title: Double-Stage Three-Phase Grid-Integrated Solar PV System With Fast Zero Attracting Normalized Least Mean Fourth Based Adaptive Control

abstract: This paper deals with a control technique of a three-phase grid connected solar photovoltaic (SPV) system, which is based on the fast zero attracting normalized least mean fourth algorithm. This control algorithm achieves the objectives of mitigation of power quality issues such as harmonics reduction and power factor correction, together with extraction of peak power generated by the PV array. The system uses a PV array, a voltage-source converter (VSC), and linear and nonlinear loads. Here, VSC is connected to a PV array to transfer the active power to the three-phase load and the grid. The dependence on tuning of proportional-integral controller is reduced because of the feedforward term of the PV power. Due to this, the system dynamic response is improved, which makes the system quite robust. The system goals are to mitigate power quality problems and to provide current conditioning while operating in coherence with a weak distribution grid, which has poor quality of power in terms of voltage distortions with imbalances. MATLAB/Simulink is used to develop the model of the proposed system. The validation of proposed control is done at varying linear and nonlinear loads and under different environmental conditions on a prototype developed in the laboratory.


title: Phonetic-Based Microtext Normalization for Twitter Sentiment Analysis

abstract: The proliferation of Web 2.0 technologies and the increasing use of computer-mediated communication resulted in a new form of written text, termed microtext. This poses new challenges to natural language processing tools which are usually designed for well-written text. This paper proposes a phonetic-based framework for normalizing microtext to plain English and, hence, improve the classification accuracy of sentiment analysis. Results demonstrated that there is a high (>0.8) similarity index between tweets normalized by our model and tweets normalized by human annotators in 85.31% of cases, and that there is an accuracy increase of >4% in terms of polarity detection after normalization.


title: Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

abstract: We present a system for training deep neural networks for object detection using synthetic images. To handle the variability in real-world data, the system relies upon the technique of domain randomization, in which the parameters of the simulator$-$such as lighting, pose, object textures, etc.$-$are randomized in non-realistic ways to force the neural network to learn the essential features of the object of interest. We explore the importance of these parameters, showing that it is possible to produce a network with compelling performance using only non-artistically-generated synthetic data. With additional fine-tuning on real data, the network yields better performance than using real data alone. This result opens up the possibility of using inexpensive synthetic data for training neural networks while avoiding the need to collect large amounts of hand-annotated real-world data or to generate high-fidelity synthetic worlds$-$both of which remain bottlenecks for many applications. The approach is evaluated on bounding box detection of cars on the KITTI dataset.


Which Factors Influence ERP Implementation Projects in Small and Medium-Sized Enterprises?


title: Relevance and lexical pragmatics

abstract: A common problem for linguists, philosophers and psychologists is that linguistically specified ('literal') word meanings are often modified in use. The literal meaning may be narrowed (e.g. drink used to mean 'alcoholic drink'), approximated (e.g. square used to mean 'squarish') or undergo metaphorical extension (e.g. rose or diamond applied to a person). Typically, narrowing, approximation and metaphorical extension are seen as distinct pragmatic processes with no common explanation: thus, narrowing has been analysed in terms of default rules (e.g. Levinson 2000), approximation has been treated as a type of pragmatic vagueness (e.g. Lewis 1983; Lasersohn 1999) and metaphorical extension is standardly seen as a case of maxim violation with resulting implicature (Grice 1989). I will propose a unified account on which narrowing, approximation and metaphorical extension are special cases of a more general pragmatic adjustment process which applies spontaneously, automatically and unconsciously to fine-tune the interpretation of virtually every word, and show how the notion of an ad hoc concept introduced by Barsalou (1987, 1992) might interact with a pragmatic comprehension procedure developed in relevance theory (Sperber & Wilson 1998; Carston 2002; Wilson & Sperber 2002) to account for a wide range of examples.


title: Mode analyzer and modal phase-flip gate for integrated photonic circuits

abstract: We propose two basic transformations which can be used for manipulating modal qubits in Ti:LiNbO3 photonic quantum circuits. These include the mode analyzer, a device that separates the even and odd components of a state into two separate spatial paths and a modal phase-flip gate which introduces a phase shift of π to the odd component of a two-mode photon state.


title: Classroom Response Systems: A Review of the Literature

abstract: As the frequency with which Classroom Response Systems (CRSs) are used is increasing, it becomes more and more important to define the affordances and limitations of these tools. Currently existing literature is largely either anecdotal or focuses on comparing CRS and non-CRS environments that are unequal in other aspects as well. In addition, the literature primarily describes situations in which the CRS is used to provide an individual as opposed to a group response. This article points to the need for a concerted research effort, one that rigorously explores conditions of use across diverse settings and pedagogies.


ESTIMATION OF THE TWO-DIMENSIONAL TOMOGRAPHY OF PHASE AND GROUP VELOCITIES AND SHEAR WAVE STRUCTURE FOR ALBORZ REGION


title: Optimal Spatial Adaptation for Patch-Based Image Denoising

abstract: A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods.


title: Flows of diffeomorphisms for multimodal image registration

abstract: We present a theoretical and computational framework for nonrigid multimodal registration. We proceed by minimization of statistical similarity criteria (global and local) in a variational framework, and use the corresponding gradients to drive a flow of diffeomorphisms allowing large deformations. This flow is introduced through a new template propagation method, by composition of small displacements. Regularization is performed using fast filtering techniques. This approach yields robust matching algorithms offering a good computational efficiency. We apply this method to compensate distortions between EPI images (fMRI) and anatomical MRI volumes.


title: Fluorescence anisotropy (polarization): from drug screening to precision medicine.

abstract: INTRODUCTION ::: Fluorescence anisotropy (FA) is one of the major established methods accepted by industry and regulatory agencies for understanding the mechanisms of drug action and selecting drug candidates utilizing a high-throughput format. ::: ::: ::: AREAS COVERED ::: This review covers the basics of FA and complementary methods, such as fluorescence lifetime anisotropy and their roles in the drug discovery process. The authors highlight the factors affecting FA readouts, fluorophore selection and instrumentation. Furthermore, the authors describe the recent development of a successful, commercially valuable FA assay for long QT syndrome drug toxicity to illustrate the role that FA can play in the early stages of drug discovery. ::: ::: ::: EXPERT OPINION ::: Despite the success in drug discovery, the FA-based technique experiences competitive pressure from other homogeneous assays. That being said, FA is an established yet rapidly developing technique, recognized by academic institutions, the pharmaceutical industry and regulatory agencies across the globe. The technical problems encountered in working with small molecules in homogeneous assays are largely solved, and new challenges come from more complex biological molecules and nanoparticles. With that, FA will remain one of the major work-horse techniques leading to precision (personalized) medicine.


title: US in peroneal tendon tear

abstract: Peroneal tendon injuries are common also due to the recent increase in sports participants involved in amateur activities. Clinical evaluation generally provides a diagnosis, but diagnostic imaging is often required to confirm a clinical suspicion and make correct management decisions. Ultrasound (US) imaging is the method of choice in the study of peroneal tendon injuries due to the high resolution of the images and the possibility of performing dynamic studies. US is furthermore non-invasive and inexpensive and provides the possibility of performing US-guided steroid infiltration of the tendon sheath or the surrounding tissues. The present paper will address the normal anatomy of the peroneal tendons and related structures, US imaging techniques and the various conditions and injuries which may affect this anatomic region. Also more expensive imaging techniques, such as computed tomography and magnetic resonance imaging, will be mentioned as well as their indications; however, they are required only in rare cases in which diagnosis remains uncertain or for pre-operative assessment.


title: Updated Nitrofurantoin Recommendations in the Elderly: A Closer Look at the Evidence.

abstract: Abstract The American Geriatrics Society 2015 Beers Criteria Update Expert Panel has revised its recommendation to avoid use of nitrofurantoin in renal impairment from patients with a creatinine clearance (Clcr) of less than 60 mL/min to those with a Clcr of less than 30 mL/min, based on two retrospective studies that identified the safety and efficacy of nitrofurantoin in this population. This change in recommendation, along with increasing resistance to both sulfamethoxazole/trimethoprim and fluoroquinolones, gave rise to the advantages of nitrofurantoin and may reflect a surge in prescribing among the elderly. The purpose of the manuscript is to provide readers with a brief review of updated evidence to confidently treat older patients with nitrofurantoin.


