title: A Wideband Reconfigurable Antenna With 360° Beam Steering for 802.11ac WLAN Applications

abstract: A novel 360° beam steering patch antenna with parasitic elements is presented in this paper. The designed antenna consists of a radiating patch and six parasitic elements, each of which is connected through a group of shorting vias controlled by p-i-n diode switches. By switching on the desired groups of the shorting vias, the electric field distribution inside substrate cavity appears at the desired beam direction. Rotationally switching on the groups of the shorting vias, the performance of 360° beam scanning is realized. To further understand operating mechanism, the antenna is modeled with equivalent circuit in terms of the on and off status of a sector of the antenna, which can be used as a design guide for shorting-vias-controlled reconfigurable microstrip patch antennas. The fabricated antenna achieves a bandwidth of 14.5%, a peak gain of 10 dBi, and the efficiency of 80.5%. The achieved beamwidths are 42° and 97° in azimuth and elevation planes, respectively. With an ability of being steered around zenith axis at six directions, the scanned beam range covers the entire 360°. The physical dimension is only $2.5\lambda _{g}$ for the size and $0.5\lambda _{g}$ for the profile. This antenna operates from 5.1 to 5.9 GHz and has significant meaning in the IEEE 802.11ac wireless local area network applications due to its capabilities of generating 360° steered beams.


title: Understanding of a convolutional neural network

abstract: The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to surpass classical methods performance in different fields; especially in pattern recognition. One of the most popular deep neural networks is the Convolutional Neural Network (CNN). It take this name from mathematical linear operation between matrixes called convolution. CNN have multiple layers; including convolutional layer, non-linearity layer, pooling layer and fully-connected layer. The convolutional and fully-connected layers have parameters but pooling and non-linearity layers don't have parameters. The CNN has an excellent performance in machine learning problems. Specially the applications that deal with image data, such as largest image classification data set (Image Net), computer vision, and in natural language processing (NLP) and the results achieved were very amazing. In this paper we will explain and define all the elements and important issues related to CNN, and how these elements work. In addition, we will also state the parameters that effect CNN efficiency. This paper assumes that the readers have adequate knowledge about both machine learning and artificial neural network.


title: Diverse and Controllable Image Captioning with Part-of-Speech Guidance

abstract: Automatically describing an image is an important capability for virtual assistants. Significant progress has been achieved in recent years on this task of image captioning. However, classical prediction techniques based on maximum likelihood trained LSTM nets don't embrace the inherent ambiguity of image captioning. To address this concern, recent variational auto-encoder and generative adversarial network based methods produce a set of captions by sampling from an abstract latent space. But, this latent space has limited interpretability and therefore, a control mechanism for captioning remains an open problem. This paper proposes a captioning technique conditioned on part-of-speech. Our method provides human interpretable control in form of part-of-speech. Importantly, part-of-speech is a language prior, and conditioning on it provides: (i) more diversity as evaluated by counting n-grams and the novel sentences generated, (ii) achieves high accuracy for the diverse captions on standard captioning metrics.


title: Rethinking Spatiotemporal Feature Learning For Video Understanding.

abstract: In this paper we study 3D convolutional networks for video understanding tasks. Our starting point is the state-of-the-art I3D model, which "inflates" all the 2D filters of the Inception architecture to 3D. We first consider "deflating" the I3D model at various levels to understand the role of 3D convolutions. Interestingly, we found that 3D convolutions at the top layers of the network contribute more than 3D convolutions at the bottom layers, while also being computationally more efficient. This indicates that I3D is better at capturing high-level temporal patterns than low-level motion signals. We also consider replacing 3D convolutions with spatiotemporal-separable 3D convolutions (i.e., replacing convolution using a k * k * k filter with 1 * k * k followed by k * 1 * 1 filters); we show that such a model, which we call S3D, is 1.5x more computationally efficient (in terms of FLOPS) than I3D, and achieves better accuracy. Finally, we explore spatiotemporal feature gating on top of S3D. The resulting model, which we call S3D-G, outperforms the state-of-the-art I3D model by 3.5% accuracy on Kinetics and reduces the FLOPS by 34%. It also achieves a new state-of-the-art performance when transferred to other action classification (UCF-101 and HMDB-51) and detection (UCF-101 and JHMDB) datasets.


title: End-to-End Deep HDR Imaging with Large Foreground Motions.

abstract: This paper proposes the first end-to-end deep framework for high dynamic range (HDR) imaging of dynamic scenes with large-scale foreground motions. In state-of-the-art deep HDR imaging such as [13], the problem is formulated as an image composition problem, by first aligning input images using optical flows which are still error-prone due to occlusion and large motions. In our end-to-end approach, HDR imaging is formulated as an image translation problem and no optical flows are used. Moreover, our simple translation network can automatically hallucinate plausible HDR details in the presence of total occlusion, saturation and under-exposure, which are otherwise almost impossible to recover by conventional optimization approaches. We perform extensive qualitative and quantitative comparisons to show that our end-to-end HDR approach produces excellent results where color artifacts and geometry distortion are significantly reduced compared with existing state-ofthe-art methods.


title: The Bittorrent P2P File-Sharing System: Measurements and Analysis

abstract: Of the many P2P file-sharing prototypes in existence, BitTorrent is one of the few that has managed to attract millions of users. BitTorrent relies on other (global) components for file search, employs a moderator system to ensure the integrity of file data, and uses a bartering technique for downloading in order to prevent users from freeriding. In this paper we present a measurement study of BitTorrent in which we focus on four issues, viz. availability, integrity, flashcrowd handling, and download performance. The purpose of this paper is to aid in the understanding of a real P2P system that apparently has the right mechanisms to attract a large user community, to provide measurement data that may be useful in modeling P2P systems, and to identify design issues in such systems.


title: Assessing dietary intake: Who, what and why of under-reporting.

abstract: Under-reporting of food intake is one of the fundamental obstacles preventing the collection of accurate habitual dietary intake data. The prevalence of under-reporting in large nutritional surveys ranges from 18 to 54% of the whole sample, but can be as high as 70% in particular subgroups. This wide variation between studies is partly due to different criteria used to identify under-reporters and also to non-uniformity of under-reporting across populations. The most consistent differences found are between men and women and between groups differing in body mass index. Women are more likely to under-report than men, and under-reporting is more common among overweight and obese individuals. Other associated characteristics, for which there is less consistent evidence, include age, smoking habits, level of education, social class, physical activity and dietary restraint. Determining whether under-reporting is specific to macronutrients or food is ::: problematic, as most methods identify only low energy intakes. Studies that have ::: attempted to measure under-reporting specific to macronutrients express nutrients as percentage of energy and have tended to find carbohydrate under-reported and protein over-reported. However, care must be taken when interpreting these results, especially when data are expressed as percentages. A logical conclusion is that food items with a negative health image (e.g. cakes, sweets, confectionery) are more likely to be under-reported, whereas those with a positive health image are more likely to be over-reported (e.g. fruits and vegetables). This also suggests that dietary fat is likely to be under-reported. However, it is necessary to distinguish between under-reporting and genuine under-eating for the duration of data collection. The key to understanding this problem, but one that has been widely neglected, concerns the processes that cause people to under-report their food intakes. The little work that has been done has simply confirmed the complexity of this issue. The importance of obtaining accurate estimates of habitual dietary intakes so as to assess health correlates of food consumption can be contrasted with the poor quality of data collected. This phenomenon should be considered a priority research area. Moreover, misreporting is not simply a nutritionist's problem, but requires a multidisciplinary approach (including psychology, sociology and physiology) to advance the understanding of under-reporting in dietary intake studies.


Street Parking Detection


title: Human Skin Based Triboelectric Nanogenerators for Harvesting Biomechanical Energy and as Self-Powered Active Tactile Sensor System

abstract: We report human skin based triboelectric nanogenerators (TENG) that can either harvest biomechanical energy or be utilized as a self-powered tactile sensor system for touch pad technology. We constructed a TENG utilizing the contact/separation between an area of human skin and a polydimethylsiloxane (PDMS) film with a surface of micropyramid structures, which was attached to an ITO electrode that was grounded across a loading resistor. The fabricated TENG delivers an open-circuit voltage up to −1000 V, a short-circuit current density of 8 mA/m2, and a power density of 500 mW/m2 on a load of 100 MΩ, which can be used to directly drive tens of green light-emitting diodes. The working mechanism of the TENG is based on the charge transfer between the ITO electrode and ground via modulating the separation distance between the tribo-charged skin patch and PDMS film. Furthermore, the TENG has been used in designing an independently addressed matrix for tracking the location and pressure of human touch. The fabri...


title: Automatic nonverbal behavior indicators of depression and PTSD: the effect of gender

abstract: Recently there has been arising interest in automatically recognizing nonverbal behaviors that are linked with psychological conditions. Work in this direction has shown great potential for cases such as depression and post-traumatic stress disorder (PTSD), however most of the times gender differences have not been explored. In this paper, we show that gender plays an important role in the automatic assessment of psychological conditions such as depression and PTSD. We identify a directly interpretable and intuitive set of predictive indicators, selected from three general categories of nonverbal behaviors: affect, expression variability and motor variability. For the analysis, we employ a semi-structured virtual human interview dataset which includes 53 video recorded interactions. Our experiments on automatic classification of psychological conditions show that a gender-dependent approach significantly improves the performance over a gender agnostic one.


