title: Broadband Circularly Polarized Crossed Bowtie Dipole Antenna Loaded With Parasitic Elements

abstract: An extremely broadband circularly polarized (CP) antenna is presented. The proposed antenna consists of a crossed bowtie dipole loaded with four unequal parasitic cross slots, four parasitic bowtie patches, and four parasitic rectangular strips. The cross slots can generate a CP mode due to the sequential phases of the crossed bowtie dipole. The parasitic bowtie patches are gap-coupled with two arms of the crossed bowtie dipole, and they are sequentially rotated along diagonals to generate an additional CP mode. The parasitic strips also generate one CP mode due to the sequentially rotated configuration parallel to the crossed bowtie dipole. By properly combining with two CP modes of the crossed bowtie dipole, broadband CP operation is achieved. The experimental and simulated results are in agreement, and the measured results show that the proposed antenna exhibits a very wide impedance bandwidth of 93.1% (2.21–6.06 GHz) and an excellent axial-ratio bandwidth of 90.9% (2.2–6.4 GHz). In addition, the proposed antenna produces a right-hand circular polarization with a peak gain of 8.6 dBi.


Modularity using Event-Driven Software Architecture


title: Trojan data layouts: right shoes for a running elephant

abstract: MapReduce is becoming ubiquitous in large-scale data analysis. Several recent works have shown that the performance of Hadoop MapReduce could be improved, for instance, by creating indexes in a non-invasive manner. However, they ignore the impact of the data layout used inside data blocks of Hadoop Distributed File System (HDFS). In this paper, we analyze different data layouts in detail in the context of MapReduce and argue that Row, Column, and PAX layouts can lead to poor system performance. We propose a new data layout, coined Trojan Layout, that internally organizes data blocks into attribute groups according to the workload in order to improve data access times. A salient feature of Trojan Layout is that it fully preserves the fault-tolerance properties of MapReduce. We implement our Trojan Layout idea in HDFS 0.20.3 and call the resulting system Trojan HDFS. We exploit the fact that HDFS stores multiple replicas of each data block on different computing nodes. Trojan HDFS automatically creates a different Trojan Layout per replica to better fit the workload. As a result, we are able to schedule incoming MapReduce jobs to data block replicas with the most suitable Trojan Layout. We evaluate our approach using three real-world workloads. We compare Trojan Layouts against Hadoop using Row and PAX layouts. The results demonstrate that Trojan Layout allows MapReduce jobs to read their input data up to 4.8 times faster than Row layout and up to 3.5 times faster than PAX layout.


title: VeriTrust: Verification for Hardware Trust

abstract: Today's integrated circuit designs are vulnerable to a wide range of malicious alterations, namely hardware Trojans (HTs). HTs serve as backdoors to subvert or augment the normal operation of infected devices, which may lead to functionality changes, sensitive information leakages, or denial of service attacks. To tackle such threats, this paper proposes a novel verification technique for hardware trust, namely VeriTrust, which facilitates to detect HTs inserted at design stage. Based on the observation that HTs are usually activated by dedicated trigger inputs that are not sensitized with verification test cases, VeriTrust automatically identifies such potential HT trigger inputs by examining verification corners. The key difference between VeriTrust and existing HT detection techniques based on “unused circuit identification” is that VeriTrust is insensitive to the implementation style of HTs. Experimental results show that VeriTrust is able to detect all HTs evaluated in this paper (constructed based on various HT design methodologies shown in this paper) at the cost of moderate extra verification time.


title: PREFER: PREdiction Model for Financial Entity Relation

abstract: The Financial Entity Identification and Information Integration (FEIII) is a competition for the understanding relationships between financial entities. To predict competitor relation between two entities, there are three challenges - 1) relevant feature extraction from the various released dataset, 2) missing entity information handling and 3) imbalance of train data handling. To solve these challenges, we propose a model named PREFER which considering 1) relation trend and context feature extraction from the release dataset, 2) K-NN estimation with concept graph of knowledge bases (Probase), and 3) oversampling from the true labeled data. From the model, we increase 34% of F1-score compared to the baseline method.


title: Remote sensing applications: an overview

abstract: Remote Sensing (RS) refers to the science of identification of earth surface features and estimation of their geo-bio-physical properties using electromagnetic radiation as a medium of interaction. Spectral, spatial, temporal and polarization signatures are major characteristics of the sensor/target, which facilitate target discrimination. Earth surface data as seen by the sensors in different wavelengths (reflected, scattered and/or emitted) is radiometrically and geometrically corrected before extraction of spectral information. RS data, with its ability for a synoptic view, repetitive coverage with calibrated sensors to detect changes, observations at different resolutions, provides a better alternative for natural resources management as compared to traditional methods. Indian Earth Observation (EO) programme has been applications-driven and national development has been its prime motivation. From Bhaskara to Cartosat, India's EO capability has increased manifold. Improvements are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. Some of the major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, marine resources, snow and glacier, disaster monitoring and mitigation, infrastructure development, etc. The paper reviews RS techniques and applications carried out using both optical and microwave sensors. It also analyses the gap areas and discusses the future perspectives.


title: A review for mobile commerce research and applications

abstract: Although a large volume of literature is available on mobile commerce (m-commerce), the topic is still under development and offers potential opportunities for further research and applications. Since the subject is at the stage of development, a review of the literature on m-commerce with the objective of bringing to the fore the state-of-art in m-commerce research and applications will initiate further research on the growth of m-commerce technologies. This paper reviews the literature on m-commerce and applications using a suitable classification scheme to identify the gap between theory and practice and future research directions. The 149 m-commerce articles are classified and the results of these are presented based on a scheme that consists of five distinct categories: m-commerce theory and research, wireless network infrastructure, mobile middleware, wireless user infrastructure, and m-commerce applications and cases. A comprehensive list of references is presented. We hope that the findings of this research will provide useful insights into the anatomy of m-commerce literature and be a good source for anyone who is interested in m-commerce. The paper also provides some future directions for research.


title: Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning

abstract: The paper proposes an inductive semi-supervised learning method, called Smooth Neighbors on Teacher Graphs (SNTG). At each iteration during training, a graph is dynamically constructed based on predictions of the teacher model, i.e., the implicit self-ensemble of models. Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low dimensional manifold. We achieve state-of-the-art results on semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular, the improvements are significant when the labels are scarce. For non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%. Our method is also effective under noisy supervision and shows robustness to incorrect labels.


title: Business culture and dishonesty in the banking industry

abstract: According to popular opinion, unethical business practices are common in the financial industry; here, the employees of a large, international bank are shown to behave, on average, honestly in a laboratory game to reveal dishonest behaviour, but when their professional identity as bank employees was rendered salient, the prevalence of dishonest behaviour increased.


title: Characterizing Dynamic Brain Responses with fMRI: A Multivariate Approach

abstract: In this paper we present a multivariate analysis of evoked hemodynamic responses and their spatiotemporal dynamics as measured with fast fMRI. This analysis uses standard multivariate statistics (MANCOVA) and the general linear model to make inferences about effects of interest and canonical variates analysis (CVA) to describe the important features of these effects. We have used these techniques to characterize the form of hemodynamic transients that are evoked during a cognitive or sensorimotor task. In particular we do not assume that the neural or hemodynamic response reaches some "steady state" but acknowledge that these physiological changes could show profound task-dependent adaptation and time-dependent changes during the task. To address this issue we have modeled hemodynamic responses using appropriate temporal basis functions and estimated their exact form within the general linear model using MANCOVA. We do not propose that this analysis is a particularly powerful way to make inferences about functional specialization (or more generally functional anatomy) because it only provides statistical inferences about the distributed (whole brain) responses evoked by different conditions. However, its application to characterizing the temporal aspects of evoked hemodynamic responses reveals some compelling and somewhat unexpected perspectives on transient but stereotyped responses to changes in cognitive or sensorimotor processing. The most remarkable observation is that these responses can be biphasic and show profound differences in their form depending on the extant task or condition. Furthermore these differences can be seen in the absence of changes in mean signal.


