title: Tainting is not pointless

abstract: Pointer tainting is a form of Dynamic Information Flow Tracking used primarily to prevent software security attacks such as buffer overflows. Researchers have also applied pointer tainting to malware and virus analysis. A recent paper by Slowinska and Bos has criticized pointer tainting as a security mechanism, arguing that it is has serious, inherent false positive and false negative defects. We present a rebuttal that addresses the confusion due to the two uses of pointer tainting in security literature. We clarify that many of the arguments against pointer tainting apply only to its use as a malware and virus analysis platform, but do not apply to the application of pointer tainting to memory corruption protection. Hence, we argue that pointer tainting remains a useful and promising technique for robust protection against memory corruption attacks.


title: Convolutional neural networks for vibrational spectroscopic data analysis

abstract: In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results.


title: Bilateral LSTM: A Two-Dimensional Long Short-Term Memory Model With Multiply Memory Units for Short-Term Cycle Time Forecasting in Re-entrant Manufacturing Systems

abstract: Forecasting short-term cycle time (CT) of wafer lots is crucial for production planning and control in the wafer manufacturing. A novel recurrent neural network called “bilateral long short-term memory (bilateral LSTM)” is proposed to model a short-term cycle time forecasting (CTF) of each re-entrant period of a wafer lot. First, a two-dimensional (2-D) architecture is designed to transmit the wafer and layer correlations by using wafer and layer connections. Subsequently, aiming to store various error signals caused by the diverse CT data, a multiply memory structure is presented to extend the capacity of constant error carousel (CEC) in the LSTM model. The experiment results indicate that the proposed model outperforms conventional models in the accuracy and stability for the short-term CTF. Further comparative experiments reveal that the 2-D architecture can enhance the prediction accuracy and the multi-CEC structure can improve the forecasting stability for the short-term CTF of wafer lots.


title: Real time object scanning using a mobile phone and cloud-based visual search engine

abstract: Computer vision and human-powered services can provide blind people access to visual information in the world around them, but their efficacy is dependent on high-quality photo inputs. Blind people often have difficulty capturing the information necessary for these applications to work because they cannot see what they are taking a picture of. In this paper, we present Scan Search, a mobile application that offers a new way for blind people to take high-quality photos to support recognition tasks. To support realtime scanning of objects, we developed a key frame extraction algorithm that automatically retrieves high-quality frames from continuous camera video stream of mobile phones. Those key frames are streamed to a cloud-based recognition engine that identifies the most significant object inside the picture. This way, blind users can scan for objects of interest and hear potential results in real time. We also present a study exploring the tradeoffs in how many photos are sent, and conduct a user study with 8 blind participants that compares Scan Search with a standard photo-snapping interface. Our results show that Scan Search allows users to capture objects of interest more efficiently and is preferred by users to the standard interface.


Automatic Syllabus Classification using Support Vector Machines


The relationship between headache, depressive tendency and family function-in high school girls in Seoul-.


Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources


title: Improving Duckworth lewis method by using machine learning

abstract: There has been lot of research has been done in the area of sports with prediction. Machine learning algorithm has been applied on most of the data to predict its outcome. In cricket also there are many research has been done to find out the outcome of the match. But there are a special case in the cricket where statistic plays an important role. When the cricket match is interrupted by the rain and then again it starts during the shortage of the time it is difficult to predict the run and overs remaining in the cricket match.To over come this issue Duck worth lewis method has been introduced. In this paper we are going to discuss the short come of the method of Duck worth lewis method. In this research we are going to improve the model of duck worth lewis method with machine learning models. At end we are going to compare the Duck worth lewis method, Duck worth lewis method with run rate as a variable and Improved version of duck worth lewis method.


title: 3D human face recognition using point signature

abstract: We present a novel face recognition algorithm based on the point signature-a representation for free-form surfaces. We treat the face recognition problem as a non-rigid object recognition problem. The rigid parts of the face of one person are extracted after registering the range data sets of faces having different facial expressions. These rigid parts are used to create a model library for efficient indexing. For a test face, models are indexed from the library and the most appropriate models are ranked according to their similarity with the test face. Verification of each model face can be quickly and efficiently identified. Experimental results with range data involving six human subjects, each with four different facial expressions, have demonstrated the validity and effectiveness of our algorithm.


title: Development and control of a hand exoskeleton for rehabilitation of hand injuries

abstract: Hand injuries are a frequent problem. The great amount of hand injuries is not only a problem for the affected people but economic consequences follow because rehabilitation takes a long time. Physical therapy after an operation is associated with high personnel expenses. To improve therapy results and reduce cost of rehabilitation a hand exoskeleton was developed. The hand exoskeleton was specifically designed to accomplish requirements of medical applications. For research on control algorithms and rehabilitation programs a prototype supporting all four degrees of freedom of one finger was build. The device can be easily attached and also be adjusted to deformed and scarred hands. In view of the fact that a lot of hand injuries affect only one finger, this prototype could already be functional in physical therapy. This paper presents the construction and the control system of the hand exoskeleton and possible applications in therapy of hand injuries. For the position control a PID controller was implemented and evaluated. The resulting control system allows following of recorded trajectories with sufficient accuracy.


title: PPFS: A Scale-Out Distributed File System for Post-Petascale Systems

abstract: The convergence of high-performance computing and big data, which has become known as the field of extreme big data, is problematic in that file creation in storage systems such as distributed file systems is not optimized. That is, the large workload leads to the simultaneous creation of many files by many processes when creating checkpoints. The need to improve the file creation processes prompted us to design a scale-out distributed file system for post-petascale systems named PPFS. PPFS consists of PPMDS, a scale-out distributed metadata server, and PPOSS, a scalable distributed storage server for flash storage. High file creation performance of PPMDS was achieved by using a key-value store for metadata storage and non-blocking distributed transactions to update multiple entries simultaneously. PPOSS depends on PPOST, an object storage system that manages underlying low-level storage, such as Fusion IO ioDrive, a flash device connected through PCI express supporting OpenNVM. High file creation performance was attained by implementing the PPFS prototype using file creation optimization, termed bulk creation, to reduce the amount of communication between PPMDS and PPOSS. Moreover, to enhance the I/O performance of PPOSS when the client process and PPOSS run on the same node, PPOSS accesses the local storage device directly. The prototype implementation of PPFS with file creation optimization achieves 119,000 operations per second for file creation when using five metadata servers and 128 client processes, thereby exceeding the performance of IndexFS 2.17 times. With local access optimization, PPOSS reached its limit at a block size of 16 KiB, an improvement of 1.5 times compared to before optimization. Furthermore, this evaluation indicates that PPFS has scalability on file creation and IO performance, that is required for post-petascale systems.


title: Design and architecture of the unified modular snake robot

abstract: The design of a hyper-redundant serial-linkage snake robot is the focus of this paper. The snake, which consists of many fully enclosed actuators, incorporates a modular architecture. In our design, which we call the Unified Snake, we consider size, weight, power, and speed tradeoffs. Each module includes a motor and gear train, an SMA wire actuated bistable brake, custom electronics featuring several different sensors, and a custom intermodule connector. In addition to describing the Unified Snake modules, we also discuss the specialized head and tail modules on the robot and the software that coordinates the motion.


title: Depression recognition based on dynamic facial and vocal expression features using partial least square regression

abstract: Depression is a typical mood disorder, and the persons who are often in this state face the risk in mental and even physical problems. In recent years, there has therefore been increasing attention in machine based depression analysis. In such a low mood, both the facial expression and voice of human beings appear different from the ones in normal states. This paper presents a novel method, which comprehensively models visual and vocal modalities, and automatically predicts the scale of depression. On one hand, Motion History Histogram (MHH) extracts the dynamics from corresponding video and audio data to represent characteristics of subtle changes in facial and vocal expression of depression. On the other hand, for each modality, the Partial Least Square (PLS) regression algorithm is applied to learn the relationship between the dynamic features and depression scales using training data, and then predict the depression scale for an unseen one. Predicted values of visual and vocal clues are further combined at decision level for final decision. The proposed approach is evaluated on the AVEC2013 dataset and experimental results clearly highlight its effectiveness and better performance than baseline results provided by the AVEC2013 challenge organiser.


title: Generating Videos with Scene Dynamics

abstract: We capitalize on large amounts of unlabeled video in order to learn a model of scene dynamics for both video recognition tasks (e.g. action classification) and video generation tasks (e.g. future prediction). We propose a generative adversarial network for video with a spatio-temporal convolutional architecture that untangles the scene's foreground from the background. Experiments suggest this model can generate tiny videos up to a second at full frame rate better than simple baselines, and we show its utility at predicting plausible futures of static images. Moreover, experiments and visualizations show the model internally learns useful features for recognizing actions with minimal supervision, suggesting scene dynamics are a promising signal for representation learning. We believe generative video models can impact many applications in video understanding and simulation.