title: Variable flux permanent magnet synchronous machine (VF-PMSM) design to meet electric vehicle traction requirements with reduced losses

abstract: Variable flux permanent magnet synchronous machines (VF-PMSMs) in which the magnetization state (MS) of low coercive force (low-H c ) permanent magnets can be actively controlled to reduce losses in applications that require wide-speed operation have been proposed recently. While prior focus has been on achieving MS manipulation without over-sizing the inverter and obtaining higher torque capability, this paper extends the design objectives to include the power requirements of an electric vehicle traction motor over its entire speed range. Finite element methods are used to study the effect of combinations of low-H c and high-H c permanent magnets arranged in either series or parallel on the performance of VF-PMSMs. It is shown that while both configurations help improve the torque density, only the series configuration can help improve the high speed power capability. Experimental results showing the variable MS property, torque-speed capability and loss reduction capability of a series magnet configuration VF-PMSM test machine are presented.


title: A Mixture Imputation-Boosted Collaborative Filter

abstract: Recommendation systems suggest products to users. Collaborative filtering (CF) systems, which base those recommendations on a database of previous ratings by various users and products, have been proven to be very effective. Since this database is typically very sparse, we consider first imputing the missing values, then making predictions based on that completed dataset. In this paper, we apply several standard imputation techniques within the framework of imputation-boosted collaborative filtering (IBCF). Each technique passes that imputed rating data to a traditional Pearson correlation-based CF algorithm, which uses that information to produce CF predictions. We also propose a novel mixture IBCF algorithm, IBCF-NBM, that uses either naive Bayes or mean imputation, depending on the sparsity of the original CF rating dataset. Our empirical results show that IBCFs are fairly accurate on CF tasks, and that IBCF-NBM significantly outperforms a representative hybrid CF system, content-boosted CF algorithm, as well as other IBCFs that use standard imputation techniques.


title: Siphodera ghanensis sp.n. (Cryptogoni-midae), a digenetic trematode from an estuarine fish from Ghana.

abstract: Siphodera ghanensis sp. n., a cryptogonimid trematode from the small intestine of an estuarine fish, Chrysichthys nigrodigitatus (Lacepede) (Bagridae) from Ghana is described. It is differentiated from the type and only species in the genus, S. vinaledwardsii (Linton, 1901) Linton, 1910, in possessing an acetabulum larger than the oral sucker, an unlobed or trilobed ovary, and a greater extent of vitellaria; it differs further in lacking uterine coils anterior to the testes, and a pitlike cavity in which the acetabulum lies. This report is the fourth in a series by the authors on the digenetic trematodes of fishes from Ghana. The worms were killed in hot water, fixed overnight in FAA, and stored in 70% alcohol plus 2% glycerine; they were stained in Mayer's carmalum, and mounted in permount. All measurements are in microns. Siphodera ghanensis sp. n. (Figs. 1, 2) Host: Chrysichthys nigrodigitatus (Lacepede) (Bagridae). Site: Small intestine. Localities: Kakum river estuary at Iture; creek near Ekotsi; Ghana. Dates: 2, 3, 18 November 1965. Specimens: USNM Helm. Coll. No. 71189 (holotype); No. 71190 (paratypes). Diagnosis (based on four mature specimens): Body elongate oval, extremities round, 2,480 to 3,085 long, 930 to 1,035 wide at testicular level; preoral space 22 to 46 long; forebody 560 to 690 long, hindbody 1,560 to 2,100 long. Tegument spined to short distance posterior to testes. Glands scattered primarily in lateral fields of forebody, confluent dorsal to prepharynx. Eyespot pigment granules scattered in forebody. Oral sucker subterminal ventral, round to slightly elongate transversely, 230 to 250 long, 230 to 263 wide. Postoral circular muscle ring present. Acetabulum muscular, not in pitlike cavity, slightly elongate transversely, 280 to 300 long, 295 to 327 wide, center at level of anterior 1/4 to 3/10 of body length. Length ratio of suckers 1:1.17 to 1.25. Prepharynx emerging from posterodorsal part of oral sucker, 92 to 123 long in well extended specimens; pharynx pyriform, with circular muscle band around anterior part, 190 to 216 long, 144 to 177 wide; esophagus shorter than prepharynx, 65 to 85 long in well-extended specimens; small, compact mass of Received for publication 5 April 1968. * This work was supported in part by a State University of New York Faculty Research Fellowship (FRF68-40-003) awarded to the senior author. glands adjacent to prepharynx, pharynx, and esophagus; cecal bifurcation 56 to 65 anterior to acetabulum; ceca cell-lined, thin-walled, extending into posttesticular space but not reaching posterior extremity. Testes nine in number, smooth, lateral to c ca, five sinistral and four dextral in two worms, reverse distribution in other two, round to longitudinally or transversely elongate, 160 to 253 long, 133 to 230 wide; row with four testes extending slightly more anteriorly than one with five, lying 190 to 295 posterior to acetabulum; posttesticular space 445 to 760 long. Cirrus sac absent. Seminal vesicle with some undulations, bipartite, 412 to 585 long, 157 to 172 wide, extending 136 to 300 posterior to acetabulum, usually overlapping ovary dorsally; chambers thin-walled, anterior chamber lined with large cells. Prostatic vesicle cell-lined, much smaller than acetabulum, surrounded by dense mass of prostate cells, lying anterodorsal to acetabulum. Ejaculatory duct very short. Genital atrium short. Genital pore median, anteroventral to acetabulum. Ovary median to submedian (right or left), longitudinally or transversely elongate, unlobed and smooth in two specimens, with three smooth lobes in other two, 270 to 335 long, 270 to 340 wide, lying 90 to 195 posterior to acetabulum. Oviduct emerging from dorsal surface of ovary. Mehlis' gland well developed, anterodorsal to ovary. Seminal receptacle large, almost round to longitudinally elongate, 170 to 198 long, 145 to 165 wide, anterodorsal to ovary. Laurer's canal winding to surface dorsal to ovary. Uterus overlapping ceca and testes ventrally; with coiled descending limb on left in two specimens, on right in other two; with coiled ascending limb to ovarian level on opposite side, sinuous anterior to ovary in median to submedian position, lacking coils anterior to testes. Metraterm muscular, thickwalled, very short, beginning at anterior margin of acetabulum. Vitellaria follicular, extending from level of acetabulum to midlength of testicular fields or to posteriormost testes, filling dorsoventral depth of body between acetabulum and testes, lying dorsal only to testes, confluent dorsally from posterior part of ovary almost to posteriormost extent of follicles; vitelline duct from each side uniting dorsal to ovary to form common duct. Eggs nu-


Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF


title: Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

abstract: Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. This paper investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.


title: Scalable Private Set Intersection Based on OT Extension

abstract: Private set intersection (PSI) allows two parties to compute the intersection of their sets without revealing any information about items that are not in the intersection. It is one of the best studied applications of secure computation and many PSI protocols have been proposed. However, the variety of existing PSI protocols makes it difficult to identify the solution that performs best in a respective scenario, especially since they were not compared in the same setting. In addition, existing PSI protocols are several orders of magnitude slower than an insecure naive hashing solution, which is used in practice. ::: ::: In this article, we review the progress made on PSI protocols and give an overview of existing protocols in various security models. We then focus on PSI protocols that are secure against semi-honest adversaries and take advantage of the most recent efficiency improvements in Oblivious Transfer (OT) extension, propose significant optimizations to previous PSI protocols, and suggest a new PSI protocol whose runtime is superior to that of existing protocols. We compare the performance of the protocols, both theoretically and experimentally, by implementing all protocols on the same platform, give recommendations on which protocol to use in a particular setting, and evaluate the progress on PSI protocols by comparing them to the currently employed insecure naive hashing protocol. We demonstrate the feasibility of our new PSI protocol by processing two sets with a billion elements each.


title: A Survey on Optical Character Recognition System

abstract: Optical Character Recognition (OCR) has been a topic of interest for many years. It is defined as the process of digitizing a document image into its constituent characters. Despite decades of intense research, developing OCR with capabilities comparable to that of human still remains an open challenge. Due to this challenging nature, researchers from industry and academic circles have directed their attentions towards Optical Character Recognition. Over the last few years, the number of academic laboratories and companies involved in research on Character Recognition has increased dramatically. This research aims at summarizing the research so far done in the field of OCR. It provides an overview of different aspects of OCR and discusses corresponding proposals aimed at resolving issues of OCR.


title: Black-Box Attacks against RNN based Malware Detection Algorithms

abstract: Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples. These works mainly focused on the detection algorithms which use features with fixed dimension, while some researchers have begun to use recurrent neural networks (RNN) to detect malware based on sequential API features. This paper proposes a novel algorithm to generate sequential adversarial examples, which are used to attack a RNN based malware detection system. It is usually hard for malicious attackers to know the exact structures and weights of the victim RNN. A substitute RNN is trained to approximate the victim RNN. Then we propose a generative RNN to output sequential adversarial examples from the original sequential malware inputs. Experimental results showed that RNN based malware detection algorithms fail to detect most of the generated malicious adversarial examples, which means the proposed model is able to effectively bypass the detection algorithms.


