title: PSHAPE: Automatically Combining Gadgets for Arbitrary Method Execution

abstract: Return-Oriented Programming (ROP) is the cornerstone of today’s exploits. Yet, building ROP chains is predominantly a manual task, enjoying limited tool support. Many of the available tools contain bugs, are not tailored to the needs of exploit development in the real world and do not offer practical support to analysts, which is why they are seldom used for any tasks beyond gadget discovery. We present PSHAPE (Practical Support for Half-Automated Program Exploitation), a tool which assists analysts in exploit development. It discovers gadgets, chains gadgets together, and ensures that side effects such as register dereferences do not crash the program. Furthermore, we introduce the notion of gadget summaries, a compact representation of the effects a gadget or a chain of gadgets has on memory and registers. These semantic summaries enable analysts to quickly determine the usefulness of long, complex gadgets that use a lot of aliasing or involve memory accesses. Case studies on nine real binaries representing 147 MiB of code show PSHAPE’s usefulness: it automatically builds usable ROP chains for nine out of eleven scenarios.


title: Ten-year results of a three-arm prospective cohort study on implants in periodontally compromised patients. Part 1: implant loss and radiographic bone loss.

abstract: OBJECTIVES ::: The aim of this study was to compare the long-term outcomes of implants placed in patients treated for periodontitis periodontally compromised patients (PCP) and in periodontally healthy patients (PHP) in relation to adhesion to supportive periodontal therapy (SPT). ::: ::: ::: MATERIAL AND METHODS ::: One hundred and twelve partially edentulous patients were consecutively enrolled in private specialist practice and divided into three groups according to their initial periodontal condition: PHP, moderate PCP and severe PCP. Perio and implant treatment was carried out as needed. Solid screws (S), hollow screws (HS) and hollow cylinders (HC) were installed to support fixed prostheses, after successful completion of initial periodontal therapy (full-mouth plaque score <25% and full-mouth bleeding score <25%). At the end of treatment, patients were asked to follow an individualized SPT program. At 10 years, clinical measures and radiographic bone changes were recorded by two calibrated operators, blinded to the initial patient classification. ::: ::: ::: RESULTS ::: Eleven patients were lost to follow-up. During the period of observation, 18 implants were removed because of biological complications. The implant survival rate was 96.6%, 92.8% and 90% for all implants and 98%, 94.2% and 90% for S-implants only, respectively, for PHP, moderate PCP and severe PCP. The mean bone loss was 0.75 (+/- 0.88) mm in PHP, 1.14 (+/- 1.11) mm in moderate PCP and 0.98 (+/- 1.22) mm in severe PCP, without any statistically significant difference. The percentage of sites, with bone loss > or =3 mm, was, respectively, 4.7% for PHP, 11.2% for moderate PCP and 15.1% for severe PCP, with a statistically significant difference between PHP and severe PCP (P<0.05). Lack of adhesion to SPT was correlated with a higher incidence of bone loss and implant loss. ::: ::: ::: CONCLUSION ::: Patients with a history of periodontitis presented a lower survival rate and a statistically significantly higher number of sites with peri-implant bone loss. Furthermore, PCP, who did not completely adhere to the SPT, were found to present a higher implant failure rate. This underlines the value of the SPT in enhancing the long-term outcomes of implant therapy, particularly in subjects affected by periodontitis, in order to control reinfection and limit biological complications.


title: G-Distillation: Reducing Overconfident Errors on Novel Samples

abstract: Counter to the intuition that unfamiliarity should lead to lack of confidence, current algorithms often make highly confident yet wrong predictions when faced with unexpected test samples from an unknown distribution different from training. Unlike all domain adaptation methods, we cannot gather an "unexpected dataset" prior to test. We propose a simple solution that reduces overconfident errors of samples from an unknown novel distribution without increasing evaluation time: train an ensemble of classifiers and then distill into a single model using both labeled and unlabeled examples. Experimentally, we investigate the overconfidence problem and evaluate our solution by creating "familiar" and "novel" test splits, where "familiar" are identically distributed with training and "novel" are not. We show that our solution yields more appropriate prediction confidences, on familiar and novel data, compared to single models and ensembles distilled on training data only. For example, we reduce confident errors in gender recognition by 94% on demographic groups different from the training data.


title: Hierarchical Edge Computing

abstract: The computing paradigm based on the giant like DC is replaced by a new paradigm. The urgency of this shift is caused by the requirements of new applications that actively use video, real-time interactivity, new mobile communication technologies, which today cannot be implemented without the usage of cloud computing and virtualization based on SDN&NFV technologies. The paper considers the requirements dictated by these applications, outlines the architecture of this new paradigm which we call Hierarchical Edge Computing (HEC). Attention is focused on the fact that all these applications are distributed, more and more became real-time applications and require guaranteed quality of service in the networking operation. The main scientific problems that need to be solved for implementing this new paradigm are discussed.


title: A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression

abstract: We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent variables prior, and the introduction of an ordinality enforcing unit. We describe how to do supervised, unsupervised and semi-supervised learning, and nominal and ordinal classification, with the model. We analyze generative properties of the approach, and the classification effectiveness under nominal and ordinal classification, using two benchmark datasets. Our results show that our model can achieve comparable results with relevant baselines in both of the classification tasks.


title: Named Entity Recognition in Bengali: A Conditional Random Field Approach

abstract: This paper reports about the development of a Named Entity Recognition (NER) system for Bengali using the statistical Conditional Random Fields (CRFs). The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the various named entity (NE) classes. A portion of the partially NE tagged Bengali news corpus, developed from the archive of a leading Bengali newspaper available in the web, has been used to develop the system. The training set consists of 150K words and has been manually annotated with a NE tagset of seventeen tags. Experimental results of the 10-fold cross validation test show the effectiveness of the proposed CRF based NER system with an overall average Recall, Precision and F-Score values of 93.8%, 87.8% and 90.7%, respectively.


title: Twisted GFSR generators

abstract: The generalized feed back shift register (GFSR) algorithm suggested by Lewis and Payne is a widely used pseudorandom number generator, but has the following serious drawbacks: (1) an initialization scheme to assure higher order equidistribution is involved and is time consuming; (2) each bit of the generated words constitutes an m -sequence based on a primitive trinomials, which shows poor randomness with respect to weight distribution; (3) a large working area is necessary; (4) the period of sequence is far shorter than the theoretical upper bound. This paper presents the twisted GFSR (TGFSR) algorithm, a slightly but essentially modified version of the GFSR, which solves all the above problems without loss of merit. Some practical TGFSR generators were implemented and passed strict empirical tests. These new generators are most suitable for simulation of a large distributive system, which requires a number of mutually independent pseudorandom number generators with compact size.


title: Pornography Detection Using Support Vector Machine

abstract: As Internet grows quickly, pornography, which is often printed into a small quantity of publication in the past, becomes one of the highly distributed information over Internet. However, pornography may be harmful to children, and may affect the efficiency of workers. In this paper, we design an easy scheme for detecting pornography. We exploit primitive information from pornography and use this knowledge for determining whether a given photo belongs to pornography or not. In the beginning, we extract skin region from photos, and find out the correlation in skin region and non-skin region. Then, we use these correlations as the input of support vector machine (SVM), an excellent tool for classification with learning abilities. After a period of training SVM model, we achieved about 75% of accuracy, 35% of false alarm rate, and only 14% of mis-detection rate. Moreover, we also provide a simple tool based on our scheme.


title: Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance

abstract: One of the major obstacles in automatic polyp detection during colonoscopy is the lack of labeled polyp training images. In this paper, we propose a framework of conditional adversarial networks to increase the number of training samples by generating synthetic polyp images. Using a normal binary form of polyp mask which represents only the polyp position as an input conditioned image, realistic polyp image generation is a difficult task in a generative adversarial networks approach. We propose an edge filtering-based combined input conditioned image to train our proposed networks. This enables realistic polyp image generations while maintaining the original structures of the colonoscopy image frames. More importantly, our proposed framework generates synthetic polyp images from normal colonoscopy images which have the advantage of being relatively easy to obtain. The network architecture is based on the use of multiple dilated convolutions in each encoding part of our generator network to consider large receptive fields and avoid much contractions of a feature map size. An image resizing with convolution for upsampling in the decoding layers is considered to prevent artifacts on generated images. We show that the generated polyp images are not only qualitatively realistic, but also help to improve polyp detection performance.


title: Patch-based Image Correlation with Rapid Filtering

abstract: This paper describes a patch-based approach for rapid image correlation or template matching. By representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Rectangle filters are applied to each image patch for fast filtering based on the integral image representation. A new method is developed for feature dimension reduction by detecting the "salient" image structures given a single image. Experiments on a variety images show the success of the method in dealing with different variations in the test images. In terms of computation time, the approach is faster than traditional methods by up to two orders of magnitude and is at least three times faster than a fast implementation of normalized cross correlation.


title: Industrial Applications of the Kalman Filter: A Review

abstract: The Kalman filter (KF) has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 1970s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing, and so on. This paper provides a brief overview of the industrial applications and implementation issues of the KF in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.