title: QCRI advanced transcription system (QATS) for the Arabic Multi-Dialect Broadcast media recognition: MGB-2 challenge

abstract: In this paper, we describe Qatar Computing Research Institute's (QCRI) speech transcription system for the 2016 Dialectal Arabic Multi-Genre Broadcast (MGB-2) challenge. MGB-2 is a controlled evaluation using 1,200 hours audio with lightly supervised transcription Our system which was a combination of three purely sequence trained recognition systems, achieved the lowest WER of 14.2% among the nine participating teams. Key features of our transcription system are: purely sequence trained acoustic models using the recently introduced Lattice free Maximum Mutual Information (LF-MMI) modeling framework; Language model rescoring using a four-gram and Recurrent Neural Network with Max- Ent connections (RNNME) language models; and system combination using Minimum Bayes Risk (MBR) decoding criterion. The whole system is built using kaldi speech recognition toolkit.


title: Compound polar codes

abstract: A capacity-achieving scheme based on polar codes is proposed for reliable communication over multi-channels which can be directly applied to bit-interleaved coded modulation schemes. We start by reviewing the ground-breaking work of polar codes and then discuss our proposed scheme. Instead of encoding separately across the individual underlying channels, which requires multiple encoders and decoders, we take advantage of the recursive structure of polar codes to construct a unified scheme with a single encoder and decoder that can be used over the multi-channels. We prove that the scheme achieves the capacity over this multi-channel. Numerical analysis and simulation results for BICM channels at finite block lengths shows a considerable improvement in the probability of error comparing to a conventional separated scheme.


title: Scalable and efficient bayes-adaptive reinforcement learning based on monte-carlo tree search

abstract: Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, planning optimally in the face of uncertainty is notoriously taxing, since the search space is enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach avoids expensive applications of Bayes rule within the search tree by sampling models from current beliefs, and furthermore performs this sampling in a lazy manner. This enables it to outperform previous Bayesian model-based reinforcement learning algorithms by a significant margin on several well-known benchmark problems. As we show, our approach can even work in problems with an in finite state space that lie qualitatively out of reach of almost all previous work in Bayesian exploration.


title: Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry

abstract: Banks are important to national, and even global, economic stability. Banking panics that follow bank insolvency or bankruptcy, especially of large banks, can severely jeopardize economic stability. Therefore, issuers and investors urgently need a credit rating indicator to help identify the financial status and operational competence of banks. A credit rating provides financial entities with an assessment of credit worthiness, investment risk, and default probability. Although numerous models have been proposed to solve credit rating problems, they have the following drawbacks: (1) lack of explanatory power; (2) reliance on the restrictive assumptions of statistical techniques; and (3) numerous variables, which result in multiple dimensions and complex data. To overcome these shortcomings, this work applies two hybrid models that solve the practical problems in credit rating classification. For model verification, this work uses an experimental dataset collected from the Bankscope database for the period 1998-2007. Experimental results demonstrate that the proposed hybrid models for credit rating classification outperform the listing models in this work. A set of decision rules for classifying credit ratings is extracted. Finally, study findings and managerial implications are provided for academics and practitioners.


title: Applying the learning rate adaptation to the matrix factorization based collaborative filtering

abstract: Matrix Factorization (MF) based Collaborative Filtering (CF) have proved to be a highly accurate and scalable approach to recommender systems. In MF based CF, the learning rate is a key factor affecting the recommendation accuracy and convergence rate; however, this essential parameter is difficult to decide, since the recommender has to keep the balance between the recommendation accuracy and convergence rate. In this work, we choose the Regularized Matrix Factorization (RMF) based CF as the base model to discuss the effect of the learning rate in MF based CF, trying to deal with the dilemma of learning rate tuning through learning rate adaptation. First of all, we empirically validate the affection caused by the change of the learning rate on the recommendation performance. Subsequently, we integrate three sophisticated learning rate adapting strategies into RMF, including the Deterministic Step Size Adaption (DSSA), the Incremental Delta Bar Delta (IDBD), and the Stochastic Meta Decent (SMD). Thereafter, by analyzing the characteristics of the parameter update in RMF, we further propose the Gradient Cosine Adaption (GCA). The experimental results on five public large datasets demonstrate that by employing GCA, RMF could maintain good balance between accuracy and convergence rate, especially with small learning rate values.


title: Malware Detection Using Deep Transferred Generative Adversarial Networks

abstract: Malicious software is generated with more and more modified features of which the methods to detect malicious software use characteristics. Automatic classification of malicious software is efficient because it does not need to store all characteristic. In this paper, we propose a transferred generative adversarial network (tGAN) for automatic classification and detection of the zero-day attack. Since the GAN is unstable in training process, often resulting in generator that produces nonsensical outputs, a method to pre-train GAN with autoencoder structure is proposed. We analyze the detector, and the performance of the detector is visualized by observing the clustering pattern of malicious software using t-SNE algorithm. The proposed model gets the best performance compared with the conventional machine learning algorithms.


title: Detection, analysis and matching of hair

abstract: We develop computational models for measuring hair appearance for comparing different people. The models and methods developed have applications to person recognition and face image indexing. An automatic hair detection algorithm is described and results reported. A multidimensional representation of hair appearance is presented and computational algorithms are described. Results on a dataset of 524 subjects are reported. Identification of people using hair attributes is compared to eigenface-based recognition along with a joint, eigenface-hair based identification.


title: Complex event processing with T-REX

abstract: Several application domains involve detecting complex situations and reacting to them. This asks for a Complex Event Processing (CEP) middleware specifically designed to timely process large amounts of event notifications as they flow from the peripheral to the center of the system, to identify the composite events relevant for the application. To answer this need we designed T-Rex, a new CEP middleware that combines expressiveness and efficiency. On the one hand, it adopts a language (TESLA) explicitly conceived to easily and naturally describe composite events. On the other hand, it provides an efficient event detection algorithm based on automata to interpret TESLA rules. Our evaluation shows that the T-Rex engine can process a large number of complex rules with a reduced overhead, even in the presence of challenging workloads.


title: Inverse Cooking: Recipe Generation From Food Images

abstract: People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously. We extensively evaluate the whole system on the large-scale Recipe1M dataset and show that (1) we improve performance w.r.t. previous baselines for ingredient prediction; (2) we are able to obtain high quality recipes by leveraging both image and ingredients; (3) our system is able to produce more compelling recipes than retrieval-based approaches according to human judgment. We make code and models publicly available.


title: Organic Charge Carriers for Perovskite Solar Cells

abstract: The photovoltaic field is currently experiencing the "perovskite revolution". These materials have been known for decades, but only recently have they been applied in solid-state solar cells to obtain outstanding power conversion efficiencies. Given that the variety of perovskites used so far is limited, a lot of attention has been devoted to the development of suitable organic charge-transport materials to improve device performance. In this article, we will focus on the most promising materials able to transport electrons or holes from a structural point of view. Thereby, we focus on organic materials owing to their ease of preparation and manipulation, and this is nicely combined with the potential tuning of their properties through chemical synthesis.


title: Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation

abstract: This research proposes a robust method for disc localization and cup segmentation that incorporates masking to avoid misclassifying areas as well as forming the structure of the cup based on edge detection. Our method has been evaluated using two fundus image datasets, namely: D-I and D-II comprising of 60 and 38 images, respectively. The proposed method of disc localization achieves an average \(F_{\mathrm{score}}\) of 0.96 and average boundary distance of 7.7 for D-I, and 0.96 and 9.1, respectively, for D-II. The cup segmentation method attains an average \(F_{\mathrm{score}}\) of 0.88 and average boundary distance of 13.8 for D-I, and 0.85 and 18.0, respectively, for D-II. The estimation errors (mean ± standard deviation) of our method for the value of vertical cup-to-disc diameter ratio against the result of the boundary by the expert of D-I and D-II have similar value, namely \(0.04 \pm 0.04\). Overall, the result of our method indicates its robustness for glaucoma evaluation.


title: AMR Dependency Parsing with a Typed Semantic Algebra

abstract: We present a semantic parser for Abstract Meaning Representations which learns to parse strings into tree representations of the compositional structure of an AMR graph. This allows us to use standard neural techniques for supertagging and dependency tree parsing, constrained by a linguistically principled type system. We present two approximative decoding algorithms, which achieve state-of-the-art accuracy and outperform strong baselines.


title: Morphological Lifting Scheme for Current Transformer Saturation Detection and Compensation

abstract: Current transformer (CT) saturation can cause protective relay mal-operation or even prevent tripping. The wave shape of the secondary current is severely distorted as the CT is forced into deep saturation when the residual flux in the core adds to the flux change caused by faults. In this paper, a morphological lifting scheme is proposed to extract features contained in the waveform of the signal. The detection of the CT saturation is accurately achieved and the points of the inflection, where the saturation begins and ends, are found with the scheme used. This paper also presents a compensation algorithm, based upon the detection results, to reconstruct healthy secondary currents. The proposed morphological lifting scheme and compensation algorithm are demonstrated on a sample power system. The simulation results clearly indicate that they can successfully detect and compensate the distorted secondary current of a saturated CT with residual flux.