title: Heavy-Hitter Detection Entirely in the Data Plane

abstract: Identifying the "heavy hitter" flows or flows with large traffic volumes in the data plane is important for several applications e.g., flow-size aware routing, DoS detection, and traffic engineering. However, measurement in the data plane is constrained by the need for line-rate processing (at 10-100Gb/s) and limited memory in switching hardware. We propose HashPipe, a heavy hitter detection algorithm using emerging programmable data planes. HashPipe implements a pipeline of hash tables which retain counters for heavy flows while evicting lighter flows over time. We prototype HashPipe in P4 and evaluate it with packet traces from an ISP backbone link and a data center. On the ISP trace (which contains over 400,000 flows), we find that HashPipe identifies 95% of the 300 heaviest flows with less than 80KB of memory.


title: Detecting the unexpected in distributed systems

abstract: Bugs in distributed systems are often hard to find. Many bugs reflect discrepancies between a system's behavior and the programmer's assumptions about that behavior. We present Pip, an infrastructure for comparing actual behavior and expected behavior to expose structural errors and performance problems in distributed systems. Pip allows programmers to express, in a declarative language, expectations about the system's communications structure, timing, and resource consumption. Pip includes system instrumentation and annotation tools to log actual system behavior, and visualization and query tools for exploring expected and unexpected behavior. Pip allows a developer to quickly understand and debug both familiar and unfamiliar systems. ::: ::: We applied Pip to several applications, including FAB, SplitStream, Bullet, and RanSub. We generated most of the instrumentation for all four applications automatically. We found the needed expectations easy to write, starting in each case with automatically generated expectations. Pip found unexpected behavior in each application, and helped to isolate the causes of poor performance and incorrect behavior.


INFERRING REWARD FUNCTIONS


title: Artificial intelligence applied to computer forensics

abstract: To be able to examine large amounts of data in a timely manner in search of important evidence during crime investigations is essential to the success of computer forensic examinations. The limitations in time and resources, both computational and human, have a negative impact in the results obtained. Thus, better use of the resources available are necessary, beyond the capabilities of the currently used forensic tools. Herein, we describe the use of Artificial Intelligence in computer forensics through the development of a multiagent system and case-based reasoning. This system is composed of specialized intelligent agents that act based on the experts knowledge of the technical domain. Their goal is to analyze and correlate the data contained in the evidences of an investigation and based on its expertise, present the most interesting evidence to the human examiner, thus reducing the amount of data to be personally analyzed. The correlation feature helps to find links between evidences that can be easily overlooked by a human expert, specially due to the amount of data involved. This system has been tested using real data and the results were very positive when compared to those obtained by the human expert alone performing the same analysis.


A qualitative study on barriers to evidence-based practice in patient counseling and advocacy in Germany


title: Flexion-relaxation and clinical features associated with chronic low back pain: A comparison of different methods of quantifying flexion-relaxation.

abstract: Objectives: The purpose of this study was to simultaneously assess 5 surface electromyography (SEMG) ratios commonly used to quantify the flexion relaxation phenomenon in chronic low back pain patients relative to clinical and musculoskeletal abnormalities. Methods: Seventy-six persons with low back pain (LBP) were assessed through SEMG in standing, flexion, maximum voluntary flexion, and extension. Additionally, participants were assessed for clinical status (pain intensity, perceived disability, and pain-related fear) and musculoskeletal abnormalities (supine straight leg raise, Faber test, and lumbar restrictions). Results: Flexion-relaxation assessed as the ratio of maximum SEMG during flexion to average SEMG during maximum voluntary flexion, and maximum SEMG during extension to average SEMG during maximum voluntary flexion, demonstrated that highest associations with clinical and musculoskeletal status. Ratio of maximum SEMG during flexion to average SEMG during maximum voluntary flexion was significantly associated with 2 measures of perceived disability (both rs = -0.31, P < 0.01), pain-related fear (r = -0.45, P < 0.001), as well as range of motion during flexion (r = 0.51, P < 0.001), and elicitation of pain during straight leg raise (r=0.30, P<0.05). Ratio of maximum SEMG during extension to average SEMG during maximum voluntary flexion was also significantly associated with these same measures, at a slightly higher magnitude, in addition to a measure of clinical pain (r= -0.29, P<0.05). Discussions: These data suggest that measures of flexion-relaxation that contrast SEMG during flexion or extension to MVF are more highly associated with clinical status compared with measures that contrast SEMG during flexion and extension, SEMG whereas standing to MVF, or SEMG during MVF alone.


title: User-side Wi-Fi evil twin attack detection using random wireless channel monitoring

abstract: Free open wireless Internet access is a complimentary Wi-Fi service offered by most coffee shops, fast food restaurants and airports to their customers. For ease of access, these Wi-Fi networks are inherently insecure where no authentication/ encryption is used to protect customers wireless data. An attacker can easily deceive a wireless customer (WC) by setting up a rogue access point (RAP) impersonating the legitimate access point (LAP). The WC connecting to the RAP becomes an easy target to the Man-In-the-Middle Attack (MIMA) and data traffic snooping. In this paper, we present a real-time client-side detection scheme to detect evil twin attack (ETA) when the attacker relies on the LAP to direct WC data to the Internet. The WC can detect ETA by monitoring multiple Wi-Fi channels in a random order looking for specific data packets sent by a dedicated sever on the Internet. Once an ETA is detected, our scheme can clearly identify whether a specific AP is a LAP or a RAP. The effectiveness of the proposed detection method was mathematically modeled, prototyped and evaluated in real life environment with a detection rate approximates to 100%.


title: Multi-Task GANs for View-Specific Feature Learning in Gait Recognition

abstract: Gait recognition is of great importance in the fields of surveillance and forensics to identify human beings since gait is the unique biometric feature that can be perceived efficiently at a distance. However, the accuracy of gait recognition to some extent suffers from both the variation of view angles and the deficient gait templates. On one hand, the existing cross-view methods focus on transforming gait templates among different views, which may accumulate the transformation error in a large variation of view angles. On the other hand, a commonly used gait energy image template loses temporal information of a gait sequence. To address these problems, this paper proposes multi-task generative adversarial networks (MGANs) for learning view-specific feature representations. In order to preserve more temporal information, we also propose a new multi-channel gait template, called period energy image (PEI). Based on the assumption of view angle manifold, the MGANs can leverage adversarial training to extract more discriminative features from gait sequences. Experiments on OU-ISIR, CASIA-B, and USF benchmark data sets indicate that compared with several recently published approaches, PEI + MGANs achieves competitive performance and is more interpretable to cross-view gait recognition.


title: Multi-view Adversarially Learned Inference for Cross-domain Joint Distribution Matching

abstract: Many important data mining problems can be modeled as learning a (bidirectional) multidimensional mapping between two data domains. Based on the generative adversarial networks (GANs), particularly conditional ones, cross-domain joint distribution matching is an increasingly popular kind of methods addressing such problems. Though significant advances have been achieved, there are still two main disadvantages of existing models, i.e., the requirement of large amount of paired training samples and the notorious instability of training. In this paper, we propose a multi-view adversarially learned inference (ALI) model, termed as MALI, to address these issues. Unlike the common practice of learning direct domain mappings, our model relies on shared latent representations of both domains and can generate arbitrary number of paired faking samples, benefiting from which usually very few paired samples (together with sufficient unpaired ones) is enough for learning good mappings. Extending the vanilla ALI model, we design novel discriminators to judge the quality of generated samples (both paired and unpaired), and provide theoretical analysis of our new formulation. Experiments on image-to-image translation, image-to-attribute generation (multi-label classification), attribute-to-image generation tasks demonstrate that our semi-supervised learning framework yields significant performance improvements over existing ones. Results on cross-modality retrieval show that our latent space based method can achieve competitive similarity search performance in relative fast speed, compared to those methods that compute similarities in the high-dimensional data space.


title: A virtual machine re-packing approach to the horizontal vs. vertical elasticity trade-off for cloud autoscaling

abstract: An automated solution to horizontal vs. vertical elasticity problem is central to make cloud autoscalers truly autonomous. Today's cloud autoscalers are typically varying the capacity allocated by increasing and decreasing the number of virtual machines (VMs) of a predefined size (horizontal elasticity), not taking into account that as load varies it may be advantageous not only to vary the number but also the size of VMs (vertical elasticity). We analyze the price/performance effects achieved by different strategies for selecting VM-sizes for handling increasing load and we propose a cost-benefit based approach to determine when to (partly) replace a current set of VMs with a different set. We evaluate our repacking approach in combination with different auto-scaling strategies. Our results show a range of 7% up to 60% cost saving in total resource utilization cost of our sample applications and workloads.


title: Efficient Defenses Against Adversarial Attacks

abstract: Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat. Adversarial samples are crafted with a deliberate intention of undermining a system. In the case of DNNs, the lack of better understanding of their working has prevented the development of efficient defenses. In this paper, we propose a new defense method based on practical observations which is easy to integrate into models and performs better than state-of-the-art defenses. Our proposed solution is meant to reinforce the structure of a DNN, making its prediction more stable and less likely to be fooled by adversarial samples. We conduct an extensive experimental study proving the efficiency of our method against multiple attacks, comparing it to numerous defenses, both in white-box and black-box setups. Additionally, the implementation of our method brings almost no overhead to the training procedure, while maintaining the prediction performance of the original model on clean samples.