title: Digital Control in Power Electronics

abstract: This book presents the reader, whether an electrical engineering student in power electronics or a design engineer, a selection of power converter control problems and their basic digital solutions, based on the most widespread digital control techniques. The presentation is primarily focused on different applications of the same power converter topology, the half-bridge voltage source inverter, considered both in its single- and three-phase implementation. This is chosen as the test case because, besides being simple and well known, it allows the discussion of a significant spectrum of the most frequently encountered digital control applications in power electronics, from digital pulse width modulation (DPWM) and space vector modulation (SVM), to inverter output current and voltage control, ending with the relatively more complex VSI applications related to the so called smart-grid scenario. This book aims to serve two purposes: (1) to give a basic, introductory knowledge of the digital control techniques applied to power converters; and (2) to raise the interest for discrete time control theory, stimulating new developments in its application to switching power converters.


title: A gap analysis of Internet-of-Things platforms

abstract: We are experiencing an abundance of Internet-of-Things (IoT) middleware solutions that provide connectivity for sensors and actuators to the Internet. To gain a widespread adoption, these middleware solutions, referred to as platforms, have to meet the expectations of different players in the IoT ecosystem, including device providers, application developers, and end-users, among others.In this article, we evaluate a representative sample of these platforms, both proprietary and open-source, on the basis of their ability to meet the expectations of different IoT users. The evaluation is thus more focused on how ready and usable these platforms are for IoT ecosystem players, rather than on the peculiarities of the underlying technological layers. The evaluation is carried out as a gap analysis of the current IoT landscape with respect to (i) the support for heterogeneous sensing and actuating technologies, (ii) the data ownership and its implications for security and privacy, (iii) data processing and data sharing capabilities, (iv) the support offered to application developers, (v) the completeness of an IoT ecosystem, and (vi) the availability of dedicated IoT marketplaces. The gap analysis aims to highlight the deficiencies of today's solutions to improve their integration to tomorrow's ecosystems. In order to strengthen the finding of our analysis, we conducted a survey among the partners of the Finnish IoT program, counting over 350 experts, to evaluate the most critical issues for the development of future IoT platforms. Based on the results of our analysis and our survey, we conclude this article with a list of recommendations for extending these IoT platforms in order to fill in the gaps.


title: Learned optimizers that outperform SGD on wall-clock and test loss.

abstract: Deep learning has shown that learned functions can dramatically outperform hand-designed functions on perceptual tasks. Analogously, this suggests that learned optimizers may similarly outperform current hand-designed optimizers, especially for specific problems. However, learned optimizers are notoriously difficult to train and have yet to demonstrate wall-clock speedups over hand-designed optimizers, and thus are rarely used in practice. Typically, learned optimizers are trained by truncated backpropagation through an unrolled optimization process. The resulting gradients are either strongly biased (for short truncations) or have exploding norm (for long truncations). In this work we propose a training scheme which overcomes both of these difficulties, by dynamically weighting two unbiased gradient estimators for a variational loss on optimizer performance. This allows us to train neural networks to perform optimization of a specific task faster than well tuned first-order methods. Moreover, by training the optimizer against validation loss (as opposed to training loss), we are able to learn optimizers that train networks to better generalization than first order methods. We demonstrate these results on problems where our learned optimizer trains convolutional networks in a fifth of the wall-clock time compared to tuned first-order methods, and with an improvement in test loss.


title: Fast Kernel Learning for Multidimensional Pattern Extrapolation

abstract: The ability to automatically discover patterns and perform extrapolation is an essential quality of intelligent systems. Kernel methods, such as Gaussian processes, have great potential for pattern extrapolation, since the kernel flexibly and interpretably controls the generalisation properties of these methods. However, automatically extrapolating large scale multidimensional patterns is in general difficult, and developing Gaussian process models for this purpose involves several challenges. A vast majority of kernels, and kernel learning methods, currently only succeed in smoothing and interpolation. This difficulty is compounded by the fact that Gaussian processes are typically only tractable for small datasets, and scaling an expressive kernel learning approach poses different challenges than scaling a standard Gaussian process model. One faces additional computational constraints, and the need to retain significant model structure for expressing the rich information available in a large dataset. In this paper, we propose a Gaussian process approach for large scale multidimensional pattern extrapolation. We recover sophisticated out of class kernels, perform texture extrapolation, inpainting, and video extrapolation, and long range forecasting of land surface temperatures, all on large multidimensional datasets, including a problem with 383,400 training points. The proposed method significantly outperforms alternative scalable and flexible Gaussian process methods, in speed and accuracy. Moreover, we show that a distinct combination of expressive kernels, a fully non-parametric representation, and scalable inference which exploits existing model structure, are critical for large scale multidimensional pattern extrapolation.


title: Biological flora of Central Europe: Ailanthus altissima (Mill.) Swingle

abstract: Abstract Ailanthus altissima (tree of heaven), Simaroubaceae, is an early successional tree, native to China and North Vietnam, which has become invasive in Europe and on all other continents except Antarctica. It is most abundant in urban habitats and along transportation corridors, but can also invade natural habitats. This paper reviews the literature on the morphology, distribution, ecology, habitat requirements, population biology, genetics, physiology, impacts, management and uses of this species.


title: Map-Based Visual-Inertial Monocular SLAM using Inertial assisted Kalman Filter

abstract: In this paper, we present a novel tightly-coupled monocular visual-inertial Simultaneous Localization and Mapping algorithm following an inertial assisted Kalman Filter and reusing the estimated 3D map. By leveraging an inertial assisted Kalman Filter, we achieve an efficient motion tracking bearing fast dynamic movement in the front-end. To enable place recognition and reduce the trajectory estimation drift, we construct a factor graph based non-linear optimization in the back-end. We carefully design a feedback mechanism to balance the front/back ends ensuring the estimation accuracy. We also propose a novel initialization method that accurately estimate the scale factor, the gravity, the velocity, and gyroscope and accelerometer biases in a very robust way. We evaluated the algorithm on a public dataset, when compared to other state-of-the-art monocular Visual-Inertial SLAM approaches, our algorithm achieves better accuracy and robustness in an efficient way. By the way, we also evaluate our algorithm in a Monocular-Inertial setup with a low cost IMU to achieve a robust and low-drift realtime SLAM system.


title: Design and analysis of defect tolerant hierarchical sorting networks

abstract: A novel hierarchical defect-tolerant sorting network is presented. The design achieves a balance in area-time cost between the odd-even transposition sort and the bitonic sort. It uses less hardware than a single-level odd-even transposition sorter and reduces the wire complexity of the bitonic sorter in VLSI or WSI (wafer scale integration) implementation. The optimal number of levels in the hierarchy is evaluated and the sorting capability of each level is derived to minimize the hardware overhead. The hierarchical sorting network is very regular in structure and hence it is easy to provide redundancy at every level of the hierarchy. Hierarchical reconfiguration is implemented by replacing the defective cells with spare cells at the bottom level first, and goes to the next higher level to perform reconfiguration if there is not enough redundancy at the current level. Simulation results show that the defect tolerant hierarchical sorting network considered achieves a significant yield increase over a nonredundant sorting network. >


title: A review of methods for automatic understanding of natural language mathematical problems

abstract: This article addresses the problem of understanding mathematics described in natural language. Research in this area dates back to early 1960s. Several systems have so far been proposed to involve machines to solve mathematical problems of various domains like algebra, geometry, physics, mechanics, etc. This correspondence provides a state of the art technical review of these systems and approaches proposed by different research groups. A unified architecture that has been used in most of these approaches is identified and differences among the systems are highlighted. Significant achievements of each method are pointed out. Major strengths and weaknesses of the approaches are also discussed. Finally, present efforts and future trends in this research area are presented.


title: ERPs reveal subliminal processing of fearful faces

abstract: To investigate whether facial expression is processed in the absence of conscious awareness, ERPs were recorded in a task in which participants had to identify the expression of masked fearful and neutral target faces. On supraliminal trials (200 ms target duration), in which identification performance was high, a sustained positivity to fearful versus neutral target faces started 140 ms after target face onset. On subliminal trials (8 ms target duration), identification performance was at chance level, but ERPs still showed systematic fear-specific effects. An early positivity to fearful target faces was present but smaller than on supraliminal trials. A subsequent enhanced N2 to fearful faces was only present for subliminal trials. In contrast, a P3 enhancement to fearful faces was observed on supraliminal but not subliminal trials. Results demonstrate rapid emotional expression processing in the absence of awareness.