title: Differential Privacy Preserving in Big Data Analytics for Connected Health

abstract: In Body Area Networks (BANs), big data collected by wearable sensors usually contain sensitive information, which is compulsory to be appropriately protected. Previous methods neglected privacy protection issue, leading to privacy exposure. In this paper, a differential privacy protection scheme for big data in body sensor network is developed. Compared with previous methods, this scheme will provide privacy protection with higher availability and reliability. We introduce the concept of dynamic noise thresholds, which makes our scheme more suitable to process big data. Experimental results demonstrate that, even when the attacker has full background knowledge, the proposed scheme can still provide enough interference to big sensitive data so as to preserve the privacy.


title: A generalized algorithm for graph-coloring register allocation

abstract: Graph-coloring register allocation is an elegant and extremely popular optimization for modern machines. But as currently formulated, it does not handle two characteristics commonly found in commercial architectures. First, a single register name may appear in multiple register classes, where a class is a set of register names that are interchangeable in a particular role. Second, multiple register names may be aliases for a single hardware register. We present a generalization of graph-coloring register allocation that handles these problematic characteristics while preserving the elegance and practicality of traditional graph coloring. Our generalization adapts easily to a new target machine, requiring only the sets of names in the register classes and a map of the register aliases. It also drops easily into a well-known graph-coloring allocator, is efficient at compile time, and produces high-quality code.


title: Tumour heterogeneity and resistance to cancer therapies

abstract: Cancer is a dynamic disease. During the course of disease, cancers generally become more heterogeneous. As a result of this heterogeneity, the bulk tumour might include a diverse collection of cells harbouring distinct molecular signatures with differential levels of sensitivity to treatment. This heterogeneity might result in a non-uniform distribution of genetically distinct tumour-cell subpopulations across and within disease sites (spatial heterogeneity) or temporal variations in the molecular makeup of cancer cells (temporal heterogeneity). Heterogeneity provides the fuel for resistance; therefore, an accurate assessment of tumour heterogeneity is essential for the development of effective therapies. Multiregion sequencing, single-cell sequencing, analysis of autopsy samples, and longitudinal analysis of liquid biopsy samples are all emerging technologies with considerable potential to dissect the complex clonal architecture of cancers. In this Review, we discuss the driving forces behind intratumoural heterogeneity and the current approaches used to combat this heterogeneity and its consequences. We also explore how clinical assessments of tumour heterogeneity might facilitate the development of more-effective personalized therapies.


title: Implementation of fast HEVC encoder based on SIMD and data-level parallelism

abstract: This paper presents several optimization algorithms for a High Efficiency Video Coding (HEVC) encoder based on single instruction multiple data (SIMD) operations and data-level parallelism. Based on the analysis of the computational complexity of HEVC encoder, we found that interpolation filter, cost function, and transform take around 68% of the total computation, on average. In this paper, several software optimization techniques, including frame-level interpolation filter and SIMD implementation for those computationally intensive parts, are presented for a fast HEVC encoder. In addition, we propose a slice-level parallelization and its load-balancing algorithm on multi-core platforms from the estimated computational load of each slice during the encoding process. The encoding speed of the proposed parallelized HEVC encoder is accelerated by approximately ten times compared to the HEVC reference model (HM) software, with minimal loss of coding efficiency.


title: Differentially private frequent subgraph mining

abstract: Mining frequent subgraphs from a collection of input graphs is an important topic in data mining research. However, if the input graphs contain sensitive information, releasing frequent subgraphs may pose considerable threats to individual's privacy. In this paper, we study the problem of frequent subgraph mining (FGM) under the rigorous differential privacy model. We introduce a novel differentially private FGM algorithm, which is referred to as DFG. In this algorithm, we first privately identify frequent subgraphs from input graphs, and then compute the noisy support of each identified frequent subgraph. In particular, to privately identify frequent subgraphs, we present a frequent subgraph identification approach which can improve the utility of frequent subgraph identifications through candidates pruning. Moreover, to compute the noisy support of each identified frequent subgraph, we devise a lattice-based noisy support derivation approach, where a series of methods has been proposed to improve the accuracy of the noisy supports. Through formal privacy analysis, we prove that our DFG algorithm satisfies e-differential privacy. Extensive experimental results on real datasets show that the DFG algorithm can privately find frequent subgraphs with high data utility.


title: Apical extrusion of debris in four different endodontic instrumentation systems: A meta-analysis

abstract: Background All endodontic instrumentation systems tested so far, promote apical extrusion of debris, which is one of the main causes of postoperative pain, flare ups, and delayed healing. Objectives Of this meta-analysis was to collect and analyze in vitro studies quantifying apically extruded debris while using Hand ProTaper (manual), ProTaper Universal (rotary), Wave One (reciprocating), and self-adjusting file (SAF; vibratory) endodontic instrumentation systems and to determine methods which produced lesser extrusion of debris apically. Methodology An extensive electronic database search was done in PubMed, Scopus, Cochrane, LILACS, and Google Scholar from inception until February 2016 using the key terms “Apical Debris Extrusion, extruded material, and manual/rotary/reciprocating/SAF systems.” A systematic search strategy was followed to extract 12 potential articles from a total of 1352 articles. The overall effect size was calculated from the raw mean difference of weight of apically extruded debris. Results Statistically significant difference was seen in the following comparisons: SAF Conclusions Apical extrusion of debris was invariably present in all the instrumentation systems analyzed. SAF system seemed to be periapical tissue friendly as it caused reduced apical extrusion compared to Rotary ProTaper and Wave One.


title: Comparison of descriptor spaces for chemical compound retrieval and classification

abstract: In recent years the development of computational techniques that build models to correctly assign chemical compounds to various classes or to retrieve potential drug-like compounds has been an active area of research. Many of the best-performing techniques for these tasks utilize a descriptor-based representation of the compound that captures various aspects of the underlying molecular graph?s topology. In this paper we compare different set of descriptors that are currently used for chemical compound classification. In this process, we also introduce four different descriptors derived from all connected fragments present in the molecular graphs. In addition, we introduce an extension to existing vector-based kernel functions to take into account the length of the fragments present in the descriptors. We experimentally evaluate the performance of the previously introduced and the new descriptors in the context of SVM-based classification and ranked-retrieval on 28 classification and retrieval problems derived from 18 datasets. Our experiments show that for both these tasks, the new descriptors consistently and statistically outperform previously developed schemes based on the widely used fingerprint- and Maccs keys-based descriptors, as well as recently introduced descriptors obtained by mining and analyzing the structure of the molecular graphs.


title: A low-power 10-Gb/s receiver with merged CTLE and DFE summer

abstract: This paper presents a wireline communication receiver with merged continuous-time linear equalizer (CTLE) and decision feedback equalizer (DFE) summer circuit. The merged circuit removes the traditional CTLE and merge it into the following DFE summer to builds linear equalization so as to significantly reduce the receiver power consumption. Analysis for the conventional equalizer and proposed circuit are presented. The proposed circuit architecture is experimentally verified at a 10 Gb/s wireline receiver with 0.45 mW/Gb/s power efficiency.


title: SMM Rootkits: A New Breed of OS Independent Malware

abstract: The emergence of hardware virtualization technology has led to the development of OS independent malware such as the Virtual Machine based rootkits (VMBRs). In this paper, we draw attention to a different but related threat that exists on many commodity systems in operation today: The System Management Mode based rootkit (SMBR). System Management Mode (SMM) is a relatively obscure mode on Intel processors used for low-level hardware control. It has its own private memory space and execution environment which is generally invisible to code running outside (e.g., the Operating System). Furthermore, SMM code is completely non-preemptible, lacks any concept of privilege level, and is immune to memory protection mechanisms. These features make it a potentially attractive home for stealthy rootkits. In this paper, we present our development of a proof of concept SMM rootkit. In it, we explore the potential of System Management Mode for malicious use by implementing a chipset level keylogger and a network backdoor capable of directly interacting with the network card to send logged keystrokes to a remote machine via UDP. The rootkit hides its memory footprint and requires no changes to the existing Operating System. It is compared and contrasted with VMBRs. Finally, techniques to defend against these threats are explored. By taking an offensive perspective we hope to help security researchers better understand the depth and scope of the problems posed by an emerging class of OS independent malware.