title: A volumetric method for building complex models from range images

abstract: A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working with one range image at a time, we first scan-convert it to a distance function, then combine this with the data already acquired using a simple additive scheme. To achieve space efficiency, we employ a run-length encoding of the volume. To achieve time efficiency, we resample the range image to align with the voxel grid and traverse the range and voxel scanlines synchronously. We generate the final manifold by extracting an isosurface from the volumetric grid. We show that under certain assumptions, this isosurface is optimal in the least squares sense. To fill gaps in the model, we tessellate over the boundaries between regions seen to be empty and regions never observed. Using this method, we are able to integrate a large number of range images (as many as 70) yielding seamless, high-detail models of up to 2.6 million triangles.


title: Miniaturization of a Spiral Antenna Using Periodic Z-Plane Meandering

abstract: A new approach for miniaturizing an ultra-wideband spiral antenna, called the periodic spiral antenna (PSA), is presented. The combination of ${\bm{z}}$ -directed meandering of the antenna element and a tapered substrate profile provides a volumetric style of miniaturization. In this design, the distributed inductance and capacitance, and therefore the impedance match, are tightly controlled as the footprint is reduced. The effectiveness of ${\bm{z}}$ -directed meandering is confirmed through a loop antenna study that demonstrates a footprint reduction of 76.7% compared to the 57.1% reduction using planar meandering. Numerical simulations and measurements show that the PSA maintains similar performance as a traditional Archimedean spiral antenna with a miniaturization factor up to 1.2 prior to dielectric loading. A sinusoidal variation in the sidewall height of the back cavity is also shown to contribute to improved antenna performance. A 0.8–3 GHz cavity-backed PSA fabricated using a tapered ULTEM ( ${{\boldsymbol{\varepsilon}}_{\bm{r}}} = {\bf{2}}.{\bf{75}}$ ) substrate and 0.81 mm diameter copper wire is described.


A Fast Taboo Search Algorithm for the Job Shop Scheduling Problem


title: AUTOMATIC LOCALIZATION OF INDOOR SOCCER PLAYERS FROM MULTIPLE CAMERAS ∗

abstract: Nowadays, there is an ever growing quest for finding sophisticated performance evaluation tools by team sports that could give them an additional inch or a quarter of a second of advantage in a competition. Using cameras to shoot the events of a game, for instance, the teams can analyze the performance of the athletes and even extrapolate the data to obtain semantical information about the behavior of the teams themselves at relatively low costs. In this context, this paper introduces a new approach for better estimating the positions of indoor soccer players using multiple cameras at all moments of a game. The setup consists of four stationary cameras set around the soccer court. Our solution relies on individual object detectors (one per camera) working in the image coordinates and a robust fusion approach working in the world coordinates in a plane that represents the soccer court. The fusion approach relies on a gradient ascent algorithm over a multimodal bidimensional mixture of Gaussians function representing all the players in the soccer court. In the experiments, we show that the proposed solution improves standard object detector approaches and greatly reduces the mean error rate of soccer player detection to a few centimeters with respect to the actual positions of the players.


title: SPATIO-SEMANTIC COHERENCE IN THE INTEGRATION OF 3D CITY MODELS

abstract: An increasing number of applications rely on 3D geoinformation. In addition to 3D geometry, these applications particularly require complex semantic information. In the context of spatial data infrastructures the needed data are drawn from distributed sources and often are thematically and spatially fragmented. Straight forward joining of 3D objects would inevitably lead to geometrical inconsistencies such as cracks, permeations, or other inconsistencies. Semantic information can help to reduce the ambiguities for geometric integration, if it is coherently structured with respect to geometry. The paper discusses these problems with special focus on virtual 3D city models and the semantic data model CityGML, an emerging standard for the representation and the exchange of 3D city models based on ISO 191xx standards and GML3. Different data qualities are analyzed with respect to their semantic and spatial structure leading to the distinction of six categories regarding the spatio-semantic coherence of 3D city models. Furthermore, it is shown how spatial data with complex object descriptions support the integration process. The derived categories will help in the future development of automatic integration methods for complex 3D geodata.


title: End-to-end Data Integrity for File Systems: A ZFS Case Study

abstract: We present a study of the effects of disk and memory corruption on file system data integrity. Our analysis focuses on Sun's ZFS, a modern commercial offering with numerous reliability mechanisms. Through careful and thorough fault injection, we show that ZFS is robust to a wide range of disk faults. We further demonstrate that ZFS is less resilient to memory corruption, which can lead to corrupt data being returned to applications or system crashes. Our analysis reveals the importance of considering both memory and disk in the construction of truly robust file and storage systems.


title: Design of Anisotropic Thermal Conductivity in Multilayer Printed Circuit Boards

abstract: The design of anisotropic thermal conductivity in multilayer printed circuit boards (PCB) is studied, where the flow of heat is manipulated through the informed layout of circuit board electrothermal traces. Three representative circuit board configurations are considered to illustrate the basic method. A baseline circuit board (Configuration 1) comprising a heat-generating device in thermal communication with a heat sensitive device by the way of a standard electrical trace connection is studied first to characterize heat flow due to conduction and convection. Building off of this baseline structure, the thermal management advantages and disadvantages of a closed heat shield for anisotropic PCB thermal conductivity are explored using the second circuit board (Configuration 2). The design of the third multilayer circuit board (Configuration 3) with optimized anisotropic PCB thermal conductivity for enhanced heat flow control is simultaneously explored. It is experimentally shown that, for PCBs subject to free convection and power densities on the order of 1–10 W/cm2, the design technique allows for noticeable (−10 °C) reduction in the temperature of the heat sensitive device without a significant (<3 °C) increase in the heat-generating device temperature. The experimental heat transfer results are verified through simulations that further confirm and explain the functionality and limitations of the heat flow control concept. The effect of Joule heating is also investigated numerically. A conceptual framework for electrothermal codesign of multilayer PCBs with integrated heat flow control and more device-dense packaging is proposed.


title: "Residence times difference" fluxgate magnetometers

abstract: We present analytical and experimental results on fluxgate magnetometers that make use of a readout technique based on residence times. This approach allows for enhancing sensitivity to weak target signals in particular when the reduction of the sensor dimensions are considered. Our approach, exploiting the inherent nonlinear character of the bistable core dynamics, is based on the time domain characterization of the transitions between the two saturation states of the hysteresis loop that is inherent in the ferromagnetic core dynamics. This readout technique can be implemented with bias signals having lower amplitude and frequency than those used in conventional fluxgate processing schemes, thus reducing the device power requirements. The efficacy of this strategy is shown through an analytical approach and via experimental results which suggests guidelines for optimal device design and realization. The experiments have been carried out on a miniaturized laboratory fluxgate prototype; this device shows numerous desirable characteristics, including very good sensitivity and resolution, as well as ease of operation and a very low cost.


title: Unified Model for Contrast Enhancement and Denoising

abstract: In this paper, we attempt a challenging task to unify two important complementary operations, i.e. contrast enhancement and denoising, that is required in most image processing applications. The proposed method is implemented using practical analog circuit configurations that can lead to near real-time processing capabilities useful to be integrated with vision sensors. Metrics used for performance includes estimation of Residual Noise Level (RNL), Structural Similarity Index Measure (SSIM), Output-to-Input Contrast Ratio (CRo_i), and its combined score (SCD). The class of contrast stretching methods has resulted in higher noise levels (RNL ≥ 7) along with increased contrast measures (CRo-i ≥ eight times than that of the input image) and SSIM ≤ 0.52. Denoising methods generates images with lesser noise levels (RNL ≤ 0.2308), poor contrast enhancements (CRo-i ≤ 1.31) and with best structural similarity (SSIM ≥ 0.85). In contrast, the proposed model offers best contrast stretching (CRo-i = 5.83), least noise (RNL = 0.02), a descent structural similarity (SSIM = 0.6453) and the highest combined score (SCD = 169).


title: Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing

abstract: Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network (CNN) architecture to localize semantic parts in 2D image and 3D space while inferring their visibility states, given a single RGB image. Our key insight is to exploit domain knowledge to regularize the network by deeply supervising its hidden layers, in order to sequentially infer intermediate concepts associated with the final task. To acquire training data in desired quantities with ground truth 3D shape and relevant concepts, we render 3D object CAD models to generate large-scale synthetic data and simulate challenging occlusion configurations between objects. We train the network only on synthetic data and demonstrate state-of-the-art performances on real image benchmarks including an extended version of KITTI, PASCAL VOC, PASCAL3D+ and IKEA for 2D and 3D keypoint localization and instance segmentation. The empirical results substantiate the utility of our deep supervision scheme by demonstrating effective transfer of knowledge from synthetic data to real images, resulting in less overfitting compared to standard end-to-end training.