title: A Survey of Synthetic Biometrics: Capabilities and Benefits

abstract: Within the Association for Computing Machinery (ACM) Special Interest Group on Computer Graphics (SIGGRAPH) community, a long-standing goal has been held for photo-realism in the generation of synthetic images—a goal that some feel has been achieved [7]. This body of work, spanning over three decades, documents achievements in modeling, animation, and rendering human subjects. The visual products range from feature films, commercial art, to video games. Computer generated “synthetic” biometrics are not widely used within the biometrics community beyond their current use as a research tool. Yet they offer a number of potential advantages that can be developed further to support the science and practical use of biometrics. They can be used to improve the understanding of a biometric system’s robustness and as an engineering tool to predict system performance. This paper surveys the state of synthetic biometrics generation, provides a glimpse at some benefits that can be obtained from their use, and discusses the issues retarding their adoption by the biometrics community. The most advanced synthetic images come from the movie industry and have yet to be fully adapted to suit biometric needs. A synthetically rendered face is illustrated in Figure 1.


title: WR-3 Band Quasi-Elliptical Waveguide Filters Using Higher Order Mode Resonances

abstract: Two types of WR-3 band quasi-elliptical waveguide bandpass filters (BPFs) using higher order mode resonators are presented based on physical cross coupling and modal bypass coupling, respectively. Under the situation of physical folded structure, a TE102-mode in an oversized waveguide resonator is utilized to reverse the magnetic field direction of main path to implement a negative cross coupling (Filter-I). Limited to the structure of Filter-I itself, the simulated 3 dB fractional bandwidth (FBW) of 8.5% is slightly narrower than desired 10% FBW. For the other case, two TE101/TE201 overmode resonant cavities with suitable input and output coupling locations are employed to generate two transmission zeros in the vicinity of the passband through modal bypass couplings (Filter-II). The benefit from the realization of the wideband source and load couplings in the fringe first and fourth oversized cavities is that the FBW of 10% is achieved in the Filter-II. The two BPFs fabricated by computer numerical control milling technology exhibit an insertion loss (IL) of about 0.7 dB and a 3 dB FBW of 8.77% centered at 257.7 GHz (Filter-I), IL of around 0.5 dB, and FBW of 9.83% with center frequency of 256.3 GHz (Filter-II), which are all in good agreement with the simulations. The performance of the two BPFs based on one or two oversized waveguide resonators is highlighted comparing with the reported similar terahertz waveguide filters.


title: The Dialog State Tracking Challenge Series

abstract: In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking, and also deeper understandings about the problem itself, including methods for evaluation.


title: Social media mining for drug safety signal detection

abstract: Adverse Drug Reactions (ADRs) represent a serious problem all over the world. They may complicate a patient's medical conditions and increase the morbidity, even mortality. Drug safety currently depends heavily on post-marketing surveillance, because pre-marketing review process cannot identify all possible adverse drug reactions in that it is limited by scale and time span. However, current post-marketing surveillance is conducted through centralized volunteering reporting systems, and the reporting rate is low. Consequently, it is difficult to detect the adverse drug reactions signals in a timely manner. To solve this problem, many researchers have explored methods to detect ADRs in electronic health records. Nevertheless, we only have access to electronic health records form particular health units. Aggregating and integrating electronic health records from multiple sources is rather challenging. With the advance of Web 2.0 technologies and the popularity of social media, many health consumers are discussing and exchanging health-related information with their peers. Many of this online discussion involve adverse drug reactions. In this work, we propose to use association mining and Proportional Reporting Ratios to mine the associations between drugs and adverse reactions from the user contributed content in social media. We have conducted an experiment using ten drugs and five adverse drug reactions. The FDA alerts are used as the gold standard to test the performance of the proposed techniques. The result shows that the metrics leverage, lift, and PRR are all promising to detect the adverse drug reactions reported by FDA. However, PRR outperformed the other two metrics.


Introduction DAISY: A Practical Tool for Semi-Automated Species Identification


title: DjiNN and Tonic: DNN as a service and its implications for future warehouse scale computers

abstract: As applications such as Apple Siri, Google Now, Microsoft Cortana, and Amazon Echo continue to gain traction, web-service companies are adopting large deep neural networks (DNN) for machine learning challenges such as image processing, speech recognition, natural language processing, among others. A number of open questions arise as to the design of a server platform specialized for DNN and how modern warehouse scale computers (WSCs) should be outfitted to provide DNN as a service for these applications. In this paper, we present DjiNN, an open infrastructure for DNN as a service in WSCs, and Tonic Suite, a suite of 7 end-to-end applications that span image, speech, and language processing. We use DjiNN to design a high throughput DNN system based on massive GPU server designs and provide insights as to the varying characteristics across applications. After studying the throughput, bandwidth, and power properties of DjiNN and Tonic Suite, we investigate several design points for future WSC architectures. We investigate the total cost of ownership implications of having a WSC with a disaggregated GPU pool versus a WSC composed of homogeneous integrated GPU servers. We improve DNN throughput by over 120x for all but one application (40x for Facial Recognition) on an NVIDIA K40 GPU. On a GPU server composed of 8 NVIDIA K40s, we achieve near-linear scaling (around 1000x throughput improvement) for 3 of the 7 applications. Through our analysis, we also find that GPU-enabled WSCs improve total cost of ownership over CPU-only designs by 4-20x, depending on the composition of the workload


title: 0.8-Volt bulk-driven flipped voltage follower

abstract: A source follower based voltage buffer that fits in with a low supply voltage is presented. This proposal is based on utilizing the Bulk-Driven Flipped Voltage Follower (BDFVF) and the Quasi-Floating Gate (QFG) technique. The proposed voltage buffer has been designed with a 0.18-μm CMOS technology. The simulated results under the single 0.8-volt power supply environment show that the proposed buffer achieves 9-MHz of -3-dB frequency with a 10-pF load, while the bias current consumption is only 4-μA.


title: Ontology based classification system for online job offers

abstract: The significance of employment in a setup of society is quite evident. Publishing jobs online opened the research opportunities to study different methods for the automation of job offers retrieval — from the internet. We need a classification mechanism, from machine learning or some other discipline, to automate job offers retrieval. In this paper, we devised an ontology-based classifier for job offers classification. More than 5000 job offers were collected from different job offers websites. The ontology-based classification works in three stages, concepts are extracted from job offers text description as feature vectors, the minimum threshold for the classification is calculated, and a classification model is developed. Rather using machine learning algorithms, we used an ontology for classification. We evaluated this classifier according to machine learning evaluation model, training, and test dataset. Our classifier showed more than 0.9 accuracy, precision, and recall for both training and test dataset. Our finding paves the way to automate the job offers classification and retrieval.


title: Coupled semi-supervised learning for information extraction

abstract: We consider the problem of semi-supervised learning to extract categories (e.g., academic fields, athletes) and relations (e.g., PlaysSport(athlete, sport)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised training using only a few labeled examples is typically unreliable because the learning task is underconstrained. This paper pursues the thesis that much greater accuracy can be achieved by further constraining the learning task, by coupling the semi-supervised training of many extractors for different categories and relations. We characterize several ways in which the training of category and relation extractors can be coupled, and present experimental results demonstrating significantly improved accuracy as a result.


Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources


title: Embedded Trench Redistribution Layers (RDL) by Excimer Laser Ablation and Surface Planer Processes

abstract: This paper reports the demonstration of 2-5 µm embedded trench formation in dry film polymer dielectrics such as Ajinomoto build-up film (ABF) and Polyimide without using chemical mechanical polishing (CMP) process. The trenches in these dielectrics were formed by excimer laser ablation, followed by metallization of trenches by copper plating processes and overburden removal with surface planer tool. The materials and processes integrated in this work are scalable to large panel fabrication at much higher throughput, for interposers and high density fan-out packaging at lower cost and higher performance than silicon interposers.


title: Performance analysis and improvement of machine learning algorithms for automatic modulation recognition over Rayleigh fading channels

abstract: Automatic modulation recognition (AMR) is becoming more important because it is usable in advanced general-purpose communication such as, cognitive radio, as well as, specific applications. Therefore, developments should be made for widely used modulation types; machine learning techniques should be employed for this problem. In this study, we have evaluated performances of different machine learning algorithms for AMR. Specifically, we have evaluated performances of artificial neural networks, support vector machines, random forest tree, k-nearest neighbor, Hoeffding tree, logistic regression, Naive Bayes and Gradient Boosted Regression Tree methods to obtain comparative results. The most preferred feature extraction methods in the literature have been used for a set of modulation types for general-purpose communication. We have considered AWGN and Rayleigh channel models evaluating their recognition performance as well as having made recognition performance improvement over Rayleigh for low SNR values using the reception diversity technique. We have compared their recognition performance in the accuracy metric, and plotted them as well. Furthermore, we have served confusion matrices for some particular experiments.


title: Learning Depth with Convolutional Spatial Propagation Network

abstract: Depth prediction is one of the fundamental problems in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for various depth estimation tasks. We can append this module to any output from a state-of-the-art (SOTA) network to improve their performances. In practice, we further extend CSPN in two aspects: 1) take a sparse depth map as additional input, which is useful for the task of sparse to dense (a.k.a depth completion); 2) we propose 3D CSPN to handle features with one additional dimension, which is effective in the task of stereo matching using 3D cost volume. For the tasks of depth completion, we experimented the proposed CPSN conjunct algorithms over NYU v2 and KITTI datasets, where we show that our proposed algorithms not only produce high quality (e.g., 30% more reduction in depth error), but also run faster (e.g., 2 to 5 × faster) than previous SOTA spatial propagation network. We also evaluated our stereo matching algorithm on the Scene Flow and KITTI Stereo datasets, and rank 1st on both the KITTI Stereo 2012 and 2015 benchmarks, which demonstrates the effectiveness of the proposed module again.