title: Perspectives on the Productivity Dilemma

abstract: Editor’s note The authors of this paper presented an All-academy session at the 2008 Academy of Management annual meeting in Anaheim, California. We were excited by the dynamic nature of the debate and felt that it related closely to critical issues in the areas of operations management, strategy, product development and international business. We thus invited the authors to write an article offering their individual and joint views on the productivity dilemma. We trust you will find it to be stimulating and thought provoking. We invite you to add your voice to the discussion by commenting on this article at the Operations and Supply Chain (OSM) Forum at http://www.journaloperationsmanagement.org/OSM.asp. – Kenneth K. Boyer and Morgan L. Swink


title: Non-Linear Text Regression with a Deep Convolutional Neural Network

abstract: Text regression has traditionally been tackled using linear models. Here we present a non-linear method based on a deep convolutional neural network. We show that despite having millions of parameters, this model can be trained on only a thousand documents, resulting in a 40% relative improvement over sparse linear models, the previous state of the art. Further, this method is flexible allowing for easy incorporation of side information such as document meta-data. Finally we present a novel technique for interpreting the effect of different text inputs on this complex non-linear model.


title: Effects of Occupational Therapy Services on Fine Motor and Functional Performance in Preschool Children

abstract: PURPOSE ::: This study examined how performance components and variables in intervention influenced fine motor and functional outcomes in preschool children. ::: ::: ::: METHOD ::: In a sample of 44 preschool-aged children with fine motor delays who received occupational therapy services, eight fine motor and functional performance assessments were administered at the beginning and end of the academic year. Data on the format and intervention activities of each occupational therapy session were recorded for 8 months. ::: ::: ::: RESULTS ::: The children received a mean of 23 sessions, in both individual and group format. Most of the sessions (81%) used fine motor activities; 29% addressed peer interaction, and 16% addressed play skills. Visual motor outcomes were influenced by the number of intervention sessions and percent of sessions with play goals. Fine motor outcomes were most influenced by the therapists' emphasis on play and peer interaction goals; functional outcomes were influenced by number of sessions and percent of sessions that specifically addressed self-care goals. ::: ::: ::: CONCLUSION ::: The influence of play on therapy outcomes suggests that a focus on play in intervention activities can enhance fine motor and visual motor performance.


title: Visual Perception of Parallel Coordinate Visualizations

abstract: Parallel coordinates is a visualization technique that provides an unbiased representation of high-dimensional data. The parallel configuration of axes treats data dimensions uniformly and is well suited for exploratory visualization. However, first-time users of parallel coordinate visualizations can find the representation confusing and difficult to understand.We used eye tracking to study how parallel coordinate visualizations are perceived, and compared the results to the optimal visual scan path required to complete the tasks. The results indicate that even first-time users quickly learn how to use parallel coordinate visualizations, pay attention to the correct task-specific areas in the visualization, and become rapidly proficient with it.


title: An Efficient Sudden-Power-Off-Recovery Design with Guaranteed Booting Time for Solid State Drives

abstract: Solid state drives (SSDs) that deliver high- bandwidth and low-latency performance have become the mainstream of storage devices in modern systems. Over the past years, there has been a great deal of researches conducted to improve the SSD performance or reliability with parallel or efficient address translation designs. On the contrary, little work is done for the optimization to guarantee the booting/recovery time of SSDs after any sudden power-off. Motivated by the fact that the fast-growing SSD capacity gradually makes existing scanning and recovering processes become infeasible and unacceptable, we propose an efficient sudden-power-off-recovery design to recover an SSD with guaranteed booting time. The proposed design was implemented on an SSD prototyping platform equipped with in-house NAND flash memories and was evaluated with various benchmarks. The results demonstrate that after sudden power-off, the prototyped SSD can be recovered with a guaranteed and bounded booting time between 80ms and 200ms.


title: Linear Discriminant Regression Classification for Face Recognition

abstract: To improve the robustness of the linear regression classification (LRC) algorithm, in this paper, we propose a linear discriminant regression classification (LDRC) algorithm to boost the effectiveness of the LRC for face recognition. We embed the Fisher criterion into the LRC as a novel discriminant regression analysis method. The LDRC attempts to maximize the ratio of the between-class reconstruction error (BCRE) over the within-class reconstruction error (WCRE) to find an optimal projection matrix for the LRC such that the LRC on that subspace can achieve a high discrimination for classification. Then, the projected coefficients are executed by the LRC for face recognition. Extensive experiments carried out on the FERET and AR face databases show that the LDRC performs better than the related regression based algorithms and shows a promising ability for face recognition.


title: Bridging centrality: graph mining from element level to group level

abstract: Despite the pervasiveness of networks as models for real world systems ranging from the Internet, the World Wide Web to gene regulation and scientific collaborations, only a limited number of metrics capable of characterizing these systems are available. The existing metrics for characterizing networks have broad specificity and lack the selectivity for many applications. The purpose of this paper is to identify and critically evaluate a metric, termed bridging centrality, which is highly selective for identifying bridges in networks. The properties of bridges are unique compared to the other network metrics. For a diverse range of data sets, we found that networks are highly susceptible to disruption but robust to loss structural integrity upon targeted deletion of bridging nodes. A novel graph clustering approach, termed `bridge cut', utilizing bridging edges as module boundary is also proposed. The modules identified by the bridge cut algorithm are more effective than the other graph clustering methods. Thus, bridging centrality is a network metric with unique properties that may aid in network analysis from element to group level in various areas including systems biology and national security applications.


Automated Detection of Lung Diseases in Chest X-Rays


title: Dummy-Based User Location Anonymization Under Real-World Constraints

abstract: According to the growth of mobile devices equipped with a GPS receiver, a variety of location-based services (LBSs) have been launched. Since location information may reveal private information, preserving location privacy has become a significant issue. Previous studies proposed methods to preserve a users’ privacy; however, most of them do not take physical constraints into consideration. In this paper, we focus on such constraints and propose a location privacy preservation method that can be applicable to a real environment. In particular, our method anonymizes the user’s location by generating dummies which we simulate to behave like real human. It also considers traceability of the user’s locations to quickly recover from an accidental reveal of the user’s location. We conduct an experiment using five users’ real GPS trajectories and compared our method with previous studies. The results show that our method ensures to anonymize the user’s location within a pre-determined range. It also avoids fixing the relative positions of the user and dummies, which may give a hint for an LBS provider to identify the real user. In addition, we conducted a user experiment with 22 participants to evaluate the robustness of our method against humans. We asked participants to observe movements of a user and dummies and try to find the real user. As a result, we confirmed that our method can anonymize the users’ locations even against human’s observation.


title: Joint rate and channel width adaptation for 802.11 MIMO wireless networks

abstract: The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems. This leap, however, adds complexity to selecting the right transmission rate. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth. ARAMIS adapts transmission rates on a per-packet basis; we believe it is the first 802.11n RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11n environments with up to a 10 fold increase in throughput.


title: Emotion recognition system using short-term monitoring of physiological signals

abstract: A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.


title: A 2.4GHz ambient RF energy harvesting system with −20dBm minimum input power and NiMH battery storage

abstract: We describe a radio frequency (RF) energy harvester and power management circuit that trickle charges a battery from incident power levels as low as -20dBm. We designed the harvester for the 2.4 GHz RF band to leverage the ubiquity of energy that is produced by Wi-Fi, Bluetooth, and other devices. This paper reports on the design and current status of the harvester and compares our performance to other published results. In this incident power regime, rectified voltages are low, so power management circuit operation in the 100mV regime is critical. This paper describes a novel rectenna design, boost converter, and battery charger for RF energy harvesting specifically tuned to this low-power regime. At −20dBm RF input power, the harvesting system (rectenna, boost converter, and battery charger) sources 5.8µJ into a rechargeable battery after 1 hour.


title: Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

abstract: DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the remaining nucleosomes follow a statistical positioning model.


title: A High-Precision CMOS Temperature Sensor with Thermistor Linear Calibration in the (−5 °C, 120 °C) Temperature Range

abstract: A high-precision Complementary Metal-Oxide-Semiconductor (CMOS) temperature sensor for (−5 °C, 120 °C) temperature range is designed and analyzed in this investigation. The proposed design is featured with a temperature range selection circuit so that the thermistor linear circuit automatically switches to a corresponding calibration loop in light of the temperature range besides the analysis of the calibration method. It resolves the problem that the temperature range of a single thermistor temperature sensor is too small. Notably, the output of the proposed design also attains a high linearity. The measurement results in a thermal chamber justifying that the output voltage is 1.96 V to 4.15 V, the maximum linearity error ≤1.4%, and the worst temperature error ≤1.1 °C in the temperature range of −5 °C to 120 °C.


title: Deep Domain Confusion: Maximizing for Domain Invariance

abstract: Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available. We propose a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant. We additionally show that a domain confusion metric can be used for model selection to determine the dimension of an adaptation layer and the best position for the layer in the CNN architecture. Our proposed adaptation method offers empirical performance which exceeds previously published results on a standard benchmark visual domain adaptation task.