title: Fast capture of textured full-body avatar with RGB-D cameras

abstract: We present a practical system which can provide a textured full-body avatar within 3 s. It uses sixteen RGB-depth (RGB-D) cameras, ten of which are arranged to capture the body, while six target the important head region. The configuration of the multiple cameras is formulated as a constraint-based minimum set space-covering problem, which is approximately solved by a heuristic algorithm. The camera layout determined can cover the full-body surface of an adult, with geometric errors of less than 5 mm. After arranging the cameras, they are calibrated using a mannequin before scanning real humans. The 16 RGB-D images are all captured within 1 s, which both avoids the need for the subject to attempt to remain still for an uncomfortable period, and helps to keep pose changes between different cameras small. All scans are combined and processed to reconstruct the photorealistic textured mesh in 2 s. During both system calibration and working capture of a real subject, the high-quality RGB information is exploited to assist geometric reconstruction and texture stitching optimization.


title: Geo-SAGE: A Geographical Sparse Additive Generative Model for Spatial Item Recommendation

abstract: With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important means to help people discover attractive and interesting venues and events, especially when users travel out of town. However, this recommendation is very challenging compared to the traditional recommender systems. A user can visit only a limited number of spatial items, leading to a very sparse user-item matrix. Most of the items visited by a user are located within a short distance from where he/she lives, which makes it hard to recommend items when the user travels to a far away place. Moreover, user interests and behavior patterns may vary dramatically across different geographical regions. In light of this, we propose Geo-SAGE, a geographical sparse additive generative model for spatial item recommendation in this paper. Geo-SAGE considers both user personal interests and the preference of the crowd in the target region, by exploiting both the co-occurrence pattern of spatial items and the content of spatial items. To further alleviate the data sparsity issue, Geo-SAGE exploits the geographical correlation by smoothing the crowd's preferences over a well-designed spatial index structure called spatial pyramid. We conduct extensive experiments and the experimental results clearly demonstrate our Geo-SAGE model outperforms the state-of-the-art.


title: Meta-Review of Augmented Reality in Education

abstract: Augmented reality has been developed decades ago, but its implementation can be enjoyed by the wider community only in the last few years, along with the increasing number of affordable smartphones which are owned by almost everyone. Although previous studies have shown that augmented reality has the potential to improve learning in students, the benefits of education by utilizing augmented reality and the context in which this technology is more effective than other educational media is still not clear. This paper tries to discuss the topic of augmented reality in the world of education by doing a meta-review of some reviews that have been done before. This paper tries to look at the trend of augmented reality research in education and also to help researchers decide which topics to explore.


title: Probabilistic model-based sentiment analysis of twitter messages

abstract: We present a machine learning approach to sentiment classification on twitter messages (tweets). We classify each tweet into two categories: polar and non-polar. Tweets with positive or negative sentiment are considered polar. They are considered non-polar otherwise. Sentiment analysis of tweets can potentially benefit different parties, such as consumers and marketing researchers, for obtaining opinions on different products and services. We present methods for text normalization of the noisy tweets and their classification with respect to the polarity. We experiment with a mixture model approach for generation of sentimental words, which are later used as indicator features of the classification model. Based on a gold standard manually annotated ensemble of tweets, with the new approach, we obtain F-scores that are relatively 10% better than a classification baseline that uses raw word n-gram features.


title: Tiled directional flow

abstract: For a real-time visualization of one of the Dutch harbors we needed a realistic looking water surface. The old shader showed the same waves everywhere, but inside a harbor waves have many different directions and sizes. To solve this problem we needed a shader capable of visualizing flow. We developed a new algorithm called Tiled Directional Flow which has several advantages over other implementations.


title: Tri-modal Person Re-identification with RGB, Depth and Thermal Features

abstract: Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body, from depth data, we compute different soft body biometrics, and from thermal data, we extract local structural information. Then, the three information types are combined in a joined classifier. The tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios.


title: SmartProvenance: A Distributed, Blockchain Based DataProvenance System

abstract: Blockchain technology has evolved from being an immutable ledger of transactions for cryptocurrencies to a programmable interactive environment for building distributed reliable applications. Although the blockchain technology has been used to address various challenges, to our knowledge none of the previous work focused on using Blockchain to develop a secure and immutable scientific data provenance management framework that automatically verifies the provenance records. In this work, we leverage Blockchain as a platform to facilitate trustworthy data provenance collection, verification, and management. The developed system utilizes smart contracts and open provenance model (OPM) to record immutable data trails. We show that our proposed framework can securely capture and validate provenance data that prevents any malicious modification to the captured data as long as the majority of the participants are honest.


title: Virtual CNN Branching: Efficient Feature Ensemble for Person Re-Identification

abstract: In this paper we introduce an ensemble method for convolutional neural network (CNN), called"virtual branching,"which can be implemented with nearly no additional parameters and computation on top of standard CNNs. We propose our method in the context of person re-identification (re-ID). Our CNN model consists of shared bottom layers, followed by"virtual"branches, where neurons from a block of regular convolutional and fully-connected layers are partitioned into multiple sets. Each virtual branch is trained with different data to specialize in different aspects, e.g., a specific body region or pose orientation. In this way, robust ensemble representations are obtained against human body misalignment, deformations, or variations in viewing angles, at nearly no any additional cost. The proposed method achieves competitive performance on multiple person re-ID benchmark datasets, including Market-1501, CUHK03, and DukeMTMC-reID.


title: Thermal expansion of moist wood

abstract: Description of an examination carried out by measuring the linear thermal expansion of wood at constant moisture content.


title: Sensor and actuator networks - Vineyard computing: sensor networks in agricultural production

abstract: Using ethnographic research methods, the authors studied the structure of the needs and priorities of people working in a vineyard to gain a better understanding of the potential for sensor networks in agriculture. We discuss an extended study of vineyard workers and their work practices to assess the potential for sensor network systems to aid work in this environment. The major purpose is to find new directions and new topics that pervasive computing and sensor networks might address in designing technologies to support a broader range of users and activities.


title: Salamandra Robotica II: An Amphibious Robot to Study Salamander-Like Swimming and Walking Gaits

abstract: In this paper, we present Salamandra robotica II: an amphibious salamander robot that is able to walk and swim. The robot has four legs and an actuated spine that allow it to perform anguilliform swimming in water and walking on the ground. The paper first presents the new robot hardware design, which is an improved version of Salamandra robotica I. We then address several questions related to body–limb coordination in robots and animals that have a sprawling posture like salamanders and lizards, as opposed to the erect posture of mammals (e.g., in cats and dogs). In particular, we investigate how the speed of locomotion and curvature of turning motions depend on various gait parameters such as the body–limb coordination, the type of body undulation (offset, amplitude, and phase lag of body oscillations), and the frequency. Comparisons with animal data are presented, and our results show striking similarities with the gaits observed with real salamanders, in particular concerning the timing of the body’s and limbs’ movements and the relative speed of locomotion.


title: Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

abstract: Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.


title: Transalg: a Tool for Translating Procedural Descriptions of Discrete Functions to SAT (Tool Paper)

abstract: In this paper we present the Transalg system, designed to produce SAT encodings for discrete functions, written as programs in a specific language. Translation of such programs to SAT is based on propositional encoding methods for formal computing models and on the concept of symbolic execution. We used the Transalg system to make SAT encodings for a number of cryptographic functions.


title: Application for the Detection of Dangerous Driving and an Associated Gamification Framework

abstract: In today's highly technology-based world, wireless devices such as smartphones, are being utilized for solving daily problems and in making our daily lives more efficient. Although smartphones are sometimes to be blamed for vehicular accidents, they can also be used for helping to avoid accidents. In this paper we consider one such possible solution (called Project Drive), in which a smartphone based application is used to bridge the gap between negative driving detection and user motivation for safer driving behavior. Project Drive leverages a hosted web service to support the Android based application. Weather data is integrated with a modular negative driving detection component to create a point based system where positive driving is rewarded. Project Drive employs user motivation and retention strategies, such as gamification and social networking, as a means to promote safe driving. Users gain badges based on positive driving events that are seen by their contacts via the in-application social feed. Points of interest, including positive driving event areas, are visible via an interactive map. A small scale prototype was developed and used for testing of the associated algorithms as well as the social aspects of the framework.