title: CMOS Capacitive Fingerprint Sensor Based on Differential Sensing Circuit with Noise Cancellation

abstract: In this paper, we introduce a differential sensing technique for CMOS capacitive fingerprint detection. It employs a new capacitive-sensing cell structure with charge sharing detection and readout circuit. The proposed technique also can eliminate the effect of parasitic capacitances by employing parasitic insensitive switched-capacitor structure and so increases the sensitivity even under severe noisy conditions. It can also overcome the performance degradation caused by various conditions of finger surface by using a differential integrator and adjusting its number of integrations. In addition, the proposed architecture allows parallel detection of all sensing channels. It can, therefore, substantially speed up the detection process compared with conventional architectures. We implemented a prototype fingerprint sensor chip with an array of 20 × 16 sensor cells using a 130 nm CMOS process. Simulation experiments demonstrated that the proposed architecture provided an SNR gain of 54 dB, whereas a conventional single line sensing gives an SNR gain of only 13 dB.


title: A Deep Learning Approach for Road Damage Detection from Smartphone Images

abstract: With recent advances in technology, it is feasible to conveniently monitor urban roads using various cameras, such as surveillance cameras, in-vehicle cameras, or smartphones, and recognize their conditions by detecting specific types of road damages in order to plan maintenance resources efficiently based on the identified spots. This paper describes a road damage type detection and classification solution submitted to the IEEE BigData Cup Challenge 2018. Our solution is based on the state-of-the-art deep learning methods for an object detection task. In particular, our approach utilizes an object detection algorithm to detect various types of road damages by training the detector on different image examples categorized into a set of damages defined by Japan Road Association. We evaluated our approach thoroughly using different versions of trained models. Our experiments show that our approach was able to achieve an F1 score up to 0.62.


title: Self-Efficacy Mechanism in Human Agency

abstract: This article addresses the centrality of the self-efficacy mechanism in human agency. Self-per- cepts of efficacy influence thought patterns, actions, and emotional arousal. In causal tests the higher the level of induced self-efficacy, the higher the perfor- mance accomplishments and the lower the emotional arousal. Different lines of research are reviewed, show- ing that the self-efficacy mechanism may have wide explanatory power. Perceived self-efficacy helps to ac- count for such diverse phenomena as changes in coping behavior produced by different modes of influence, level of physiological stress reactions, self-regulation of refractory behavior, resignation and despondency to failure experiences, self-debilitating effects of proxy control and illusory inefficaciousness, achievement strivings, growth of intrinsic interest, and career pur- suits. The influential role of perceived collective effi- cacy in social change is analyzed, as are the social con- ditions conducive to development of collective inefficacy. Psychological theorizing and research tend to cen- ter on issues concerning either acquisition of knowledge or execution of response patterns. As a result the processes governing the interrelation- ship between knowledge and action have been largely neglected (Newell, 1978). Some of the re- cent efforts to bridge this gap have been directed at the biomechanics problem—how efferent com- mands of action plans guide the production of ap- propriate response patterns (Stelmach, 1976,1978). Others have approached the matter in terms of algorithmic knowledge, which furnishes guides for executing action sequences (Greeno, 1973; Newell, 1973). ,


title: Magnetically coupled buck converters

abstract: The use of coupled inductors is now a typical approach to decrease the current ripple in multiphase power converters. Minimizing current ripple increases efficiency, while coupled inductors support a fast transient performance at the same time. This paper proposes a new approach: coupling of inductors between the different power converters with independent outputs, to decrease current ripple and improve efficiency. This method effectively extends benefits of both multiphase performance and coupled inductors to the single phase solutions.


title: A Cost and Power Efficient Image Compressor VLSI Design With Fuzzy Decision and Block Partition for Wireless Sensor Networks

abstract: This paper presents a novel hardware-oriented image compression algorithm and its very large-scale integration (VLSI) implementation for wireless sensor networks. The proposed novel image compression algorithm consists of a fuzzy decision, block partition, digital halftoning, and block truncation coding (BTC) techniques. A novel variable-size block partition technique was used in the proposed algorithm to improve image quality and compression performance. In addition, eight different types of blocks were encoded by Huffman coding according to probability to increase the compression ratio further. In order to achieve the low-cost and low-power characteristics, a novel iteration-based BTC training module was created to get representative levels and meet the requirement of wireless sensor networks. A prediction and a modified Golomb–Rice coding modules were designed to encode the information of representative levels to achieve higher compression performance. The proposed algorithm was realized by a VLSI technique with an UMC 0.18- $\mu \text{m}$ CMOS process. The synthesized gate counts and core area of this design were 6.4 k gate counts and 60 000 $\mu \text{m}^{2}$ , respectively. The operating frequency and power consumption were 100 MHz and 3.11 mW respectively. Compared with previous JPEG, JPEG-LS, and fixed-size BTC-based designs, this work reduced 20.9% gate counts more than previous designs. Moreover, the proposed design required only a one-line-buffer memory rather than a frame-buffer memory required by previous designs.


title: Object exchange across heterogeneous information sources

abstract: We address the problem of providing integrated access to diverse and dynamic information sources. We explain how this problem differs from the traditional database integration problem and we focus on one aspect of the information integration problem, namely information exchange. We define an object-based information exchange model and a corresponding query language that we believe are well suited for integration of diverse information sources. We describe how, the model and language have been used to integrate heterogeneous bibliographic information sources. We also describe two general-purpose libraries we have implemented for object exchange between clients and servers. >


title: Deep autoencoder neural networks for gene ontology annotation predictions

abstract: The annotation of genomic information is a major challenge in biology and bioinformatics. Existing databases of known gene functions are incomplete and prone to errors, and the bimolecular experiments needed to improve these databases are slow and costly. While computational methods are not a substitute for experimental verification, they can help in two ways: algorithms can aid in the curation of gene annotations by automatically suggesting inaccuracies, and they can predict previously-unidentified gene functions, accelerating the rate of gene function discovery. In this work, we develop an algorithm that achieves both goals using deep autoencoder neural networks. With experiments on gene annotation data from the Gene Ontology project, we show that deep autoencoder networks achieve better performance than other standard machine learning methods, including the popular truncated singular value decomposition.


title: Kettlebell swing, snatch, and bottoms-up carry: back and hip muscle activation, motion, and low back loads.

abstract: The intent of this study was to quantify spine loading during different kettlebell swings and carries. No previously published studies of tissue loads during kettlebell exercises could be found. Given the popularity of kettlebells, this study was designed to provide an insight into the resulting joint loads. Seven male subjects participated in this investigation. In addition, a single case study of the kettlebell swing was performed on an accomplished kettlebell master. Electromyography, ground reaction forces (GRFs), and 3D kinematic data were recorded during exercises using a 16-kg kettlebell. These variables were input into an anatomically detailed biomechanical model that used normalized muscle activation; GRF; and spine, hip, and knee motion to calculate spine compression and shear loads. It was found that kettlebell swings create a hip-hinge squat pattern characterized by rapid muscle activation-relaxation cycles of substantial magnitudes (~50% of a maximal voluntary contraction [MVC] for the low back extensors and 80% MVC for the gluteal muscles with a 16-kg kettlebell) resulting in about 3,200 N of low back compression. Abdominal muscular pulses together with the muscle bracing associated with carries create kettlebell-specific training opportunities. Some unique loading patterns discovered during the kettlebell swing included the posterior shear of the L4 vertebra on L5, which is opposite in polarity to a traditional lift. Thus, quantitative analysis provides an insight into why many individuals credit kettlebell swings with restoring and enhancing back health and function, although a few find that they irritate tissues.


title: Advances in Geometric Morphometrics

abstract: Geometric morphometrics is the statistical analysis of form based on Cartesian landmark coordinates. After separating shape from overall size, position, and orientation of the landmark configurations, the resulting Procrustes shape coordinates can be used for statistical analysis. Kendall shape space, the mathematical space induced by the shape coordinates, is a metric space that can be approximated locally by a Euclidean tangent space. Thus, notions of distance (similarity) between shapes or of the length and direction of developmental and evolutionary trajectories can be meaningfully assessed in this space. Results of statistical techniques that preserve these convenient properties—such as principal component analysis, multivariate regression, or partial least squares analysis—can be visualized as actual shapes or shape deformations. The Procrustes distance between a shape and its relabeled reflection is a measure of bilateral asymmetry. Shape space can be extended to form space by augmenting the shape coordinates with the natural logarithm of Centroid Size, a measure of size in geometric morphometrics that is uncorrelated with shape for small isotropic landmark variation. The thin-plate spline interpolation function is the standard tool to compute deformation grids and 3D visualizations. It is also central to the estimation of missing landmarks and to the semilandmark algorithm, which permits to include outlines and surfaces in geometric morphometric analysis. The powerful visualization tools of geometric morphometrics and the typically large amount of shape variables give rise to a specific exploratory style of analysis, allowing the identification and quantification of previously unknown shape features.


title: Measuring Word Relatedness Using Heterogeneous Vector Space Models

abstract: Noticing that different information sources often provide complementary coverage of word sense and meaning, we propose a simple and yet effective strategy for measuring lexical semantics. Our model consists of a committee of vector space models built on a text corpus, Web search results and thesauruses, and measures the semantic word relatedness using the averaged cosine similarity scores. Despite its simplicity, our system correlates with human judgements better or similarly compared to existing methods on several benchmark datasets, including WordSim353.