title: Exposing digital forgeries in color filter array interpolated images

abstract: With the advent of low-cost and high-resolution digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital cameras, for example, employ a single sensor in conjunction with a color filter array (CFA), and then interpolate the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms.


title: Does our speech change when we cannot gesture

abstract: Do people speak differently when they cannot use their hands? Previous studies have suggested that speech becomes less fluent and more monotonous when speakers cannot gesture, but the evidence for this claim remains inconclusive. The present study attempts to find support for this claim in a production experiment in which speakers had to give addressees instructions on how to tie a tie; half of the participants had to perform this task while sitting on their hands. Other factors that influence the ease of communication, such as mutual visibility and previous experience, were also taken into account. No evidence was found for the claim that the inability to gesture affects speech fluency or monotony. An additional perception task showed that people were also not able to hear whether someone gestures or not.


title: Table-to-text Generation by Structure-aware Seq2seq Learning

abstract: Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the \texttt{WIKIBIO} dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on this https URL


title: 2-year follow-up of trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer: a randomised controlled trial

abstract: Summary Background Trastuzumab—a humanised monoclonal antibody against HER2—has been shown to improve disease-free survival after chemotherapy in women with HER2-positive early breast cancer. We investigated the drug's effect on overall survival after a median follow-up of 2 years in the Herceptin Adjuvant (HERA) study. Methods HERA is an international multicentre randomised trial that compared 1 or 2 years of trastuzumab treatment with observation alone after standard neoadjuvant or adjuvant chemotherapy in women with HER2-positive node positive or high-risk node negative breast cancer. 5102 women participated in the trial; we analysed data from 1703 women who had been randomised for treatment with trastuzumab for 1 year and 1698 women from the control group, with median follow-up of 23·5 months (range 0–48 months). The primary endpoint of the trial was disease-free survival. Here, we assess overall survival, a secondary endpoint. Analyses were done on an intent-to-treat basis. This trial is registered with the European Clinical Trials Database, number 2005–002385–11. Findings 97 (5·7%) patients randomised to observation alone and 58 (3·4%) patients randomised to 1 year of treatment with trastuzumab were lost to follow-up. 172 women stopped trastuzumab prematurely. 59 deaths were reported for trastuzumab and 90 in the control group. The unadjusted hazard ratio (HR) for the risk of death with trastuzumab compared with observation alone was 0·66 (95% CI 0·47–0·91; p=0·0115). 218 disease-free survival events were reported with trastuzumab compared with 321 in the control group. The unadjusted HR for the risk of an event with trastuzumab compared with observation alone was 0·64 (0·54–0·76; p Interpretation Our results show that 1 year of treatment with trastuzumab after adjuvant chemotherapy has a significant overall survival benefit after a median follow-up of 2 years. The emergence of this benefit after only 2 years reinforces the importance of trastuzumab in the treatment of women with HER2-positive early breast cancer.


title: Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks

abstract: This paper presents a study on detecting cyberattacks on industrial control systems (ICS) using unsupervised deep neural networks, specifically, convolutional neural networks. The study was performed on a SecureWater Treatment testbed (SWaT) dataset, which represents a scaled-down version of a real-world industrial water treatment plant. e suggest a method for anomaly detection based on measuring the statistical deviation of the predicted value from the observed value.We applied the proposed method by using a variety of deep neural networks architectures including different variants of convolutional and recurrent networks. The test dataset from SWaT included 36 different cyberattacks. The proposed method successfully detects the vast majority of the attacks with a low false positive rate thus improving on previous works based on this data set. The results of the study show that 1D convolutional networks can be successfully applied to anomaly detection in industrial control systems and outperform more complex recurrent networks while being much smaller and faster to train.


title: Protego: A passive intrusion detection system for Android smartphones

abstract: With the proliferation of smartphones, the security threats have correspondingly increased. Although some form of security mechanisms like authentication and encryption have been provided on platforms such as Android, these alone cannot mitigate all the forms of threats. Thus, the need for an intrusion detection system for smartphones has become immensely important. In this project, we capitalize on earlier approaches of host-based intrusion detection systems and behavior-based intrusion detection systems for Android smartphones to design and implement a host-based, behavior-based passive intrusion detection system, Protego, for Android smartphones. There are two versions - static and dynamic, each with its own novel approach. The static version of Protego improves predictive performance by implementing feature reduction, thus increasing classifier accuracy significantly. The novelty of dynamic approach is that it analyzes live traffic with a minimum delay (in the order of milliseconds). This opens up a variety of use cases for our system, especially in the business world where information security on smartphones is of utmost importance. We have illustrated this by also extending Protego to devise a solution for BYOD (bring your own device), a growing trend in the corporate world, by using the IDS to detect other malicious activities like peer-to-peer traffic from torrent clients.


title: An Ontology-Based Information Extraction (OBIE) Framework for Analyzing Initial Public Offering (IPO) Prospectus

abstract: With the large amounts of information associated with the Initial Public Offering (IPO) process, an intelligent tool is needed for assisting the decision-making activities for both the investors and the underwriters. Even though a large body of related studies exists in extant literature, minimum attention has been devoted to the aspect of understanding hidden semantics within the informative contents of IPO prospectus. In this paper, we present a framework for processing the textual content of IPO prospectus based on an emerging technique named Ontology Based Information Extraction (OBIE). Preliminary results indicates that the framework is capable of meeting the design requirements identified. Moreover, lessons learned during the design and implementation span technical and organizational considerations and can serve as guidance for future research and development in related areas.


title: Effective Suppression of Surface Recombination in ZnO Nanorods Arrays during the Growth Process

abstract: ZnO nanorods arrays are respectively prepared under different vapor pressures with opening (OZN) or sealing (SZN) of the beaker. The results from time-resolved photoluminescence measurements indica ...


title: An unexpected unity among methods for interpreting model predictions

abstract: Understanding why a model made a certain prediction is crucial in many data science fields. Interpretable predictions engender appropriate trust and provide insight into how the model may be improved. However, with large modern datasets the best accuracy is often achieved by complex models even experts struggle to interpret, which creates a tension between accuracy and interpretability. Recently, several methods have been proposed for interpreting predictions from complex models by estimating the importance of input features. Here, we present how a model-agnostic additive representation of the importance of input features unifies current methods. This representation is optimal, in the sense that it is the only set of additive values that satisfies important properties. We show how we can leverage these properties to create novel visual explanations of model predictions. The thread of unity that this representation weaves through the literature indicates that there are common principles to be learned about the interpretation of model predictions that apply in many scenarios.


title: Modeling and Comparison of Machine and Converter Losses for PWM and PAM in High-Speed Drives

abstract: The interaction of the machine and the converter is becoming increasingly important, especially for high-speed drives, mainly due to the effect of the converter modulation on the machine losses. The allocation of the losses to different components of the drive system needs to be known in order to choose the ideal machine and modulation match. In this paper, individual models are introduced for calculating the rotor, copper, core and inverter losses, taking the modulation type into account. These models are developed considering two typical high-speed permanent-magnet synchronous motor topologies (slotted and slotless machines) driven by PulseAmplitude-Modulation (PAM) and Pulse-Width-Modulation (PWM) converters. The models are applied to two off-the-shelf machines and a converter operating both with PAM and PWM. The test bench used to experimentally verify the models is also described and the model results are compared to the measurements. The results show that PAM produces a higher overall efficiency for the high-speed machines considered in this work. However, PWM can be used to move the losses from the rotor to the converter at the expense of decreasing the overall drive efficiency. The possible benefits of these results are discussed.


title: Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings

abstract: Commercial buildings contribute to 19% of the primary energy consumption in the US, with HVAC systems accounting for 39.6% of this usage. To reduce HVAC energy use, prior studies have proposed using wireless occupancy sensors or even cameras for occupancy based actuation showing energy savings of up to 42%. However, most of these solutions require these sensors and the associated network to be designed, deployed, tested and maintained within existing buildings which is significantly costly. We present Sentinel, a system that leverages existing WiFi infrastructure in commercial buildings along with smartphones with WiFi connectivity carried by building occupants to provide fine-grained occupancy based HVAC actuation. We have implemented Sentinel on top of RESTful web services, and demonstrate that it is scalable and compatible with legacy building management. We show that Sentinel accurately determines the occupancy in office spaces 86% of the time, with 6.2% false negative errors. We high-light the reasons for the inaccuracies, mostly attributed to aggressive power management by smartphones. Finally, we actuate 23% of the HVAC zones within a commercial building using Sentinel for one day and measure HVAC electrical energy savings of 17.8%.


