title: RC-Delay Chain-Based RF Time to Digital Converter

abstract: An RC delay Chain high-speed Time-to-Digital Converter (TDC) is presented. It focuses on the detection of phase difference among Radio-frequency range oscillators. Compared with other TDC solutions, the RC-delay chain can provide configurable granularity, higher resolution, as well as simplicity of implementation.


title: Single-Phase Single-Stage Transformer less Grid-Connected PV System

abstract: In this paper, a single-phase, single-stage current source inverter-based photovoltaic system for grid connection is proposed. The system utilizes transformer-less single-stage conversion for tracking the maximum power point and interfacing the photovoltaic arrays to the grid. The maximum power point is maintained with a fuzzy logic controller. A proportional-resonant controller is used to control the current injected into the grid. To improve the power quality and system efficiency, a double-tuned parallel resonant circuit is proposed to attenuate the second- and fourth- order harmonics at the inverter dc side. A modified carrier-based modulation technique for the current source inverter is proposed to magnetize the dc-link inductor by shorting one of the bridge converter legs after every active switching cycle. Simulation and practical results validate and confirm the dynamic performance and power quality of the proposed system.


title: Towards Explainable NLP: A Generative Explanation Framework for Text Classification

abstract: Building explainable systems is a critical problem in the field of Natural Language Processing (NLP), since most machine learning models provide no explanations for the predictions. Existing approaches for explainable machine learning systems tend to focus on interpreting the outputs or the connections between inputs and outputs. However, the fine-grained information is often ignored, and the systems do not explicitly generate the human-readable explanations. To better alleviate this problem, we propose a novel generative explanation framework that learns to make classification decisions and generate fine-grained explanations at the same time. More specifically, we introduce the explainable factor and the minimum risk training approach that learn to generate more reasonable explanations. We construct two new datasets that contain summaries, rating scores, and fine-grained reasons. We conduct experiments on both datasets, comparing with several strong neural network baseline systems. Experimental results show that our method surpasses all baselines on both datasets, and is able to generate concise explanations at the same time.


title: Convolutional Neural Networks to Enhance Coded Speech

abstract: Enhancing coded speech suffering from far-end acoustic background noise, quantization noise, and potentially transmission errors is a challenging task. In this paper, we propose two postprocessing approaches applying convolutional neural networks either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time-domain approach follows an end-to-end fashion, whereas the cepstral domain approach uses analysis–synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves perceptual evaluation of speech quality by up to 0.25 mean opinion score listening quality objective points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec. In a subjective comparison category rating listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized postfilter by 0.36 CMOS points, and obtains a clear preference of 1.77 CMOS points compared to legacy G.711, even better than uncoded speech with statistical significance . The source code for the cepstral domain approach to enhance G.711-coded speech is made available. 1 1 https://github.com/ifnspaml/Enhancement-Coded-Speech .


title: Approximations For The Maximum Acyclic Subgraph Problem

abstract: Given a directed graph G = (V,A), the maximum acyclic subgraph problem is to compute a subset, A′, of arcs of maximum size or total weight so that G′ = (V,A′) is acyclic. We discuss several approximation algorithms for this problem. Our main result is an O(|A| + d3max) algorithm that produces a solution with at least a fraction 12+ω(1dmax) of the number of arcs in an optimal solution. Here, dmax is the maximum vertex degree in G.


title: Plasticity versus Adaptation of Ambient-Temperature Flowering Response

abstract: It is challenging to understand how plants adapt flowering time to novel environmental conditions, such as global warming, while maintaining plasticity in response to daily fluctuating temperatures. A recent study shows a role for transposons and highlights the need to investigate how these different responses evolved.


title: Optimized stratified sampling for approximate query processing

abstract: The ability to approximately answer aggregation queries accurately and efficiently is of great benefit for decision support and data mining tools. In contrast to previous sampling-based studies, we treat the problem as an optimization problem where, given a workload of queries, we select a stratified random sample of the original data such that the error in answering the workload queries using the sample is minimized. A key novelty of our approach is that we can tailor the choice of samples to be robust, even for workloads that are “similar” but not necessarily identical to the given workload. Finally, our techniques recognize the importance of taking into account the variance in the data distribution in a principled manner. We show how our solution can be implemented on a database system, and present results of extensive experiments on Microsoft SQL Server that demonstrate the superior quality of our method compared to previous work.


Using Context Information for Dialog Act Classification in DNN Framework


title: Discrete Region Competition for Unknown Numbers of Connected Regions

abstract: We present a discrete unsupervised multiregion-competition algorithm for image segmentation over different energy functionals. The number of regions present in an image does not need to be known a priori, nor their photometric properties. The algorithm jointly estimates the number of regions, their photometries, and their contours. The required regularization is provided by defining a region as a connected set of pixels. The evolving contours in the image are represented by computational particles that move as driven by an energy-minimization algorithm. We present an efficient discrete algorithm that allows minimizing a range of well-known energy functionals under the topological constraint of regions being connected components. The presented framework and algorithms are implemented in the open-source Insight Toolkit image-processing library.


title: Detection and tracking of pianist hands and fingers

abstract: Current MIDI recording and transmitting technology allows teachers to teach piano playing remotely (or off-line): a teacher plays a MIDI-keyboard at one place and a student observes the played piano keys on another MIDI-keyboard at another place. What this technology does not allow is to see how the piano keys are played, namely: which hand and finger was used to play a key. In this paper we present a video recognition tool that makes it possible to provide this information. A video-camera is mounted on top of the piano keyboard and video recognition techniques are then used to calibrate piano image with MIDI sound, then to detect and track pianist hands and then to annotate the fingers that play the piano. The result of the obtained video annotation of piano playing can then be shown on a computer screen for further perusal by a piano teacher or a student.


title: The Emergence of Spanking Among a Representative Sample of Children Under 2 Years of Age in North Carolina

abstract: Spanking is common in the United States but less common in many European countries in which it has been outlawed. Being spanked has been associated with child abuse victimization, poor self-esteem, impaired parent-child relationships, and child and adult mental health, substance abuse, and behavioral consequences. Being spanked as a child has also been shown to increase the likelihood of abusing one’s own children or spouse as an adult. Spanking of very young children less than two is almost never recommended even among experts that consider spanking as reasonable in some circumstances. Using a cross-sectional anonymous telephone survey, we describe spanking rates among a representative sample of North Carolina mothers of children less than two years old and the association of spanking with demographic characteristics. A substantial proportion of mothers admit to spanking their very young children. The rate of spanking in the last year among all maternal respondents was 30%. Over 5% of the mothers of three month olds reported spanking. Over 70% of the mothers of 23 month olds reported spanking. Increased spanking was associated with higher age of the child and lower maternal age. With every month of age, a child had 27% increased odds of being spanked. Early spanking has been shown to be associated with poor cognitive development in early childhood. Further, early trauma has been shown to have significant effects on the early developing brain. It is therefore critical that health and human services professionals addres the risk of corporal punishment as a method of discipline early in the life of the child. The spanking of very young children may be an appropriate locus for policy and legislative debates regarding corporal punishment.


title: Social Support and Health: A Review of Physiological Processes Potentially Underlying Links to Disease Outcomes

abstract: Social support has been reliably related to lower rates of morbidity and mortality. An important issue concerns the physiological mechanisms by which support influences such health endpoints. In this review, I examine evidence linking social support to changes in cardiovascular, neuroendocrine, and immune function. Consistent with epidemiological evidence, social support appears to be related to more positive “biological profiles” across these disease-relevant systems. Recent research on immune-mediated inflammatory processes is also starting to provide data on more integrative physiological mechanisms potentially linking social support to health. The implications of these links, along with future research directions are discussed.


title: An Automata-Theoretic Approach to Linear Temporal Logic

abstract: The automata-theoretic approach to linear temporal logic uses the theory of automata as a unifying paradigm for program specification, verification, and synthesis. Both programs and specifications are in essence descriptions of computations. These computations can be viewed as words over some alphabet. Thus, programs and specifications can be viewed as descriptions of languages over some alphabet. The automata-theoretic perspective considers the relationships between programs and their specifications as relationships between languages. By translating programs and specifications to automata, questions about programs and their specifications can be reduced to questions about automata. More specifically, questions such as satisfiability of specifications and correctness of programs with respect to their specifications can be reduced to questions such as nonemptiness and containment of automata.


title: Survey of Visual Question Answering: Datasets and Techniques

abstract: Visual question answering (or VQA) is a new and exciting problem that combines natural language processing and computer vision techniques. We present a survey of the various datasets and models that have been used to tackle this task. The first part of the survey details the various datasets for VQA and compares them along some common factors. The second part of this survey details the different approaches for VQA, classified into four types: non-deep learning models, deep learning models without attention, deep learning models with attention, and other models which do not fit into the first three. Finally, we compare the performances of these approaches and provide some directions for future work.