title: Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising

abstract: Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula that characterizes the allowed undersampling of generalized sparse objects. The formula applies to Approximate Message Passing (AMP) algorithms for compressed sensing, which are here generalized to employ denoising operators besides the traditional scalar soft thresholding denoiser. This paper gives several examples including scalar denoisers not derived from convex penalization -- the firm shrinkage nonlinearity and the minimax nonlinearity -- and also nonscalar denoisers -- block thresholding, monotone regression, and total variation minimization. Let the variables eps = k/N and delta = n/N denote the generalized sparsity and undersampling fractions for sampling the k-generalized-sparse N-vector x_0 according to y=Ax_0. Here A is an n\times N measurement matrix whose entries are iid standard Gaussian. The formula states that the phase transition curve delta = delta(eps) separating successful from unsuccessful reconstruction of x_0 by AMP is given by: delta = M(eps| Denoiser), where M(eps| Denoiser) denotes the per-coordinate minimax mean squared error (MSE) of the specified, optimally-tuned denoiser in the directly observed problem y = x + z. In short, the phase transition of a noiseless undersampling problem is identical to the minimax MSE in a denoising problem.


title: Genetically Identical Irises Have Texture Similarity That Is Not Detected By

abstract: As the standard iris biometric algorithm ''sees'' them, the left and right irises of the same person are as different as irises of unrelated people. Similarly, in terms of iris biometric matching, the eyes of identical twins are as different as irises of unrelated people. The left and right eyes of an individual or the eyes of identical twins are examples of genetically identical irises. In experiments with human observers viewing pairs of iris images acquired using an iris biometric system, we have found that there is recognizable similarity in the left and right irises of an individual and in the irises of identical twins. This result suggests that iris texture analysis different from that performed in the standard iris biometric algorithm may be able to answer questions that iris biometrics cannot answer.


title: An Evaluation of BPMN Modeling Tools

abstract: Various BPMN modeling tools are available and it is close to impossible to understand their functional differences without simply trying them out. This paper presents an evaluation framework and presents the outcomes of its application to a set of five BPMN modeling tools. We report on various differences, for example with respect to the level of BPMN support and the “smart” support a modeler may experience using these tools. Interestingly, while tools that are provided by commercial parties do offer more integration options with other tools in BPM suites, they cannot be said to outperform academic tools in other respects.


title: VAIN: Attentional Multi-agent Predictive Modeling

abstract: Multi-agent predictive modeling is an essential step for understanding physical, social and team-play systems. Recently, Interaction Networks (INs) were proposed for the task of modeling multi-agent physical systems, INs scale with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce VAIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that VAIN is effective for multi-agent predictive modeling. Our method is evaluated on tasks from challenging multi-agent prediction domains: chess and soccer, and outperforms competing multi-agent approaches.


A Systematic Approach for Design a Low-Cost Mobility Assistive Device for Elderly People


title: A Case Study of the Relationship between Local Pen Action and Three Dimensional Shapes of Handwritten Strokes

abstract: In this article, we performed a case study of the relationship between local pen action and three dimensional shapes of handwritten strokes on paper sheet. The purpose of the study is to enrich the knowledge effective for forensic handwriting examination. Samples for analysis were one Japanese Hiragana character written by one participant. Online and offline handwritings were captured simultaneously by using ink pen tablet. The type of pen was ballpoint pen, and characters were written on paper for plain copy placed on the tablet. The position and pen pressure information were captured at 200 Hz. The precision of pen position was 0.25 mm, and the pen pressure information was at 15 bit. Experimental results showed that the depth information of overall area of character was related with the density of handwritten strokes. On the other hand, the local shape of handwritten stroke was considered to be related with local pen action. As the pen pressure increased, the depth and the width of handwritten strokes increased. In addition, the influence of pen pressure spread widely around handwritten strokes. However, the local shape was not only dependent on pen pressure but also on pen speed.


70 . The use of Sen ’ s capability approach in ICT 4 D : an exploratory review


title: A low noise readout circuit for integrated electrochemical biosensor arrays

abstract: This paper presents a low noise electrochemical interface circuit that is tuned to the needs of protein-based biosensor arrays and compatible with the formation of fully integrated biochemical microsystems. The circuit includes an integrated potentiostat and highly sensitive amperometric readout amplifier. It achieves good noise performance while supporting biosensor output currents from 10 pA to 10 /spl mu/A to suit a wide range of sensitivities and electrode areas. Implemented in 0.5 /spl mu/m CMOS process, it is compatible with a post-CMOS process to realize an integrated biosensor array.


title: Clickstream analysis for crowd-based object segmentation with confidence

abstract: With the rapidly increasing interest in machine learning based solutions for automatic image annotation, the availability of reference annotations for algorithm training is one of the major bottlenecks in the field. Crowdsourcing has evolved as a valuable option for low-cost and large-scale data annotation; however, quality control remains a major issue which needs to be addressed. To our knowledge, we are the first to analyze the annotation process to improve crowd-sourced image segmentation. Our method involves training a regressor to estimate the quality of a segmentation from the annotator's clickstream data. The quality estimation can be used to identify spam and weight individual annotations by their (estimated) quality when merging multiple segmentations of one image. Using a total of 29,000 crowd annotations performed on publicly available data of different object classes, we show that (1) our method is highly accurate in estimating the segmentation quality based on clickstream data, (2) outperforms state-of-the-art methods for merging multiple annotations. As the regressor does not need to be trained on the object class that it is applied to it can be regarded as a low-cost option for quality control and confidence analysis in the context of crowd-based image annotation.


title: PingPongPlus: Design of an Athletic-Tangible Interface for Computer-Supported Cooperative Play

abstract: This paper introduces a novel interface for digitally-augmentedcooperative play. We present the concept of the athletic-tangibleinterface, a new class of interaction which uses tangible objectsand full-body motion in physical spaces with digital augmentation.We detail the implementation of PingPongPlus, a reactive ping-pongtable, which features a novel sound-based ball tracking technology.The game is augmented and transformed with dynamic graphics andsound, determined by the position of impact, and the rhythm andstyle of play. A variety of different modes of play and initialexperiences with PingPongPlus are also described.


title: ESCRT‐III and Vps4: a dynamic multipurpose tool for membrane budding and scission

abstract: Complex molecular machineries bud, scission and repair cellular membranes. Components of the multi-subunit endosomal sorting complex required for transport (ESCRT) machinery are enlisted when multivesicular bodies are generated, extracellular vesicles are formed, the plasma membrane needs to be repaired, enveloped viruses bud out of host cells, defective nuclear pores have to be cleared, the nuclear envelope must be resealed after mitosis and for final midbody abscission during cytokinesis. While some ESCRT components are only required for specific processes, the assembly of ESCRT-III polymers on target membranes and the action of the AAA-ATPase Vps4 are mandatory for every process. In this review, we summarize the current knowledge of structural and functional features of ESCRT-III/Vps4 assemblies in the growing pantheon of ESCRT-dependent pathways. We describe specific recruitment processes for ESCRT-III to different membranes, which could be useful to selectively inhibit ESCRT function during specific processes, while not affecting other ESCRT-dependent processes. Finally, we speculate how ESCRT-III and Vps4 might function together and highlight how the characterization of their precise spatiotemporal organization will improve our understanding of ESCRT-mediated membrane budding and scission in vivo.