title: Near-optimal dynamic trajectory generation and control of an omnidirectional vehicle

abstract: Abstract This paper describes a computationally inexpensive, yet high performance trajectory generation algorithm for omnidirectional vehicles. It is shown that the associated non-linear control problem can be made tractable by restricting the set of admissible control functions. The resulting problem is linear with coupled control efforts and a near-optimal control strategy is shown to be piecewise constant (bang–bang type). A very favorable trade-off between optimality and computational efficiency is achieved. The proposed algorithm is based on a small number of evaluations of simple closed-form expressions and is thus extremely efficient. The low computational cost makes this method ideal for path planning in dynamic environments.


title: Long-term working memory.

abstract: To account for the large demands on working memory during text comprehension and expert performance, the traditional models of working memory involving temporary storage must be extended to include working memory based on storage in long-term memory. In the proposed theoretical framework cognitive processes are viewed as a sequence of stable states representing end products of processing. In skilled activities, acquired memory skills allow these end products to be stored in long-term memory and kept directly accessible by means of retrieval cues in short-term memory, as proposed by skilled memory theory. These theoretical claims are supported by a review of evidence on memory in text comprehension and expert performance in such domains as mental calculation, medical diagnosis, and chess.


title: Edge computing in IoT: Preliminary results on modeling and performance analysis

abstract: Edge computing, the subsidiary model of Cloud computing, is aiming to push computational resources closer to the edge of network. Its objectives are to improve network latency and ensure efficiency of performed tasks. However Internet of Things (IoT) devices usually are of limited processing power or/and connecting to network episodically, mostly due to power saving necessity and mobility. Thus in the paper investigation of the performance of edge computing for IoT is presented. Multiple simulations by EdgeNetworkCloudSim of different topologies, their parameters, together with considered IoT device primary limitations, are carried out. Simulation results confirm the specificity of edge computations for IoT and gives some practical insights.


title: Visibility in bad weather from a single image

abstract: Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light. In computer vision, the absorption and scattering processes are commonly modeled by a linear combination of the direct attenuation and the airlight. Based on this model, a few methods have been proposed, and most of them require multiple input images of a scene, which have either different degrees of polarization or different atmospheric conditions. This requirement is the main drawback of these methods, since in many situations, it is difficult to be fulfilled. To resolve the problem, we introduce an automated method that only requires a single input image. This method is based on two basic observations: first, images with enhanced visibility (or clear-day images) have more contrast than images plagued by bad weather; second, airlight whose variation mainly depends on the distance of objects to the viewer, tends to be smooth. Relying on these two observations, we develop a cost function in the framework of Markov random fields, which can be efficiently optimized by various techniques, such as graph-cuts or belief propagation. The method does not require the geometrical information of the input image, and is applicable for both color and gray images.


title: Forensic analysis of Telegram Messenger on Android smartphones

abstract: Abstract In this paper we present a methodology for the forensic analysis of the artifacts generated on Android smartphones by Telegram Messenger, the official client for the Telegram instant messaging platform, which provides various forms of secure individual and group communication, by means of which both textual and non-textual messages can be exchanged among users, as well as voice calls. Our methodology is based on the design of a set of experiments suitable to elicit the generation of artifacts and their retention on the device storage, and on the use of virtualized smartphones to ensure the generality of the results and the full repeatability of the experiments, so that our findings can be reproduced and validated by a third-party. In this paper we show that, by using the proposed methodology, we are able (a) to identify all the artifacts generated by Telegram Messenger, (b) to decode and interpret each one of them, and (c) to correlate them in order to infer various types of information that cannot be obtained by considering each one of them in isolation. As a result, in this paper we show how to reconstruct the list of contacts, the chronology and contents of the messages that have been exchanged by users, as well as the contents of files that have been sent or received. Furthermore, we show how to determine significant properties of the various chats, groups, and channels in which the user has been involved (e.g., the identifier of the creator, the date of creation, the date of joining, etc.). Finally, we show how to reconstruct the log of the voice calls made or received by the user. Although in this paper we focus on Telegram Messenger, our methodology can be applied to the forensic analysis of any application running on the Android platform.


Explorer Large Scale Personality Classification of Bloggers


title: Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth

abstract: Enabled by Web 2.0 technologies social media provide an unparalleled platform for consumers to share their product experiences and opinions---through word-of-mouth (WOM) or consumer reviews. It has become increasingly important to understand how WOM content and metrics thereof are related to consumer purchases and product sales. By integrating network analysis with text sentiment mining techniques, we propose product comparison networks as a novel construct, computed from consumer product reviews. To test the validity of these product ranking measures, we conduct an empirical study based on a digital camera dataset from Amazon.com. The results demonstrate significant linkage between network-based measures and product sales, which is not fully captured by existing review measures such as numerical ratings. The findings provide important insights into the business impact of social media and user-generated content, an emerging problem in business intelligence research. From a managerial perspective, our results suggest that WOM in social media also constitutes a competitive landscape for firms to understand and manipulate.


title: Cortical Proprioceptive Processing Is Altered by Aging

abstract: Proprioceptive perception is impaired with aging, but little is known about aging-related deterioration of proprioception at the cortical level. Corticokinematic coherence (CKC) between limb kinematic and magnetoencephalographic (MEG) signals reflects cortical processing of proprioceptive afference. We, thus, compared CKC strength to ankle movements between younger and older subjects, and examined whether CKC predicts postural stability. Fifteen younger (range 18–31 yrs) and eight older (66–73 yrs) sedentary volunteers were seated in MEG, while their right and left ankle joints were moved separately at 2 Hz (for 4 min each) using a novel MEG-compatible ankle-movement actuator. Coherence was computed between foot acceleration and MEG signals. CKC strength at the movement frequency (F0) and its first harmonic (F1) was quantified. In addition, postural sway was quantified during standing eyes-open and eyes-closed tasks to estimate motor performance. CKC peaked in the gradiometers over the vertex, and was significantly stronger (~76%) at F0 for the older than younger subjects. At F1, only the dominant-leg CKC was significantly stronger (~15%) for the older than younger subjects. In addition, CKC (at F1) was significantly stronger in the non-dominant than dominant leg, but only in the younger subjects. Postural sway was significantly (~64%) higher in the older than younger subjects when standing with eyes closed. Regression models indicated that CKC strength at F1 in the dominant leg and age were the only significant predictors for postural sway Our results indicated that aging-related cortical-proprioceptive processing is altered by aging. Stronger CKC may reflect poorer cortical-proprioceptive processing, and not solely the amount of proprioceptive afference as suggested earlier. In combination with ankle-movement actuator, CKC can be efficiently used to unravel proprioception-related-neuronal mechanisms and the related plastic changes in aging, rehabilitation, motor-skill acquisition, motor disorders etc.


title: Physiological regulation of the heat shock response by glutamine: implications for chronic low-grade inflammatory diseases in age-related conditions

abstract: Aging is an intricate process modulated by different molecular and cellular events, such as genome instability, epigenetic and transcriptional changes, molecular damage, cell death and senescence, inflammation, and metabolic dysfunction. Particularly, protein quality control (chaperone systems) tends to be negatively affected by aging, thus leading to cellular senescence in metabolic tissues and, as a consequence, to the increasing dissemination of inflammation throughout the body. The heat shock (HS) response and its associated expression of the 70 kDa family of heat shock proteins (HSP70), which are anti-inflammatory molecular chaperones, are found to be markedly decreased during muscle inactivity and aging, while evidence supports the loss of HSP70 as a key mechanism which may drive muscle atrophy, contractile dysfunction, and reduced regenerative capacity. In addition, abnormal stress response is linked with higher incidence of neurodegenerative diseases as well as low-grade inflammatory diseases that are associated with physical inactivity and obesity. Therefore, strategies to increase or, at least, to maintain the levels of HSP70, and its accompanying HS response to stress, are key to reduce biological cell dysfunctions that occur in aging. In this sense, physical exercise is of note as it is the most powerful inducer of the HS response, comparable only to heat stress and fever-like conditions. On the other hand, the amino acid l-glutamine, whose production within the skeletal muscle and liberation into the blood stream is dependent on muscle activity, is a potentializer of HSP70 expression and HS response, particularly via its entering in hexosamine biosynthetic pathway (HBP). Herein, we discuss the collaborative role of glutamine (and its donors/precursors) and physical exercise (mostly responsible for glutamine release into the circulation) as potential tools to increase HSP70 expression and the HS response in the elderly.


title: Semi-dense Piecewise Planar Stereo Reconstruction Using SymStereo and PEARL

abstract: We propose an algorithm for two-frame stereo reconstruction that generates an accurate semi-dense piecewise planar model of the 3D scene. First, we use the SymStereo framework [1], [2] for obtaining a semi-dense stereo reconstruction, which exploits symmetry cues for estimating depth along virtual cut planes. Next, the energy-based multi-model fitting algorithm PEARL [3] is applied for estimating the plane models, and labeling the lines of intersection between the virtual and epipolar planes. This procedure involves three steps: (i) generate an initial set of plane hypotheses by extracting line cuts from each virtual cut plane, (ii) inlier classification, and (iii) a plane model refinement step. We provide experiments of our approach on a variety of challenging indoor and outdoor stereo pairs. The results show that we successfully detect and reconstruct the planar surfaces contained in the scene, as well as are able to obtain plausible piecewise planar descriptions of non-planar objects. In contrast to existing approaches, the accuracy of our plane hypotheses are not limited by an initial 3D reconstruction, but are iteratively refined after each plane labeling.