title: Generalization in Metric Learning: Should the Embedding Layer Be Embedding Layer?

abstract: This work studies deep metric learning under small to medium scale as we believe that better generalization could be a contributing factor to the improvement of previous fine-grained image retrieval methods; it should be considered when designing future techniques. In particular, we investigate using other layers in a deep metric learning system (besides the embedding layer) for feature extraction and analyze how well they perform on training data and generalize to testing data. From this study, we suggest a new regularization practice where one can add or choose a more optimal layer for feature extraction. State-of-the-art performance is demonstrated on 3 fine-grained image retrieval benchmarks: Cars-196, CUB-200-2011, and Stanford Online Product.


title: Development of a cloud-based RTAB-map service for robots

abstract: With the recent advances in wireless communication and cloud computing technology, conventional robots can enhance their capabilities on the fly. A number of functionalities can be offered to robots for which they were not initially designed for such as heavy computation offloading, navigation, map building, etc. In this paper, we have offered a cloud-enabled framework for robots where along with offloading their computation they can also use navigation, map building, path planning, etc. as a service. In order to validate the working of our framework, a RTAB-map (Real Time Appearance Based Mapping) service is built for mobile robots. In this, we have incorporated SOA (Service Oriented Architecture) design to standardized the RTAB-map service, i.e., service listing are defined via WSDL (Web Service Definition Language) and communicated over SOAP (Simple Object Access Protocol) protocol respectively. Some simulation results of service execution and cloud usage analysis are also presented to support the proposed system.


title: Ontology-Based Data Access for Extracting Event Logs from Legacy Data: The onprom Tool and Methodology

abstract: Process mining aims at discovering, monitoring, and improving business processes by extracting knowledge from event logs. In this respect, process mining can be applied only if there are proper event logs that are compatible with accepted standards, such as extensible event stream (XES). Unfortunately, in many real world set-ups, such event logs are not explicitly given, but instead are implicitly represented in legacy information systems. In this work, we exploit a framework and associated methodology for the extraction of XES event logs from relational data sources that we have recently introduced. Our approach is based on describing logs by means of suitable annotations of a conceptual model of the available data, and builds on the ontology-based data access (OBDA) paradigm for the actual log extraction. Making use of a real-world case study in the services domain, we compare our novel approach with a more traditional extract-transform-load based one, and are able to illustrate its added value. We also present a set of tools that we have developed and that support the OBDA-based log extraction framework. The tools are integrated as plugins of the ProM process mining suite.


title: Fat Grafting in Facial Rejuvenation

abstract: Abstract Patients with significant facial atrophy and age-related loss of facial fat generally achieve suboptimal improvement from both surface treatments of facial skin and surgical lifts. Restoring lost facial volume by fat grafting is a powerful technique that is now acknowledged by most plastic surgeons and other physicians engaged in treating the aging face as one of the most important advances in aesthetic surgery. Properly performed, the addition of fat to areas of the face that have atrophied because of age or disease can produce a significant and sustained improvement in appearance that is unobtainable by other means.


title: Dial2Desc: End-to-end Dialogue Description Generation

abstract: We first propose a new task named Dialogue Description (Dial2Desc). Unlike other existing dialogue summarization tasks such as meeting summarization, we do not maintain the natural flow of a conversation but describe an object or an action of what people are talking about. The Dial2Desc system takes a dialogue text as input, then outputs a concise description of the object or the action involved in this conversation. After reading this short description, one can quickly extract the main topic of a conversation and build a clear picture in his mind, without reading or listening to the whole conversation. Based on the existing dialogue dataset, we build a new dataset, which has more than one hundred thousand dialogue-description pairs. As a step forward, we demonstrate that one can get more accurate and descriptive results using a new neural attentive model that exploits the interaction between utterances from different speakers, compared with other baselines.


title: Robust end-to-end deep audiovisual speech recognition

abstract: Speech is one of the most effective ways of communication among humans. Even though audio is the most common way of transmitting speech, very important information can be found in other modalities, such as vision. Vision is particularly useful when the acoustic signal is corrupted. Multi-modal speech recognition however has not yet found wide-spread use, mostly because the temporal alignment and fusion of the different information sources is challenging. ::: This paper presents an end-to-end audiovisual speech recognizer (AVSR), based on recurrent neural networks (RNN) with a connectionist temporal classification (CTC) loss function. CTC creates sparse "peaky" output activations, and we analyze the differences in the alignments of output targets (phonemes or visemes) between audio-only, video-only, and audio-visual feature representations. We present the first such experiments on the large vocabulary IBM ViaVoice database, which outperform previously published approaches on phone accuracy in clean and noisy conditions.


title: Understanding the most satisfying and unsatisfying user experiences: Emotions, psychological needs, and context

abstract: The aim of this research was to study the structure of the most satisfying and unsatisfying user experiences in terms of experienced emotions, psychological needs, and contextual factors. 45 university students wrote descriptions of their most satisfying and unsatisfying recent user experiences and analyzed those experiences using the Positive and Negative Affect Schedule (PANAS) method for experienced emotions, a questionnaire probing the salience of 10 psychological needs, and a self-made set of rating scales for analyzing context. The results suggested that it was possible to capture variations in user experiences in terms of experienced emotions, fulfillment of psychological needs, and context effectively by using psychometric rating scales. The results for emotional experiences showed significant differences in 16 out of 20 PANAS emotions between the most satisfying and unsatisfying experiences. The results for psychological needs indicated that feelings of autonomy and competence emerged as highly salient in the most satisfying experiences and missing in the unsatisfying experiences. High self-esteem was also notably salient in the most satisfying experiences. The qualitative results indicated that most of the participants' free-form qualitative descriptions, especially for the most unsatisfying user experiences, gave important information about the pragmatic aspects of the interaction, but often omitted information about hedonic and social aspects of user experience.


A Workplace Study of the Adoption of Information Visualization Systems


title: NChecker: saving mobile app developers from network disruptions

abstract: Most of today's mobile apps rely on the underlying networks to deliver key functions such as web browsing, file synchronization, and social networking. Compared to desktop-based networks, mobile networks are much more dynamic with frequent connectivity disruptions, network type switches, and quality changes, posing unique programming challenges for mobile app developers. As revealed in this paper, many mobile app developers fail to handle these intermittent network conditions in the mobile network programming. Consequently, network programming defects (NPDs) are pervasive in mobile apps, causing bad user experiences such as crashes, data loss, etc. Despite the development of network libraries in the hope of lifting the developers' burden, we observe that many app developers fail to use these libraries properly and still introduce NPDs. In this paper, we study the characteristics of the real-world NPDs in Android apps towards a deep understanding of their impacts, root causes, and code patterns. Driven by the study, we build NChecker, a practical tool to detect NPDs by statically analyzing Android app binaries. NChecker has been applied to hundreds of real Android apps and detected 4180 NPDs from 285 randomly-selected apps with a 94+% accuracy. Our further analysis of these defects reveals the common mistakes of app developers in working with the existing network libraries' abstractions, which provide insights for improving the usability of mobile network libraries.


title: Cluster-based patent retrieval

abstract: Through the recent NTCIR workshops, patent retrieval casts many challenging issues to information retrieval community. Unlike newspaper articles, patent documents are very long and well structured. These characteristics raise the necessity to reassess existing retrieval techniques that have been mainly developed for structure-less and short documents such as newspapers. This study investigates cluster-based retrieval in the context of invalidity search task of patent retrieval. Cluster-based retrieval assumes that clusters would provide additional evidence to match user's information need. Thus far, cluster-based retrieval approaches have relied on automatically-created clusters. Fortunately, all patents have manually-assigned cluster information, international patent classification codes. International patent classification is a standard taxonomy for classifying patents, and has currently about 69,000 nodes which are organized into a five-level hierarchical system. Thus, patent documents could provide the best test bed to develop and evaluate cluster-based retrieval techniques. Experiments using the NTCIR-4 patent collection showed that the cluster-based language model could be helpful to improving the cluster-less baseline language model.


title: GLEU: Automatic Evaluation of Sentence-Level Fluency

abstract: In evaluating the output of language technology applications—MT, natural language generation, summarisation—automatic evaluation techniques generally conflate measurement of faithfulness to source content with fluency of the resulting text. In this paper we develop an automatic evaluation metric to estimate fluency alone, by examining the use of parser outputs as metrics, and show that they correlate with human judgements of generated text fluency. We then develop a machine learner based on these, and show that this performs better than the individual parser metrics, approaching a lower bound on human performance. We finally look at different language models for generating sentences, and show that while individual parser metrics can be ‘fooled’ depending on generation method, the machine learner provides a consistent estimator of fluency.