title: Deep adversarial networks for biomedical image segmentation utilizing unannotated images

abstract: Semantic segmentation is a fundamental problem in biomedical image analysis. In biomedical practice, it is often the case that only limited annotated data are available for model training. Unannotated images, on the other hand, are easier to acquire. How to utilize unannotated images for training effective segmentation models is an important issue. In this paper, we propose a new deep adversarial network (DAN) model for biomedical image segmentation, aiming to attain consistently good segmentation results on both annotated and unannotated images. Our model consists of two networks: (1) a segmentation network (SN) to conduct segmentation; (2) an evaluation network (EN) to assess segmentation quality. During training, EN is encouraged to distinguish between segmentation results of unannotated images and annotated ones (by giving them different scores), while SN is encouraged to produce segmentation results of unannotated images such that EN cannot distinguish these from the annotated ones. Through an iterative adversarial training process, because EN is constantly “criticizing” the segmentation results of unannotated images, SN can be trained to produce more and more accurate segmentation for unannotated and unseen samples. Experiments show that our proposed DAN model is effective in utilizing unannotated image data to obtain considerably better segmentation.


title: A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory

abstract: A functional defined on the class of generalized characteristic functions (fuzzy sets), called “entropy”, is introduced using no probabilistic concepts in order to obtain a global measure of the indefiniteness connected with the situations described by fuzzy sets. This “entropy” may be regarded as a measure of a quantity of information which is not necessarily related to random experiments. Some mathematical properties of this functional are analyzed and some considerations on its applicability to pattern analysis are made.


title: A novel framework for automatic trimap generation using the Gestalt laws of grouping

abstract: In this paper, we are concerned with unsupervised natural image matting. Due to the under-constrained nature of the problem, image matting algorithms are usually provided with user interactions, such as scribbles or trimaps. This is a very tedious task and may even become impractical for some applications. For unsupervised matte calculation, we can either adopt a technique that supports an unsupervised mode for alpha map calculation, or we may automate the process of acquiring user interactions provided for a matting algorithm. Our proposed technique contributes to both approaches and is based on spectral matting. The latter is the only technique in the literature that supports automatic matting but it suffers from critical limitations among which is the unreliable unsupervised operation. Stressing on that drawback, spectral matting may produce erroneous mattes in the absence of guiding scribbles or trimaps. Using the Gestalt laws of grouping, we propose a method that automatically produces more truthful mattes than spectral matting. In addition, it can be used to generate trimaps, eliminating the required user interactions and making it possible to harness the powers of matting techniques that are better than spectral matting but don't support unsupervised operation. The main contribution of this research is the introduction of the Gestalt laws of grouping to the matting problem.


title: Image Crowd Counting Using Convolutional Neural Network and Markov Random Field

abstract: In this paper, we propose a method called Convolutional Neural Network-Markov Random Field (CNN-MRF) to estimate the crowd count in a still image. We first divide the dense crowd visible image into overlapping patches and then use a deep convolutional neural network to extract features from each patch image, followed by a fully connected neural network to regress the local patch crowd count. Since the local patches have overlapping portions, the crowd count of the adjacent patches has a high correlation. We use this correlation and the Markov random field to smooth the counting results of the local patches. Experiments show that our approach significantly outperforms the state-of-the-art methods on UCF and Shanghaitech crowd counting datasets.


title: Stupid Tutoring Systems, Intelligent Humans

abstract: The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development, and discuss the potential of educational data mining driving human decision-making as an alternate paradigm for online learning, focusing on intelligence amplification rather than artificial intelligence.


title: 70-nm impact-ionization metal-oxide-semiconductor (I-MOS) devices integrated with tunneling field-effect transistors (TFETs)

abstract: 70-nm I-MOS devices have been integrated with 70-nm TFETs for the first time by adopting a novel process method. The integration of the I-MOS device with the TFET is meaningful in that it compensates for weak points of each device and implements both high-performance and low-power functionality on the same substrate. Additionally, by using SOI substrate and modifying mask layout, ON/OFF current ratio of the I-MOS device is increased dramatically by a factor of more than 1000 compared with our previous works. Finally, we have investigated the applicability of the I-MOS device to inverter and 6T-SRAM cell by measurement and mixed-mode simulation. When applied to 6T-SRAM cell, CI-MOS case shows 22.6 % improvement in SNM without much penalty in cell area


title: Curvature-based Methods for Brain Network Analysis

abstract: The human brain forms functional networks on all spatial scales. Modern fMRI scanners allow to resolve functional brain data in high resolutions, allowing to study large-scale networks that relate to cognitive processes. The analysis of such networks forms a cornerstone of experimental neuroscience. Due to the immense size and complexity of the underlying data sets, efficient evaluation and visualization remain a challenge for data analysis. In this study, we combine recent advances in experimental neuroscience and applied mathematics to perform a mathematical characterization of complex networks constructed from fMRI data. We use task-related edge densities [Lohmann et al., 2016] for constructing networks of task-related changes in synchronization. This construction captures the dynamic formation of patterns of neuronal activity and therefore represents efficiently the connectivity structure between brain regions. Using geometric methods that utilize Forman-Ricci curvature as an edge-based network characteristic [Weber et al., 2017], we perform a mathematical analysis of the resulting complex networks. We motivate the use of edge-based characteristics to evaluate the network structure with geometric methods. The geometric features could aid in understanding the connectivity and interplay of brain regions in cognitive processes.


title: Understanding integer overflow in C/C++

abstract: Integer overflow bugs in C and C++ programs are difficult to track down and may lead to fatal errors or exploitable vulnerabilities. Although a number of tools for finding these bugs exist, the situation is complicated because not all overflows are bugs. Better tools need to be constructed — but a thorough understanding of the issues behind these errors does not yet exist. We developed IOC, a dynamic checking tool for integer overflows, and used it to conduct the first detailed empirical study of the prevalence and patterns of occurrence of integer overflows in C and C++ code. Our results show that intentional uses of wraparound behaviors are more common than is widely believed; for example, there are over 200 distinct locations in the SPEC CINT2000 benchmarks where overflow occurs. Although many overflows are intentional, a large number of accidental overflows also occur. Orthogonal to programmers' intent, overflows are found in both well-defined and undefined flavors. Applications executing undefined operations can be, and have been, broken by improvements in compiler optimizations. Looking beyond SPEC, we found and reported undefined integer overflows in SQLite, PostgreSQL, SafeInt, GNU MPC and GMP, Firefox, GCC, LLVM, Python, BIND, and OpenSSL; many of these have since been fixed. Our results show that integer overflow issues in C and C++ are subtle and complex, that they are common even in mature, widely used programs, and that they are widely misunderstood by developers.


title: Dermatological disease detection using image processing and machine learning

abstract: Dermatological diseases are the most prevalent diseases worldwide. Despite being common, its diagnosis is extremely difficult and requires extensive experience in the domain. In this research paper, we provide an approach to detect various kinds of these diseases. We use a dual stage approach which effectively combines Computer Vision and Machine Learning on clinically evaluated histopathological attributes to accurately identify the disease. In the first stage, the image of the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction. The second stage involves the use of Machine learning algorithms to identify diseases based on the histopathological attributes observed on analysing of the skin. Upon training and testing for the six diseases, the system produced an accuracy of up to 95 percent.


title: Current Research in Risk-aware Business Process Management―Overview, Comparison, and Gap Analysis

abstract: The management of risks in business processes has been a subject of active research in the past few years. Many benefits can potentially be obtained by integrating the two traditionally-separated fields of risk management and business process management, including the ability to minimize risks in business processes (by design) and to mitigate risks at run time. In the past few years, an increasing amount of research aimed at delivering such an integrated system has been proposed. However, these research efforts vary in terms of their scope, goals, and functionality. Through systematic collection and evaluation of relevant literature, this paper compares and classifies current approaches in the area of risk-aware business process management in order to identify and explain relevant research gaps. The process through which relevant literature is collected, filtered, and evaluated is also detailed.


title: Analysis of Nonideal Behaviors Based on INL/DNL Plots for SAR ADCs

abstract: This paper presents a comprehensive investigation of several important error sources for the successive-approximation register (SAR) analog-to-digital converters (ADCs). The error sources that we discuss in this paper include the dynamic comparator offset, the dynamic gain error of digital-to-analog converter (DAC), the capacitor mismatch of capacitive DAC, the incomplete settling of DAC, the undershoot of reference voltage, and the input signal coupling. The integral/differential nonlinearities (INL/DNL) of SAR ADCs that are resulted from these error sources are analyzed and addressed. A diagnostic procedure is presented to identify the possible error sources based on the INL/DNL plots. In addition, design suggestions for overcoming these problems are also offered and recommended in this paper.