Prevalence, incidence, and age at diagnosis in Marfan Syndrome


title: Multi-Camera Coordination and Control in Surveillance Systems: A Survey

abstract: The use of multiple heterogeneous cameras is becoming more common in today's surveillance systems. In order to perform surveillance tasks, effective coordination and control in multi-camera systems is very important, and is catching significant research attention these days. This survey aims to provide researchers with a state-of-the-art overview of various techniques for multi-camera coordination and control (MC3) that have been adopted in surveillance systems. The existing literature on MC3 is presented through several classifications based on the applicable architectures, frameworks and the associated surveillance tasks. Finally, a discussion on the open problems in surveillance area that can be solved effectively using MC3 and the future directions in MC3 research is presented


title: Variational Autoencoders for Collaborative Filtering

abstract: We extend variational autoencoders (VAEs) to collaborative filtering for implicit feedback. This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference to learn this powerful generative model. Despite widespread use in language modeling and economics, the multinomial likelihood receives less attention in the recommender systems literature. We introduce a different regularization parameter for the learning objective, which proves to be crucial for achieving competitive performance. Remarkably, there is an efficient way to tune the parameter using annealing. The resulting model and learning algorithm has information-theoretic connections to maximum entropy discrimination and the information bottleneck principle. Empirically, we show that the proposed approach significantly outperforms several state-of-the-art baselines, including two recently-proposed neural network approaches, on several real-world datasets. We also provide extended experiments comparing the multinomial likelihood with other commonly used likelihood functions in the latent factor collaborative filtering literature and show favorable results. Finally, we identify the pros and cons of employing a principled Bayesian inference approach and characterize settings where it provides the most significant improvements.


title: An Algorithm for Data Hiding in Radiographic Images and ePHI/R Application

abstract: Telemedicine is the use of Information and Communication Technology (ICT) for clinical health care from a distance. The exchange of radiographic images and electronic patient health information/records (ePHI/R) for diagnostic purposes has the risk of confidentiality, ownership identity, and authenticity. In this paper, a data hiding technique for ePHI/R is proposed. The color information in the cover image is used for key generation, and stego-images are produced with ideal case. As a result, the whole stego-system is perfectly secure. This method includes the features of watermarking and steganography techniques. The method is applied to radiographic images. For the radiographic images, this method resembles watermarking, which is an ePHI/R data system. Experiments show promising results for the application of this method to radiographic images in ePHI/R for both transmission and storage purpose.


PROMOTING POSITIVE YOUTH DEVELOPMENT: THEORETICAL AND EMPIRICAL BASES


title: On dataless hierarchical text classification

abstract: In this paper, we systematically study the problem of dataless hierarchical text classification. Unlike standard text classification schemes that rely on supervised training, dataless classification depends on understanding the labels of the sought after categories and requires no labeled data. Given a collection of text documents and a set of labels, we show that understanding the labels can be used to accurately categorize the documents. This is done by embedding both labels and documents in a semantic space that allows one to compute meaningful semantic similarity between a document and a potential label. We show that this scheme can be used to support accurate multiclass classification without any supervision. We study several semantic representations and show how to improve the classification using boot-strapping. Our results show that bootstrapped dataless classification is competitive with supervised classification with thousands of labeled examples.


title: Benchmarking Ultrawideband Phased Antenna Arrays: Striving for Clearer and More Informative Reporting Practices

abstract: This article presents embedded element design and measurement data for an ultrawideband (UWB) flared-notch (Vivaldi) array. The U.S. Naval Research Laboratory (NRL) built this all-metal dual-polarized phased array specifically as a benchmarking reference for other planar phased-array apertures in the 2-21.2-GHz frequency range. It is suitable for comparative reporting of new array radiator performance, a practice favored by the Navy for effective technology readiness level (TRL) assessment. The 256-port (16 t 16, 7-mm array lattice spacing) slant-linear array is precision machined to ensure consistent agreement between measured performance results and predictions from simulated full-wave models, making the array desirable for benchmarking new array hardware as well as validating computational modeling tools. The optimally sampled aperture has excellent matching and zero scan blindness out to 80d in all scan planes, for grating-lobe-free scans over roughly 2-21.2 GHz. Benchmarking is tailored to highlight cross-polarization (cross-pol) performance in all scan planes, an important metric that should be tested during array radiator design. This data can be made available to design engineers for comparative evaluation of new UWB radiator performance under a cooperative research and development agreement (CRADA). NRL provides this and similar benchmarking data sets (and related measurement services) in the 0.8-75-GHz range.


title: Handbook of Elliptic and Hyperelliptic Curve Cryptography

abstract: Preface Introduction to Public-Key Cryptography Mathematical Background Algebraic Background Background on p-adic Numbers Background on Curves and Jacobians Varieties Over Special Fields Background on Pairings Background on Weil Descent Cohomological Background on Point Counting Elementary Arithmetic Exponentiation Integer Arithmetic Finite Field Arithmetic Arithmetic of p-adic Numbers Arithmetic of Curves Arithmetic of Elliptic Curves Arithmetic of Hyperelliptic Curves Arithmetic of Special Curves Implementation of Pairings Point Counting Point Counting on Elliptic and Hyperelliptic Curves Complex Multiplication Computation of Discrete Logarithms Generic Algorithms for Computing Discrete Logarithms Index Calculus Index Calculus for Hyperelliptic Curves Transfer of Discrete Logarithms Applications Algebraic Realizations of DL Systems Pairing-Based Cryptography Compositeness and Primality Testing-Factoring Realizations of DL Systems Fast Arithmetic Hardware Smart Cards Practical Attacks on Smart Cards Mathematical Countermeasures Against Side-Channel Attacks Random Numbers-Generation and Testing References


title: Collection, exploration and analysis of crowdfunding social networks

abstract: Crowdfunding is a recent financing phenomenon that is gaining wide popularity as a means for startups to raise seed funding for their companies. This paper presents our initial results at understanding this phenomenon using an exploratory data driven approach. We have developed a big data platform for collecting and managing data from multiple sources, including company profiles (CrunchBase and AngelList) and social networks (Facebook and Twitter). We describe our data collection process that allows us to gather data from diverse sources at high throughput. Using Spark as our analysis tool, we study the impact of social engagement on startup fund raising success. We further define novel metrics that allow us to quantify the behavior of investors to follow and make investment decisions as communities rather than individuals. Finally, we explore visualization techniques that allow us to visualize communities of investors that make decisions in a close-knit fashion vs looser communities where investors largely make independent decisions. We conclude with a discussion on our ongoing research on causality analysis and new community detection algorithms.


title: Learning to Predict Streaming Video QoE: Distortions, Rebuffering and Memory

abstract: Mobile streaming video data accounts for a large and increasing percentage of wireless network traffic. The available bandwidths of modern wireless networks are often unstable, leading to difficulties in delivering smooth, high-quality video. Streaming service providers such as Netflix and YouTube attempt to adapt their systems to adjust in response to these bandwidth limitations by changing the video bitrate or, failing that, allowing playback interruptions (rebuffering). Being able to predict end user' quality of experience (QoE) resulting from these adjustments could lead to perceptually-driven network resource allocation strategies that would deliver streaming content of higher quality to clients, while being cost effective for providers. Existing objective QoE models only consider the effects on user QoE of video quality changes or playback interruptions. For streaming applications, adaptive network strategies may involve a combination of dynamic bitrate allocation along with playback interruptions when the available bandwidth reaches a very low value. Towards effectively predicting user QoE, we propose Video Assessment of TemporaL Artifacts and Stalls (Video ATLAS): a machine learning framework where we combine a number of QoE-related features, including objective quality features, rebuffering-aware features and memory-driven features to make QoE predictions. We evaluated our learning-based QoE prediction model on the recently designed LIVE-Netflix Video QoE Database which consists of practical playout patterns, where the videos are afflicted by both quality changes and rebuffering events, and found that it provides improved performance over state-of-the-art video quality metrics while generalizing well on different datasets. The proposed algorithm is made publicly available at this http URL release_v2.rar.