title: Calamintha nepeta (L.) Savi and Micromeria thymifolia (Scop.) Fritsch cultivated in Italy

abstract: Calamintha nepeta and Micromeria thymifolia have been traditionally used in the Mediterranean area as condiments and medicinal plants for a long time. Whereas in parts of Italy C. nepeta (special recipes have been developed in Lazio and Tuscany) is also an established garden plant showing different evolutionary products and their interaction among each other and the wild progenitor, M. thymifolia is being developed into a new crop plant. Both plants and their uses are described with regard to Italy. There is a marked tendency to broaden the use of condiments and spices which results in new crop plants which have to be documented and elaborated in further studies. Many species of Labiatae are predisposed to use by man and new items can be found even in areas which have to be considered as well studied.


title: Comparative Study of Optical Unipolar Codes for Incoherent DS-OCDMA system

abstract: In incoherent Direct Sequence Optical Code Division Multiple Access system (DSOCDMA), the Multiple Access Interference (MAI) is one of the main limitations. To mitigate the MAI, many types of codes can be used to remove the contributions from users. In this paper, we study two types of unipolar codes used in DS-OCDMA system incoherent which are optical orthogonal codes (OOC) and the prime code (PC). We developed the characteristics of these codes i,e factors correlations, and the theoretical upper bound of the probability of error. The simulation results showed that PC codes have better performance than OOC codes.


title: A feature selection method based on minimum redundancy maximum relevance for learning to rank

abstract: Learning to rank has considered as a promising approach for ranking in information retrieval. In recent years feature selection for learning to rank introduced as a crucial issue. Reducing the feature set by removing irrelevant and redundant features can improve the prediction performance. In this paper we address the problem of filter feature selection for ranking. We propose to apply minimum redundancy maximum relevance (mRMR) method that select feature subset based on importance of features and similarity between them. We reweight the component of mRMR to balance between importance and similarity. We apply two methods for measuring the similarity between features and two methods for evaluating importance. Experimental results on two standard datasets from Letor demonstrate that the proposed algorithm 1)outperform two stateof- the-art learning to rank algorithms in term of accuracy, 2) learn a more spars model compared to a feature selection model for ranking.


title: Empty the basket - a shape based learning approach for grasping piles of unknown objects

abstract: This paper presents a novel approach to emptying a basket filled with a pile of objects. Form, size, position, orientation and constellation of the objects are unknown. Additional challenges are to localize the basket and treat it as an obstacle, and to cope with incomplete point cloud data. There are three key contributions. First, we introduce Height Accumulated Features (HAF) which provide an efficient way of calculating grasp related feature values. The second contribution is an extensible machine learning system for binary classification of grasp hypotheses based on raw point cloud data. Finally, a practical heuristic for selection of the most robust grasp hypothesis is introduced. We evaluate our system in experiments where a robot was required to autonomously empty a basket with unknown objects on a pile. Despite the challenging scenarios, our system succeeded each time.


title: Performance evaluation of a robot-assisted catheter operating system with haptic feedback

abstract: In this paper, a novel robot-assisted catheter operating system (RCOS) has been proposed as a method to reduce physical stress and X-ray exposure time to physicians during endovascular procedures. The unique design of this system allows the physician to apply conventional bedside catheterization skills (advance, retreat and rotate) to an input catheter, which is placed at the master side to control another patient catheter placed at the slave side. For this purpose, a magnetorheological (MR) fluids-based master haptic interface has been developed to measure the axial and radial motions of an input catheter, as well as to provide the haptic feedback to the physician during the operation. In order to achieve a quick response of the haptic force in the master haptic interface, a hall sensor-based closed-loop control strategy is employed. In slave side, a catheter manipulator is presented to deliver the patient catheter, according to position commands received from the master haptic interface. The contact forces between the patient catheter and blood vessel system can be measured by designed force sensor unit of catheter manipulator. Four levels of haptic force are provided to make the operator aware of the resistance encountered by the patient catheter during the insertion procedure. The catheter manipulator was evaluated for precision positioning. The time lag from the sensed motion to replicated motion is tested. To verify the efficacy of the proposed haptic feedback method, the evaluation experiments in vitro are carried out. The results demonstrate that the proposed system has the ability to enable decreasing the contact forces between the catheter and vasculature.


title: Image processing for driver's safety and vehicle control using raspberry Pi and webcam

abstract: This paper gives real time approach for detection of driver drowsiness due to fatigue or intoxication. According to survey made by government, 22% accidents are due to drowsiness and 33% accidents are due to alcohol drinking. Actually to develop such a technique in car which can detect the drowsiness of driver is a big task. To complete this task image processing technique can be more useful. The camera is supported by embedded system in which Raspbian OS is used. This embeddd system is serially interfaced with another micro controller with RS232 protocol via serial communication, which will detect the real time siuation of the driver and switch on the alarm when drowsiness is detected the system switches off the car ignition power source by microcontroller based signal. A GSM module is introduced in the system which sends sms to the person whose contact is saved in the system.


title: Aerial Mapping of Forests Affected by Pathogens Using UAVs, Hyperspectral Sensors, and Artificial Intelligence

abstract: The environmental and economic impacts of exotic fungal species on natural and plantation forests have been historically catastrophic. Recorded surveillance and control actions are challenging because they are costly, time-consuming, and hazardous in remote areas. Prolonged periods of testing and observation of site-based tests have limitations in verifying the rapid proliferation of exotic pathogens and deterioration rates in hosts. Recent remote sensing approaches have offered fast, broad-scale, and affordable surveys as well as additional indicators that can complement on-ground tests. This paper proposes a framework that consolidates site-based insights and remote sensing capabilities to detect and segment deteriorations by fungal pathogens in natural and plantation forests. This approach is illustrated with an experimentation case of myrtle rust (Austropuccinia psidii) on paperbark tea trees (Melaleuca quinquenervia) in New South Wales (NSW), Australia. The method integrates unmanned aerial vehicles (UAVs), hyperspectral image sensors, and data processing algorithms using machine learning. Imagery is acquired using a Headwall Nano-Hyperspec ® camera, orthorectified in Headwall SpectralView ® , and processed in Python programming language using eXtreme Gradient Boosting (XGBoost), Geospatial Data Abstraction Library (GDAL), and Scikit-learn third-party libraries. In total, 11,385 samples were extracted and labelled into five classes: two classes for deterioration status and three classes for background objects. Insights reveal individual detection rates of 95% for healthy trees, 97% for deteriorated trees, and a global multiclass detection rate of 97%. The methodology is versatile to be applied to additional datasets taken with different image sensors, and the processing of large datasets with freeware tools.


title: Personalizing Information Retrieval Using Search Behaviors and Time Constraints

abstract: Studies have examined how time constraints influence search behaviors; however, no effort has been spent on how time constraints may help predict document usefulness for personalization purposes. This study aims to fill this gap by researching the relationships between time constraints, search behaviors, and usefulness judgments. A controlled lab experiment was conducted with 40 participants searching for four tasks of two types (fact finding and information understanding), under two time conditions (with or without time constraints). Results show that time constraints and usefulness had interaction effects on first dwell time; while usefulness had positive relationship with total dwell time. Results indicate that knowing time constraints helps predict document usefulness from dwell time. The findings provide implications on personalization in information search.


title: Rotor Position Estimation for PMSM Based on Sliding Mode Observer

abstract: Most methods to estimate the rotor position of permanent magnetic synchronous motor (PMSM) suffer from the motor parameters, which are difficult to be applied due to the complicated algorithm. This paper presents a sliding mode observer (SMO) to estimate the rotor position of PMSM. The SMO makes use of the error between the estimated current and measured current. And the phase lag, which is caused by the low pass filter, is compensated in a proper way. The convergence of the SMO is guaranteed. A novel estimated speed is used to improve the systematic stability. High performance sensorless drive of the PMSM based on SMO is established and the whole system is simulated by matlab. Simulations show the effect of variation of the torque and speed on the estimated rotor angle. From these experiments, it is found that this method is more robust and easier to be applied.


title: Semantic Segmentation of RGBD Videos with Recurrent Fully Convolutional Neural Networks

abstract: Semantic segmentation of videos using neural networks is currently a popular task, the work done in this field is however mostly on RGB videos. The main reason for this is the lack of large RGBD video datasets, annotated with ground truth information at the pixel level. In this work, we use a synthetic RGBD video dataset to investigate the contribution of depth and temporal information to the video segmentation task using convolutional and recurrent neural network architectures. Our experiments show the addition of depth information improves semantic segmentation results and exploiting temporal information results in higher quality output segmentations.