title: Doubly-Efficient zkSNARKs Without Trusted Setup

abstract: We present a zero-knowledge argument for NP with low communication complexity, low concrete cost for both the prover and the verifier, and no trusted setup, based on standard cryptographic assumptions. Communication is proportional to d log G (for d the depth and G the width of the verifying circuit) plus the square root of the witness size. When applied to batched or data-parallel statements, the prover's runtime is linear and the verifier's is sub-linear in the verifying circuit size, both with good constants. In addition, witness-related communication can be reduced, at the cost of increased verifier runtime, by leveraging a new commitment scheme for multilinear polynomials, which may be of independent interest. These properties represent a new point in the tradeoffs among setup, complexity assumptions, proof size, and computational cost. We apply the Fiat-Shamir heuristic to this argument to produce a zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) in the random oracle model, based on the discrete log assumption, which we call Hyrax. We implement Hyrax and evaluate it against five state-of-the-art baseline systems. Our evaluation shows that, even for modest problem sizes, Hyrax gives smaller proofs than all but the most computationally costly baseline, and that its prover and verifier are each faster than three of the five baselines.


title: Online Segment to Segment Neural Transduction

abstract: We introduce an online neural sequence to sequence model that learns to alternate between encoding and decoding segments of the input as it is read. By independently tracking the encoding and decoding representations our algorithm permits exact polynomial marginalization of the latent segmentation during training, and during decoding beam search is employed to find the best alignment path together with the predicted output sequence. Our model tackles the bottleneck of vanilla encoder-decoders that have to read and memorize the entire input sequence in their fixed-length hidden states before producing any output. It is different from previous attentive models in that, instead of treating the attention weights as output of a deterministic function, our model assigns attention weights to a sequential latent variable which can be marginalized out and permits online generation. Experiments on abstractive sentence summarization and morphological inflection show significant performance gains over the baseline encoder-decoders.


title: Adaptive Paired Page Prebackup Scheme for MLC NAND Flash Memory

abstract: Multilevel cell (MLC) NAND flash memory is more cost effective compared with single-level cell NAND flash memory as it can store two or more bits in a memory cell. However, in MLC flash memory, a programming operation can corrupt the paired page under abnormal termination. In order to solve the paired page problem, a backup scheme is generally used, which inevitably causes performance degradation and shortens the lifespan of flash memory. In this paper, we propose a more efficient paired page prebackup scheme for MLC flash memory. It adaptively exploits interleaving, copyback operations, and parity data to reduce the prebackup overhead. In experiments, the proposed scheme reduced the backup overhead by up to 78%.


Summarization using Attentive Sequence-to-Sequence RNNs


title: Functional electrical stimulation after spinal cord injury: current use, therapeutic effects and future directions

abstract: Repair of the injured spinal cord by regeneration therapy remains an elusive goal. In contrast, progress in medical care and rehabilitation has resulted in improved health and function of persons with spinal cord injury (SCI). In the absence of a cure, raising the level of achievable function in mobility and self-care will first and foremost depend on creative use of the rapidly advancing technology that has been so widely applied in our society. Building on achievements in microelectronics, microprocessing and neuroscience, rehabilitation medicine scientists have succeeded in developing functional electrical stimulation (FES) systems that enable certain individuals with SCI to use their paralyzed hands, arms, trunk, legs and diaphragm for functional purposes and gain a degree of control over bladder and bowel evacuation. This review presents an overview of the progress made, describes the current challenges and suggests ways to improve further FES systems and make these more widely available.


title: Discovery of Evolving Semantics through Dynamic Word Embedding Learning

abstract: During the course of human language evolution, the semantic meanings of words keep evolving with time. The understanding of evolving semantics enables us to capture the true meaning of the words in different usage contexts, and thus is critical for various applications, such as machine translation. While it is naturally promising to study word semantics in a time-aware manner, traditional methods to learn word vector representation do not adequately capture the change over time. To this end, in this paper, we aim at learning time-aware vector representation of words through dynamic word embedding modeling. Specifically, we first propose a method that captures time-specific semantics and across-time alignment simultaneously in a way that is robust to data sparsity. Then, we solve the resulting optimization problem using a scalable coordinate descent method. Finally, we perform the empirical study on New York Times data to learn the temporal embeddings and develop multiple evaluations that illustrate the semantic evolution of words, discovered from news media. Moreover, our qualitative and quantitative tests indicate that the our method not only reliably captures the semantic evolution over time, but also onsistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.


title: Centralized, Parallel, and Distributed Information Processing during Collective Sensemaking

abstract: Widespread rumoring can hinder attempts to make sense of what is going on during disaster scenarios. Understanding how and why rumors spread in these contexts could assist in the design of systems that facilitate timely and accurate sensemaking. We address a basic question in this line: To what extent does rumor evolution occur (1) through reliance on a centralized information source, (2) in parallel information silos, or (3) through a web of complex informational interactions? We develop a conceptual model and associated analysis algorithms that allow us to distinguish between these possibilities. We analyze a case of rumoring on Twitter during the Boston Marathon Bombing. We find that rumor spreading was predominantly a parallel process in this case, which is consistent with a hypothesis that information silos may underlie the persistence of false rumors. Special attention towards detecting and resolving parallel information threads during collective sensemaking may hence be warranted.


title: Performance analysis of high gain isolated soft switched DC-DC converter for thermoelectric energy harvesting system using MPPT control strategies

abstract: This paper proposes a soft-switched isolated step-up DC-DC converter for thermoelectric energy harvesting application. The soft switching technique such as Zero-Current Switching (ZCS), Zero Voltage Switching (ZVS) schemes together with the accurate MPPT algorithm is adopted for enhancing the overall efficiency of the proposed converter. A ThermoElectric generator (TEG) is used as a DC power source. TEG is a low-voltage and high-current solid-state device, hence a high gain isolated DC-DC boost converter has been used to increase the overall efficiency of the converter. The Low-rated lossless snubber is also used in the proposed converter; hence it is expected to achieve high efficiency for step-up applications. The conversion efficiency obtained is about 92%. Perturb and Observe (P&O), Incremental Conductance (INC) and Modified Incremental Conductance (MINC) tracking techniques are incorporated in this study to track the maximum power from the ThermoElectric source. The proposed converter has been modeled and simulated using PSIM software for investigating their performance effectiveness and the obtained results are presented in this paper.


title: A closed-form solution to uncalibrated photometric stereo via diffuse maxima

abstract: In this paper we propose a novel solution to uncalibrated photometric stereo. Our approach is to eliminate the so-called generalized bas relief (GBR) ambiguity by exploiting points where the Lambertian reflection is maximal. We demonstrate several noteworthy properties of these maxima: 1) Closed-form solution: A single diffuse maximum constrains the GBR ambiguity to a semi-circle in 3D space; 2) Efficiency: As few as two diffuse maxima in different images identify a unique solution; 3) GBR-invariance: The estimation error of the GBR parameters is completely independent of the true parameters. Furthermore, our algorithm is remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80% of the observations). The method is validated on real data and achieves state-of-the-art results.


title: A Deep Learning SAR Target Classification Experiment on MSTAR Dataset

abstract: Yet another deep learning method is proposed in this paper for the problem of automated target recognition (ATR) in Synthetic Aperture Radar (SAR) images. Deep convolutional neural networks (CNN) classifiers have been demonstrated on benchmark datasets like MSTAR to outperform classical machine learning algorithms based on formal feature extraction. Most of these deep learning solutions use amplitude information only and reverse the SAR classification problem to a simple image classification task. In this paper we analyze the potential of using additional radar information, such as phase information, in the deep learning process.


title: Image classification with CNN-based Fisher vector coding

abstract: Fisher vector coding methods have been demonstrated to be effective for image classification. With the help of convolutional neural networks (CNN), several Fisher vector coding methods have shown state-of-the-art performance by adopting the activations of a single fully-connected layer as region features. These methods generally exploit a diagonal Gaussian mixture model (GMM) to describe the generative process of region features. However, it is difficult to model the complex distribution of high-dimensional feature space with a limited number of Gaussians obtained by unsupervised learning. Simply increasing the number of Gaussians turns out to be inefficient and computationally impractical. To address this issue, we re-interpret a pre-trained CNN as the probabilistic discriminative model, and present a CNN based Fisher vector coding method, termed CNN-FVC. Specifically, activations of the intermediate fully-connected and output soft-max layers are exploited to derive the posteriors, mean and covariance parameters for Fisher vector coding implicitly. To further improve the efficiency, we convert the pre-trained CNN to a fully convolutional one to extract the region features. Extensive experiments have been conducted on two standard scene benchmarks (i.e. SUN397 and MIT67) to evaluate the effectiveness of the proposed method. Classification accuracies of 60.7% and 82.1% are achieved on the SUN397 and MIT67 benchmarks respectively, outperforming previous state-of-the-art approaches. Furthermore, the method is complementary to GMM-FVC methods, allowing a simple fusion scheme to further improve performance to 61.1% and 83.1% respectively.