title: Taxonomic perspective of plant species yielding vegetable oils used in cosmetics and skin care products

abstract: A search conducted to determine the plants yielding vegetable oils resulted in 78 plant species with potential use in cosmetics and skin care products. The taxonomic position of these plant species is described with a description of vegetable oils from these plants and their use in cosmetic and skin care products. These species belonged to 74 genera and 45 plant families and yielded 79 vegetable oils. Family Rosaceae had highest number of vegetable oil yielding species (five species). Most of the species were distributed in two families (Anacardiaceae and Asteraceae) containing four species each, followed by seven families (Boraginaceae, Brassicaceae, Clausiaceae, Cucubitaceae, Euphorbiaceae, Fabaceae and Lamaceae) containing three species each of oil yielding plants. Five families (Apiaceae, Dipterocarpaceae, Malvaceae, Rubiaceae and Sapotaceae) have two species each of vegetable oil yielding plants. Two monocotyledonous families Arecaceae and Poaceae contained three species each of oil yielding plants. Remaining 28 vegetable oil yielding species were distributed in 28 plant families, which included two species of gymnosperms distributed in family Cupressaceae and Pinaceae. These vegetable oils are natural and can be used as the base for mixing ones own aromatherapy massage or bath oil, or if preferred can be used as ready blended massage oils or bath oils. Apart from their medicinal value, vegetable oils have heat contents approximately 90% that of diesel fuel and are potential alternate fuel candidates. By combining the skills and efforts of biologists, food scientists, plant breeders and oil companies it may be possible to develop reasonably priced vegetable oils with enhanced levels of functional ingredients. With growing trend of using vegetable oils in food preparation and body and skin care, the health benefits in reducing heart disease and body and skin ailments could be substantial. Key Words: Taxonomy, vegetable oils, uses, cosmetics, skin care products. African Journal of Biotechnology Vol.4(1) 2005: 36-44


title: Entity disambiguation in anonymized graphs using graph kernels

abstract: This paper presents a novel method for entity disambiguation in anonymized graphs using local neighborhood structure. Most existing approaches leverage node information, which might not be available in several contexts due to privacy concerns, or information about the sources of the data. We consider this problem in the supervised setting where we are provided only with a base graph and a set of nodes labelled as ambiguous or unambiguous. We characterize the similarity between two nodes based on their local neighborhood structure using graph kernels; and solve the resulting classification task using SVMs. We give empirical evidence on two real-world datasets, comparing our approach to a state-of-the-art method, highlighting the advantages of our approach. We show that using less information, our method is significantly better in terms of either speed or accuracy or both. We also present extensions of two existing graphs kernels, namely, the direct product kernel and the shortest-path kernel, with significant improvements in accuracy. For the direct product kernel, our extension also provides significant computational benefits. Moreover, we design and implement the algorithms of our method to work in a distributed fashion using the GraphLab framework, ensuring high scalability.


How perceived risk affects online buying


title: Study on nature of inflation and its relationship with GDP growth rate: a Case Study on Bangladesh

abstract: The rapid rise in food prices has been a burden on the poor in developing countries. In many countries such as in Bangladesh, food price inflation is higher than aggregate inflation and contributing to underlying inflationary pressures. Losing the purchasing power and increasing the cost of production indicates the high rate of inflation. Sometimes high inflation has adverse impact on growth through a variety of channels. It will increase uncertainty in investment and withdrawal of saving for consumption support. In south Asia- Bangladesh, Pakistan has experienced double digit inflation rate. However inflation and growth rate called real GDP growth rate have positive and negative relationship depending on situation. Due to supply gap for natural disaster cause rapid growth of inflation rate mainly contributed by food price and regression analysis reflect a positive relationship with inflation and GDP. Inflation fluctuates all the time because of the fluctuation of the money supply. But in recent years, we came to know that international affairs are influencing to increase the inflation rate. So M3 expansion and global price hike also contrite to inflationary pressure. Consistent budget deficit and exchange rate deteriorate the economic growth which directly relates with inflation and also can increase unemployment as a result of high inflation due o increased production cost resources may be reduced. So authority may work on inflation control for the clear future signals.


title: Extractive Summarization using Continuous Vector Space Models

abstract: Automatic summarization can help users extract the most important pieces of information from the vast amount of text digitized into electronic form everyday. Central to automatic summarization is the notion of similarity between sentences in text. In this paper we propose the use of continuous vector representations for semantically aware representations of sentences as a basis for measuring similarity. We evaluate different compositions ::: for sentence representation on a standard dataset using the ROUGE evaluation measures. Our experiments show that the evaluated methods improve the performance of a state-of-the-art summarization framework and strongly indicate the benefits of continuous word vector representations for automatic summarization.


Similarity, Separability, and the Triangle Inequality


title: Single-pixel imaging via compressive sampling

abstract: In this article, the authors present a new approach to building simpler, smaller, and cheaper digital cameras that can operate efficiently across a broader spectral range than conventional silicon-based cameras. The approach fuses a new camera architecture based on a digital micromirror device with the new mathematical theory and algorithms of compressive sampling.


title: On random walk based graph sampling

abstract: Random walk based graph sampling has been recognized as a fundamental technique to collect uniform node samples from a large graph. In this paper, we first present a comprehensive analysis of the drawbacks of three widely-used random walk based graph sampling algorithms, called re-weighted random walk (RW) algorithm, Metropolis-Hastings random walk (MH) algorithm and maximum-degree random walk (MD) algorithm. Then, to address the limitations of these algorithms, we propose two general random walk based algorithms, named rejection-controlled Metropolis-Hastings (RCMH) algorithm and generalized maximum-degree random walk (GMD) algorithm. We show that RCMH balances the tradeoff between the limitations of RW and MH, and GMD balances the tradeoff between the drawbacks of RW and MD. To further improve the performance of our algorithms, we integrate the so-called delayed acceptance technique and the non-backtracking random walk technique into RCMH and GMD respectively. We conduct extensive experiments over four real-world datasets, and the results demonstrate the effectiveness of the proposed algorithms.


title: Physics-based hand interaction with virtual objects

abstract: We propose an intuitive user interaction system with virtual objects by directly using user's hands through a physics simulation. This system aims to provide a more direct way to manipulate and interact with virtual objects and to induce more realistic reactions of the virtual objects in the interaction than the existing methods. The key contribution of this paper is to model the deformations of the user's hands in two ways: global deformation by the hand poses and the local deformation by contacts with objects. By the two deformation models, objects can be manipulated with better stability and robustness without any predefinition of interaction methods depending on the object shapes, by solely using a physics simulation. The experiment shows that the proposed method enables a user to intuitively manipulate virtual object accurately.


title: Revisiting the Description-to-Behavior Fidelity in Android Applications

abstract: Since more than 96% of mobile malware targets on Android platform, various techniques based on static code analysis or dynamic behavior analysis have been proposed to detect malicious applications. As malware is becoming more complicated and stealthy, recent research proposed a promising detection approach that looks for the inconsistency between an application's permissions and its description. In this paper, we revisit this approach and find that using description and permission will lead to many false positives. Therefore, we propose employing app's privacy policy and its bytecode to enhance description and permission for malware detection. It is non-trivial to automatically analyze privacy policy and perform the cross-verification among these four kinds of software artifacts including, privacy policy, bytecode, description, and permissions. We propose a novel data flow model for analyzing privacy policy, and develop a novel system, named TAPVerifier, for carrying out investigation of individual software artifacts and conducting the cross-verification. The experimental results show that TAPVerifier can analyze privacy policy with a high accuracy and recall rate. More importantly, integrating privacy policy and code level information removes 8.1%-65.5% false positives of existing systems based on description and permission.