title: A Double-Layered Timber Plate Shell - Computational Methods for Assembly, Prefabrication, and Structural Design

abstract: This paper presents a new lightweight construction system for doubly-curved shells, built from two interconnected layers of structural wood veneer plates. The system uses integral through-tenon joints for a fast, precise, and simple assembly, allowing for the construction of a series of differently shaped shells without a costly mould or support structure. Instead, inclined joints cut with a 5-axis CNC milling machine embed the correct location and angle between plates into the shape of the parts. This constrains the relative motions between joined parts to one assembly path. To take advantage of the benefits of such connectors, the constrained assembly paths must be considered in the fundamental design of the system, allowing for the insertion of each plate. This imposes additional constraints in the segmentation process of doubly-curved shells. In order to meet the requirements and resolve the multi-constraint system, we use a global, non-linear optimisation approach. Developed as a close collaboration between architects, computer scientists and structural engineers, the paper includes an experimental analysis of the influence of parametric modifications in the shape of connectors on their load-bearing performance.


title: The Brain on Stress: Vulnerability and Plasticity of the Prefrontal Cortex over the Life Course

abstract: The prefrontal cortex (PFC) is involved in working memory and self-regulatory and goal-directed behaviors and displays remarkable structural and functional plasticity over the life course. Neural circuitry, molecular profiles, and neurochemistry can be changed by experiences, which influence behavior as well as neuroendocrine and autonomic function. Such effects have a particular impact during infancy and in adolescence. Behavioral stress affects both the structure and function of PFC, though such effects are not necessarily permanent, as young animals show remarkable neuronal resilience if the stress is discontinued. During aging, neurons within the PFC become less resilient to stress. There are also sex differences in the PFC response to stressors. While such stress and sex hormone-related alterations occur in regions mediating the highest levels of cognitive function and self-regulatory control, the fact that they are not necessarily permanent has implications for future behavior-based therapies that harness neural plasticity for recovery.


Integrating Computational Methods to Investigate the Macroecology of Microbiomes


title: A Robust Method for Identification of Paper Currency Using Otsu’s Thresholding

abstract: In this modern world, the correct identification of paper currency is an essential need for the automation of current systems. The automatic currency identification method is highly required to reduce the human power in recognizing the currency and its associated value. Sometimes, the damaged or blurred currency and the complex design of the currency make the task of identification much difficult. In this paper, a method is proposed that can identify paper currency by considering the identification marks given on the currency. Specifically, this study focuses on the identification of Pakistani paper currency. The proposed method makes use of the fundamental image processing techniques for differentiating the major denominations. Particularly, the proposed algorithm uses Otsu’s thresholding method and circulatory matrix criteria for locating the special identity marks given on the currency. The effectiveness of the proposed approach has been shown by performing various experiments on all of the major denominations of Pakistani paper currency with varying intensities. The Proposed method shows quite satisfactory results in terms of accuracy and efficiency.


title: Random key predistribution schemes for sensor networks

abstract: Key establishment in sensor networks is a challenging problem because asymmetric key cryptosystems are unsuitable for use in resource constrained sensor nodes, and also because the nodes could be physically compromised by an adversary. We present three new mechanisms for key establishment using the framework of pre-distributing a random set of keys to each node. First, in the q-composite keys scheme, we trade off the unlikeliness of a large-scale network attack in order to significantly strengthen random key predistribution's strength against smaller-scale attacks. Second, in the multipath-reinforcement scheme, we show how to strengthen the security between any two nodes by leveraging the security of other links. Finally, we present the random-pairwise keys scheme, which perfectly preserves the secrecy of the rest of the network when any node is captured, and also enables node-to-node authentication and quorum-based revocation.


title: Natural tongue physique identification using hybrid deep learning methods

abstract: Traditional Chinese Medicine (TCM) illustrates that the physique determines the susceptibility of human to certain diseases and treatment programs for illness. Tongue diagnosis is an important way to identify the physique, but now it is performed by the doctor’s professional experience and the design of a questionnaire. Consequently, accurate physique identification cannot be obtained easily. In this paper, we propose a new method to identify the physique through wild tongue images using hybrid deep learning methods. It begins with constructing a large number of tongue images that are taken in natural conditions, instead of in a controlled environment. Based on the resulting database, a new method of tongue coating detection is put forward that applies a rapid deep learning method to complete the initial tongue coating detection, and then utilizes another deep learning method, a calibration neural network, to further improve the accuracy of tongue detection. Finally, an effective deep learning method is applied to identify the tongue physique. Experiments validate the proposed method, illustrating that physique identification can be performed well using hybrid deep learning methods.


title: WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming

abstract: We present a novel weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.


title: Design of spherical parallel mechanisms for application to laparoscopic surgery

abstract: This paper addresses an optimal study of workspace for spherical parallel mechanism for laparoscopic surgery. The spherical parallel manipulator has been selected because of its characteristics. Two designs have been studied for maximizing their workspaces; a haptic device, as part of training system, and a laparoscope holding mechanism. The laparoscope holding mechanism has to satisfy additional constraints by minimizing the occupied space above the patient. The objective is to solve design problem to offer the maximal workspace for such mechanisms. The design of a haptic interface and the laparoscope holding mechanism based on the optimal parameters are presented. This paper presents a Genetic Algorithm (GA) approach for selecting optimal design parameters for maximizing workspace of spherical parallel mechanism.


title: Transformer-isolated gate drive design for SiC JFET phase-leg module

abstract: In order to take full advantage of the SiC devices' high-temperature and high-frequency capabilities, a transformer isolated gate driver is designed for the SiC JFET phase leg module to achieve a fast switching speed of 26V/ns and a small cross-talking voltage of 4.2V in a 650V and 5A inductive load test. Transformer isolated gate drive circuits suitable for high-temperature applications are compared with respect to different criteria. Based on the comparison, an improved edge triggered gate drive topology is proposed. Then, using the proposed gate drive topology, special issues in the phase-leg gate drive design are discussed. Several strategies are implemented to improve the phase-leg gate drive performance and alleviate the cross-talking issue. Simulation and experimental results are given for verification purposes.


title: Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

abstract: As a variant of Convolutional Neural Networks (CNNs) in Deep Learning, the Fully Convolutional Network (FCN) model achieved state-of-the-art performance for natural image semantic segmentation. In this paper, an accurate classification approach for high resolution remote sensing imagery based on the improved FCN model is proposed. Firstly, we improve the density of output class maps by introducing Atrous convolution, and secondly, we design a multi-scale network architecture by adding a skip-layer structure to make it capable for multi-resolution image classification. Finally, we further refine the output class map using Conditional Random Fields (CRFs) post-processing. Our classification model is trained on 70 GF-2 true color images, and tested on the other 4 GF-2 images and 3 IKONOS true color images. We also employ object-oriented classification, patch-based CNN classification, and the FCN-8s approach on the same images for comparison. The experiments show that compared with the existing approaches, our approach has an obvious improvement in accuracy. The average precision, recall, and Kappa coefficient of our approach are 0.81, 0.78, and 0.83, respectively. The experiments also prove that our approach has strong applicability for multi-resolution image classification.


title: Passive mixing in a three-dimensional serpentine microchannel

abstract: A three-dimensional serpentine microchannel design with a "C shaped" repeating unit is presented in this paper as a means of implementing chaotic advection to passively enhance fluid mixing. The device is fabricated in a silicon wafer using a double-sided KOH wet-etching technique to realize a three-dimensional channel geometry. Experiments using phenolphthalein and sodium hydroxide solutions demonstrate the ability of flow in this channel to mix faster and more uniformly than either pure molecular diffusion or flow in a "square-wave" channel for Reynolds numbers from 6 to 70. The mixing capability of the channel increases with increasing Reynolds number. At least 98% of the maximum intensity of reacted phenolphthalein is observed in the channel after five mixing segments for Reynolds numbers greater than 25. At a Reynolds number of 70, the serpentine channel produces 16 times more reacted phenolphthalein than a straight channel and 1.6 times more than the square-wave channel. Mixing rates in the serpentine channel at the higher Reynolds numbers are consistent with the occurrence of chaotic advection. Visualization of the interface formed in the channel between streams of water and ethyl alcohol indicates that the mixing is due to both diffusion and fluid stirring.


title: Medical Volume Rendering Techniques

abstract: Medical visualization is the use of computers to create 3D images from medical imaging data sets, almost all surgery and cancer treatment in the developed world relies on it.Volume visualization techniques includes iso-surface visualization, mesh visualization and point cloud visualization techniques, these techniques have revolutionized medicine. Much of modern medicine relies on the 3D imaging that is possible with magnetic resonance imaging (MRI) scanners, functional magnetic resonance imaging (fMRI)scanners, positron emission tomography (PET) scanners, ultrasound imaging (US) scanners, X-Ray scanners, bio-marker microscopy imaging scanners and computed tomography (CT) scanners, which make 3D images out of 2D slices. The primary goal of this report is the application-oriented optimization of existing volume rendering methods providing interactive frame rates. Techniques are presented for traditional alpha-blending rendering, surface-shaded display, maximum intensity projection (MIP), and fast previewing with fully interactive parameter control. Different preprocessing strategies are proposed for interactive iso-surface rendering and fast previewing, such as the well-known marching cube algorithm.


title: SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

abstract: As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.