title: Propofol infusion syndrome

abstract: The clinical features of propofol infusion syndrome (PRIS) are acute refractory bradycardia leading to asystole, in the presence of one or more of the following: metabolic acidosis (base deficit > 10 mmol.l(-1)), rhabdomyolysis, hyperlipidaemia, and enlarged or fatty liver. There is an association between PRIS and propofol infusions at doses higher than 4 mg.kg(-1).h(-1) for greater than 48 h duration. Sixty-one patients with PRIS have been recorded in the literature, with deaths in 20 paediatric and 18 adult patients. Seven of these patients (four paediatric and three adult patients) developed PRIS during anaesthesia. It is proposed that the syndrome may be caused by either a direct mitochondrial respiratory chain inhibition or impaired mitochondrial fatty acid metabolism mediated by propofol. An early sign of cardiac instability associated with the syndrome is the development of right bundle branch block with convex-curved ('coved type') ST elevation in the right praecordial leads (V1 to V3) of the electrocardiogram. Predisposing factors include young age, severe critical illness of central nervous system or respiratory origin, exogenous catecholamine or glucocorticoid administration, inadequate carbohydrate intake and subclinical mitochondrial disease. Treatment options are limited. Haemodialysis or haemoperfusion with cardiorespiratory support has been the most successful treatment.


title: Stretchable liquid tactile sensor for robot-joints

abstract: In this paper, we propose a stretchable tactile sensor composed of a pair of silicone rubber channels filled with electro conductive liquid. When a force was applied to this channel, its length and cross-sectional area deforms. By measuring the resistance change of the electro conductive liquid in the channel, its deformation can be measured. The proposed tactile sensor is composed of two parallel channel filled with electro conductive liquid, therefore, by comparing the resistance changes of each channel to the deformation, only the contacting force can be measured independently. Since a liquid is used for the sensing material, the proposed liquid tactile sensor can be easily attached to movable portions as the joints of robots. In the paper, we measured the sensing characteristics of the liquid tactile sensor to the stretch, bend, and contact force. Finally, the efficiency of the sensor was demonstrated by measuring the contact force from 0 to 3.0N by attaching the 20% stretched liquid tactile sensor to curved surfaces with 0.05mm−1 in curvature.


title: A survey of mobility models for ad hoc network research

abstract: In the performance evaluation of a protocol for an ad hoc network, the protocol should be tested under realistic conditions including, but not limited to, a sensible transmission range, limited buffer space for the storage of messages, representative data traffic models, and realistic movements of the mobile users (i.e., a mobility model). This paper is a survey of mobility models that are used in the simulations of ad hoc networks. We describe several mobility models that represent mobile nodes whose movements are independent of each other (i.e., entity mobility models) and several mobility models that represent mobile nodes whose movements are dependent on each other (i.e., group mobility models). The goal of this paper is to present a number of mobility models in order to offer researchers more informed choices when they are deciding upon a mobility model to use in their performance evaluations. Lastly, we present simulation results that illustrate the importance of choosing a mobility model in the simulation of an ad hoc network protocol. Specifically, we illustrate how the performance results of an ad hoc network protocol drastically change as a result of changing the mobility model simulated.


title: On coding for real-time streaming under packet erasures

abstract: We consider a real-time streaming system where messages created at regular time intervals at a source are encoded for transmission to a receiver over a packet erasure link; the receiver must subsequently decode each message within a given delay from its creation time. We study a bursty erasure model in which all erasure patterns containing erasure bursts of a limited length are admissible. For certain classes of parameter values, we provide code constructions that asymptotically achieve the maximum message size among all codes that allow decoding under all admissible erasure patterns. We also study an i.i.d. erasure model in which each transmitted packet is erased independently with the same probability; the objective is to maximize the decoding probability for a given message size. We derive an upper bound on the decoding probability for any time-invariant code, and show that the gap between this bound and the performance of a family of time-invariant intrasession codes is small in the high reliability regime.


title: Scaling up POMDPs for Dialog Management: The ``Summary POMDP'' Method

abstract: Partially observable Markov decision processes (POMDPs) have been shown to be a promising framework for dialog management in spoken dialog systems. However, to date, POMDPs have been limited to artificially small tasks. In this work, we present a novel method called a "summary POMDP" for scaling slot-filling POMDP-based dialog managers to cope with tasks of a realistic size. An example dialog problem incorporating a user model built from real dialog data is presented. A dialog manager is created using this method and evaluated using a second user model created from held-out dialog data. Results confirm that summary POMDP policies scale well, and also show that summary POMDP policies are reasonably robust to variations in user behavior


title: Multipath Ghosts in Radar Imaging: Physical Insight and Mitigation Strategies

abstract: Most algorithms commonly exploited for radar imaging are based on linear models that describe only direct scattering events from the targets in the investigated scene. This assumption is rarely verified in practical scenarios where the objects to be imaged interact with each other and with surrounding environment producing undesired multipath signals. These signals manifest in radar images as “ghosts" that usually impair the reliable identification of the targets. The recent literature in the field is attempting to provide suitable techniques for multipath suppression from one side and from the other side is focusing on the exploitation of the additional information conveyed by multipath to improve target detection and localization. This work addresses the first problem with a specific focus on multipath ghosts caused by target-to-target interactions. In particular, the study is performed with regard to metallic scatterers by means of the linearized inverse scattering approach based on the physical optics (PO) approximation. A simple model is proposed in the case of point-like targets to gain insight into the ghosts problem so as to devise possible measurement and processing strategies for their mitigation. Finally, the effectiveness of these methods is assessed by reconstruction results obtained from full-wave synthetic data.


title: Detecting and tracking people in real time with RGB-D camera

abstract: A PEI representation is proposed to alleviate overlapping in original image domain while preserving information of all pixels.A human plausible candidates locating technique is proposed to quickly reduce search space of the detector.Two novel features are proposed to characterize human shape and appearance in 3D space.A single-pass, progressive refinement framework enables the system to achieve high accuracy at real time. We propose a novel approach to automatic detection and tracking of people taking different poses in cluttered and dynamic environments using a single RGB-D camera. The original RGB-D pixels are transformed to a novel point ensemble image (PEI), and we demonstrate that human detection and tracking in 3D space can be performed very effectively with this new representation. The detector in the first phase quickly locates human physiquewise plausible candidates, which are then further carefully filtered in a supervised learning and classification second phase. Joint statistics of color and height are computed for data association to generate final 3D motion trajectories of tracked individuals. Qualitative and quantitative experimental results obtained on the publicly available office dataset, mobile camera dataset and the real-world clothing store dataset we created show very promising results.


title: Soft robotics: Definition and research issues

abstract: Soft robots have been a buzz word in the field of robotics. However, there are several definitions of soft robots in literature with their varying degrees of usefulness to providing guidelines for designing and constructing a soft robot. This paper attempts to provide a comprehensive definition of soft robots with the goals that the definition should be of (i) generality and (ii) practicality. By generality, it is meant that the definition gives the signature to soft robots and is all inclusive, and by practicality, it is meant that the definition provides a general guideline for practicing designers to design and construct soft robots to particular applications. A salient point in our definition of soft robots is the concept of softness, which is defined from the perspective of a receiving object - particularly as the stress and other damage quantities (e.g., deflection) created in the receiving object when the receiving object interacts with the soft system per se. A further contribution is the provision of definitions to soft sensor, soft actuator along with power generator, soft controller, and soft mechanism. Finally, research issues on soft robots are outlined.


title: Video Object Segmentation by Learning Location-Sensitive Embeddings

abstract: We address the problem of video object segmentation which outputs the masks of a target object throughout a video given only a bounding box in the first frame. There are two main challenges to this task. First, the background may contain similar objects as the target. Second, the appearance of the target object may change drastically over time. To tackle these challenges, we propose an end-to-end training network which accomplishes foreground predictions by leveraging the location-sensitive embeddings which are capable to distinguish the pixels of similar objects. To deal with appearance changes, for a test video, we propose a robust model adaptation method which pre-scans the whole video, generates pseudo foreground/background labels and retrains the model based on the labels. Our method outperforms the state-of-the-art methods on the DAVIS and the SegTrack v2 datasets.