High Power Outphasing Modulation


title: Robust Visual Place Recognition with Graph Kernels

abstract: A novel method for visual place recognition is introduced and evaluated, demonstrating robustness to perceptual aliasing and observation noise. This is achieved by increasing discrimination through a more structured representation of visual observations. Estimation of observation likelihoods are based on graph kernel formulations, utilizing both the structural and visual information encoded in covisibility graphs. The proposed probabilistic model is able to circumvent the typically difficult and expensive posterior normalization procedure by exploiting the information available in visual observations. Furthermore, the place recognition complexity is independent of the size of the map. Results show improvements over the state-of-theart on a diverse set of both public datasets and novel experiments, highlighting the benefit of the approach.


title: Comorbid depression, chronic pain, and disability in primary care

abstract: Objectives: The objectives of this study were to provide estimates of the prevalence and strength of association between major depression and chronic pain in a primary care population and to examine the clinical burden associated with the two conditions, singly and together. Methods: A random sample of Kaiser Permanente patients who visited a primary care clinic was mailed a questionnaire assessing major depressive disorder (MDD), chronic pain, pain-related disability, somatic symptom severity, panic disorder, other anxiety, probable alcohol abuse, and health-related quality of life (HRQL). Instruments included the Patient Health Questionnaire, SF-8, and Graded Chronic Pain Questionnaire. A total of 5808 patients responded (54% of those eligible to participate). Results: Among those with MDD, a significantly higher proportion reported chronic (i.e., nondisabling or disabling) pain than those without MDD (66% versus 43%, respectively). Disabling chronic pain was present in 41% of those with MDD versus 10% of those without MDD. Respondents with comorbid depression and disabling chronic pain had significantly poorer HRQL, greater somatic symptom severity, and higher prevalence of panic disorder than other respondents. The prevalence of probable alcohol abuse/dependence was significantly higher among persons with MDD compared with individuals without MDD regardless of pain or disability level. Compared with participants without MDD, the prevalence of other anxiety among those with MDD was more than sixfold greater regardless of pain or disability level. Conclusions: Chronic pain is common among those with MDD. Comorbid MDD and disabling chronic pain are associated with greater clinical burden than MDD alone. Key words: major depression, chronic pain, comorbidity, disability, epidemiologic comorbidity. MDD major depressive disorder; HRQL health-related quality of life; HMO health maintenance organization; PHQ Patient Health Questionnaire; GCPS Graded Chronic Pain Scale; CP chronic pain; DCP disabling chronic pain; GAD generalized anxiety disorder; SCID Structured Clinical Interview for DSM-III-R; PRIME-MD Primary Care Evaluation of Mental Disorders; CI confidence interval; DSM-IV Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition; DSM-III-R Diagnostic and Statistical Manual of Mental Disorders, Third Edition Revised.


title: Multi-label classification for colon cancer using histopathological images

abstract: Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.


title: Subconscious Crowdsourcing: A feasible data collection mechanism for mental disorder detection on social media

abstract: Mental disorders are currently affecting millions of people from different cultures, age groups and geographic regions. The challenge of mental disorders is that they are difficult to detect on suffering patients, thus presenting an alarming number of undetected cases and misdiagnosis. In this paper, we aim at building predictive models that leverage language and behavioral patterns, used particularly in social media, to determine whether a user is suffering from two cases of mental disorder. These predictive models are made possible by employing a novel data collection process, coined as Subconscious Crowdsourcing, which helps to collect a faster and more reliable dataset of patients. Our experiments suggest that extracting specific language patterns and social interaction features from reliable patient datasets can greatly contribute to further analysis and detection of mental disorders.


title: Baidu driving dataset and end-to-end reactive control model

abstract: End-to-end autonomous driving system has obtained great progress recently. In this paper, we will introduce our open source dataset: Baidu Driving Dataset(BDD), and our end-to-end reactive control model trained on BDD. The BDD comes from Baidu street view project, which generates millions of kilometers driving data every year. Among them, we publish 10000 kilometers driving data for end-to-end autonomous driving research. The BDD consists of two parts: forward images and vehicle motion attitude. The vehicle motion attitude is derived from real time kinematic GPS location data with standard deviation of 3 centimeters. Our reactive control model consists of lateral control and longitudinal control. We employ curvature instead of steering angle for lateral control, and leverage acceleration, not throttle or brake, for longitudinal control. CNN network is employed for lateral control model, mapping a single image from forward camera directly to corresponding curvature. For longitudinal control, stacked convolutional LSTM is used to extract spatial and temporal features from a sequence of frames, and to map the features with longitudinal control commands. The demo and data are in http://roadhackers.baidu.com. To the best of our knowledge, it is the first time that both lateral and longitudinal control are implemented in an end-to-end style.


Unsupervised Neural Categorization for Scientific Publications


title: A CONTOUR TREE BASED VISUALIZATION FOR EXPLORING DATA WITH UNCERTAINTY

abstract: Uncertainty is a common and crucial issue in scientific data. The exploration and analysis of three-dimensional (3D) and large two-dimensional (2D) data with uncertainty information demand an effective visualization augmented with both user interaction and relevant context. The contour tree has been exploited as an efficient data structure to guide exploratory visualization. This paper proposes an interactive visualization tool for exploring data with quantitative uncertainty representations. First, we introduce a balanced planar hierarchical contour tree layout integrated with tree view interaction, allowing users to quickly navigate between levels of detail for contours of large data. Further, uncertainty information is attached to a planar contour tree layout to avoid the visual cluttering and occlusion in viewing uncertainty in 3D data or large 2D data. For the first time, the uncertainty information is explored as a combination of the data-level uncertainty which represents the uncertainty concerning the numerical values of the data, the contour variability which quantifies the positional variation of contours, and the topology variability which reveals the topological variation of contour trees. This information provides a new insight into how the uncertainty exists with and relates to the features of the data. The experimental results show that this new visualization facilitates a quick and accurate selection of prominent contours with high or low uncertainty and variability.


title: The real time hardware of Smart Medicine Dispenser to Reduce the Adverse Drugs Reactions

abstract: In this paper we present a proof-of-concept for the solution of a problem of Adverse Drug Reactions (ADRs) due to human error. ADRs due to carelessness or human mistakes is very common in elder people or in people who are administered with multiple prescriptions for the day, but they face difficulty in tracking their medications over time. This paper deals with the development of a Smart Medicine Dispenser to manage the very real problem of patients confusing the dosage and timing of their medications, particularly vulnerable patients or patients on numerous medications. We have devised a device named SMART MEDICINE DISPENSER (SMD) which will contain all the prescribed doses for a fortnight or a month and will dispense them at accurate timings. This device will monitor the patient's medication habits and will also reduce the risk of overdose or taking the wrong medication. We have used the basic yet brilliant concept of electromagnetism in our design with compare to exiting product, and the components used are also easily available making SMD very inexpensive as compared to the current solutions in the market.


title: CityPersons: A Diverse Dataset for Pedestrian Detection

abstract: Convnets have enabled significant progress in pedestrian detection recently, but there are still open questions regarding suitable architectures and training data. We revisit CNN design and point out key adaptations, enabling plain FasterRCNN to obtain state-of-the-art results on the Caltech dataset. To achieve further improvement from more and better data, we introduce CityPersons, a new set of person annotations on top of the Cityscapes dataset. The diversity of CityPersons allows us for the first time to train one single CNN model that generalizes well over multiple benchmarks. Moreover, with additional training with CityPersons, we obtain top results using FasterRCNN on Caltech, improving especially for more difficult cases (heavy occlusion and small scale) and providing higher localization quality.


title: Scholarly paper recommendation via user's recent research interests

abstract: We examine the effect of modeling a researcher's past works in recommending scholarly papers to the researcher. Our hypothesis is that an author's published works constitute a clean signal of the latent interests of a researcher. A key part of our model is to enhance the profile derived directly from past works with information coming from the past works' referenced papers as well as papers that cite the work. In our experiments, we differentiate between junior researchers that have only published one paper and senior researchers that have multiple publications. We show that filtering these sources of information is advantageous -- when we additionally prune noisy citations, referenced papers and publication history, we achieve statistically significant higher levels of recommendation accuracy.


title: Facial Landmark Detection with Tweaked Convolutional Neural Networks

abstract: We present a novel convolutional neural network (CNN) design for facial landmark coordinate regression. We examine the intermediate features of a standard CNN trained for landmark detection and show that features extracted from later, more specialized layers capture rough landmark locations. This provides a natural means of applying differential treatment midway through the network, tweaking processing based on facial alignment. The resulting Tweaked CNN model (TCNN) harnesses the robustness of CNNs for landmark detection, in an appearance-sensitive manner without training multi-part or multi-scale models. Our results on standard face landmark detection and face verification benchmarks show TCNN to surpasses previously published performances by wide margins.


title: Complex Gated Recurrent Neural Networks

abstract: Complex numbers have long been favoured for digital signal processing, yet complex representations rarely appear in deep learning architectures. RNNs, widely used to process time series and sequence information, could greatly benefit from complex representations. We present a novel complex gated recurrent cell, which is a hybrid cell combining complex-valued and norm-preserving state transitions with a gating mechanism. The resulting RNN exhibits excellent stability and convergence properties and performs competitively on the synthetic memory and adding task, as well as on the real-world tasks of human motion prediction.