title: Analysis of Factor Influencing the Tablet Acceptance for Library Information Services: A Combination of UTAUT and TTF Model

abstract: Increasingly, powerful mobile technology, like the tablet, empowers learners to seek information in mobile learning scenarios virtually anywhere and anytime. It is important for the academic library to look at how students use their tablets for library information seeking tasks to fully understand the nature of the patron's task and the need for the tablet. This study investigates the function of mobile technology based on the information seeking task, with particular reference to the appropriateness of tablet characteristics for the information seeking task in a library online database, downloading and reading from the screen, the effect of the patron's perception on their behavioural intention (BI) to use the tablet, and a measure of the patron's acceptance. Adapting the unified theory of acceptance and use of technology (UTAUT) model requires re-specification to suit a specific information system. The re-specific model introduces new constructs and new measuring instruments. New constructs were used for exogenous predictors of the UTAUT variables as well as including integration variables namely technology characteristics and task characteristics that were explicitly contained in the task-technology fit (TTF) model. These extensions and integrations of UTAUT have been valuable in expanding our understanding of technology adoption and extending the boundaries of the theory. Our research found that TTF and performance expectancy (PE) have a direct effect on actual use (AU). In addition, we also found a significant effect of the TTF on PE which in turn influences BI to accept the tablet for library information seeking tasks. This shows the importance of TTF. Besides, the results showed that interaction with library search systems revealed the strongest effect among the exogenous variables, which ultimately determines the AU of the tablet for library information seeking tasks.


title: Life cycle assessment of renewable energy sources

abstract: Importance of Life Cycle Assessment of Renewable Energy Sources.- Key Issues in Conducting Life Cycle Assessment of Renewable Energy Sources.- Life Cycle Assessment of Agricultural Production Systems with Main Emphasis in Biogas and Bioethanol Production.- Life Cycle Assessment of Biomethane from Lignocellulosic Biomass.- Life Cycle Assessment of Biodiesel from Palm Oil.- Environmental Sustainability Assessment of Ethanol from Cassava and Sugarcane Molasses in a Life Cycle Perspective.- Comparison of Algal Biodiesel Production Pathways using Life Cycle Assessment Tool.- Sustainability of (H2+CH4) by Anaerobic Digestion via EROI approach and LCA evaluations.- Life cycle assessment of wind energy.- Comparing various indicators for the LCA of residential photovoltaic systems.- Hydropower Life-Cycle Inventories: Methodological Considerations and Results Based on a Brazilian Experience.- Comparison of Various Life Cycle Assessment of Different Renewable Energy Sources.


title: Interprofessional Education for Whom? — Challenges and Lessons Learned from Its Implementation in Developed Countries and Their Application to Developing Countries: A Systematic Review

abstract: BACKGROUND ::: Evidence is available on the potential efficacy of interprofessional education (IPE) to foster interprofessional cooperation, improve professional satisfaction, and improve patient care. While the intention of the World Health Organization (WHO) is to implement IPE in all countries, evidence comes from developed countries about its efficiency, challenges, and barriers to planning and implementing IPE. We therefore conducted this review to examine challenges of implementing IPE to suggest possible pathways to overcome the anticipated challenges in developing countries. ::: ::: ::: METHODS ::: We searched for literatures on IPE in PubMed/MEDLINE, CINAHL, PsycINFO, and ERIC databases. We examined challenges or barriers and initiatives to overcome them so as to suggest methods to solve the anticipated challenges in developing countries. We could not conduct a meta-analysis because of the qualitative nature of the research question and the data; instead we conducted a meta-narrative of evidence. ::: ::: ::: RESULTS ::: A total of 40 out of 2,146 articles were eligible for analyses in the current review. Only two articles were available from developing countries. Despite the known benefits of IPE, a total of ten challenges or barriers were common based on the retrieved evidence. They included curriculum, leadership, resources, stereotypes and attitudes, variety of students, IPE concept, teaching, enthusiasm, professional jargons, and accreditation. Out of ten, three had already been reported in developing countries: IPE curriculum, resource limitations, and stereotypes. ::: ::: ::: CONCLUSION ::: This study found ten important challenges on implementing IPE. They are curriculum, leadership, resources, stereotypes, students' diversity, IPE concept, teaching, enthusiasm, professional jargons, and accreditation. Although only three of them are already experienced in developing countries, the remaining seven are potentially important for developing countries, too. By knowing these challenges and barriers in advance, those who implement IPE programs in developing countries will be much more prepared, and can enhance the program's potential success.


Together we stand: Siamese Networks for Similar Question Retrieval


title: Stationary and nonstationary learning characteristics of the LMS adaptive filter

abstract: This paper describes the performance characteristics of the LMS adaptive filter, a digital filter composed of a tapped delay line and adjustable weights, whose impulse response is controlled by an adaptive algorithm. For stationary stochastic inputs, the mean-square error, the difference between the filter output and an externally supplied input called the "desired response," is a quadratic function of the weights, a paraboloid with a single fixed minimum point that can be sought by gradient techniques. The gradient estimation process is shown to introduce noise into the weight vector that is proportional to the speed of adaptation and number of weights. The effect of this noise is expressed in terms of a dimensionless quantity "misadjustment" that is a measure of the deviation from optimal Wiener performance. Analysis of a simple nonstationary case, in which the minimum point of the error surface is moving according to an assumed first-order Markov process, shows that an additional contribution to misadjustment arises from "lag" of the adaptive process in tracking the moving minimum point. This contribution, which is additive, is proportional to the number of weights but inversely proportional to the speed of adaptation. The sum of the misadjustments can be minimized by choosing the speed of adaptation to make equal the two contributions. It is further shown, in Appendix A, that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value. When the eigenvalues are highly disparate (λ max /λ min > 10), an algorithm similar to LMS but based on Newton's method would approach this theoretical limit very closely.


title: Structure from Motion with Line Segments Under Relaxed Endpoint Constraints

abstract: We present a novel structure from motion pipeline, which estimates motion and wiry 3D structure from imaged line segments across multiple views. Although the position and orientation of line segments can be determined more accurately than point features, the instability of their endpoints and the fact that lines are not constrained by epipolar geometry diverted most research focus away to point-based methods. In our approach, we tackle the problem of instable endpoints by utilizing relaxed constraints on their positions, both during matching and as well in the following bundle adjustment stage. Furthermore, we gain efficiency in estimating trifocal image relations by decoupling rotation and translation. To this end, a novel linear solver for relative translation estimation given rotations from five line correspondences in three views is introduced. Extensive experiments on long image sequences show that our line-based structure from motion pipeline advantageously complements point-based methods, giving more meaningful 3D representation for indoor scenarios.


title: A virtual try-on system in augmented reality using RGB-D cameras for footwear personalization

abstract: Abstract This paper presents a system for design evaluation of footwear using commercial depth-sensing technologies. In a mixed reality environment, the system allows users to virtually try on 3D shoe models in a live video stream. A two-stage object tracking algorithm was developed to correctly align shoe models to moving feet during the try-on process. Color markers on the user's foot enabled markerless tracking. Tracking was driven by an iterative closest point (ICP) algorithm that superimposed the captured depth data and predefined reference foot models. Test data showed that the two-stage approach resulted in increased positional accuracy compared with tracking using only surface registration. Trimming the reference model using the instant view angle increased the computational efficiency of the ICP algorithm. The proposed virtual try-on function is an effective tool for realizing human-centered design. This study also demonstrated a new application of RGB-D cameras to product design.


title: Common-Centroid Capacitor Layout Generation Considering Device Matching and Parasitic Minimization

abstract: In analog layout design, the accuracy of capacitance ratios correlates closely with both the matching properties among the ratioed capacitors and the induced parasitics due to interconnecting wires. However, most of the previous works only emphasized the matching properties of a common-centroid placement, but ignored the induced parasitics after it is routed. This paper addresses the parasitic issue in addition to device matching during common-centroid capacitor layout generation. To effectively minimize the routing-induced parasitics, a novel common-centroid placement style, distributed connected unit capacitors, is presented. Based on the placement style, the ratioed capacitor layout generation flow and algorithms are proposed to simultaneously optimize the matching properties of a common-centroid placement and minimize the induced parasitics. Experimental results show that the proposed approach can greatly reduce area, wirelength, and routing-induced parasitics, and guarantee the best matching quality after routing.