A Distributed Cyber-security Framework for Heterogeneous Environments


title: Training Simplification and Model Simplification for Deep Learning : A Minimal Effort Back Propagation Method

abstract: We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top- $k$ k elements (in terms of magnitude) are kept. As a result, only $k$ k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of the time we only need to update fewer than 5 percent of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy.


title: Online variance minimization

abstract: We consider the following type of online variance minimization problem: In every trial t our algorithms get a covariance matrix C t and try to select a parameter vector w t?1 such that the total variance over a sequence of trials $\sum_{t=1}^{T} (\boldsymbol {w}^{t-1})^{\top} \boldsymbol {C}^{t}\boldsymbol {w}^{t-1}$ is not much larger than the total variance of the best parameter vector u chosen in hindsight. Two parameter spaces in ? n are considered--the probability simplex and the unit sphere. The first space is associated with the problem of minimizing risk in stock portfolios and the second space leads to an online calculation of the eigenvector with minimum eigenvalue of the total covariance matrix $\sum_{t=1}^{T} \boldsymbol {C}^{t}$ . For the first parameter space we apply the Exponentiated Gradient algorithm which is motivated with a relative entropy regularization. In the second case, the algorithm has to maintain uncertainty information over all unit directions u. For this purpose, directions are represented as dyads uu ? and the uncertainty over all directions as a mixture of dyads which is a density matrix. The motivating divergence for density matrices is the quantum version of the relative entropy and the resulting algorithm is a special case of the Matrix Exponentiated Gradient algorithm. In each of the two cases we prove bounds on the additional total variance incurred by the online algorithm over the best offline parameter.


title: An Electromagnetic Interference (EMI) Reduced High-Efficiency Switching Power Amplifier

abstract: The design of a new high-efficiency switching power amplifier with an ultralow-power spread spectrum clock generator (SSCG) is first reported in this paper. An effective low-power frequency modulation method is first proposed to reduce the electromagnetic interference of the pulse width modulation class D power amplifier without degrading its power efficiency. Also, a simple RC voltage feedback circuit is used to reduce the total harmonic distortion (THD). This amplifier proves to be a cost-effective solution for designing high fidelity and high efficiency audio power amplifiers for portable applications. Measurement results show that the power efficiency and THD can reach 90% and 0.05%, respectively. The power dissipation of the SSCG is only 112 ?W. The harmonic peaks of the switching frequency are greatly reduced when the SSCG technique is applied to the amplifier design. The impact of the SSCG on the THD of the class D power amplifier is also first reported in this paper. This switching power amplifier is implemented using a Taiwan Semiconductor Manufacture Company (TSMC) 0.35- ?m CMOS process.


title: Iyengar yoga for treating symptoms of osteoarthritis of the knees: a pilot study.

abstract: Objectives: The American College of Rheumatology (ACR) Guidelines for the medical management of osteoarthritis (OA) emphasize the use of nonpharmacologic interventions including exercise. Implementation of an exercise program can be difficult for patients, and little is known about the benefits of alternative therapies such as yoga. The aim of this pilot study was to assess the feasibility of using yoga in the tradition of B.K.S. Iyengar to treat the symptoms of osteoarthritis of the knee. Design: Participants were instructed in modified Iyengar yoga postures during 90-minute classes once weekly for 8 weeks. Subjects: Participants met ACR criteria for osteoarthritis of the knee and completed a medical history and physical examination, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Arthritis Impact Measurement Scale 2 (AIMS2), Patient Global Assessment (GA) by Visual Analog Scale (VAS), Physician GA by VAS, and 50-foot Walk Time before and following an 8-week course of yoga instruc...


title: Amino acid substitution matrices from protein blocks.

abstract: Methods for alignment of protein sequences typically measure similarity by using a substitution matrix with scores for all possible exchanges of one amino acid with another. The most widely used matrices are based on the Dayhoff model of evolutionary rates. Using a different approach, we have derived substitution matrices from about 2000 blocks of aligned sequence segments characterizing more than 500 groups of related proteins. This led to marked improvements in alignments and in searches using queries from each of the groups.


title: Less is More : Trading a little Bandwidth for Ultra-Low Latency in the Data Center

abstract: Traditional measures of network goodness--goodput, quality of service, fairness--are expressed in terms of bandwidth. Network latency has rarely been a primary concern because delivering the highest level of bandwidth essentially entails driving up latency--at the mean and, especially, at the tail. Recently, however, there has been renewed interest in latency as a primary metric for mainstream applications. In this paper, we present the HULL (High-bandwidth Ultra-Low Latency) architecture to balance two seemingly contradictory goals: near baseline fabric latency and high bandwidth utilization. HULL leaves 'bandwidth headroom' using Phantom Queues that deliver congestion signals before network links are fully utilized and queues form at switches. By capping utilization at less than link capacity, we leave room for latency sensitive traffic to avoid buffering and the associated large delays. At the same time, we use DCTCP, a recently proposed congestion control algorithm, to adaptively respond to congestion and to mitigate the bandwidth penalties which arise from operating in a bufferless fashion. HULL further employs packet pacing to counter burstiness caused by Interrupt Coalescing and Large Send Offloading. Our implementation and simulation results show that by sacrificing a small amount (e.g., 10%) of bandwidth, HULL can dramatically reduce average and tail latencies in the data center.


title: Web-CAT: automatically grading programming assignments

abstract: This demonstration introduces participants to using Web-CAT, an open-source automated grading system. Web-CAT is customizable and extensible, allowing it to support a wide variety of programming languages and assessment strategies. Web-CAT is most well-known as the system that "grades students on how well they test their own code," with experimental evidence that it offers greater learning benefits than more traditional output-comparison grading. Participants will learn how to set up courses, prepare reference tests, set up assignments, and allow graders to manually grade for design.


title: Modeling and Optimizing Tensile Strength and Yield Point on a Steel Bar Using an Artificial Neural Network With Taguchi Particle Swarm Optimizer

abstract: A Taguchi particle swarm optimization (TPSO) with a three-layer feedforward artificial neural network (ANN) is used to model and optimize the chemical composition of a steel bar. The novel contribution of a TPSO is the use of a Taguchi method mechanism to exploit better solutions in the search space through iterations, the use of the conventional non-linear PSO to increase convergence speed, and the use of random movement for particle diversity. The exploration and exploitation capability of the TPSO were confirmed by performance comparisons with other PSO-based algorithms in solving high-dimensional global numerical optimization problems. Experiments in this paper showed that the TPSO provides higher computational efficiency and higher robustness when solving problems involving seven non-linear benchmark functions, including three unimodal functions, one multimodal functions, two rotated functions, and one shifted functions. The results for the computational experiments show that the TPSO outperforms other PSO-based algorithms reported in the literature. Finally, the results obtained by a TPSO-based ANN model of the chemical composition of the steel bar were consistent with the actual data. That is, the proposed TPSO with three-layer feedforward ANN can be used in practical applications.


title: What's different about social media networks? a framework and research agenda

abstract: In recent years, we have witnessed the rapid proliferation and widespread adoption of a new class of information technologies, commonly known as social media. Researchers often rely on social network analysis (SNA) when attempting to understand these technologies, often without considering how the novel capabilities of social media platforms might affect the underlying theories of SNA, which were developed primarily through studies of offline social networks. This article outlines several key differences between traditional offline social networks and online social media networks by juxtaposing an established typology of social network research with a well-regarded definition of social media platforms that articulates four key features. The results show that at four major points of intersection, social media has considerable theoretical implications for SNA. In exploring these points of intersection, this study outlines a series of theoretically distinct research questions for SNA in social media contexts. These points of intersection offer considerable opportunities for researchers to investigate the theoretical implications introduced by social media and lay the groundwork for a robust social media agenda potentially spanning multiple disciplines.


Integration of deep bottleneck features for audio-visual speech recognition.


title: Data quality management in the public domain: a case study within the Dutch justice system

abstract: The need for anonymity preservation within the justice domain requires the introduction of a trusted third party as an intermediary, while integrating individual databases within its boundaries. After the trusted third party encrypts records, it is no longer possible to perform checks on the data quality and correct data anomalies. Therefore, this research examines the concepts of data quality, data integration, record linkage and trusted third party and, then, combines these with four expert interviews in order to identify ways to assess and improve data quality while linking privacy-sensitive data. Next, the trusted data linkage framework (TDLF) is presented to aid data quality management while combining citizens' privacy-sensitive data from different organisations. Finally, we evaluate the framework in a case study to demonstrate how the quality of structured judicial data can be managed prior to its encryption, while using multiple databases as sources of data and a different final recipient.


title: A compact near field UHF RFID reader antenna using SRR

abstract: In this paper, a compact RFID reader antenna is proposed, which can be externally extended on mobile terminal. Using the structure of Split Ring Resonator (SRR) can arouse strong magnetic resonance; therefore, it can work better in RFID near field communication system based on inductive coupling. Meanwhile, the advantage of two-dimensional structure and electrically small will be helpful to fabrication and antenna's miniaturization.


title: A Sequence-to-Sequence Model for User Simulation in Spoken Dialogue Systems

abstract: User simulation is essential for generating enough data to train a statistical spoken dialogue system. Previous models for user simulation suffer from several drawbacks, such as the inability to take dialogue history into account, the need of rigid structure to ensure coherent user behaviour, heavy dependence on a specific domain, the inability to output several user intentions during one dialogue turn, or the requirement of a summarized action space for tractability. This paper introduces a data-driven user simulator based on an encoder-decoder recurrent neural network. The model takes as input a sequence of dialogue contexts and outputs a sequence of dialogue acts corresponding to user intentions. The dialogue contexts include information about the machine acts and the status of the user goal. We show on the Dialogue State Tracking Challenge 2 (DSTC2) dataset that the sequence-to-sequence model outperforms an agenda-based simulator and an n-gram simulator, according to F-score. Furthermore, we show how this model can be used on the original action space and thereby models user behaviour with finer granularity.