title: Time series analysis of temporal networks

abstract: A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks.


title: Computer vision elastography: speckle adaptive motion estimation for elastography using ultrasound sequences

abstract: We present the development and validation of an image based speckle tracking methodology, for determining temporal two-dimensional (2-D) axial and lateral displacement and strain fields from ultrasound video streams. We refine a multiple scale region matching approach incorporating novel solutions to known speckle tracking problems. Key contributions include automatic similarity measure selection to adapt to varying speckle density, quantifying trajectory fields, and spatiotemporal elastograms. Results are validated using tissue mimicking phantoms and in vitro data, before applying them to in vivo musculoskeletal ultrasound sequences. The method presented has the potential to improve clinical knowledge of tendon pathology from carpel tunnel syndrome, inflammation from implants, sport injuries, and many others.


title: A Phylogenetic Analysis of the Brassicales Clade Based on an Alignment-Free Sequence Comparison Method

abstract: Phylogenetic analyses reveal the evolutionary derivation of species. A phylogenetic tree can be inferred from multiple sequence alignments of proteins or genes. The alignment of whole genome sequences of higher eukaryotes is a computational intensive and ambitious task as is the computation of phylogenetic trees based on these alignments. To overcome these limitations, we here used an alignment-free method to compare genomes of the Brassicales clade. For each nucleotide sequence a Chaos Game Representation (CGR) can be computed, which represents each nucleotide of the sequence as a point in a square defined by the four nucleotides as vertices. Each CGR is therefore a unique fingerprint of the underlying sequence. If the CGRs are divided by grid lines each grid square denotes the occurrence of oligonucleotides of a specific length in the sequence (Frequency Chaos Game Representation, FCGR). Here, we used distance measures between FCGRs to infer phylogenetic trees of Brassicales species. Three types of data were analyzed because of their different characteristics: (A) Whole genome assemblies as far as available for species belonging to the Malvidae taxon. (B) EST data of species of the Brassicales clade. (C) Mitochondrial genomes of the Rosids branch, a supergroup of the Malvidae. The trees reconstructed based on the Euclidean distance method are in general agreement with single gene trees. The Fitch-Margoliash and Neighbor joining algorithms resulted in similar to identical trees. Here, for the first time we have applied the bootstrap re-sampling concept to trees based on FCGRs to determine the support of the branchings. FCGRs have the advantage that they are fast to calculate, and can be used as additional information to alignment based data and morphological characteristics to improve the phylogenetic classification of species in ambiguous cases.


title: Cancer-related fatigue—mechanisms, risk factors, and treatments

abstract: Fatigue is one of the most common adverse effects of cancer that might persist for years after treatment completion in otherwise healthy survivors. Cancer-related fatigue causes disruption in all aspects of quality of life and might be a risk factor of reduced survival. The prevalence and course of fatigue in patients with cancer have been well characterized and there is growing understanding of the underlying biological mechanisms. Inflammation seems to have a key role in fatigue before, during, and after cancer-treatment. However, there is a considerable variability in the presentation of cancer-related fatigue, much of which is not explained by disease-related or treatment-related characteristics, suggesting that host factors might be important in the development and persistence of this symptom. Indeed, longitudinal studies have identified genetic, biological, psychosocial, and behavioural risk factors associated with cancer-related fatigue. Although no current gold-standard treatment for fatigue is available, a variety of intervention approaches have shown beneficial effects in randomized controlled trials, including physical activity, psychosocial, mind-body, and pharmacological treatments. This Review describes the mechanisms, risk factors, and possible interventions for cancer-related fatigue, focusing on recent longitudinal studies and randomized trials that have targeted fatigued patients.


title: Implementation of Stereo Vision Semi-Global Block Matching Methods for Distance Measurement

abstract: Stereo vision has become an attractive topic research in the last decades. Many implementations such as the autonomous car, 3D movie, 3D object generation, are produced using this technique. The advantages of using two cameras in stereo vision are the disparity map between images. Disparity map will produce distance estimation of the object. Distance measurement is a crucial parameter for an autonomous car. The distance between corresponding points between the left and right images must be precisely measured to get an accurate distance. One of the most challenging in stereo vision is to find corresponding points between left and right images (stereo matching). This paper proposed distance measurement using stereo vision using Semi-Global Block Matching algorithm for stereo matching purpose. The object is captured using a calibrated stereo camera. The images pair then optimized using WLS Filter to reduce noises. The implementation results of this algorithm are furthermore converted to a metric unit for distance measurement. The result shows that the stereo vision distance measurement using Semi-Global Block Matching gives a good result. The obtained best result of this work contains error of less than 1% for 1m distance


title: Power Control for D2D Underlay Cellular Networks with Imperfect CSI

abstract: Device-to-Device communications underlying the cellular infrastructure is a technology that has recently been proposed as a promising solution to enhance cellular network capabilities. However, interference is the major challenge since the same resources are shared by both systems. Therefore, interference management techniques are required to keep the interference under control. In this work, in order to mitigate interference, we consider centralized and distributed power control algorithms in a one-cell random network model. Differently from previous works, we are assuming that the channel state information may be imperfect and include estimation errors. We evaluate how this uncertainty impacts performances. In the centralized approach, we derive the optimal powers that maximize the coverage probability and the rate of the cellular user while scheduling as many D2D links as possible. These powers are computed at the base station (BS) and then delivered to the users, and hence the name "centralized". For the distributed method, the on-off power control and the truncated channel inversion are proposed. Expressions of coverage probabilities are established in function of D2D links intensity, pathloss exponent and estimation error variance.


title: Long-term memory, sleep, and the spacing effect.

abstract: Many studies have shown that memory is enhanced when study sessions are spaced apart rather than massed. This spacing effect has been shown to have a lasting benefit to long-term memory when the study phase session follows the encoding session by 24 hours. Using a spacing paradigm we examined the impact of sleep and spacing gaps on long-term declarative memory for Swahili–English word pairs by including four spacing delay gaps (massed, 12 hours same-day, 12 hours overnight, and 24 hours). Results showed that a 12-hour spacing gap that includes sleep promotes long-term memory retention similar to the 24-hour gap. The findings support the importance of sleep to the long-term benefit of the spacing effect.


title: Humanoid robot indoor navigation based on 2D bar codes: application to the NAO robot

abstract: In this paper, we put forward a method for humanoid robot indoor localization and navigation, using 2D bar codes, running on embedded hardware. Our approach is based on camera image processing and the detection of 2D bar codes stuck to the walls. The particularities of our approach are: it works without odometry, fast localization (≈1s) that allows for kidnapping and falls, and it does not require the manual edition of a map (thus no extra computer is needed).


title: Monitoring the Application-Layer DDoS Attacks for Popular Websites

abstract: Distributed denial of service (DDoS) attack is a continuous critical threat to the Internet. Derived from the low layers, new application-layer-based DDoS attacks utilizing legitimate HTTP requests to overwhelm victim resources are more undetectable. The case may be more serious when such attacks mimic or occur during the flash crowd event of a popular Website. Focusing on the detection for such new DDoS attacks, a scheme based on document popularity is introduced. An Access Matrix is defined to capture the spatial-temporal patterns of a normal flash crowd. Principal component analysis and independent component analysis are applied to abstract the multidimensional Access Matrix. A novel anomaly detector based on hidden semi-Markov model is proposed to describe the dynamics of Access Matrix and to detect the attacks. The entropy of document popularity fitting to the model is used to detect the potential application-layer DDoS attacks. Numerical results based on real Web traffic data are presented to demonstrate the effectiveness of the proposed method.