title: Parallel Feature Pyramid Network for Object Detection

abstract: Recently developed object detectors employ a convolutional neural network (CNN) by gradually increasing the number of feature layers with a pyramidal shape instead of using a featurized image pyramid. However, the different abstraction levels of CNN feature layers often limit the detection performance, especially on small objects. To overcome this limitation, we propose a CNN-based object detection architecture, referred to as a parallel feature pyramid (FP) network (PFPNet), where the FP is constructed by widening the network width instead of increasing the network depth. First, we adopt spatial pyramid pooling and some additional feature transformations to generate a pool of feature maps with different sizes. In PFPNet, the additional feature transformation is performed in parallel, which yields the feature maps with similar levels of semantic abstraction across the scales. We then resize the elements of the feature pool to a uniform size and aggregate their contextual information to generate each level of the final FP. The experimental results confirmed that PFPNet increases the performance of the latest version of the single-shot multi-box detector (SSD) by mAP of 6.4% AP and especially, 7.8% \(\text {AP}_\text {small}\) on the MS-COCO dataset.


title: Safer driving agreements in the Australian context: can they be effective?

abstract: Prior research suggests that greater parental involvement in the safety and learning of their young novice driver can have a positive impact on their childrs safety. Safer driving agreements, which typically involve a formal statement of driving conditions and restrictions ratified by a driver and another party, most often parents, are an increasingly common initiative to enhance young novice driver safety. However, there are few formal evaluations of such initiatives and the limited available research suggests only modest differences in traffic violations, and minimal impacts on crash involvement. The current paper reports on an assessment of the potential efficacy of safer driving agreements in the Australian context, via a literature review and extensive stakeholder and community consultations. Specifically, discussions were conducted with an expert panel of United States researchers and program developers; a survey was completed with Australian police, transport and motoring stakeholders; and focus groups and surveys were completed with young drivers and parents. Overall, results suggested mixed understanding of, and support for, safer driving agreements in Australia, with issues relating to voluntary participation and accurate monitoring of behaviour cited as major barriers. Indeed, the potential effectiveness of the initiative was largely perceived as being limited to those young drivers who are already safety conscious, and as being dependent on existing strong relationships with parents (e.g., trust, honesty and respect). Implications of the study and recommendations for future research are discussed.


title: Revisiting Web Data Extraction Using In-Browser Structural Analysis and Visual Cues in Modern Web Designs

abstract: Recent trends in website design have an impact on methods used for web data extraction. Many existing methods rely on structural analysis of web pages and, with the introduction of CSS, table-based layouts are no longer used, while responsive design means that layout and presentation are dependent on browsing context which also makes the use of visual clues more complex. We present DeepDesign, a system that semi-automatically extracts data records from web pages based on a combination of structural and visual features. It runs in a general-purpose browser, taking advantage of direct access to the complete CSS3 spectrum and the capability to trigger and execute JavaScript in the page. The user sees record matching in real-time and dynamically adapts the process if required. We present the details of the matching algorithms and provide an evaluation of them based on the top ten Alexa websites.


Dutch football prediction using machine learning classifiers


title: SkinWatch: skin gesture interaction for smart watch

abstract: We propose SkinWatch, a new interaction modality for wearable devices. SkinWatch provides gesture input by sensing deformation of the skin under a wearable wrist device, also known as a smart watch. A gesture command that is matched by learning data and two-dimensional linear input recognizes the gestures. The sensing part is small, thin, and stable, to accept accurate input via a user's skin. We also implement an anti-error mechanism to prevent unexpected input when the user moves or rotates his or her forearm. The whole sensor costs less than $1.50 and the sensor layer does not exceed a height of more than 3 mm in this prototype. We demonstrate sample applications with a practical task; using two-finger skin gesture input.


title: Health, ethics and environment: A qualitative study of vegetarian motivations

abstract: Abstract This qualitative study explored the motivations of vegetarians by means of online ethnographic research with participants in an international message board. The researcher participated in discussions on the board, gathered responses to questions from 33 participants, and conducted follow-up e-mail interviews with 18 of these participants. Respondents were predominantly from the US, Canada and the UK. Seventy per cent were females, and ages ranged from 14 to 53, with a median of 26 years. Data were analysed using a thematic approach. While this research found that health and the ethical treatment of animals were the main motivators for participants’ vegetarianism, participants reported a range of commitments to environmental concerns, although in only one case was environmentalism a primary motivator for becoming a vegetarian. The data indicate that vegetarians may follow a trajectory, in which initial motivations are augmented over time by other reasons for sustaining or further restricting their diet.


title: An Advanced Power Electronics Interface for Electric Vehicles Applications

abstract: Power electronics interfaces play an increasingly important role in the future clean vehicle technologies. This paper proposes a novel integrated power electronics interface (IPEI) for battery electric vehicles (BEVs) in order to optimize the performance of the powertrain. The proposed IPEI is responsible for the power-flow management for each operating mode. In this paper, an IPEI is proposed and designed to realize the integration of the dc/dc converter, on-board battery charger, and dc/ac inverter together in the BEV powertrain with high performance. The proposed concept can improve the system efficiency and reliability, can reduce the current and voltage ripples, and can reduce the size of the passive and active components in the BEV drivetrains compared to other topologies. In addition, low electromagnetic interference and low stress in the power switching devices are expected. The proposed topology and its control strategy are designed and analyzed by using MATLAB/Simulink. The simulation results related to this research are presented and discussed. Finally, the proposed topology is experimentally validated with results obtained from the prototypes that have been built and integrated in our laboratory based on TMS320F2808 DSP.


title: Forensic neuropathology : a practical review of the fundamentals

abstract: "Forensic Neuropathology" provides superior visual examples of the more commonly encountered conditions in forensic neuropathology and answers questions that arise regarding neuropathologic findings. The work includes values for frequently-encountered clinical assessments, and contains a more comprehensive summary of aging/dating of various neuropathological processes than is available in any other single current source. General pathology residents, forensic pathology and neuropathology fellows, and general pathologists and clinicians involved in referred cases will find this book extremely useful, as will individuals in allied fields such as law enforcement officers and attorneys."Forensic Neuropathology" aims to provide a concise summary of practical information frequently needed in forensic neuropathology cases. It includes selected material previously known but perhaps not significantly emphasized in current literature. Where possible, it suggests aging/dating parameters for certain neuropathological findings relevant to forensic neuropathology testimony. As a selective reference, the volume emphasizes practical issues and focuses on the most commonly encountered issues among neuropathology and medical examiner professionals. It features over 800 high-quality full-color photographs, gross and microscopic as well as illustrative line drawings. It includes use of actual cases, briefly summarized and illustrated to emphasize key principles. It focuses on the most-commonly encountered cases as relate to forensic incident and covers these aspects in depth and detail.


title: Data mining library reuse patterns using generalized association rules

abstract: In this paper, we show how data mining can be used to discover library reuse patterns in existing applications. Specifically, we consider the problem of discovering library classes and member functions that are typically reused in combination by application classes. This paper improves upon our earlier research using “association rules” [8] by taking into account the inheritance hierarchy using “generalized association rules”. This turns out to be a non-trivial but worthwhile endeavor. By browsing generalized association rules, a developer can discover patterns in library usage in a way that takes into account inheritance relationships. For example, such a rule might tell us that application classes that inherit from a particular library class often instantiate another class or one of its descendents . We illustrate the approach using our tool, CodeWeb, by demonstrating characteristic ways in which applications reuse classes in the KDE application framework.


title: Tweeting the Drug War : Empowerment , Intimidation , and Regulation in Social Media

abstract: In this paper, we describe how people living in armedconflict environments use social media as a participatory news platform. We investigate this by analyzing the microblogging practices of people living amid the Mexican Drug War. This paper provides a descriptive analysis of the phenomenon by mixing quantitative observations, content analysis, interviews, and case studies. We characterize the volume, temporal attributes, and information sharing methods. We focus on how citizens use social media to alert and disseminate information about acute violent events, and to interact with other people in their localities. We describe how social media might start to function in lieu of damaged state and news media apparatuses, in particular, through the emergence of communities that congregate around hashtags and the citizens that curate them. Finally, we explore the tensions among citizens, media actors, and the government in light of generalized violence and distrust in institutions and citizens. We end by outlining the implications for system design and governmental intervention.


title: Harnessing the Cloud for Securely Outsourcing Large-Scale Systems of Linear Equations

abstract: Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. However, how to protect customers' confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud. Because applying traditional approaches like Gaussian elimination or LU decomposition (aka. direct method) to such large-scale LEs would be prohibitively expensive, we build the secure LE outsourcing mechanism via a completely different approach—iterative method, which is much easier to implement in practice and only demands relatively simpler matrix-vector operations. Specifically, our mechanism enables a customer to securely harness the cloud for iteratively finding successive approximations to the LE solution, while keeping both the sensitive input and output of the computation private. For robust cheating detection, we further explore the algebraic property of matrix-vector operations and propose an efficient result verification mechanism, which allows the customer to verify all answers received from previous iterative approximations in one batch with high probability. Thorough security analysis and prototype experiments on Amazon EC2 demonstrate the validity and practicality of our proposed design.