Starter Cultures in Food Production


title: PDLK: Plagiarism detection using linguistic knowledge

abstract: Abstract Plagiarism is described as the reuse of someone else's previous ideas, work or even words without sufficient attribution to the source. This paper presents a method to detect external plagiarism using the integration of semantic relations between words and their syntactic composition. The problem with the available methods is that they fail to capture the meaning in comparison between a source document sentence and a suspicious document sentence, when two sentences have same surface text (the words are the same) or they are a paraphrase of each other. Therefore it causes inaccurate or unnecessary matching results. However, this method can improve the performance of plagiarism detection because it is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. It is executed by computing the semantic and syntactic similarity of the sentence-to-sentence. Besides, the proposed method expands the words in sentences to tackle the problem of information limit. It bridges the lexical gaps for semantically similar contexts that are expressed in a different wording. This method is also capable to identify various kinds of plagiarism such as the exact copied text, paraphrasing, transformation of sentences and changing of word structure in the sentences. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11. The experimental results also displayed that the proposed method demonstrates better performance as compared to other existing techniques on PAN-PC-10 and PAN-PC-11 datasets.


title: Searching optimal product bundles by means of GA-based Engine and Market Basket Analysis

abstract: The product bundling problem is a challenging task in the e-Commerce domain. We propose a generative engine in order to find the bundle of products that best satisfies user requirements and, at the same time, seller needs such as the minimization of the dead stocks and the maximization of net income. The proposed system named Intelligent Bundle Suggestion and Generation (IBSAG) is designed in order to satisfy these requirements. Market Basket Analysis supports the system in user requirement elicitation task. Experimental results prove the ability of system in finding the optimal tradeoff between different and conflicting constraints.


title: A light non-monotonic knowledge-base for service robots

abstract: In this paper a Non-Monotonic Knowledge-Base (KB) for practical applications in service robots is presented. The KB is defined as a conceptual hierarchy with inheritance that supports the expression of defaults and exceptions. All classes and individuals, with their properties and relations, can be updated dynamically and the KB-System supports non-monotonic behavior. Non-monotonicity is handled on the basis of a specificity criteria, such that more specific properties and relations have precedence over more general ones. The system supports the expression of conceptual (or terminological) and factual (or assertional) knowledge, which are used in inference in a coherent and consistent way. The KB-System is embedded within the IOCA Architecture, where knowledge about how to communicate and interact with the world, and also knowledge of the particular interpretation situation are represented. The cognitive architecture is structured around a main communication cycle, and queries and conceptual inferences are performed on demand during the interaction of the robot with other agents or the world. The overall structure of the KB with its main interpreter and supporting utilities as well as the embedding of the KB-system in the robot’s architecture are also presented. The KB-System is illustrated with a case study in service robots scenarios, where a practical non-monotonic KB is required. Finally, the implementation of the KB-System in the robot Golem-III is described.


title: Pendra goes Dutch: lessons for the CE mark in Europe

abstract: The development of a truly non-invasive continuous glucose sensor is an elusive goal. We describe the rise and fall of the Pendra device. In 2000 the company Pendragon Medical introduced a truly non-invasive continuous glucose-monitoring device. This system was supposed to work through so-called impedance spectroscopy. Pendra was Conformite Europeenne (CE) approved in May 2003. For a short time the Pendra was available on the Dutch direct-to-consumer market. A post-marketing reliability study was performed in six type 1 diabetes patients. Mean absolute difference between Pendra glucose values and values obtained through self-monitoring of blood glucose was 52%; the Pearson’s correlation coefficient was 35.1%; and a Clarke error grid showed 4.3% of the Pendra readings in the potentially dangerous zone E. We argue that the CE certification process for continuous glucose sensors should be made more transparent, and that a consensus on specific requirements for continuous glucose sensors is needed to prevent patient exposure to potentially dangerous situations.


title: Improved Spelling Error Detection and Correction for Arabic

abstract: A spelling error detection and correction application is based on three main components: a dictionary (or reference word list), an error model and a language model. While most of the attention in the literature has been directed to the language model, we show how improvements in any of the three components can lead to significant cumulative improvements in the overall performance of the system. We semi-automatically develop a dictionary of 9.3 million fully inflected Arabic words using a morphological transducer and a large corpus. We improve the error model by analysing error types and creating an edit distance based re-ranker. We also improve the language model by analysing the level of noise in different sources of data and selecting the optimal subset to train the system on. Testing and evaluation experiments show that our system significantly outperforms Microsoft Word 2010, OpenOffice Ayaspell and Google Docs.


title: Firmware over the air for automotive, Fotamotive

abstract: The technologies in the automotive industry are becoming smarter and more advanced than before. Complexity of vehicle electronics is becoming a greater issue and often caused by more interfaces, more connections, more displays, more user demand features and more suppliers. This means there is a great need to supply system updates to ensure higher quality and improve functionality. This paper introduces a new solution for vehicle updates that will help OEMs to reduce cost, reduce recalls, increase quality, ease vehicle updates and take better control of vehicles fleet in the market. This new solution consists of updating vehicle ECUs over the air without the need of a physical connection to the vehicle. We are presenting Fotamotive architecture, process flow chart, system components, vehicle cloud management, simulation and results.


title: An Interleaved Totem-Pole Boost Bridgeless Rectifier With Reduced Reverse-Recovery Problems For Power Factor Correction

abstract: An interleaved totem-pole boost bridgeless rectifier with reduced reverse-recovery problems for power factor correction is proposed in this paper. The proposed converter consists of two interleaved and intercoupled totem-pole boost bridgeless converter cells. The two cells operate in phase-shift mode. Thus, the input current can be continuous with low ripple. For the individual cells, they operate alternatively in discontinuous current mode and the maximum duty ratio is 50%, which allows shifting the diode current with low di/dt rate to achieve zero-current switching off. Zero-voltage switching is achieved in the MOSFETs under low line input. Furthermore, the merits of totem-pole topology are inherited. The common-mode (CM) noise interference is rather low. And the potential capacity of bidirectional power conversion is retained. In brief, the conduction losses are reduced, reverse-recovery process is improved, and high efficiency is achieved. The interleaved totem-pole cell can also be applied to bidirectional dc/dc converters and ac/dc converters. Finally, an 800 W, 100 kHz experimental prototype was built to verify the theoretical analysis and feasibility of the proposed converter, whose efficiency is above 95.5% at full load under 90 V.


title: The Role of Visual Perception in Data Visualization

abstract: Abstract This paper presents a perceptually motivated formal framework for effective visualization of relational data. In this framework, the intended structure of data and the perceptual structure of visualizations are formally and uniformly defined in terms of relations that are induced on data and visual elements by data and visual attributes, respectively. Visual attributes are analyzed and classified from the perceptual point of view and in terms of perceptual relations that they induce on visual elements. The presented framework satisfies a necessary condition for effective data visualizations. This condition is formulated in terms of a structure preserving map between the intended structure of data and the perceptual structure of visualization.