Author Obfuscation: Attacking the State of the Art in Authorship Verification


title: Model-based data-driven approach for sleep apnea detection

abstract: Obstructive sleep apnea (OSA) is a serious sleep disorder affecting millions of people worldwide. There is a great need to develop an efficient, low-cost OSA detection method. Traditional OSA detection methods are purely data-driven and hence their detection performance greatly depends on the quality and quantity of the sensor data. Several mathematical models of the human cardiorespiratory system exist which can generate different physiological signals that are hard to measure using current sensor technology. In this paper, we propose a new framework for OSA detection in which we fuse the sensor data with the physiological signal data from the cardiorespiratory system models. Multivariate Gaussian processes (GPs) are used to capture and model the physiological signal variations among different individuals. We define the multivariate GP covariance function using the sum of separable kernel functions form and estimate the corresponding hyperparameters by maximizing the GP marginal likelihood function. We detect OSA using the heart rate signal on a window-by-window basis using a likelihood ratio test. We conduct several experiments on both simulated and real data to show the effectiveness of the proposed OSA detection framework. We also compare with other purely data-driven OSA detection methods to demonstrate the advantage of the proposed OSA detection fusion framework.


title: Buildings, Beauty, and the Brain: A Neuroscience of Architectural Experience

abstract: A burgeoning interest in the intersection of neuroscience and architecture promises to offer biologically inspired insights into the design of spaces. The goal of such interdisciplinary approaches to architecture is to motivate construction of environments that would contribute to peoples' flourishing in behavior, health, and well-being. We suggest that this nascent field of neuroarchitecture is at a pivotal point in which neuroscience and architecture are poised to extend to a neuroscience of architecture. In such a research program, architectural experiences themselves are the target of neuroscientific inquiry. Here, we draw lessons from recent developments in neuroaesthetics to suggest how neuroarchitecture might mature into an experimental science. We review the extant literature and offer an initial framework from which to contextualize such research. Finally, we outline theoretical and technical challenges that lie ahead.


title: Learning Neurosymbolic Generative Models via Program Synthesis

abstract: Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can't easily reproduce these structures. We propose to address this problem by incorporating programs representing global structure into the generative model---e.g., a 2D for-loop may represent a configuration of windows. Furthermore, we propose a framework for learning these models by leveraging program synthesis to generate training data. On both synthetic and real-world data, we demonstrate that our approach is substantially better than the state-of-the-art at both generating and completing images that contain global structure.


title: Fe-Doped CoP Nanoarray: A Monolithic Multifunctional Catalyst for Highly Efficient Hydrogen Generation

abstract: An Fe-doped CoP nanoarray behaves as a robust 3D monolithic multifunctional catalyst for electrolytic and hydrolytic hydrogen evolution with high activity. Its two-electrode electrolyzer needs a cell voltage of only 1.60 V for 10 mA cm-2 water-splitting current. It also catalyzes effectively NaBH4 hydrolysis with a low activation energy of ≈39.6 kJ mol-1 and a hydrogen generation rate of 6.06 L min-1 g-1 .


From Customer Relationship Management ( CRM ) to Supplier Relationship Management ( SRM )


Losses, Gains, and Hyperbolic Discounting: An Experimental Approach to Information Security Attitudes and Behavior


title: A fast accumulative motion orientation model based on integral image for video smoke detection

abstract: Video smoke detection has many advantages over traditional methods, such as fast response, non-contact, and so on. But most of video smoke detection systems usually have high false alarms. In order to improve the performance of video smoke detection, we propose an accumulative motion model based on the integral image by fast estimating the motion orientation of smoke. But the estimation is not very precise due to block sum. Not very accurate estimation will affect the subsequent decision. To reduce this influence, the accumulation of the orientation over time is performed to compensate results for the inaccuracy of orientation. The model is able to mostly eliminate the disturbance of artificial lights and non-smoke moving objects by using the accumulation of motion. The model together with chrominance detection can correctly detect the existence of smoke. Experimental results show that our algorithm has good robustness for smoke detection.


title: Robot-assisted Therapy in Stroke Rehabilitation

abstract: Research into rehabilitation robotics has grown rapidly and the number of therapeutic rehabilitation robots has expanded dramatically during the last two decades. Robotic rehabilitation therapy can deliver high-dosage and high-intensity training, making it useful for patients with motor disorders caused by stroke or spinal cord disease. Robotic devices used for motor rehabilitation include end-effector and exoskeleton types; herein, we review the clinical use of both types. One application of robot-assisted therapy is improvement of gait function in patients with stroke. Both end-effector and the exoskeleton devices have proven to be effective complements to conventional physiotherapy in patients with subacute stroke, but there is no clear evidence that robotic gait training is superior to conventional physiotherapy in patients with chronic stroke or when delivered alone. In another application, upper limb motor function training in patients recovering from stroke, robot-assisted therapy was comparable or superior to conventional therapy in patients with subacute stroke. With end-effector devices, the intensity of therapy was the most important determinant of upper limb motor recovery. However, there is insufficient evidence for the use of exoskeleton devices for upper limb motor function in patients with stroke. For rehabilitation of hand motor function, either end-effector and exoskeleton devices showed similar or additive effects relative to conventional therapy in patients with chronic stroke. The present evidence supports the use of robot-assisted therapy for improving motor function in stroke patients as an additional therapeutic intervention in combination with the conventional rehabilitation therapies. Nevertheless, there will be substantial opportunities for technical development in near future.


title: The Music Ontology

abstract: In this paper, we overview some Semantic Web technologies and describe the Music Ontology: a formal framework for dealing with music-related information on the Semantic Web, including editorial, cultural and acoustic information. We detail how this ontology can act as a grounding for more domain-specific knowledge representation. In addition, we describe current projects involving the Music Ontology and interlinked repositories of musicrelated knowledge.


title: Combatting rendering latency

abstract: Latency or lag in an interactive graphics system is the delay between user input and displayed output. We have found latency and the apparent bobbing and swimming of objects that it produces to be a serious problem for head-mounted display (HMD) and augmented reality applications. At UNC, we have been investigating a number of ways to reduce latency; we present two of these. Slats is an experimental rendering system for our Pixel-Planes 5 graphics machine guaranteeing a constant single NTSC field of latency. This guaranteed response is especially important for predictive tracking. Just-in-time pixels is an attempt to compensate for rendering latency by rendering the pixels in a scanned display based on their position in the scan.


title: Empirical Studies of Pair Programming for CS/SE Teaching in Higher Education: A Systematic Literature Review

abstract: The objective of this paper is to present the current evidence relative to the effectiveness of pair programming (PP) as a pedagogical tool in higher education CS/SE courses. We performed a systematic literature review (SLR) of empirical studies that investigated factors affecting the effectiveness of PP for CS/SE students and studies that measured the effectiveness of PP for CS/SE students. Seventy-four papers were used in our synthesis of evidence, and 14 compatibility factors that can potentially affect PP's effectiveness as a pedagogical tool were identified. Results showed that students' skill level was the factor that affected PP's effectiveness the most. The most common measure used to gauge PP's effectiveness was time spent on programming. In addition, students' satisfaction when using PP was overall higher than when working solo. Our meta-analyses showed that PP was effective in improving students' grades on assignments. Finally, in the studies that used quality as a measure of effectiveness, the number of test cases succeeded, academic performance, and expert opinion were the quality measures mostly applied. The results of this SLR show two clear gaps in this research field: 1) a lack of studies focusing on pair compatibility factors aimed at making PP an effective pedagogical tool and 2) a lack of studies investigating PP for software design/modeling tasks in conjunction with programming tasks.


title: Confocal absorption spectral imaging of MoS2: optical transitions depending on the atomic thickness of intrinsic and chemically doped MoS2.

abstract: We performed a nanoscale confocal absorption spectral imaging to obtain the full absorption spectra (over the range 1.5–3.2 eV) within regions having different numbers of layers and studied the variation of optical transition depending on the atomic thickness of the MoS2 film. Three distinct absorption bands corresponding to A and B excitons and a high-energy background (BG) peak at 2.84 eV displayed a gradual redshift as the MoS2 film thickness increased from the monolayer, to the bilayer, to the bulk MoS2 and this shift was attributed to the reduction of the gap energy in the Brillouin zone at the K-point as the atomic thickness increased. We also performed n-type chemical doping of MoS2 films using reduced benzyl viologen (BV) and the confocal absorption spectra modified by the doping showed a strong dependence on the atomic thickness: A and B exciton peaks were greatly quenched in the monolayer MoS2 while much less effect was shown in larger thickness and the BG peak either showed very small quenching for 1 L MoS2 or remained constant for larger thicknesses. Our results indicate that confocal absorption spectral imaging can provide comprehensive information on optical transitions of microscopic size intrinsic and doped two-dimensional layered materials.