Early and dynamic student achievement prediction in e-learning courses using neural networks


title: Improving predictive inference under covariate shift by weighting the log-likelihood function

abstract: Abstract A class of predictive densities is derived by weighting the observed samples in maximizing the log-likelihood function. This approach is effective in cases such as sample surveys or design of experiments, where the observed covariate follows a different distribution than that in the whole population. Under misspecification of the parametric model, the optimal choice of the weight function is asymptotically shown to be the ratio of the density function of the covariate in the population to that in the observations. This is the pseudo-maximum likelihood estimation of sample surveys. The optimality is defined by the expected Kullback–Leibler loss, and the optimal weight is obtained by considering the importance sampling identity. Under correct specification of the model, however, the ordinary maximum likelihood estimate (i.e. the uniform weight) is shown to be optimal asymptotically. For moderate sample size, the situation is in between the two extreme cases, and the weight function is selected by minimizing a variant of the information criterion derived as an estimate of the expected loss. The method is also applied to a weighted version of the Bayesian predictive density. Numerical examples as well as Monte-Carlo simulations are shown for polynomial regression. A connection with the robust parametric estimation is discussed.


title: An integrated CMOS current-sensing circuit for low-Voltage current-mode buck regulator

abstract: An integrated current-sensing circuit for low-voltage buck regulator is presented. The minimum achievable supply voltage of the proposed current-sensing circuit is 1.2 V implemented in a CMOS technology with V/sub TH/=0.85 V, and the current-sensing accuracy is higher than 94%. With the developed current-sensing circuit, a buck regulator, which is able to operate at a 1.2-V supply, is implemented. A maximum output current of 120 mA and power-conversion efficiency higher than 89% are achieved.


398 Trappenberg Chapter XVI Continuous Attractor Nueral


Semi-Supervised Classification by Low Density Separation


title: Data Warehousing Based Computer Forensics Investigation Framework

abstract: In this paper, we have proposed the design of an efficient computer forensics investigation framework. The proposed framework improves the investigation efficiency using Data Warehouse (DW) concept, which provides a selective evidence identification, collection and analysis. So, only relevant data is investigated instead of investigating the entire user data. The proposed framework consists of a Data Warehouse Engine (DWE) to selectively identify, collect and analyze digital evidences from multiple digital resources. A Digital Evidence Preservation (DEP) mechanism is also introduced for preservation of the collected digital evidences whose authenticity is ensured using cryptographic techniques. An access control mechanism is implemented to allow only authorized investigator to access the preserved digital evidences. The DEP mechanism provides court of law with a Secure Forensic Audit Trial (SFAT) that helps in tracking happened activities on the collected evidences for ensuring the authenticity and reliability of the presented digital evidence.


title: Design of Compliant Mechanisms : Applications to MEMS

abstract: Compliant mechanisms are single-piece flexible structures that deliver the desired motion by undergoing elastic deformation as opposed to jointed rigid body motions of conventional mechanisms. Compliance in design leads to jointless, no-assembly (Fig. 1), monolithic mechanical devices and is particularly suited for applications with small range of motions. The compliant windshield wiper shown in Fig. 1 illustrates this paradigm of no-assembly. Conventional flexural mechanisms employ flexural joints that connect relatively rigid links as depicted in Fig. 2. Reduced fatigue life, high stress concentration and difficulty in fabrication are some of the drawbacks of flexural joints. Our focus is on designing compliant mechanisms with distributed compliance which employs flexural links (see Fig. 3) and have no joints (neither pin nor flexural joints) for improved reliability, performance, and ease of manufacture. Distributed compliant mechanisms derive their flexibility due to topology and shape of the material continuum rather than concentrated flexion at few regions. This paper focuses on the unique methodology employed to design jointless mechanisms with distributed compliance. The paper also illustrates a compliant stroke amplification mechanism that was recently designed, fabricated and tested for MEMS application.


title: Friends only: examining a privacy-enhancing behavior in facebook

abstract: Privacy practices in social network sites often appear paradoxical, as content-sharing behavior stands in conflict with the need to reduce disclosure-related harms. In this study we explore privacy in social network sites as a contextual information practice, managed by a process of boundary regulation. Drawing on a sample survey of undergraduate Facebook users, we examine a particular privacy-enhancing practice: having a friends-only Facebook profile. Particularly, we look at the association between network composition, expectancy violations, interpersonal privacy practices and having a friends-only profile. We find that expectancy violations by weak ties and increased levels of interpersonal privacy management are positively associated with having a friends-only profile. We conclude with a discussion of how these findings may be integrated into the design of systems to facilitate interaction while enhancing individual privacy.


title: Clustering with Constraints: Feasibility Issues and the k-Means Algorithm

abstract: Recent work has looked at extending the k-Means algorithm to incorporate background information in the form of instance level must-link and cannot-link constraints. We introduce two ways of specifying additional background information in the form of δ and constraints that operate on all instances but which can be interpreted as conjunctions or disjunctions of instance level constraints and hence are easy to implement. We present complexity results for the feasibility of clustering under each type of constraint individually and several types together. A key finding is that determining whether there is a feasible solution satisfying all constraints is, in general, NP-complete. Thus, an iterative algorithm such as k-Means should not try to find a feasible partitioning at each iteration. This motivates our derivation of a new version of the k-Means algorithm that minimizes the constrained vector quantization error but at each iteration does not attempt to satisfy all constraints. Using standard UCI datasets, we find that using constraints improves accuracy as others have reported, but we also show that our algorithm reduces the number of iterations until convergence. Finally, we illustrate these benefits and our new constraint types on a complex real world object identification problem using the infra-red detector on an Aibo robot.


title: Goal-oriented Dialogue Policy Learning from Failures

abstract: Reinforcement learning methods have been used for learning dialogue policies. However, learning an effective dialogue policy frequently requires prohibitively many conversations. This is partly because of the sparse rewards in dialogues, and the very few successful dialogues in early learning phase. Hindsight experience replay (HER) enables learning from failures, but the vanilla HER is inapplicable to dialogue learning due to the implicit goals. In this work, we develop two complex HER methods providing different tradeoffs between complexity and performance, and, for the first time, enabled HER-based dialogue policy learning. Experiments using a realistic user simulator show that our HER methods perform better than existing experience replay methods (as applied to deep Q-networks) in learning rate.


title: Learning binary hash codes for large-scale image search

abstract: Algorithms to rapidly search massive image or video collections are critical for many vision applications, including visual search, content-based retrieval, and non-parametric models for object recognition. Recent work shows that learned binary projections are a powerful way to index large collections according to their content. The basic idea is to formulate the projections so as to approximately preserve a given similarity function of interest. Having done so, one can then search the data efficiently using hash tables, or by exploring the Hamming ball volume around a novel query. Both enable sub-linear time retrieval with respect to the database size. Further, depending on the design of the projections, in some cases it is possible to bound the number of database examples that must be searched in order to achieve a given level of accuracy.


Iterative Noise Injection for Scalable Imitation Learning


title: Financial Forecasting Using Support Vector Machines

abstract: The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing it with a multi-layer perceptron trained by the Back Propagation (BP) algorithm. SVMs forecast better than BP based on the criteria of Normalised Mean Square Error (NMSE), Mean Absolute Error (MAE), Directional Symmetry (DS), Correct Up (CP) trend and Correct Down (CD) trend. S&P 500 daily price index is used as the data set. Since there is no structured way to choose the free parameters of SVMs, the generalisation error with respect to the free parameters of SVMs is investigated in this experiment. As illustrated in the experiment, they have little impact on the solution. Analysis of the experimental results demonstrates that it is advantageous to apply SVMs to forecast the financial time series.


title: The Query Change Model: Modeling Session Search as a Markov Decision Process

abstract: Modern information retrieval (IR) systems exhibit user dynamics through interactivity. These dynamic aspects of IR, including changes found in data, users, and systems, are increasingly being utilized in search engines. Session search is one such IR task—document retrieval within a session. During a session, a user constantly modifies queries to find documents that fulfill an information need. Existing IR techniques for assisting the user in this task are limited in their ability to optimize over changes, learn with a minimal computational footprint, and be responsive. This article proposes a novel query change retrieval model (QCM), which uses syntactic editing changes between consecutive queries, as well as the relationship between query changes and previously retrieved documents, to enhance session search. We propose modeling session search as a Markov decision process (MDP). We consider two agents in this MDP: the user agent and the search engine agent. The user agent’s actions are query changes that we observe, and the search engine agent’s actions are term weight adjustments as proposed in this work. We also investigate multiple query aggregation schemes and their effectiveness on session search. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and TREC 2012.