title: Social anxiety and social norms in individualistic and collectivistic countries

abstract: Although cultural values are hypothesized to have an impact on the level of social anxiety and anxiety-related impairment experienced by individuals within a culture [1], cultural research is still in its infancy [2]. The majority of studies that have addressed cultural influences on psychopathology have focused on the dimension of individualism versus collectivism, a factor shown to vary across countries [3]. Individualistic cultures are said to value the expression and assertion of individual desires whereas collectivist cultures are believed to give greater priority to the maintenance of group harmony [4, 5]. Extant findings consistently indicate that people living in East Asian countries [1, 6] as well as those that migrated from East Asian to western countries [7, 8] report higher levels of social anxiety than those in individualistic cultures. ::: ::: Heinrichs and colleagues [1] postulated that cultural norms regarding social behavior may explain the relationship between cultural values and social anxiety. To address this issue, these researchers developed an innovative measure that used social vignettes to evaluate cultural norms for the acceptability of socially reticent and withdrawn versus attention-seeking and outgoing behaviors. They conducted a large cross-cultural study that compared individualistic and collectivistic countries and found that collectivistic countries (Japan, South Korea, and Spain) showed higher levels of social anxiety and greater acceptance towards socially reticent and withdrawn behaviors on a cultural level than did individualistic countries (Australia, Canada, Germany, the Netherlands, and the USA). These results may suggest that within collectivist countries, strict social norms designed to ensure group harmony may evoke social anxiety due to feared negative consequences if those norms are violated (stringent norm hypothesis). ::: ::: Despite those findings, the relationship between individualistic/collectivist values and social anxiety is far from clear. A cross-cultural study by Arrindell and colleagues [9], which examined the association between mean self-reported phobic symptoms and Hofsteade’s dimensions in eleven countries, failed to find a significant association between country individualism scores and social fears. Those findings appear to be inconsistent with the conclusions of Heinrichs et al. In addition, some researchers argue that individuals in collectivist societies, particularly Asian cultures, are more willing to engage in self-criticism and hence are more willing to endorse negative attributes than individuals in individualistic societies [7, 8, 10]. According to this view, higher self-reported levels of social anxiety may reflect a response style related to collectivistic values and low independent self-construal rather than a genuine difference in social anxiety between cultures (response style hypothesis). Furthermore, some researchers found a lower prevalence of social anxiety disorder in East Asians countries [11 - 13], which has led to speculations that individuals from collectivistic countries may report more social anxiety but suffer less impairment due to cultural acceptance of socially reticent behaviors [2]. In sum, while social norms may indeed affect social anxiety, the specific relationship between collectivist values and social anxiety has yet to be identified. ::: ::: Another shortcoming of the existing literature is that most cross-cultural studies of social anxiety included only East Asian countries as examples of collectivist cultures and failed to include countries from Latin America or Africa [1, 6, 7], both of which are also characterized by collectivist values. Critical analysis of the concept of individualism [14] also supports the idea that the higher levels of social anxiety and lower prevalence of social anxiety disorder found in East Asian countries may be limited to these countries and are not a general characteristic of collectivistic cultures. For example, Allik and McCrae [15] found that people in Latin American countries displayed similar patterns of personality traits to those found in European and North American countries while people in East Asian and African countries displayed different patterns. Furthermore, the study by Arrindell et al. [9] revealed that whereas Guatemala and Venezuela had the lowest individualism scores of eleven countries studied, they did not display significantly higher levels of social fears. Consequently, replication of the Heinrichs et al. [1] results in collectivistic countries other than East Asia is needed to verify whether collectivistic countries do indeed show higher mean levels of social anxiety and greater cultural acceptance of socially reticent behaviors. Therefore, the current study sought to extend previous research findings by examining two geographically distinct collectivist cultures, namely East Asian and Latin American cultures. ::: ::: We chose to re-examine the results of Heinrichs et al. [1] in the Latin American culture because research points to unique Latin American cultural scripts, or social norms, that promote sociability and might therefore be relevant to social anxiety. For example, people from Mexico have been found to be more sociable and extraverted than Americans when considering behavioral data [16]: These authors found that Mexicans talked more and spent more time socializing compared to Americans in their daily lives, as measured with an Electronically Activated Recorder. These results fit the cultural script of simpatia, first described by Triandis, Marin, Lisansky, and Betancourt, [17] which is assumed to be a specific characteristic of Latin American culture. According to this cultural script, people are expected to be likeable, fun, easygoing, polite, and respectful. Avoiding conflicts and refraining from criticizing others by emphasizing positive and de-emphasizing negative behaviors are highly valued behaviors in Latin America [18]. According to these findings, Latin American cultures may provide higher rates of modeling sociable, non-anxious behaviors coupled with lower prevalence of negative learning experiences (being criticized and rejected by important others) and therefore may evoke less social anxiety than is the case in individualistic or East Asian countries. These findings would challenge the conclusions of Heinrichs et al. [1], who assumed all collectivistic countries to be characterized by higher levels of social anxiety. To the best of our knowledge, no study has investigated this issue. The results of the Arrindell et al. [9] study is consistent with the possibility that Latin American and East Asian countries may differ significantly despite their shared valuing of group harmony. However, Arrindell et al. did not directly test group differences to determine whether different collectivistic countries do indeed differ on levels of social anxiety. Research that compares Latin American collectivist countries with individualistic European and North American countries on the one hand, and with East Asian collectivist countries on the other, would provide a good test of the link between collectivist values per se and social anxiety. ::: ::: Extant research also indicates that it is important to consider both gender and age when examining cultural differences. A study conducted on undergraduate students from the United States indicates that gender affected the association between social anxiety and self-construal. Specifically, independent self-construal showed negative associations with social anxiety only in men, while a reversed pattern was found in women [19]. In addition, Heinrichs et al. found a significant main effect of age when comparing collectivistic and individualistic countries on social norms. Furthermore, community studies have consistently found a higher prevalence of social anxiety disorders in women and younger populations [11]. These results highlight the need to investigate genders separately and to control for age when conducting cross cultural research on social anxiety and social norms. ::: ::: In this study, we collected data from students in those countries included by Heinrichs et al. [1] to replicate results within this new sample. To address our primary question, we also collected data from two Latin American collectivistic countries, Costa Rica and Ecuador, to compare with the individualistic and East Asian countries. We predicted that the findings would replicate the results of Heinrichs et al. [1] in the individualistic and East Asian countries, i.e., we expected higher levels of social anxiety and greater cultural acceptance toward socially reticent behavior in the collectivistic East Asian countries. Based on the simpatia script specific to Latin America and the reasoning described above, we predicted that there would be less acceptance of socially reticent behaviors and lower levels of social anxiety in Latin American countries in comparison to individualistic and East Asian countries.


title: Cognitive signals for brain-machine interfaces in posterior parietal cortex include continuous 3D trajectory commands.

abstract: Cortical neural prosthetics extract command signals from the brain with the goal to restore function in paralyzed or amputated patients. Continuous control signals can be extracted from the motor cortical areas, whereas neural activity from posterior parietal cortex (PPC) can be used to decode cognitive variables related to the goals of movement. Because typical activities of daily living comprise both continuous control tasks such as reaching, and tasks benefiting from discrete control such as typing on a keyboard, availability of both signals simultaneously would promise significant increases in performance and versatility. Here, we show that PPC can provide 3D hand trajectory information under natural conditions that would be encountered for prosthetic applications, thus allowing simultaneous extraction of continuous and discrete signals without requiring multisite surgical implants. We found that limb movements can be decoded robustly and with high accuracy from a small population of neural units under free gaze in a complex 3D point-to-point reaching task. Both animals’ brain-control performance improved rapidly with practice, resulting in faster target acquisition and increasing accuracy. These findings disprove the notion that the motor cortical areas are the only candidate areas for continuous prosthetic command signals and, rather, suggests that PPC can provide equally useful trajectory signals in addition to discrete, cognitive variables. Hybrid use of continuous and discrete signals from PPC may enable a new generation of neural prostheses providing superior performance and additional flexibility in addressing individual patient needs.


title: Virtual reality-enhanced stroke rehabilitation

abstract: A personal computer (PC)-based desktop virtual reality (VR) system was developed for rehabilitating hand function in stroke patients. The system uses two input devices, a CyberGlove and a Rutgers Master II-ND (RMII) force feedback glove, allowing user interaction with a virtual environment. This consists of four rehabilitation routines, each designed to exercise one specific parameter of hand movement: range, speed, fractionation or strength. The use of performance-based target levels is designed to increase patient motivation and individualize exercise difficulty to a patient's current state. Pilot clinical trials have been performed using the above system combined with noncomputer tasks, such as pegboard insertion or tracing of 2D patterns. Three chronic stroke patients used this rehabilitation protocol daily for two weeks. Objective measurements showed that each patient showed improvement on most of the hand parameters over the course of the training. Subjective evaluation by the patients was also positive. This technical report focuses on this newly developed technology for VR rehabilitation.


title: Wi-Fi based indoor localization and tracking using sigma-point Kalman filtering methods

abstract: Estimating the location of people and tracking them in an indoor environment poses a fundamental challenge in ubiquitous computing. The accuracy of explicit positioning sensors such as GPS is often limited for indoor environments. In this study, we evaluate the feasibility of building an indoor location tracking system that is cost effective for large scale deployments, can operate over existing Wi-Fi networks, and can provide flexibility to accommodate new sensor observations as they become available. At the core of our system is a novel location and tracking algorithm using a sigma-point Kalman smoother (SPKS) based Bayesian inference approach. The proposed SPKS fuses a predictive model of human walking with a number of low-cost sensors to track 2D position and velocity. Available sensors include Wi-Fi received signal strength indication (RSSI), binary infrared (IR) motion sensors, and binary foot-switches. Wi-Fi signal strength is measured using a receiver tag developed by Ekahau Inc. The performance of the proposed algorithm is compared with a commercially available positioning engine, also developed by Ekahau Inc. The superior accuracy of our approach over a number of trials is demonstrated.