title: Uncovering Temporal Context for Video Question and Answering

abstract: In this work, we introduce Video Question Answering in temporal domain to infer the past, describe the present and predict the future. We present an encoder-decoder approach using Recurrent Neural Networks to learn temporal structures of videos and introduce a dual-channel ranking loss to answer multiple-choice questions. We explore approaches for finer understanding of video content using question form of "fill-in-the-blank", and managed to collect 109,895 video clips with duration over 1,000 hours from TACoS, MPII-MD, MEDTest 14 datasets, while the corresponding 390,744 questions are generated from annotations. Extensive experiments demonstrate that our approach significantly outperforms the compared baselines.


title: Perceptual quality assessment of 3D dynamic meshes

abstract: Nowadays, 3D mesh animations have been increasingly used in various applications, e.g., in digital entertainment and physically based simulation. In many applications, it is possible that a surface animation undergoes some lossy operations which can impair its perceptual quality. Since the end users of mesh animations are often human beings, the perceptual quality assessment of 3D dynamic meshes has recently emerged as an interesting and important research problem. In this paper, we will first of all present the results of a large-scale subjective experimental study carried out to collect human opinion scores of the perceptual quality of distorted dynamic meshes. We integrate in our subjective study a large set of 276 impaired mesh sequences with different distortion intensities and categories, such as spatial and temporal visual masking simulation, lossy compression and network transmission error. The constructed database is publicly available and can serve as ground-truth data for the performance evaluation of objective metrics of dynamic mesh perceptual quality. A comparative study is then conducted to benchmark existing objective metrics. The main finding of this objective study is that in general model-based quality metrics outperform image- and video-based quality metrics, but none of the existing metrics can achieve high correlation with human opinion scores on the whole subjective database and for all kinds of distortions. Finally, a simple but effective full-reference objective metric is proposed for the perceptual quality assessment of 3D dynamic meshes, which incorporates both spatial and temporal features and which provides a satisfactory correlation value when tested on the subjective database. HighlightsA large-scale, publicly available subjective database of 3D dynamic meshes.A set of new experiments to study the spatial and temporal visual masking effects.New and comprehensive comparative study of the existing objective metrics.A new, simple but effective objective metric for dynamic mesh perceptual quality.Simple measures of 3D mesh spatial information and temporal information.


Dummy Bit Rate Matching for UMTS LTE


title: Computer Networks as Social Networks: Collaborative Work, Telework, and Virtual Community

abstract: When computer networks link people as well as machines, they become social networks. Such computer-supported social networks (CSSNs) are becoming important bases of virtual communities, computer-supported cooperative work, and telework. Computer-mediated communication such as electronic mail and computerized conferencing is usually text-based and asynchronous. It has limited social presence, and on-line communications are often more uninhibited, creative, and blunt than in-person communication. Nevertheless, CSSNs sustain strong, intermediate, and weak ties that provide information and social support in both specialized and broadly based relationships. CSSNs foster virtual communities that are usually partial and narrowly focused, although some do become encompassing and broadly based. CSSNs accomplish a wide variety of cooperative work, connecting workers within and between organizations who are often physically dispersed. CSSNs also link teleworkers from their homes or remote work centers to main organi...


Digital Discrimination : The Case of Airbnb


title: Establishing the credibility of qualitative research findings: the plot thickens

abstract: Journal of Advanced Nursing 30(2),374–380Establishing the credibility of qualitative research ﬁndings: the plot thickensQualitative research is increasingly recognized and valued and its unique placein nursing research is highlighted by many. Despite this, some nurse researcherscontinue to raise epistemological issues about the problems of objectivity andthe validity of qualitative research ﬁndings. This paper explores the issuesrelating to the representativeness or credibility of qualitative research ﬁndings.It therefore critiques the existing distinct philosophical and methodologicalpositions concerning the trustworthiness of qualitative research ﬁndings, whichare described as follows: quantitative studies should be judged using the samecriteria and terminology as quanitative studies; it is impossible, in a meaningfulway, for any criteria to be used to judge qualitative studies; qualitative studiesshould be judged using criteria that are developed for and ﬁt the qualitativeparadigm; and the credibility of qualitative research ﬁndings could be estab-lished by testing out the emerging theory by means of conducting a deductivequantitative study. The authors conclude by providing some guidelines forestablishing the credibility of qualitative research ﬁndings.Keywords: qualitative research, representativeness, establishing credibility,nursing researchINTRODUCTION


title: Diabetes Data Analysis and Prediction Model Discovery Using RapidMiner

abstract: Data mining techniques have been extensively applied in bioinformatics to analyze biomedical data. In this paper, we choose the Rapid-I?s RapidMiner as our tool to analyze a Pima Indians Diabetes Data Set, which collects the information of patients with and without developing diabetes. The discussion follows the data mining process. The focus will be on the data preprocessing, including attribute identification and selection, outlier removal, data normalization and numerical discretization, visual data analysis, hidden relationships discovery, and a diabetes prediction model construction.


title: A Bayesian Framework for Reinforcement Learning

abstract: The reinforcement learning problem can be decomposed into two parallel types of inference: (i) estimating the parameters of a model for the underlying process; (ii) determining behavior which maximizes return under the estimated model. Following Dearden, Friedman and Andre (1999), it is proposed that the learning process estimates online the full posterior distribution over models. To determine behavior, a hypothesis is sampled from this distribution and the greedy policy with respect to the hypothesis is obtained by dynamic programming. By using a different hypothesis for each trial appropriate exploratory and exploitative behavior is obtained. This Bayesian method always converges to the optimal policy for a stationary process with discrete states.


title: Score-Performance Matching using HMMs

abstract: In this paper we will describe an implementation of a score-performance matching, capable of score following, based on a stochastic approach using Hidden Markov Models.


Homotopy Preserving Approximate Voronoi Diagram of 3 D Polyhedron


title: Tensor Canonical Correlation Analysis for Action Classification

abstract: We introduce a new framework, namely tensor canonical correlation analysis (TCCA) which is an extension of classical canonical correlation analysis (CCA) to multidimensional data arrays (or tensors) and apply this for action/gesture classification in videos. By tensor CCA, joint space-time linear relationships of two video volumes are inspected to yield flexible and descriptive similarity features of the two videos. The TCCA features are combined with a discriminative feature selection scheme and a nearest neighbor classifier for action classification. In addition, we propose a time-efficient action detection method based on dynamic learning of subspaces for tensor CCA for the case that actions are not aligned in the space-time domain. The proposed method delivered significantly better accuracy and comparable detection speed over state-of-the-art methods on the KTH action data set as well as self-recorded hand gesture data sets.


Smart Recommendation for an Evolving E-Learning System


title: Parallel structure and tuning of a fuzzy PID controller

abstract: In this paper, a parallel structure of fuzzy PID control systems is proposed. It is associated with a new tuning method which, based on gain margin and phase margin specifications, determines the parameters of the fuzzy PID controller. In comparison with conventional PID controllers, the proposed fuzzy PID controller shows higher control gains when system states are away from equilibrium and, at the same time, retains a lower profile of control signals. Consequently, better control performance is achieved. With the proposed formula, the weighting factors of a fuzzy logic controller can be systematically selected according to the plant under control. By virtue of using the simplest structure of fuzzy logic control, the stability of the nonlinear control system can be analyzed and a sufficient BIBO stability condition is given. The superior performance of the proposed controller is demonstrated through an experimental example.


title: Semantic feature modelling

abstract: Abstract Almost all current feature modelling systems are parametric, history-based modelling systems. These systems suffer from a number of shortcomings with regard to the modelling process. In particular, they lack a complete specification of feature semantics, and therefore fail to maintain the meaning of the features during modelling. Also, modelling operations sometimes are hampered by the model history, and occasionally even have ill-defined semantics. In the semantic feature modelling approach presented here, the semantics of features is well defined and maintained during all modelling operations. The result of these operations is independent of the order of feature creation, and is well defined. The specification of feature classes, the structure and functionality of the feature model, in particular the Cellular Model, and the model validity maintenance scheme are described. The advantages and disadvantages of the approach, compared to current feature modelling approaches, are pointed out.


title: Integration of Distributed Enterprise Applications: A Survey

abstract: Many industrial enterprises acquire disparate systems and applications over the years. The need to integrate these different systems and applications is often prominent for satisfying business requirements and needs. In an effort to help researchers in industrial informatics understand the state-of-the-art of the enterprise application integration, we examined the architectures and technologies for integrating distributed enterprise applications, illustrated their strengths and weaknesses, and identified research trends and opportunities in this increasingly important area.