SDN AND ITS USE-CASES-NV AND NFV A State-ofthe-Art Survey


title: Impact of test-driven development on productivity, code and tests: A controlled experiment

abstract: Context: Test-driven development is an approach to software development, where automated tests are written before production code in highly iterative cycles. Test-driven development attracts attention as well as followers in professional environment; however empirical evidence of its superiority regarding its effect on productivity, code and tests compared to test-last development is still fairly limited. Moreover, it is not clear if the supposed benefits come from writing tests before code or maybe from high iterativity/short development cycles. Objective: This paper describes a family of controlled experiments comparing test-driven development to micro iterative test-last development with emphasis on productivity, code properties (external quality and complexity) and tests (code coverage and fault-finding capabilities). Method: Subjects were randomly assigned to test-driven and test-last groups. Controlled experiments were conducted for two years, in an academic environment and in different developer contexts (pair programming and individual programming contexts). Number of successfully implemented stories, percentage of successful acceptance tests, McCabe's code complexity, code coverage and mutation score indicator were measured. Results: Experimental results and their selective meta-analysis show no statistically significant differences between test-driven development and iterative test-last development regarding productivity (@g^2(6)=4.799, p=1.0, r=.107, 95% CI (confidence interval): -.149 to .349), code complexity (@g^2(6)=8.094, p=.46, r=.048, 95% CI: -.254 to .341), branch coverage (@g^2(6)=13.996, p=.059, r=.182, 95% CI: -.081 to .421), percentage of acceptance tests passed (one experiment, Mann-Whitney U=125.0, p=.98, r=.066) and mutation score indicator (@g^2(4)=3.807, p=.87, r=.128, 95% CI: -.162 to .398). Conclusion: According to our findings, the benefits of test-driven development compared to iterative test-last development are small and thus in practice relatively unimportant, although effects are positive. There is an indication of test-driven development endorsing better branch coverage, but effect size is considered small.


title: Learning classification models with soft-label information

abstract: Objective Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training phase with soft labels that reflect how strongly the human expert feels about the original class labels. ::: ::: Materials and methods Two types of methods that can learn improved binary classification models from soft labels are proposed. The first relies on probabilistic/numeric labels, the other on ordinal categorical labels. We study and demonstrate the benefits of these methods for learning an alerting model for heparin induced thrombocytopenia. The experiments are conducted on the data of 377 patient instances labeled by three different human experts. The methods are compared using the area under the receiver operating characteristic curve (AUC) score. ::: ::: Results Our AUC results show that the new approach is capable of learning classification models more efficiently compared to traditional learning methods. The improvement in AUC is most remarkable when the number of examples we learn from is small. ::: ::: Conclusions A new classification learning framework that lets us learn from auxiliary soft-label information provided by a human expert is a promising new direction for learning classification models from expert labels, reducing the time and cost needed to label data.


title: A new method for mispronunciation detection using Support Vector Machine based on Pronunciation Space Models

abstract: This paper presents two new ideas for text dependent mispronunciation detection. Firstly, mispronunciation detection is formulated as a classification problem to integrate various predictive features. A Support Vector Machine (SVM) is used as the classifier and the log-likelihood ratios between all the acoustic models and the model corresponding to the given text are employed as features for the classifier. Secondly, Pronunciation Space Models (PSMs) are proposed to enhance the discriminative capability of the acoustic models for pronunciation variations. In PSMs, each phone is modeled with several parallel acoustic models to represent pronunciation variations of that phone at different proficiency levels, and an unsupervised method is proposed for the construction of the PSMs. Experiments on a database consisting of more than 500,000 Mandarin syllables collected from 1335 Chinese speakers show that the proposed methods can significantly outperform the traditional posterior probability based method. The overall recall rates for the 13 most frequently mispronounced phones increase from 17.2%, 7.6% and 0% to 58.3%, 44.3% and 29.5% at three precision levels of 60%, 70% and 80%, respectively. The improvement is also demonstrated by a subjective experiment with 30 subjects, in which 53.3% of the subjects think the proposed method is better than the traditional one and 23.3% of them think that the two methods are comparable.


title: Anaerobic treatment of methanol condensate from pulp mill compared with anaerobic treatment of methanol using mesophilic UASB reactors.

abstract: The feasibility of anaerobic treatment of methanol condensate from pulp and paper mill in UASB reactor was investigated and compared with the anaerobic treatment of methanol. The UASB reactor treating methanol condensate was operated for 480days with minimum problems of overload. COD removal from methanol condensate and methanol under normal operating conditions ranged from 84-86% to 86-98%, respectively. Under optimal conditions (OLR=5.0gCODL(-1)day(-1), COD(influent)=11.40gL(-1)) a methane yield of 0.29 NL CH(4) per g COD(removed) (at standard temperature and pressure) was achieved during the treatment of methanol condensate. The recovery time of the microorganisms after several overloads was decreasing each time probably due to the adaptation to methanol condensate. These findings indicate that methanol condensate can be efficiently treated in a UASB reactor with the benefit of biogas production. As a bonus effect of the treatment, much of the smell of the feed was eliminated. (Less)


title: Challenges and Solutions for End-to-End Time-Sensitive Optical Networking

abstract: Time sensitive applications rely on deterministic low latency, highly reactive, and trustable networks, as opposed to today's best effort internet. We review existing solutions and propose in this paper Deterministic Dynamic Network as a solution for end-to-end time-sensitive optical networking.


title: A novel efficient substitution-box design based on firefly algorithm and discrete chaotic map

abstract: Substitution boxes are essential nonlinear components responsible to impart strong confusion and security in most of modern symmetric ciphers. Constructing efficient S-boxes has been a prominent topic of interest for security experts. With an aim to construct cryptographically efficient S-box, a novel scheme based on firefly (FA) optimization and chaotic map is proposed in this paper. The anticipated approach generates initial S-box using chaotic map. The meta-heuristic FA is applied to find notable configuration of S-box that satisfies the criterions by guided search for near-optimal features by minimizing fitness function. The performance of proposed approach is assessed through well-established criterions such as bijectivity, nonlinearity, strict avalanche criteria, bit independence criteria, differential uniformity, and linear approximation probability. The obtained experimental results are compared with some recently investigated S-boxes to demonstrate that the proposed scheme has better proficiency of constructing efficient S-boxes.


title: Image Segmentation by a Genetic Fuzzy c-Means Algorithm Using Color and Spatial Information

abstract: This paper describes a new clustering algorithm for color image segmentation. We combine the classical fuzzy c-means algorithm (FCM) with a genetic algorithm (GA), and we modify the objective function of the FCM for taking into account the spatial information of image data and the intensity inhomogeneities. An application to medical images is presented. Experiments show that the proposed algorithm provides a useful method for image segmentation, without the need of a prefiltering step for background estimation. Moreover, the segmentation of noise images is effectively improved.


title: Sensible Privacy: How We Can Protect Domestic Violence Survivors Without Facilitating Misuse

abstract: Privacy is a concept with real life ties and implications. Privacy infringement has the potential to lead to serious consequences for the stakeholders involved, hence researchers and organisations have developed various privacy enhancing techniques and tools. However, there is no solution that fits all, and there are instances where privacy solutions could be misused, for example to hide nefarious activities. Therefore, it is important to provide suitable measures and to make necessary design tradeoffs in order to avoid such misuse. This short paper aims to make a case for the need of careful consideration when designing a privacy solution, such that the design effectively addresses the user requirements while at the same time minimises the risk of inadvertently assisting potential offenders. In other words, this paper strives to promote "sensible privacy" design, which deals with the complex challenges in balancing privacy, usability and accountability. We illustrate this idea through a case study involving the design of privacy solutions for domestic violence survivors. This is the main contribution of the paper. The case study presents specific user requirements and operating conditions, which coupled with the attacker model, provide a complex yet interesting scenario to explore. One example of our solutions is described in detail to demonstrate the feasibility of our approach.


title: Recent and Emerging Topics in Wireless Industrial Communications: A Selection

abstract: In this paper we discuss a selection of promising and interesting research areas in the design of protocols and systems for wireless industrial communications. We have selected topics that have either emerged as hot topics in the industrial communications community in the last few years (like wireless sensor networks), or which could be worthwhile research topics in the next few years (for example cooperative diversity techniques for error control, cognitive radio/opportunistic spectrum access for mitigation of external interferences).


title: The Team Brain: Soccer Analysis and Battles of Minds

abstract: Soccer is one of the most successful sports of our times, and for various reasons. One such reason is its level of unpredictability: surprising results can occur, and it is still unclear what are the secrets that make a team winning or losing within a single game. In this paper we tackle the problem of soccer analysis from a different perspective: we transform every team, together with the playfield, into a network structure (the Team Brain), and we shift perspective, turning soccer games into battles of minds. We show how this approach proves to be successful, allowing to better understand what the recipes for the success of a team can be, and also getting a new, somehow surprising, view of what are the really important critical areas of a soccer field.


title: Biosignal Data Augmentation Based on Generative Adversarial Networks

abstract: In this paper, we propose a synthetic generationmethod for time-series data based on generative adversarial networks (GANs) and apply it to data augmentation for biosinal classification. GANs are a recently proposed framework for learning a generative model, where two neural networks, one generating synthetic data and the other discriminating synthetic and real data, are trained while competing with each other. In the proposed method, each neural network in GANs is developed based on a recurrent neural network using long short-term memories, thereby allowing the adaptation of the GANs framework to time-series data generation. In the experiments, we confirmed the capability of the proposed method for generating synthetic biosignals using the electrocardiogram and electroencephalogram datasets. We also showed the effectiveness of the proposed method for data augmentation in the biosignal classification problem.