title: Effects of Service Quality and Customer Satisfaction on Repurchase Intention in Restaurants on University of Cape Coast Campus

abstract: This study sought to examine the effects of service quality and customer satisfaction on the repurchase intentions of customers of restaurants on University of Cape Coast Campus. The survey method was employed involving a convenient sample of 200 customers of 10 restaurants on the University of Cape Coast Campus. A modified DINESERV scale was used to measure customers’ perceived service quality. The results of the study indicate that four factors accounted for 50% of the variance in perceived service quality, namely; responsiveness-assurance, empathy-equity, reliability and tangibles. Service quality was found to have a significant effect on customer satisfaction. Also, both service quality and customer satisfaction had significant effects on repurchase intention. However, customer satisfaction could not moderate the effect of service quality on repurchase intention. This paper adds to the debate on the dimensions of service quality and provides evidence on the effects of service quality and customer satisfaction on repurchase intention in a campus food service context.


title: Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018

abstract: This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma Detection" (MICCAI 2018). Although our team has a long experience with melanoma classification and moderate experience with lesion segmentation, the ISIC Challenge 2018 was the very first time we worked on lesion attribute detection. For each task we submitted 3 different ensemble approaches, varying combinations of models and datasets. Our best results on the official testing set, regarding the official metric of each task, were: 0.728 (segmentation), 0.344 (attribute detection) and 0.803 (classification). Those submissions reached, respectively, the 56th, 14th and 9th places.


title: Self-Compassion in Clinical Practice

abstract: Self-compassion is conceptualized as containing 3 core components: self-kindness versus self-judgment, common humanity versus isolation, and mindfulness versus overidentification, when relating to painful experiences. Research evidence demonstrates that self-compassion is related to psychological flourishing and reduced psychopathology. Mindful Self-Compassion (MSC) is an 8-week training program, meeting 2.5 hours each week, designed to help participants cultivate self-compassion. MSC contains a variety of meditations (e.g., loving-kindness, affectionate breathing) as well as informal practices for use in daily life (e.g., soothing touch, self-compassionate letter writing). A detailed clinical case illustrates the journey of a client through the 8 weeks of MSC training, describing the key features of each session and the client's response.


title: SHAPE MEASURES FOR CONTENT BASED IMAGE RETRIEVAL: A COMPARISON

abstract: Abstract A great deal of work has been done on the evaluation of information retrieval systems for alphanumeric data. The same thing can not be said about the newly emerging multimedia and image database systems. One of the central concerns in these systems is the automatic characterization of image content and retrieval of images based on similarity of image content. In this paper, we discuss effectiveness of several shape measures for content based similarity retrieval of images. The different shape measures we have implemented include outline based features (chain code based string features, Fourier descriptors, UNL Fourier features), region based features (invariant moments, Zernike moments, pseudo-Zernike moments), and combined features (invariant moments & Fourier descriptors, invariant moments & UNL Fourier features). Given an image, all these shape feature measures (vectors) are computed automatically, and the feature vector can either be used for the retrieval purpose or can be stored in the database for future queries. We have tested all of the above shape features for image retrieval on a database of 500 trademark images. The average retrieval efficiency values computed over a set of fifteen representative queries for all the methods is presented. The output of a sample shape similarity query using all the features is also shown.


Macroeconomic Conditions and Corporate Tax Planning


title: Time-dependent covariates in the Cox proportional-hazards regression model.

abstract: The Cox proportional-hazards regression model has achieved widespread use in the analysis of time-to-event data with censoring and covariates. The covariates may change their values over time. This article discusses the use of such time-dependent covariates, which offer additional opportunities but must be used with caution. The interrelationships between the outcome and variable over time can lead to bias unless the relationships are well understood. The form of a time-dependent covariate is much more complex than in Cox models with fixed (non-time-dependent) covariates. It involves constructing a function of time. Further, the model does not have some of the properties of the fixed-covariate model; it cannot usually be used to predict the survival (time-to-event) curve over time. The estimated probability of an event over time is not related to the hazard function in the usual fashion. An appendix summarizes the mathematics of time-dependent covariates.


title: Tilt set-point correction system for balancing robot using PID controller

abstract: Balancing robot is a robot that relies on two wheels in the process of movement. Basically, to be able to remain standing balanced, the control requires an angle value to be used as tilt set-point. That angle value is a balance point of the robot itself which is the robot's center of gravity. Generally, to find the correct balance point, requires manual measurement or through trial and error, depends on the robot's mechanical design. However, when the robot is at balance state and its balance point changes because of the mechanical moving parts or bringing a payload, the robot will move towards the heaviest side and then fall. In this research, a cascade PID control system is developed for balancing robot to keep it balanced without changing the set-point even if the balance point changes. Two parameter is used as feedback for error variable, angle and distance error. When the robot is about to fall, distance taken from the starting position will be calculated and used to correct angle error so that the robot will still balance without changing the set-point but manipulating the control's error value. Based on the research that has been done, payload that can be brought by the robot is up to 350 grams.


title: Functional Near Infrared Spectroscopy (fNIRS): An Emerging Neuroimaging Technology with Important Applications for the Study of Brain Disorders

abstract: Functional near-infrared spectroscopy (fNIRS) is an emerging functional neuroimaging technology offering a relatively non-invasive, safe, portable, and low-cost method of indirect and direct monitoring of brain activity. Most exciting is its potential to allow more ecologically valid investigations that can translate laboratory work into more realistic everyday settings and clinical environments. Our aim is to acquaint clinicians and researchers with the unique and beneficial characteristics of fNIRS by reviewing its relative merits and limitations vis-a-vis other brain-imaging technologies such as functional magnetic resonance imaging (fMRI). We review cross-validation work between fMRI and fNIRS, and discuss possible reservations about its deployment in clinical research and practice. Finally, because there is no comprehensive review of applications of fNIRS to brain disorders, we also review findings from the few studies utilizing fNIRS to investigate neurocognitive processes associated with neurologic...


title: Automatic Reverse Engineering of Malware Emulators

abstract: Malware authors have recently begun using emulation technology to obfuscate their code. They convert native malware binaries into bytecode programs written in a randomly generated instruction set and paired with a native binary emulator that interprets the bytecode. No existing malware analysis can reliably reverse this obfuscation technique. In this paper, we present the first work in automatic reverse engineering of malware emulators. Our algorithms are based on dynamic analysis. We execute the emulated malware in a protected environment and record the entire x86 instruction trace generated by the emulator. We then use dynamic data-flow and taint analysis over the trace to identify data buffers containing the bytecode program and extract the syntactic and semantic information about the bytecode instruction set. With these analysis outputs, we are able to generate data structures, such as control-flow graphs, that provide the foundation for subsequent malware analysis. We implemented a proof-of-concept system called Rotalume and evaluated it using both legitimate programs and malware emulated by VMProtect and Code Virtualizer. The results show that Rotalume accurately reveals the syntax and semantics of emulated instruction sets and reconstructs execution paths of original programs from their bytecode representations.


title: Building Your Own Reading List Anytime via Embedding Relevance, Quality, Timeliness and Diversity

abstract: During every summer holidays, several editions of reading lists are recommended and emerged on mass media, e.g., New York Times, and BBC. However, these reading lists are built for whole people with general topics for some purposes. What if we expect the books of a specific topic at a specific moment? How to generate the requested reading list for our own automatically? In this paper, we propose a searching framework for building a topical reading list anytime, where the Relevance (between topics and books), Quality (of books), Timeliness (of popularities) and Diversity (of results) are embedded into vector representations respectively based on user-generated contents and statistics on social media. We collected 8,197 real-world topics from 198 diverse groups on Librarything.com. The proposed methods are evaluated on the topic collection and the public benchmarks Social Book Search 2012-2016 (SBS). Experimental results demonstrate the robustness and effectiveness of our framework.


title: Multi-accent deep neural network acoustic model with accent-specific top layer using the KLD-regularized model adaptation.