title: Statistical Voice Conversion Based on Wavenet

abstract: This paper proposes a voice conversion technique based on WaveNet to directly generate target audio waveforms from acoustic features of a source speaker. In voice conversion based on statistical models, the relation between acoustic features, such as spectral parameters, extracted from source and target audio waveforms is generally modeled using statistical models, such as Gaussian mixture models and neural networks. Although modeling the relation between acoustic features is reasonable and efficient, these models are not optimized for predicting target audio waveforms because the vocoder parameters are used as intermediate representations. To overcome this problem, we developed a voice conversion method to model the relation between target audio waveforms and acoustic features extracted from source audio waveforms using WaveNet, which is a generative model for audio waveforms. The proposed model can directly generate converted audio waveforms without vocoders. Experimental results indicate that the proposed method can generate a more naturally sounding converted speech than that using a conventional DNN method.


title: Fast Vehicle Detection with Lateral Convolutional Neural Network

abstract: In this paper, we propose a fast vehicle detector for traffic surveillance. We first explore using different feature layers from a deep residual network to perform vehicle detection. Experiment results show that the high-resolution features from earlier feature layers contain more structural information, which is good to achieve fine-grained localization but yields low recall rates. The low-resolution features in the deep layers contain semantically strong information, which is good to represent the objectness but too coarse to achieve accurate localization. Therefore, we decouple the localization and objectness prediction from a single layer. Instead, we employ a lateral network that takes the features from earlier layers as input and outputs the localization residual. Our proposed detector can achieve fast detection at a rate of 28 frames/s, and a mean average precision (mAP) of 67.25% in the DETRAC vehicle detection benchmark.


title: Tactile and Haptic Illusions

abstract: This paper surveys the research literature on robust tactile and haptic illusions. The illusions are organized into two categories. The first category relates to objects and their properties, and is further differentiated in terms of haptic processing of material versus geometric object properties. The second category relates to haptic space, and is further differentiated in terms of the observer's own body versus external space. The illusions are initially described and where possible addressed in terms of their functional properties and/or underlying neural processes. The significance of these illusions for the design of tactile and haptic displays is also discussed. We conclude by briefly considering a number of important general themes that have emerged in the materials surveyed.


title: Avoiding flex cracks in ceramic capacitors: Analytical tool for a reliable failure analysis and guideline for positioning cercaps on PCBs

abstract: Abstract In every electronic assembly line where ceramic capacitors are used and printed circuit boards are depaneled the quality risk “flex cracks” is widely known. Unfortunately flex cracks in “cercaps” always extend under the metal terminations of the capacitors and electrical tests only reveal about 1% of the affected parts. With a new method—etching away the terminations and looking at the otherwise hidden cracks—it is possible to identify all sources of mechanical bending and warping. In the course of failure analysis it is helpful to know that most of the time not only the failed ceramic capacitor shows a crack pattern but all the surrounding cercaps as well. Well-founded knowledge of different crack patterns and failure modes also allows us to discover unsafe bending and warping lines on the PCB. This gives us a guideline on how to place the ceramic capacitors in optimal orientation not only to depaneling lines but also in the vicinity of mounting and screw openings. Finally we will review the different kinds of cercaps with internal layouts that prevent boards from failing even if flex cracks should show up.


title: Transferring , Translating and Transforming : An Integrative Framework for Managing Knowledge across Boundaries 1

abstract: The paper examines managing knowledge across boundaries in settings where innovation is desired. Innovation is a useful context because it allows us to explore the negative consequences of the path-dependent nature of knowledge. A framework is developed that describes three progressively complex boundaries--syntactic, semantic, and pragmatic--and three progressively complex processes--transfer, translation, and transformation. The framework is used to specify the practical and political mismatches that occur when innovation is desired and how this relates to the common knowledge that actors use to share and assess each other's domain-specific knowledge. The development and use of a collaborative engineering tool in the early stages of a vehicle's development is presented to illustrate the conceptual and prescriptive value of the framework. The implication of this framework on key topics in the organization theory and strategy literatures is then discussed.


title: The Spread of Knowledge Management in SMEs: A Scenario in Evolution

abstract: This paper deals with knowledge management (KM) in small and medium enterprises (SMEs). Through a literature review, three research questions have been identified concerning the barriers hindering the spread of KM practices in SMEs, the main knowledge management systems (KMSs) adopted by SMEs and the impact of the use of KM practices on SME performance. The research questions were subsequently addressed through a field analysis conducted on a sample of SMEs. The empirical evidence highlights that the scenario has changed in the space of but a few years. Although SMEs are generally characterized by poor financial and human resources, they are able to overcome the barriers preventing the spread of KM practices. The SMEs investigated perceive the strategic value of KM and consequently adopt a variety of KMSs. Nevertheless, such systems are generally outdated in comparison with cheaper, more recent, and user-friendly applications. Finally, the paper emphasizes that the use of KM practices can contribute to the overall growth of SMEs by simultaneously and significantly enhancing their performance.


title: Results from a Content Analysis of User Reviews

abstract: Advanced mobile technology continues to shape professional environments. Smart cell phones, pocket computers and laptop computers reduce the need of users to remain close to a wired information system infrastructure and allow for task performance in many different contexts. Among the consequences are changes in technology requirements, such as the need to limit weight and size of the devices. In the current paper, we focus on the factors that users find important in mobile devices. Based on a content analysis of online user reviews that was followed by structural equation modeling, we found four factors to be significantly related with overall user evaluation, namely functionality, portability, performance, and usability. Besides the practical relevance for technology developers and managers, our research results contribute to the discussion about the extent to which previously established theories of technology adoption and use are applicable to mobile technology. We also discuss the methodological suitability of online user reviews for the assessment of user requirements, and the complementarity of automated and non-automated forms of content analysis.


title: Novel and Simple High-Frequency Single-Port Vector Network Analyzer

abstract: Portable, accurate, and relatively inexpensive high-frequency vector network analyzers (VNAs) have great utility for a wide range of applications, encompassing microwave circuit characterization, reflectometry, imaging, material characterization, and nondestructive testing to name a few. To meet the rising demand for VNAs possessing the aforementioned attributes, we present a novel and simple VNA design based on a standing-wave probing device and an electronically controllable phase shifter. The phase shifter is inserted between a device under test (DUT) and a standing-wave probing device. The complex reflection coefficient of the DUT is then obtained from multiple standing-wave voltage measurements taken for several different values of the phase shift. The proposed VNA design eliminates the need for expensive heterodyne detection schemes required for tuned-receiver-based VNA designs. Compared with previously developed VNAs that operate based on performing multiple power measurements, the proposed VNA utilizes a single power detector without the need for multiport hybrid couplers. In this paper, the efficacy of the proposed VNA is demonstrated via numerical simulations and experimental measurements. For this purpose, measurements of various DUTs obtained using an X-band (8.2-12.4 GHz) prototype VNA are presented and compared with results obtained using an Agilent HP8510C VNA. The results show that the proposed VNA provides highly accurate vector measurements with typical errors on the order of 0.02 and 1° for magnitude and phase, respectively.


title: A fungal pathogen secretes plant alkalinizing peptides to increase infection

abstract: The fungal pathogen Fusarium oxysporum is shown to use a functional homologue of the plant regulatory peptide RALF (rapid alkalinization factor) to induce alkalinization and cause disease in plants.


title: Do School-Based Tutoring Programs Significantly Improve Student Performance on Standardized Tests?

abstract: AbstractThis study used a pre-post, nonequivalent control group design to examine the impact of an in-district, after-school tutoring program on eighth grade students’ standardized test scores in l...


title: Spike detection: a review and comparison of algorithms

abstract: For algorithm developers, this review details recent approaches to the problem, compares the accuracy of various algorithms, identifies common testing issues and proposes some solutions. For the algorithm user, e.g. electroencephalograph (EEG) technician or neurologist, this review provides an estimate of algorithm accuracy and comparison to that of human experts. Manuscripts dated from 1975 are reviewed. Progress since Frost's 1985 review of the state of the art is discussed. Twenty-five manuscripts are reviewed. Many novel methods have been proposed including neural networks and high-resolution frequency methods. Algorithm accuracy is less than that of experts, but the accuracy of experts is probably less than what is commonly believed. Larger record sets will be required for expert-level detection algorithms.


title: TicToc: Time Traveling Optimistic Concurrency Control

abstract: Concurrency control for on-line transaction processing (OLTP) database management systems (DBMSs) is a nasty game. Achieving higher performance on emerging many-core systems is difficult. Previous research has shown that timestamp management is the key scalability bottleneck in concurrency control algorithms. This prevents the system from scaling to large numbers of cores. In this paper we present TicToc, a new optimistic concurrency control algorithm that avoids the scalability and concurrency bottlenecks of prior T/O schemes. TicToc relies on a novel and provably correct data-driven timestamp management protocol. Instead of assigning timestamps to transactions, this protocol assigns read and write timestamps to data items and uses them to lazily compute a valid commit timestamp for each transaction. TicToc removes the need for centralized timestamp allocation, and commits transactions that would be aborted by conventional T/O schemes. We implemented TicToc along with four other concurrency control algorithms in an in-memory, shared-everything OLTP DBMS and compared their performance on different workloads. Our results show that TicToc achieves up to 92% better throughput while reducing the abort rate by 3.3x over these previous algorithms.