title: Policy Gradient Based Reinforcement Learning Approach for Autonomous Highway Driving

abstract: The paper presents the application of the Policy Gradient reinforcement learning method in the area of vehicle control. The purpose of the research presented is to design an end-to-end behavior control of a kinematic vehicle model placed in a simulated highway environment, by using reinforcement learning approach. The environment model for the surrounding traffic uses microscopic simulation to provide different situations for the agent. The environment sensing model is based on high-level sensor information, e.g. the odometry, lane position and surrounding vehicle states that can be reached from the current automotive sensors, such as camera and radar based systems. The objectives were reached through the definition of a rewarding system, with subrewards and penalties enforcing desired speed, lane keeping, keeping right and avoiding collision. After the description of the theoretical basis the environment model with the reward function is detailed. Finally the experiments with the learning process are presented and the results of the evaluation are given from some aspects of control quality and safety.


title: Training a text classifier with a single word using Twitter Lists and domain adaptation

abstract: Annotating data is a common bottleneck in building text classifiers. This is particularly problematic in social media domains, where data drift requires frequent retraining to maintain high accuracy. In this paper, we propose and evaluate a text classification method for Twitter data whose only required human input is a single keyword per class. The algorithm proceeds by identifying exemplar Twitter accounts that are representative of each class by analyzing Twitter Lists (human-curated collections of related Twitter accounts). A classifier is then fit to the exemplar accounts and used to predict labels of new tweets and users. We develop domain adaptation methods to address the noise and selection bias inherent to this approach, which we find to be critical to classification accuracy. Across a diverse set of tasks (topic, gender, and political affiliation classification), we find that the resulting classifier is competitive with a fully supervised baseline, achieving superior accuracy on four of six datasets despite using no manually labeled data.


title: CroTIS-Crowdsourcing Based Traffic Information System

abstract: Road Traffic congestion is a perennial problem across the globe. The nature of the problems associated with vehicular traffic is not uniform across the globe. In the recent past, several solutions were proposed to improve traffic management. However most of these solutions cater to the traffic scenarios in the developed countries where traffic is lane based and the issues being encountered is quite different from that of developing countries. The existing solutions cannot be readily applied to the traffic situation of developing countries. In this paper, we propose an architecture which addresses the challenges of real time traffic management of non-lane based and chaotic traffic of developing countries. The big data collected for traffic analysis can be used to provide predictive analysis and patterns to enable the Traffic Department to manage the traffic better.


title: Vision-Based Navigation II: Error Analysis for a Navigation Algorithm based on Optical-Flow and a Digital Terrain Map

abstract: The paper deals with the error analysis of a navigation algorithm that uses as input a sequence of images acquired by a moving camera and a Digital Terrain Map (DTM) of the region been imaged by the camera during the motion. The main sources of error are more or less straightforward to identify: camera resolution, structure of the observed terrain and DTM accuracy, field of view and camera trajectory. After characterizing and modeling these error sources in the framework of the CDTM algorithm, a closed form expression for their effect on the pose and motion errors of the camera can be found. The analytic expression provides a priori measurements for the accuracy in terms of the parameters mentioned above.


title: Real Time Speed Estimation of Moving Vehicles from Side View Images from an Uncalibrated Video Camera

abstract: In order to estimate the speed of a moving vehicle with side view camera images, velocity vectors of a sufficient number of reference points identified on the vehicle must be found using frame images. This procedure involves two main steps. In the first step, a sufficient number of points from the vehicle is selected, and these points must be accurately tracked on at least two successive video frames. In the second step, by using the displacement vectors of the tracked points and passed time, the velocity vectors of those points are computed. Computed velocity vectors are defined in the video image coordinate system and displacement vectors are measured by the means of pixel units. Then the magnitudes of the computed vectors in image space should be transformed to the object space to find the absolute values of these magnitudes. This transformation requires an image to object space information in a mathematical sense that is achieved by means of the calibration and orientation parameters of the video frame images. This paper presents proposed solutions for the problems of using side view camera images mentioned here.


title: Recommender systems with social regularization

abstract: Although Recommender Systems have been comprehensively analyzed in the past decade, the study of social-based recommender systems just started. In this paper, aiming at providing a general method for improving recommender systems by incorporating social network information, we propose a matrix factorization framework with social regularization. The contributions of this paper are four-fold: (1) We elaborate how social network information can benefit recommender systems; (2) We interpret the differences between social-based recommender systems and trust-aware recommender systems; (3) We coin the term Social Regularization to represent the social constraints on recommender systems, and we systematically illustrate how to design a matrix factorization objective function with social regularization; and (4) The proposed method is quite general, which can be easily extended to incorporate other contextual information, like social tags, etc. The empirical analysis on two large datasets demonstrates that our approaches outperform other state-of-the-art methods.


title: How affective technologies can influence intimate interactions and improve social connectedness

abstract: Affective computing holds the promise of creating effortless, integrated, and automatic ways of communicating emotions within our intimate social network. This could augment awareness systems and connectedness devices, reducing loneliness and improving health and well-being. Through two experiments, we investigate the effects of quantity and automaticity of emotion communication on perceived intimacy in mediated settings. In the first experiment (N=48), we manipulated the number of communicated emoticons. Results show that increases in communicated emoticon quantity lead to strong increases in perceived intimacy. In the second experiment (N=34), we compare automatic and user-initiated communication of emoticons. Results show that user-initiated communication of emoticons is experienced as more intimate than automatic communication. These results are discussed in light of the interpersonal process model of intimacy and can help the design of applications aimed at improving social interactions through affective communication technology.


title: Leaky-Wave Antennas

abstract: This paper gives a basic review and a summary of recent developments for leaky-wave antennas (LWAs). An LWA uses a guiding structure that supports wave propagation along the length of the structure, with the wave radiating or “leaking” continuously along the structure. Such antennas may be uniform, quasi-uniform, or periodic. After reviewing the basic physics and operating principles, a summary of some recent advances for these types of structures is given. Recent advances include structures that can scan to endfire, structures that can scan through broadside, structures that are conformal to surfaces, and structures that incorporate power recycling or include active elements. Some of these novel structures are inspired by recent advances in the metamaterials area.


title: Cheating, Breakup, and Divorce: Is Facebook Use to Blame?

abstract: Abstract The purpose of the present study was to investigate the relationship between using the social networking site known as Facebook and negative interpersonal relationship outcomes. A survey of 205 Facebook users aged 18–82 was conducted using a 16-question online survey to examine whether high levels of Facebook use predicted negative relationship outcomes (breakup/divorce, emotional cheating, and physical cheating). It was hypothesized that those with higher levels of Facebook use would demonstrate more negative relationship outcomes than those with lower use. The study then examined whether these relationships were mediated by Facebook-related conflict. Furthermore, the researchers examined length of relationship as a moderator variable in the aforementioned model. The results indicate that a high level of Facebook usage is associated with negative relationship outcomes, and that these relationships are indeed mediated by Facebook-related conflict. This series of relationships only holds for those...


title: Long-range IoT technologies: the dawn of

abstract: The last years have seen the widespread diusion of novel Low Power Wide Area Network (LPWAN ) technologies, which are gain- ing momentum and commercial interest as enabling technologies for the Internet of Things. In this paper we discuss some of the most interest- ing LPWAN solutions, focusing in particular on LoRa TM , one of the last born and most promising technologies for the wide-area IoT.


title: Human error and commercial aviation accidents: an analysis using the human factors analysis and classification system.

abstract: OBJECTIVE ::: The aim of this study was to extend previous examinations of aviation accidents to include specific aircrew, environmental, supervisory, and organizational factors associated with two types of commercial aviation (air carrier and commuter/ on-demand) accidents using the Human Factors Analysis and Classification System (HFACS). ::: ::: ::: BACKGROUND ::: HFACS is a theoretically based tool for investigating and analyzing human error associated with accidents and incidents. Previous research has shown that HFACS can be reliably used to identify human factors trends associated with military and general aviation accidents. ::: ::: ::: METHOD ::: Using data obtained from both the National Transportation Safety Board and the Federal Aviation Administration, 6 pilot-raters classified aircrew, supervisory, organizational, and environmental causal factors associated with 1020 commercial aviation accidents that occurred over a 13-year period. ::: ::: ::: RESULTS ::: The majority of accident causal factors were attributed to aircrew and the environment, with decidedly fewer associated with supervisory and organizational causes. Comparisons were made between HFACS causal categories and traditional situational variables such as visual conditions, injury severity, and regional differences. ::: ::: ::: CONCLUSION ::: These data will provide support for the continuation, modification, and/or development of interventions aimed at commercial aviation safety. ::: ::: ::: APPLICATION ::: HFACS provides a tool for assessing human factors associated with accidents and incidents.