title: An electronic-game framework for evaluating coevolutionary algorithms

abstract: One of the common artificial intelligence applications in electronic games consists of making an artificial agent learn how to execute some determined task successfully in a game environment. One way to perform this task is through machine learning algorithms capable of learning the sequence of actions required to win in a given game environment. There are several supervised learning techniques able to learn the correct answer for a problem through examples. However, when learning how to play electronic games, the correct answer might only be known by the end of the game, after all the actions were already taken. Thus, not being possible to measure the accuracy of each individual action to be taken at each time step. A way for dealing with this problem is through Neuroevolution, a method which trains Artificial Neural Networks using evolutionary algorithms. In this article, we introduce a framework for testing optimization algorithms with artificial agent controllers in electronic games, called EvoMan, which is inspired in the action-platformer game Mega Man II. The environment can be configured to run in different experiment modes, as single evolution, coevolution and others. To demonstrate some challenges regarding the proposed platform, as initial experiments we applied Neuroevolution using Genetic Algorithms and the NEAT algorithm, in the context of competitively coevolving two distinct agents in this game.


title: Generalized face anti-spoofing by detecting pulse from face videos

abstract: Face biometric systems are vulnerable to spoofing attacks. Such attacks can be performed in many ways, including presenting a falsified image, video or 3D mask of a valid user. A widely used approach for differentiating genuine faces from fake ones has been to capture their inherent differences in (2D or 3D) texture using local descriptors. One limitation of these methods is that they may fail if an unseen attack type, e.g. a highly realistic 3D mask which resembles real skin texture, is used in spoofing. Here we propose a robust anti-spoofing method by detecting pulse from face videos. Based on the fact that a pulse signal exists in a real living face but not in any mask or print material, the method could be a generalized solution for face liveness detection. The proposed method is evaluated first on a 3D mask spoofing database 3DMAD to demonstrate its effectiveness in detecting 3D mask attacks. More importantly, our cross-database experiment with high quality REAL-F masks shows that the pulse based method is able to detect even the previously unseen mask type whereas texture based methods fail to generalize beyond the development data. Finally, we propose a robust cascade system combining two complementary attack-specific spoof detectors, i.e. utilize pulse detection against print attacks and color texture analysis against video attacks.


title: Cost optimization of supply chain network of departmental store

abstract: Supply chain is a network of many entities, some of which are suppliers, manufacturers, wholesalers and retailers, through which raw materials are acquired, transformed, produced and delivered to the end-customer. An effective and efficient way of managing this network is called as Supply Chain Management. In this paper, a multi-objective algorithm is used to solve a three stage supply chain problem. The three stage supply chain or three entities considered are suppliers, wholesalers and a retailer. Typically all supply chain problems are characterized by conflicting objectives of each entity. This work specifically deals with the implementation of an algorithm which makes a trade-off between these conflicting objectives and enables any retailer to obtain the most optimal solution (supply chain) using the concept of pareto-optimality. It considers three major objectives- transportation cost, cost of product and the capacity of the wholesaler. Thus the optimization obtained will be reflected in the entire chain and will not be biased to any one entity in particular.


title: Machine Learning Approaches to Power System Security Assessment

abstract: The paper discusses a framework that uses machine learning and other automatic-learning methods to assess power-system security. The framework exploits simulation models in parallel to screen diverse simulation scenarios of a system, yielding a large database. Using data mining techniques, the framework extracts synthetic information about the simulated system's main features from this database.


title: Regions Matter: How Localized Social Capital Affects Innovation and External Knowledge Acquisition

abstract: To introduce new products, firms often use knowledge from other organizations. Drawing on social capital theory and the relational view of the firm, we argue that geographically localized social capital affects a firm's ability to innovate through various external channels. Combining data on social capital at the regional level, with a large-scale data set of the innovative activities of a representative sample of 2,413 Italian manufacturing firms from 21 regions, and controlling for a large set of firm and regional characteristics, we find that being located in a region characterized by a high level of social capital leads to a higher propensity to innovate. We find also that being located in an area characterized by a high degree of localized social capital is complementary to firms' investments in internal research and development (R&D) and that such a location positively moderates the effectiveness of externally acquired R&D on the propensity to innovate.


title: Multiobjective Identification of Controlling Areas in Neuronal Networks

abstract: In this paper, we investigate the multiobjective identification of controlling areas in the neuronal network of a cat's brain by considering two measures of controllability simultaneously. By utilizing nondominated sorting mechanisms and composite differential evolution (CoDE), a reference-point-based nondominated sorting composite differential evolution (RP-NSCDE) is developed to tackle the multiobjective identification of controlling areas in the neuronal network. The proposed RP-NSCDE shows its promising performance in terms of accuracy and convergence speed, in comparison to nondominated sorting genetic algorithms II. The proposed method is also compared with other representative statistical methods in the complex network theory, single objective, and constraint optimization methods to illustrate its effectiveness and reliability. It is shown that there exists a tradeoff between minimizing two objectives, and therefore pareto fronts (PFs) can be plotted. The developed approaches and findings can also be applied to coordination control of various kinds of real-world complex networks including biological networks and social networks, and so on.


title: Numerical solution of optimal control problems by direct collocation

abstract: By an appropriate discretization of control and state variables, a constrained optimal control problem is transformed into a finite dimensional nonlinear program which can be solved by standard SQP-methods [10]. Convergence properties of the discretization are derived. Prom a solution of this method known as direct collocation, these properties are used to obtain reliable estimates of adjoint variables. In the presence of active state constraints, these estimates can be significantly improved by including the switching structure of the state constraint into the optimization procedure. Two numerical examples are presented.


Motivational Models of Substance Use


title: Lessons learned from natural infection: focusing on the design of protective T cell vaccines for HIV/AIDS

abstract: CD8 + cytotoxic T lymphocyte (CTL) responses are crucial in establishing the control of persistent virus infections. Population studies of HIV-1-infected individuals suggest that CD8 + CTL responses targeting epitopes that take the greatest toll on virus replication are instrumental in immune control. A major question for vaccine design is whether incorporating epitopes responsible for controlling a persistent virus will translate into protection from natural infection or serve solely as a fail-safe mechanism to prevent overt disease in infected individuals. Here, we discuss qualitative parameters of the CD8 + CTL response and mechanisms operative in the control of persistent virus infections and suggest new strategies for design and delivery of HIV vaccines.


title: Security in the Internet of Things: A Survey on Application Layer Protocols

abstract: The rapid development of technology nowadays led people to a new and revolutionary concept, named the Internet of Things. This model imposes that all "objects", such as personal objects (smartphones, notebooks, smart watches, tablets etc), electronic equipment embed with sensors and other environmental elements are always connected to a common network. Therefore, one can access any resource at any time, by using a device recognized in the network. While the IoT may be economically and socially beneficial, the implementation of such a system poses many difficulties, risks and security issues that must be taken into consideration. Nowadays, the architecture of the Internet must be updated and rethought in order to interconnect trillions of devices and to ensure interoperability between them. Nevertheless, the most important problem is the security requirements of the IoT, which is probably one of the main reasons of the relatively slow development of this field. This paper presents the most important application layer protocols that are currently used in the IoT context: CoAP, MQTT, XMPP. We discuss them both separately and by comparison, with focus on the security provided by these protocols. Finally, we provide some future research opportunities and conclusions.


title: Wearable Device-Based Gait Recognition Using Angle Embedded Gait Dynamic Images and a Convolutional Neural Network

abstract: The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN's input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns.


title: Differentiable Dynamic Programming for Structured Prediction and Attention

abstract: Dynamic programming (DP) solves a variety of structured combinatorial problems by iteratively breaking them down into smaller subproblems. In spite of their versatility, DP algorithms are usually non-differentiable, which hampers their use as a layer in neural networks trained by backpropagation. To address this issue, we propose to smooth the max operator in the dynamic programming recursion, using a strongly convex regularizer. This allows to relax both the optimal value and solution of the original combinatorial problem, and turns a broad class of DP algorithms into differentiable operators. Theoretically, we provide a new probabilistic perspective on backpropagating through these DP operators, and relate them to inference in graphical models. We derive two particular instantiations of our framework, a smoothed Viterbi algorithm for sequence prediction and a smoothed DTW algorithm for time-series alignment. We showcase these instantiations on two structured prediction tasks and on structured and sparse attention for neural machine translation.