CONTRIBUTIONS OF BANKING SECTOR IN ECONOMIC GROWTH : A Case of Pakistan


title: Continuous authentication of mobile user: Fusion of face image and inertial Measurement Unit data

abstract: Mobile devices can carry large amounts of personal data, but are often left unsecured. PIN locks are inconvenient to use and thus have seen low adoption (33% of users). While biometrics are beginning to be used for mobile device authentication, they are used only for initial unlock. Mobile devices secured with only login authentication are still vulnerable to data theft when in an unlocked state. This paper introduces our work on a face-based continuous authentication system that operates in an unobtrusive manner. We present a methodology for fusing mobile device (unconstrained) face capture with gyroscope, accelerometer, and magnetometer data to correct for camera orientation and, by extension, the orientation of the face image. Experiments demonstrate (i) improvement of face recognition accuracy from face orientation correction, and (ii) efficacy of the prototype continuous authentication system.


title: Towards Multi-Purpose Spectral Rhythm Features: An Application to Dance Style, Meter and Tempo Estimation

abstract: This paper addresses the extraction of multipurpose spectral rhythm features that simultaneously tackle a variety of rhythm analysis tasks, namely, dance style classification, meter estimation, and tempo estimation. The term spectral rhythm features emanates from the origin of the extracted features, which is the periodicity function PF, a spectral representation that encapsulates the salience of the rhythm frequencies. Two dimensionality reduction techniques applied on the PF to extract expressive and compact features are compared, namely, a linear transformation resulting from Principal Component Analysis and a nonlinear mapping derived from a Restricted Boltzmann Machine. Subsequently, the derived features were used as input to an SVM classifier for each task. Moreover, an additional method is proposed that reformulates the well-studied tempo estimation task as a combination of multiple binary classification sub-problems. Evaluation was performed on a large number of datasets demonstrating that the same set of features learned from the PF provide a robust rhythmic representation that achieved comparable results to the current state-of-the-art methods for the aforementioned tasks.


title: Host intrusion detection using system call argument-based clustering combined with Bayesian classification

abstract: We deal in this paper with anomaly-based host intrusion detection using system call traces produced by a host's kernel. In addition to the sequences, we leverage system call arguments, contextual information and domain level knowledge to produce clusters for each individual system call. These clusters are then used to rewrite process sequences of system calls obtained from kernel logs. The new sequences are then fed to a naive Bayes supervised classifier (SC2.2) that builds class conditional probabilities from Markov modeling of system call sequences. The results of our proposed two-stage (that is clustering followed by classification) intrusion detection system on the 1999 DARPA dataset from the MIT Lincoln Lab show significant performance improvements in terms of false positive rate, while maintaining a high detection rate when compared with other classifiers. The two-stage classifier fares also better than classification alone with SC2.2 on system calls without arguments and contextual knowledge.


title: Specialized Support Vector Machines for Open-set Recognition

abstract: Often, when dealing with real-world recognition problems, we do not need, and often cannot have, knowledge of the entire set of possible classes that might appear during operational testing. In such cases, we need to think of robust classification methods able to deal with the"unknown"and properly reject samples belonging to classes never seen during training. Notwithstanding, almost all existing classifiers to date were mostly developed for the closed-set scenario, i.e., the classification setup in which it is assumed that all test samples belong to one of the classes with which the classifier was trained. In the open-set scenario, however, a test sample can belong to none of the known classes and the classifier must properly reject it by classifying it as unknown. In this work, we extend upon the well-known Support Vector Machines (SVM) classifier and introduce the Specialized Support Vector Machines (SSVM), which is suitable for recognition in open-set setups. SSVM balances the empirical risk and the risk of the unknown and ensures that the region of the feature space in which a test sample would be classified as known (one of the known classes) is always bounded, ensuring a finite risk of the unknown. In this work, we also highlight the properties of the SVM classifier related to the open-set scenario, and provide necessary and sufficient conditions for an RBF SVM to have bounded open-space risk.


title: Differential-drive CMOS rectifier for UHF RFIDs with 66% PCE at −12 dBm Input

abstract: A high efficiency differential CMOS rectifier circuit for UHF RFIDs was developed. The rectifier has a cross-coupled bridge configuration and is driven by a differential RF input. Differential-drive topology enables simultaneous low ON-resistance and small reverse leakage of diode-connected MOS transistors, resulting in large power conversion efficiency(PCE), especially under small RF input power conditions. The differential-drive rectifier was fabricated with 0.18-mum CMOS technology, and the measured performance was compared with those of other types of rectifiers. Dependence of the PCE on an input RF signal frequency and output loading conditions was also evaluated. 66% of PCE was achieved under conditions of 953 MHz, -12 dBm RF input and 10 KOmega DC output load. This is twice as large as that of the state-of-the-art rectifier circuit. The peak PCE increases with a decrease in operation frequency and with an increase in output load resistance.


title: A multipath based height estimation of targets for radar systems

abstract: This paper presents an approach to classify the heights of targets with radar systems. The algorithm is based on the analysis of the superposition of several reflections at a high target. The result is a superposition of the different received signals. On the contrary to a high target, a low target has only one reflection point and one propagation path. In this paper, a technique is proposed to detect the superposition and consequently classify targets as low or high. Finally the algorithm is evaluated with measurements.


title: Lithium addition in antidepressant-resistant depression: Effects on platelet 5-HT, plasma 5-HT and plasma 5-HIAA concentration

abstract: The efficacy of the addition of lithium to an established course of antidepressant treatment can be explained by a synergistic effect of the two drugs on central 5-HT neurotransmission. In the present study we investigated the effect of lithium addition on the 5-HT concentration in plasma and platelets and the concentration of 5-HIAA. Thirty-nine depressed inpatients who fulfilled the DSM-IV criteria for major depressive disorder and who did not respond to monotherapy with either imipramine or fluvoxamine participated in this study. Concentration of 5-HT in both plasma and platelets did not change significantly during lithium addition. The 5-HT ratio (plasma concentration/platelet concentration) shows a small non-significant increase after 3 weeks lithium addition. The mean concentration of 5-HIAA shows a significant increase during lithium addition; with no difference between the imipramine and the fluvoxamine sample. The increments in 5-HIAA concentration during lithium addition are indicative of an increased 5-HT turnover.


title: Diagnostic accuracy in child sexual abuse medical evaluation: Role of experience, training, and expert case review

abstract: OBJECTIVES: (1) The purpose of this study was to assess the ability of clinicians who examine children for suspected sexual abuse to recognize and interpret normal and abnormal ano-genital findings in magnified photographs using an online survey format. (2) Determine which factors in education, clinical practice, and case review correlate with correct responses to the survey questions. METHODS: Between July and December 2007, medical professionals participated in a web-based survey. Participants answered questions regarding their professional background, education, clinical experience, and participation in case review. After viewing photographs and clinical information from 20 cases, participants answered 41 questions regarding diagnosis and medical knowledge. Answers chosen by an expert panel were used as the correct answers for the survey. RESULTS: The mean number of correct answers among the 141 first-time survey respondents was 31.6 (SD 5.9, range 15-41). Child Abuse Pediatricians (CAP) had mean total scores which were significantly higher than Pediatricians (Ped) (34.8 vs. 30.1, p Language: en


title: Accurate and Linear Time Pose Estimation from Points and Lines

abstract: The Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3D-to-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms, the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.


title: The Role of Interactivity in Student Satisfaction and Persistence in Online Learning

abstract: Enrollment in online courses is rapidly increasing and attrition rates remain high. This paper presents a literature review addressing the role of interactivity in student satisfaction and persistence in online learning. Empirical literature was reviewed through the lens of Bandura's social cognitive theory, Anderson's interaction equivalency theorem, and Tinto's social integration theory. Findings suggest that interactivity is an important component of satisfaction and persistence for online learners, and that preferences for types of online interactivity vary according to type of learner. Student–instructor interaction was also noted to be a primary variable in online student satisfaction and persistence.


title: Gremlin: Systematic Resilience Testing of Microservices

abstract: Modern Internet applications are being disaggregated into a microservice-based architecture, with services being updated and deployed hundreds of times a day. The accelerated software life cycle and heterogeneity of language runtimes in a single application necessitates a new approach for testing the resiliency of these applications in production infrastructures. We present Gremlin, a framework for systematically testing the failure-handling capabilities of microservices. Gremlin is based on the observation that microservices are loosely coupled and thus rely on standard message-exchange patterns over the network. Gremlin allows the operator to easily design tests and executes them by manipulating inter-service messages at the network layer. We show how to use Gremlin to express common failure scenarios and how developers of an enterprise application were able to discover previously unknown bugs in their failure-handling code without modifying the application.