title: Non-local Neural Networks

abstract: Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our non-local models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image recognition, our non-local models improve object detection/segmentation and pose estimation on the COCO suite of tasks. Code is available at this https URL .


title: Analog/RF Solutions Enabling Compact Full-Duplex Radios

abstract: In-band full-duplex sets challenging requirements for wireless communication radios, in particular their capability to prevent receiver sensitivity degradation due to self-interference (transmit signals leaking into its own receiver). Previously published self-interference rejection designs require bulky components and/or antenna structures. This paper addresses this form-factor issue. First, compact radio transceiver feasibility bottlenecks are identified analytically, and tradeoff equations in function of link budget parameters are presented. These derivations indicate that the main bottlenecks can be resolved by increasing the isolation in analog/RF. Therefore, two design ideas are proposed, which provide attractive analog/RF-isolation and allow integration in compact radios. The first design proposal targets compact radio devices, such as small-cell base stations and tablet computers, and combines a dual-port polarized antenna with a self-tunable cancellation circuit. The second design proposal targets even more compact radio devices such as smartphones and sensor network nodes. This design builds on a tunable electrical balance isolator/duplexer in combination with a single-port miniature antenna. The electrical balance circuit can be implemented for scaled CMOS technology, facilitating low cost and dense integration.


title: Ontologies: How can They be Built?

abstract: Ontologies are an important component in many areas, such as knowledge management and organization, electronic commerce and information retrieval and extraction. Several methodologies for ontology building have been proposed. In this article, we provide an overview of ontology building. We start by characterizing the ontology building process and its life cycle. We present the most representative methodologies for building ontologies from scratch, and the proposed techniques, guidelines and methods to help in the construction task. We analyze and compare these methodologies. We describe current research issues in ontology reuse. Finally, we discuss the current trends in ontology building and its future challenges, namely, the new issues for building ontologies for the Semantic Web.


title: Human-level control through deep reinforcement learning

abstract: The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.


title: A 0.6-V 82-dB 28.6- $\mu$W Continuous-Time Audio Delta-Sigma Modulator

abstract: The design of a low-voltage low-power fourth-order single-bit continuous-time Delta-Sigma modulator is presented in this paper for audio applications. The modulator employs an input-feedforward topology in order to reduce internal signal swings, thus relaxes the linearity and slew rate requirements on amplifiers leading to low-voltage operation and low-power consumption. The energy efficiency is further improved by embedding the summation of feedforward paths into the quantizer. For low-voltage operation, a gain-enhanced fully-differential amplifier and a body-driven rail-to-rail input CMFB circuit are developed. The modulator, implemented in a 0.13-μm standard CMOS technology with a core area of 0.11 mm2, achieves an 82-dB dynamic range (DR), and a 79.1-dB peak signal-to-noise and distortion ratio (SNDR) over a 20-kHz signal bandwidth. The power consumption of the modulator is 28.6 μW under a 0.6-V supply voltage. The achieved performance make it one of the best among state-of-the-art sub-1-V modulators in terms of two widely used figures of merit.


title: Predicting and Eliciting Addressee's Emotion in Online Dialogue

abstract: While there have been many attempts to estimate the emotion of an addresser from her/his utterance, few studies have explored how her/his utterance affects the emotion of the addressee. This has motivated us to investigate two novel tasks: predicting the emotion of the addressee and generating a response that elicits a specific emotion in the addressee’s mind. We target Japanese Twitter posts as a source of dialogue data and automatically build training data for learning the predictors and generators. The feasibility of our approaches is assessed by using 1099 utterance-response pairs that are built by five human workers.


title: Proving theorems by pattern recognition I

abstract: Certain preliminary results on doing mathematics by machines (“mechanical mathematics”) were reported in an earlier paper [20]. The writer suggested developing inferential analysis as a branch of applied logic and as a sister discipline of numerical analysis. This analogy rests on the basic distinction of pure existence proofs, elegant procedures which in theory always terminate, and efficient procedures which are more complex to describe but can more feasibly be carried out in practice. In contrast with pure logic, the chief emphasis of inferential analysis is on the efficiency of algorithms, which is usually attained by paying a great deal more attention to the detailed structures of the problems and their solutions, to take advantage of possible systematic short cuts. The possibilities of much more elaborate calculations by machines provide an incentive to studying a group of rather minute questions which were formerly regarded as of small theoretical interest. When the range of actual human computation was narrow, there seemed little point in obtaining faster procedures which were still far beyond what was feasible. Furthermore, on account of the versatility of machines, it now appears that as more progress is made, strategies in the search for proofs, or what are often called heuristic methods, will also gradually become part of the subject matter of inferential analysis. An analogous situation in numerical analysis would be, for example, to make the machine choose to apply different tricks such as taking the Fourier transform to obtain a solution of some differential equation.


title: Scaling SPADE to Big provenance

abstract: Provenance middleware (such as SPADE) lets individuals and applications use a common framework for reporting, storing, and querying records that characterize the history of computational processes and resulting data artifacts. Previous efforts have addressed a range of issues, from instrumentation techniques to applications in the domains of scientific reproducibility and data security. Here we report on our experience adapting SPADE to handle large provenance data sets. In particular, we describe two motivating case studies, several challenges that arose from managing provenance at scale, and our approach to address each concern.


title: A high-resolution multi-channel time-to-digital converter (TDC) for high-energy physics and biomedical imaging applications

abstract: This paper presents the design of a wide-range multi-channel time-to-digital converter (TDC) for high-energy physics and biomedical imaging applications. The TDC architecture is based on coarse-fine two-level conversion scheme. Double 10-bit gray counters are designed for coarse conversion while a multiphase sampling technology based on array of delay-locked loops is proposed for fine conversion. The resolution range is achieved from 71 ps to 142 ps by use of a reference clock from 100 MHz to 50 MHz. The measured range of the TDC is 10 µs. A prototype chip of 3-channel TDC for PET imaging system is designed and fabricated in AMS 0.35 µm CMOS technology. The area of the chip is 8.4 mm2 in size. The differential nonlinearity is ±0.1 LSB. The integral nonlinearity is ±0.1 LSB. The circuits will be extended to 64 channels for small animal PET imaging system.


title: Moringa oleifera oil: a possible source of biodiesel.

abstract: Biodiesel is an alternative to petroleum-based conventional diesel fuel and is defined as the mono-alkyl esters of vegetable oils and animal fats. Biodiesel has been prepared from numerous vegetable oils, such as canola (rapeseed), cottonseed, palm, peanut, soybean and sunflower oils as well as a variety of less common oils. In this work, Moringa oleifera oil is evaluated for the first time as potential feedstock for biodiesel. After acid pre-treatment to reduce the acid value of the M. oleifera oil, biodiesel was obtained by a standard transesterification procedure with methanol and an alkali catalyst at 60 C and alcohol/oil ratio of 6:1. M. oleifera oil has a high content of oleic acid (>70%) with saturated fatty acids comprising most of the remaining fatty acid profile. As a result, the methyl esters (biodiesel) obtained from this oil exhibit a high cetane number of approximately 67, one of the highest found for a biodiesel fuel. Other fuel properties of biodiesel derived from M. oleifera such as cloud point, kinematic viscosity and oxidative stability were also determined and are discussed in light of biodiesel standards such as ASTM D6751 and EN 14214. The 1 H NMR spectrum of M. oleifera methyl esters is reported. Overall, M. oleifera oil appears to


title: Spectral Mixture Kernels for Multi-Output Gaussian Processes

abstract: Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear combinations of independent, latent, single-output Gaussian processes (GPs). This resulted in cross-covariance functions with limited parametric interpretation, thus conflicting with the ability of single-output GPs to understand lengthscales, frequencies and magnitudes to name a few. On the contrary, current approaches to MOGP are able to better interpret the relationship between different channels by directly modelling the cross-covariances as a spectral mixture kernel with a phase shift. We extend this rationale and propose a parametric family of complex-valued cross-spectral densities and then build on Cramer's Theorem (the multivariate version of Bochner's Theorem) to provide a principled approach to design multivariate covariance functions. The so-constructed kernels are able to model delays among channels in addition to phase differences and are thus more expressive than previous methods, while also providing full parametric interpretation of the relationship across channels. The proposed method is first validated on synthetic data and then compared to existing MOGP methods on two real-world examples.


title: Benchmarking Traversal Operations over Graph Databases

abstract: A significant number of graph database systems has emerged in the past few years. Most aim at the management of the property graph data structure: where graph elements can be assigned with properties. In this paper, we address the need to compare the performance of different graph databases, and discuss the challenges of developing fair benchmarking methodologies. We believe that, compared to other database systems, the ability to efficiently traverse over the graph topology is unique to graph databases. As such, we focus our attention on the benchmarking of traversal operations. We describe the design of the graph traversal benchmark and present its results. The benchmark provides the means to compare the performance of different data management systems and gives us insight into the abilities and limitations of modern graph databases.