title: A comparative study of different classifiers for automatic personality prediction

abstract: Personality is described as a fairly fixed feature of an individual which indicates individual's preferences. Personality has been shown to be relevant to many types of interactions such as in predicting movie preferences, social relationships, personality and music, and correlation between personality and job performance. Predicting personality from social media become the current trend as the information extracted can be utilized to improve the users' experiences with various computerized interfaces. Thus, many algorithms have been performed to predict personality from social media. In this paper, we compared the performance of several classifiers provided in WEKA namely Bayes, Functions, Rules, Trees, and Meta in predicting student's personality. Based on adopted framework, the profile data of undergraduate students were extracted from Twitter, analyzed, and then classified in the automatic personality prediction. Four features with significant correlation from the profile data have been selected to map into Big Five personality model. Only extraversion dimension of the Big Five was considered in this study. A 10-fold cross validation was used to evaluate the classifiers. Several parameters that were observed in the performance of the classifiers are classification accuracy, F-measure, time taken to build the model, Kappa statistic, and training errors. Experimental evaluation demonstrated that OneR algorithm is the best classifier in terms of the accuracy, F-measure, and Kappa statistic.


title: Embedded controller in farmers pump by solar energy (Automation of solarised water pump)

abstract: The primary aim of this project is to develop and atomize the solar farmers pump (water pump) considering the power supply, direct current (DC), Alternating current (AC), inverter frequency, a well, water level in the well, submersible monoblock pump. Here we introduce an advanced technique with GSM module. The solar pumps which work by utilizing the energy from the SOLAR ARRAYS and the power from the PV are stored in a battery. The power from the battery is inverted and given to the pump for irrigation. This pump is also controlled by the GSM module. The use of GSM mode is to start and stop working of pumps using mobile phone; the water level monitoring is also done by the GSM mode which this also provides the message for each hour. This is done with the embedded C in PIC16F877A microcontroller. The main advantage of this project is optimizing the power and also saving government's free subsidiary electricity (22% of total power production in India). This proves an efficient and economy way of irrigation and this will automate the agriculture sector.


title: Developing Nomad for Robotic Exploration of the Atacama Desert

abstract: Recent years have seen renewed attention to planetary exploration, and robotics is recognized as essential to many upcoming missions. In this article we describe the ongoing efforts of the Nomad project to develop robots for planetary and terrestrial exploration. The project is developing, demonstrating, and evaluating systems capable of long-distance, long-duration missions. In 1997 this work has resulted in the Atacama Desert Trek, in which a mobile robot named Nomad, explored the Atacama Desert of northern Chile. Nomad's 45-day objective was to travel 200 km across the rugged, barren landscape. We then will describe the technologies for Nomad's transforming chassis, high data-rate communication, safeguarded teleoperation and autonomy, panoramic imaging and visualization, and remote science. We then focus on issues of long-distance, long-duration exploration and describe the events and results of the Atacama Desert Trek.


title: Selling Off Privacy at Auction

abstract: Real-Time Bidding (RTB) and Cookie Matching (CM) are transforming the advertising landscape to an extremely dynamic market and make targeted advertising considerably permissive. The emergence of these technologies allows companies to exchange user data as a product and therefore raises important concerns from privacy perspectives. In this paper, we perform a privacy analysis of CM and RTB and quantify the leakage of users' browsing histories due to these mechanisms. We study this problem on a corpus of users' Web histories, and show that using these technologies, certain companies can significantly improve their tracking and profiling capabilities. We detect $41$ companies serving ads via RTB and over $125$ using Cookie Matching. We show that $91\%$ of users in our dataset were affected by CM and in certain cases, $27\%$ of users' Web browsing histories could be leaked to 3rd-party companies through RTB. We expose a design characteristic of RTB systems to observe the prices which advertisers pay for serving ads to Web users. We leverage this feature and provide important insights into these prices by analyzing different user profiles and visiting contexts. Our study shows the variation of prices according to context information including visiting site, time and user's physical location. We experimentally confirm that users with known Web browsing history are evaluated higher than new comers, that some user profiles are more valuable than others, and that users' intents, such as looking for a commercial product, are sold at higher prices than users' Web browsing histories. In addition, we show that there is a huge gap between users' perception of the value of their personal information and its actual value on the market. A recent study by Carrascal et al. showed that, on average, users evaluate the price of the disclosure of their presence on a Web site to EUR 7. We show that user's Web browsing history elements are routinely being sold off for less than $\$0.0005$.


title: BC-PDS: Protecting Privacy and Self-Sovereignty through BlockChains for OpenPDS

abstract: In the Big Data era, personal metadata may will become a new type of corporate asset, however there have already been a growing public concern about user’s privacy mined from metadata. In this paper we address the problem of implementing the self-sovereignty of personal metadata on the existing OpenPDS/SafeAnswers framework according to the Windhover Principle. In order to do that, we propose a new framework, called BlocakChain-based Personal Data Store (BCPDS), to realize two basic properties: notary and autonomy. This framework, firstly introduces the BlockChain as a notary, into OpenPDS/SafeAnswers for secure storage of personal meta-data instead of the original database. Next, we present an AutoNomybased Access Control (ANAC) to improve the SafeAnswers module, where ANAC is a new mechanism that enforces access based on the relationship among all authorized users and metadata’s owner. In addition, we also propose General Access Structure (GAS) and threshold secret sharing scheme in BlockChain as an implementation method for our BC-PDS framework.


title: Performance analysis for Vedic multiplier using modified full adders

abstract: Area efficient architecture is today's major concern in the field of VLSI, Digital signal processing circuits, cryptographic algorithms, wireless communications and Internet of Things (IOT). Majority of the architectures use multiplication. Realization of multiplication by using repetitive addition and shift and add methods consumes more area, power and delay. Vedic is one of the efficient multipliers. Design of Vedic multiplier using different sutras reduces area and power. From the structure of Vedic multiplier, it is clearly observed that there is scope to design an efficient architecture. In this research, Vedic multiplier is designed using modified full adder which consumes less number of LUT's, slices and delay when compared to normal conventional Vedic multiplier. Simulation and synthesis are carried on XILINX ISE 12.2 software. FPGA results of the proposed multiplier show that number of LUT's is less by 13.8% in the modified Vedic Multiplier (4×4) and less by 7.5% in modified Vedic Multiplier(8×8). Delay is less by 10% in modified Vedic Multiplier (4×4) and 7.2 % in modified Vedic Multiplier (8×8).


title: Mapping the road to fun: Natural video game controllers, presence, and game enjoyment

abstract: This investigation examines how video game interactivity can affect presence and game enjoyment. Interactivity in the form of natural mapping has been advocated as a possible contributor to presence experiences, yet few studies to date have investigated this potential. The present work formulates a preliminary typology of natural mapping and addresses how several types of mapping impact the experience of a video game, with the expectation that more natural mapping leads to increased spatial presence affecting enjoyment. Two studies were conducted. In the first study, 48 participants played a golfing video game using one of two controller types (Nintendo Wiimote or gamepad). In the second, 78 participants played a driving video game using an even more natural controller (steering wheel) or one of three other controller types. Participants then completed measures of perceived naturalness, presence, and enjoyment. Results of both studies were generally consistent with expectations.


title: Jalangi: a selective record-replay and dynamic analysis framework for JavaScript

abstract: JavaScript is widely used for writing client-side web applications and is getting increasingly popular for writing mobile applications. However, unlike C, C++, and Java, there are not that many tools available for analysis and testing of JavaScript applications. In this paper, we present a simple yet powerful framework, called Jalangi, for writing heavy-weight dynamic analyses. Our framework incorporates two key techniques: 1) selective record-replay, a technique which enables to record and to faithfully replay a user-selected part of the program, and 2) shadow values and shadow execution, which enables easy implementation of heavy-weight dynamic analyses. Our implementation makes no special assumption about JavaScript, which makes it applicable to real-world JavaScript programs running on multiple platforms. We have implemented concolic testing, an analysis to track origins of nulls and undefined, a simple form of taint analysis, an analysis to detect likely type inconsistencies, and an object allocation profiler in Jalangi. Our evaluation of Jalangi on the SunSpider benchmark suite and on five web applications shows that Jalangi has an average slowdown of 26X during recording and 30X slowdown during replay and analysis. The slowdowns are comparable with slowdowns reported for similar tools, such as PIN and Valgrind for x86 binaries. We believe that the techniques proposed in this paper are applicable to other dynamic languages.


title: A robust and efficient method for steady state patterns in reaction-diffusion systems

abstract: An inhomogeneous steady state pattern of nonlinear reaction-diffusion equations with no-flux boundary conditions is usually computed by solving the corresponding time-dependent reaction-diffusion equations using temporal schemes. Nonlinear solvers (e.g., Newton's method) take less CPU time in direct computation for the steady state; however, their convergence is sensitive to the initial guess, often leading to divergence or convergence to spatially homogeneous solution. Systematically numerical exploration of spatial patterns of reaction-diffusion equations under different parameter regimes requires that the numerical method be efficient and robust to initial condition or initial guess, with better likelihood of convergence to an inhomogeneous pattern. Here, a new approach that combines the advantages of temporal schemes in robustness and Newton's method in fast convergence in solving steady states of reaction-diffusion equations is proposed. In particular, an adaptive implicit Euler with inexact solver (AIIE) method is found to be much more efficient than temporal schemes and more robust in convergence than typical nonlinear solvers (e.g., Newton's method) in finding the inhomogeneous pattern. Application of this new approach to two reaction-diffusion equations in one, two, and three spatial dimensions, along with direct comparisons to several other existing methods, demonstrates that AIIE is a more desirable method for searching inhomogeneous spatial patterns of reaction-diffusion equations in a large parameter space.