abstract: We propose a multi-accent deep neural network acoustic model with an accent-specific top layer and shared bottom hidden layers. The accent-specific top layer is used to model the distinct accent specific patterns. The shared bottom hidden layers allow maximum knowledge sharing between the native and the accent models. This design is particularly attractive when considering deploying such a system to a live speech service due to its computational efficiency. We applied the KL-divergence (KLD) regularized model adaptation to train the accent-specific top layer. On the mobile short message dictation task (SMD), with 1K, 10K, and 100K British or Indian accent adaptation utterances, the proposed approach achieves 18.1%, 26.0%, and 28.5% or 16.1%, 25.4%, and 30.6% word error rate reduction (WERR) for the British and the Indian accent respectively against a baseline cross entropy (CE) model trained from 400 hour data. On the 100K utterance accent adaptation setup, comparable performance gain can be obtained against a baseline CE model trained with 2000 hour data. We observe smaller yet significant WER reduction on a baseline model trained using the MMI sequence-level criterion. Index Terms: Accent speech recognition, model adaptation, KL-divergence regularization


title: Analysing Twitter and web queries for flu trend prediction

abstract: BackgroundSocial media platforms encourage people to share diverse aspects of their daily life. Among these, shared health related information might be used to infer health status and incidence rates for specific conditions or symptoms. In this work, we present an infodemiology study that evaluates the use of Twitter messages and search engine query logs to estimate and predict the incidence rate of influenza like illness in Portugal.ResultsBased on a manually classified dataset of 2704 tweets from Portugal, we selected a set of 650 textual features to train a Naïve Bayes classifier to identify tweets mentioning flu or flu-like illness or symptoms. We obtained a precision of 0.78 and an F-measure of 0.83, based on cross validation over the complete annotated set. Furthermore, we trained a multiple linear regression model to estimate the health-monitoring data from the Influenzanet project, using as predictors the relative frequencies obtained from the tweet classification results and from query logs, and achieved a correlation ratio of 0.89 (p < 0.001). These classification and regression models were also applied to estimate the flu incidence in the following flu season, achieving a correlation of 0.72.ConclusionsPrevious studies addressing the estimation of disease incidence based on user-generated content have mostly focused on the english language. Our results further validate those studies and show that by changing the initial steps of data preprocessing and feature extraction and selection, the proposed approaches can be adapted to other languages. Additionally, we investigated whether the predictive model created can be applied to data from the subsequent flu season. In this case, although the prediction result was good, an initial phase to adapt the regression model could be necessary to achieve more robust results.


36 Generalizability Theory


title: Mining Mobile User Preferences for Personalized Context-Aware Recommendation

abstract: Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.


title: A Multi-View Ensemble Classification Model for Clinically Actionable Genetic Mutations

abstract: This paper presents details of our winning solutions to the task IV of NIPS 2017 Competition Track entitled Classifying Clinically Actionable Genetic Mutations. The machine learning task aims to classify genetic mutations based on text evidence from clinical literature with promising performance. We develop a novel multi-view machine learning framework with ensemble classification models to solve the problem. During the Challenge, feature combinations derived from three views including document view, entity text view, and entity name view, which complements each other, are comprehensively explored. As the final solution, we submitted an ensemble of nine basic gradient boosting models which shows the best performance in the evaluation. The approach scores 0.5506 and 0.6694 in terms of logarithmic loss on a fixed split in stage-1 testing phase and 5-fold cross validation respectively, which also makes us ranked as a top-1 team out of more than 1,300 solutions in NIPS 2017 Competition Track IV.


Zero-Knowledge Proof of Decryption for FHE Ciphertexts


title: DTW-D: time series semi-supervised learning from a single example

abstract: Classification of time series data is an important problem with applications in virtually every scientific endeavor. The large research community working on time series classification has typically used the UCR Archive to test their algorithms. In this work we argue that the availability of this resource has isolated much of the research community from the following reality, labeled time series data is often very difficult to obtain. The obvious solution to this problem is the application of semi-supervised learning; however, as we shall show, direct applications of off-the-shelf semi-supervised learning algorithms do not typically work well for time series. In this work we explain why semi-supervised learning algorithms typically fail for time series problems, and we introduce a simple but very effective fix. We demonstrate our ideas on diverse real word problems.


title: Energy-Aware Mobile Learning:Opportunities and Challenges

abstract: As mobile devices are becoming more powerful and affordable they are increasingly used for mobile learning activities. By enabling learners' access to educational content anywhere and anytime, mobile learning has both the potential to provide online learners with new opportunities, and to reach less privileged categories of learners that lack access to traditional e-learning services. Among the many challenges with mobile learning, the battery-powered nature of mobile devices and in particular their limited battery life, stands out as one issue that can significantly limit learners' access to educational content while on the move. Adaptation and personalisation solutions have widely been considered for overcoming the differences between learners and between the characteristics of their mobile devices. However, while various energy saving solutions have been proposed in order to provide mobile users with extended device usage time, the areas of adaptive mobile learning and energy conservation in wireless communications failed to meet under the same umbrella. This paper bridges the two areas by presenting an overview of adaptive mobile learning systems as well as how these can be extended to make them energy-aware. Furthermore, the paper surveys various approaches for energy measurement, modelling and adaptation, three major aspects that have to be considered in order to deploy energy-aware mobile learning systems. Discussions on the applicability and limitations of these approaches for mobile learning are also provided.


title: Baby And Child Heroes In Ancient Greece

abstract: Thank you very much for reading baby and child heroes in ancient greece. Maybe you have knowledge that, people have search numerous times for their favorite books like this baby and child heroes in ancient greece, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their desktop computer. baby and child heroes in ancient greece is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the baby and child heroes in ancient greece is universally compatible with any devices to read.


title: Multiple adaptive kernel size KLMS for Beijing PM2.5 prediction

abstract: The kernel least mean square (KLMS) algorithm is an efficient non-linear adaptive filter that operates in the reproducing kernel Hilbert space (RKHS). In realistic applications of system identification or time series prediction, there are usually multiple inputs that demand multiple kernels or kernel parameters. This paper proposes to use a tensor product kernel for KLMS that accommodates multiple inputs. Furthermore, instead of arbitrarily setting kernel parameters, appropriate kernel sizes can be chosen by a gradient descent based adaptive algorithm that minimizes the square of instant error, which helps KLMS to better capture the underlying system mechanism. Effectiveness of the proposed algorithm is shown by experiments conducted for both simulated dataset and an important real-world problem - Beijing PM2.5 prediction.


title: Remineralisation – the buzzword for early MI caries management

abstract: Minimal intervention (MI) dentistry aims to preserve dental tissues first and restore only when indicated, thus remineralisation of initial (non-cavitated) caries lesions, an integral part of caries management, is an essential treatment strategy in MI. With this understanding, dental practitioners are increasingly embracing the principle of non-operative treatment of initial caries lesions. The purpose of this review was to summarise the most recent literature published in non-operative management of dental caries. Three electronic databases (MEDLINE, EMBASE, Cochrane CENTRAL) were searched, and clinical studies, systematic reviews and meta-analysis were included. This report outlines the strategies and numerous therapeutic materials available to aid in arrestment/remineralisation of initial caries lesions on root and coronal surfaces. However, the level of evidence of effect is variable, as well as the availability in different parts of the world. Options available to practitioners will vary when placing emphasis on the level of evidence supporting them. Strong clinical evidence support the effectiveness of pits/fissure sealants for therapeutic management of active initial caries on occlusal surfaces, and fluoride varnishes for remineralisation of caries lesions on root and coronal smooth surfaces, including proximal surfaces. Other materials formulated to enhance the effectiveness of any chosen remineralisation strategy were discussed. However, it is absolutely necessary that all caries remineralisation treatment be complemented with general behavioural modification in oral health through motivational interviewing directed towards change in oral hygiene to control plaque, dietary attitude modification to reduce the frequency of intake of fermentable sugars, and establishment of risk-based recall visits.


title: Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train

abstract: For the past 5 years, the ILSVRC competition and the ImageNet dataset have attracted a lot of interest from the Computer Vision community, allowing for state-of-the-art accuracy to grow tremendously. This should be credited to the use of deep artificial neural network designs. As these became more complex, the storage, bandwidth, and compute requirements increased. This means that with a non-distributed approach, even when using the most high-density server available, the training process may take weeks, making it prohibitive. Furthermore, as datasets grow, the representation learning potential of deep networks grows as well by using more complex models. This synchronicity triggers a sharp increase in the computational requirements and motivates us to explore the scaling behaviour on petaflop scale supercomputers. In this paper we will describe the challenges and novel solutions needed in order to train ResNet-50 in this large scale environment. We demonstrate above 90\% scaling efficiency and a training time of 28 minutes using up to 104K x86 cores. This is supported by software tools from Intel's ecosystem. Moreover, we show that with regular 90 - 120 epoch train runs we can achieve a top-1 accuracy as high as 77\% for the unmodified ResNet-50 topology. We also introduce the novel Collapsed Ensemble (CE) technique that allows us to obtain a 77.5\% top-1 accuracy, similar to that of a ResNet-152, while training a unmodified ResNet-50 topology for the same fixed training budget. All ResNet-50 models as well as the scripts needed to replicate them will be posted shortly.


title: ZigBee Based Intelligent Helmet for Coal Miners

abstract: A cost effective ZigBee-based wireless mine supervising system is presented in this article. This scheme used intelligent helmets as voice terminal and ultra-low-power nodes of wireless sensor network.The programme adopted ZigBee wireless technology to build wireless sensor networks, realized real-time surveillance with early-warning intelligence on methane,temperature, humidity in mining area, and used speech communication to reduce potential safety problems in coal production.


title: FatVAP: Aggregating AP Backhaul Capacity to Maximize Throughput

abstract: It is increasingly common that computers in residential and hotspot scenarios see multiple access points (APs). These APs often provide high speed wireless connectivity but access the Internet via independent, relatively low-speed DSL or cable modem links. Ideally, a client would simultaneously use all accessible APs and obtain the sum of their backhaul bandwidth. Past work can connect to multiple APs, but can neither aggregate AP backhaul bandwidth nor can it maintain concurrent TCPs across them. ::: ::: This paper introduces FatVAP, an 802.11 driver that aggregates the bandwidth available at accessible APs and also balances their loads. FatVAP has three key features. First, it chooses the APs that are worth connecting to and connects with each AP just long enough to collect its available bandwidth. Second, it ensures fast switching between APs without losing queued packets, and hence is the only driver that can sustain concurrent high throughput TCP connections across multiple APs. Third, it works with unmodified APs and is transparent to applications and the rest of the network stack. We experiment with FatVAP both in our lab and hotspots and residential deployments. Our results show that, in today's deployments, FatVAP immediately delivers to the end user a median throughput gain of 2.6x, and reduces the median response time by 2.8x.


title: Text Steganography in SMS

abstract: One of the services used in mobile phone is the short message service (SMS) which is widely used by the public in all parts of the world especially in Asia and Europe. This service enables people to write and exchange short messages via mobile phone. Due to the limited size of SMS, lack of a proper keyboard on the mobile phone and to improve the speed of typing, new abbreviations have been invented for different words and phrases which has lead to the invention of a new language called SMS-texting. One of the main issues in communication is information security and privacy. There are many methods for secret communication and many researchers are working on steganography. In steganography the data is hidden in a cover media such as picture or text. The present paper offers a new method for secret exchange of information through SMS by using and developing abbreviation text steganography with the use of the invented language of SMS-texting. This project has been implemented by J2ME (Java 2 Micro Edition) programming language and tested on a Nokia N71 mobile phone.


title: VerIDeep: Verifying Integrity of Deep Neural Networks through Sensitive-Sample Fingerprinting

abstract: Deep learning has become popular, and numerous cloud-based services are provided to help customers develop and deploy deep learning applications. Meanwhile, various attack techniques have also been discovered to stealthily compromise the model's integrity. When a cloud customer deploys a deep learning model in the cloud and serves it to end-users, it is important for him to be able to verify that the deployed model has not been tampered with, and the model's integrity is protected. ::: We propose a new low-cost and self-served methodology for customers to verify that the model deployed in the cloud is intact, while having only black-box access (e.g., via APIs) to the deployed model. Customers can detect arbitrary changes to their deep learning models. Specifically, we define \texttt{Sensitive-Sample} fingerprints, which are a small set of transformed inputs that make the model outputs sensitive to the model's parameters. Even small weight changes can be clearly reflected in the model outputs, and observed by the customer. Our experiments on different types of model integrity attacks show that we can detect model integrity breaches with high accuracy ($>$99\%) and low overhead ($<$10 black-box model accesses).


title: Going beyond LTM in the MTL: A synthesis of neuropsychological and neuroimaging findings on the role of the medial temporal lobe in memory and perception

abstract: Studies in rats and non-human primates suggest that medial temporal lobe (MTL) structures play a role in perceptual processing, with the hippocampus necessary for spatial discrimination, and the perirhinal cortex for object discrimination. Until recently, there was little convergent evidence for analogous functional specialisation in humans, or for a role of the MTL in processes beyond long-term memory. A recent series of novel human neuropsychological studies, however, in which paradigms from the animal literature were adapted and extended, have revealed findings remarkably similar to those seen in rats and monkeys. These experiments have demonstrated differential effects of distinct stimulus categories on performance in tasks for which there was no explicit requirement to remember information across trials. There is also accruing complementary evidence from functional neuroimaging that MTL structures show differential patterns of activation for scenes and objects, even on simple visual discrimination tasks. This article reviews some of these key studies and discusses the implications of these new findings for existing accounts of memory. A non-modular view of memory is proposed in which memory and perception depend upon the same anatomically distributed representations (emergent memory account). The limitations and criticisms of this theory are discussed and a number of outstanding questions proposed, including key predictions that can be tested by future studies.


title: Pre-service Teachers’ use of improvised and virtual laboratory experimentation in Science teaching

abstract: This research surveyed 11 purposely sampled Bindura University of Science Education (Zimbabwe) Bachelor of Science Education Honous Part III pre-service science teachers’ use of improvised and virtual laboratory experimentation in science teaching. A self-designed four-point Likert scale twenty-item questionnaire was used. SPSS Version 10 was used to analyse data. Frequencies, means, standard deviations and standard error means were used to systematically evaluate variables on pre-service teachers’ use of the technological resources. Most pre-service teachers (90.9%) used improvised laboratory experimentation in their teaching. However, although the pre-service teachers knew the value and benefits of virtual experimentation, they did not use the technology in their teaching. The methods course (Pedagogics in Science Teaching) for the pre-service science teachers should be more focused on specific instructional uses of technology and technical skills rather than familiarisation with technology, to effectively integrate technology in their teaching to support student-centred learning.


abstract: A half-bridge resonant inverter with three-phase PWM rectifier for induction heating is proposed in this paper. An adjustable frequency limit is introduced to maintain the inverter's switching frequency at a value higher than the resonant frequency for zero voltage operation. In case of no work piece in the induction coil, the dc bus can be adjusted while the switching frequency of the inverter remains at the limit. The important advantages of the proposed system are reduced switching loss, small size of filter and faster response at a no load condition. The proposed technique is implemented on the created hardware setup. The inverter operates at in a frequency range of 20–40 kHz, while the three-phase PWM rectifier runs at 23.8 kHz to deliver output power at 1.5 kW.


title: Miniaturized UWB Log-Periodic Square Fractal Antenna

abstract: In this letter, the log-periodic square fractal geometry is presented for the design of a miniaturized patch antenna for the ultra-wideband (UWB) services (3.1-10.6 GHz). A miniaturization factor of 23% is achieved with a constant and stable gain in the desired band. The radiation pattern is broadside, which finds suitable applications in the UWB radars and medical imaging. Furthermore, the time-domain performance of the proposed antenna is investigated. A prototype model of the proposed antenna is fabricated and measured as a proof of concept.


title: Relations among EEG-alpha asymmetry, BIS/BAS, and dispositional optimism

abstract: a b s t r a c t Past research has been unable to address whether the activity in the frontal hemispheres is related to the direction of motivation (approach versus withdrawal) or valence of emotion (positive versus negative). The present study was an attempt to address this question by using a standardized low-resolution brain electromagnetic tomography (sLORETA) which provides EEG localization measures that are independent of the recording reference. Resting EEG, self-report measures of Behavioral Activation and Inhibition System (BAS and BIS) strength, dispositional optimism and a measure of hedonic tone, were collected from 51 unselected undergraduates. Three measures of cortical activation were obtained: (a) alpha asymmetry at conventional scalp sites, (b) anterior and posterior source alpha asymmetries (sLORETA method), (c) posterior versus frontal delta and theta activity. Both alpha asymmetry measures (conventional EEG and sLORETA) yielded significant frontal and parietal asymmetry correlation patterns. Neither measure identified significant associations between resting posterior versus frontal delta and theta activity personality traits. Higher BAS was uniquely related to greater left-sided activation in the middle frontal gyrus (BA11). Optimism was associated with higher activations in the left-superior frontal gyrus (BA10) and in the right-posterior cingulate cortex (BA31). © 2013 Elsevier B.V. All rights reserved.


title: Knowledge-based graph document modeling

abstract: We propose a graph-based semantic model for representing document content. Our method relies on the use of a semantic network, namely the DBpedia knowledge base, for acquiring fine-grained information about entities and their semantic relations, thus resulting in a knowledge-rich document model. We demonstrate the benefits of these semantic representations in two tasks: entity ranking and computing document semantic similarity. To this end, we couple DBpedia's structure with an information-theoretic measure of concept association, based on its explicit semantic relations, and compute semantic similarity using a Graph Edit Distance based measure, which finds the optimal matching between the documents' entities using the Hungarian method. Experimental results show that our general model outperforms baselines built on top of traditional methods, and achieves a performance close to that of highly specialized methods that have been tuned to these specific tasks.