title: On the Economic Significance of Ransomware Campaigns: A Bitcoin Transactions Perspective

abstract: Bitcoin cryptocurrency system enables users to transact securely and pseudo-anonymously by using an arbitrary number of aliases (Bitcoin addresses). Cybercriminals exploit these characteristics to commit immutable and presumably untraceable monetary fraud, especially via ransomware; a type of malware that encrypts files of the infected system and demands ransom for decryption. In this paper, we present our comprehensive study on all recent ransomware and report the economic impact of such ransomware from the Bitcoin payment perspective. We also present a lightweight framework to identify, collect, and analyze Bitcoin addresses managed by the same user or group of users (cybercriminals, in this case), which includes a novel approach for classifying a payment as ransom. To verify the correctness of our framework, we compared our findings on CryptoLocker ransomware with the results presented in the literature. Our results align with the results found in the previous works except for the final valuation in USD. The reason for this discrepancy is that we used the average Bitcoin price on the day of each ransom payment whereas the authors of the previous studies used the Bitcoin price on the day of their evaluation. Furthermore, for each investigated ransomware, we provide a holistic view of its genesis, development, the process of infection and execution, and characteristic of ransom demands. Finally, we also release our dataset that contains a detailed transaction history of all the Bitcoin addresses we identified for each ransomware.


title: The ImageCLEF 2013 Plant Identification Task

abstract: The ImageCLEF's plant identification task provides a testbed for a system-oriented evaluation of plant identification about 250 species trees and herbaceous plants based on detailed views of leaves, flowers, fruits, stems and bark or some entire views of the plants. Two types of image content are considered: SheetAsBackgroud which contains only leaves in a front of a generally white uniform background, and NaturalBackground which contains the 5 kinds of detailed views with unconstrained conditions, directly photographed on the plant. The main originality of this data is that it was specifically built through a citizen sciences initiative conducted by Tela Botanica, a French social network of amateur and expert botanists. This makes the task closer to the conditions of a real-world application. This overview presents more precisely the resources and assessments of task, summarizes the retrieval approaches employed by the participating groups, and provides an analysis of the main evaluation results. With a total of twelve groups from nine countries and with a total of thirty three runs submitted, involving distinct and original methods, this third year task confirms Image Retrieval community interest for biodiversity and botany, and highlights further challenging studies in plant identification.


title: The human body and millimeter-wave wireless communication systems: Interactions and implications

abstract: With increasing interest in millimeter-wave wireless communications, investigations on interactions between the human body and millimeter-wave devices are becoming important. This paper gives examples of today's regulatory requirements, and provides an example for a 60 GHz transceiver. Also, the propagation characteristics of millimeter-waves in the presence of the human body are studied, and four models representing different body parts are considered to evaluate thermal effects of millimeter-wave radiation on the body. Simulation results show that about 34% to 42% of the incident power is reflected at the skin surface at 60 GHz. This paper shows that power density is not suitable to determine exposure compliance when millimeter wave devices are used very close to the body. A temperature-based technique for the evaluation of safety compliance is proposed in this paper.


title: A Cyber Physical Interface for Automation Systems—Methodology and Examples

abstract: Cyber physical systems (CPS) in a manufacturing and automation context can be referred to different manufacturing process, including design, simulation, control, and verification. However, for data analytics, the concept of CPS is relatively new, and a standard methodology is lacking on how to incorporate this type of interface for automation applications. This study discusses a modeling methodology for a cyber physical interface and presents the five levels of information for a cyber physical system, that range from the data connection level to the system configuration level. In order to achieve this awareness and health state of the machine and system, a technical approach that uses adaptive health monitoring algorithms is presented. Lastly, an experimental study on a machine tool ball screw is highlighted, in which a predictive model and a cyber physical interface is developed for this application. The outcomes from this study demonstrate that machine health state awareness is feasible, and the core technologies can aim mechanical systems systematically develop its CPS. This can lead to additional product revenue for the manufacturers, and also a potential competitive edge in the market place.


title: Brain structure and dynamics across scales: in search of rules

abstract: Louis Henry Sullivan, the father of skyscrapers, famously stated ‘Form ever follows function’. In this short review, we will focus on the relationship between form (structure) and function (dynamics) in the brain. We summarize recent advances on the quantification of directed- and weighted-mesoscopic connectivity of mammalian cortex, the exponential distance rule for mesoscopic and microscopic circuit wiring, a spatially embedded random model of inter-areal cortical networks, and a large-scale dynamical circuit model of money's cortex that gives rise to a hierarchy of timescales. These findings demonstrate that inter-areal cortical networks are dense (hence such concepts as ‘small-world’ need to be refined when applied to the brain), spatially dependent (therefore purely topological approach of graph theory has limited applicability) and heterogeneous (consequently cortical areas cannot be treated as identical ‘nodes’).


title: Integer Linear Programming for Discourse Parsing

abstract: In this paper we present the first, to the best of our knowledge, discourse parser that is able to predict non-tree DAG structures. We use Integer Linear Programming (ILP) to encode both the objective function and the constraints as global decoding over local scores. Our underlying data come from multi-party chat dialogues, which require the prediction of DAGs. We use the dependency parsing paradigm, as has been done in the past (Muller et al., 2012; Li et al., 2014; Afantenos et al., 2015), but we use the underlying formal framework of SDRT and exploit SDRT's notions of left and right distributive relations. We achieve an F-measure of 0.531 for fully labeled structures which beats the previous state of the art.


title: Complete genome sequence of Enterococcus durans KLDS6.0933, a potential probiotic strain with high cholesterol removal ability

abstract: Background ::: Enterococci are commensal bacteria in the mammalian gastrointestinal tract which play an important role in the production of various fermented foods. Thus, certain enterococcal strains are commonly used as probiotics to confer health benefits to human and animals. Enterococcus durans KLDS6.0933 is a potential probiotic strain with high cholesterol removal ability, which was isolated from traditional naturally fermented cream in Inner Mongolia of China. To better understand the genetic basis of the probiotic properties of this strain, the whole-genome sequence was performed using the PacBio RSII platform.


title: The pharmacodynamic effect of sirolimus: Individual variation of cytokine mRNA expression profiles in human whole blood samples

abstract: Abstract Sirolimus (SRL) has become an important alternative to calcineurin inhibitors due to its unique mechanism of action. Since rejection and poor graft outcome are still frequent problems despite therapeutic-range blood concentrations, pharmacodynamic measurements of its immunosuppressive effects would be of great clinical value to optimize treatment in individual patients. We performed a human whole blood assay using real time cytokine RT-PCR for the pharmacodynamic assessment of SRL. IL-2, IL-4 and IL-6 mRNA levels were quantitatively determined upon T-cell-specific stimulation in healthy individuals ( n =11; in vitro ) and in kidney-transplant patients ( n =3; ex vivo ). Furthermore, IL-2 protein secretion and T-cell proliferation was measured. After 24 h incubation we observed a stronger suppression of IL-2 and IL-4 mRNA expression upon SRL addition ( p p p p in vitro and ex vivo effects. We describe an individual degree of SRL-sensitivity that may correlate with clinical efficacy. Rather than analysis of one single peak, we suggest determination of two absolute cytokine mRNA peak levels for the pharmacodynamic assessment of SRL. However, prospective clinical studies are necessary to determine whether individual degrees of SRL-sensitivity correlate with clinical outcome.


title: Captcha as Graphical Passwords—A New Security Primitive Based on Hard AI Problems

abstract: Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. In this paper, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.


title: A novel one-step triggered “signal-on/off” electrochemical sensing platform for lead based on the dual-signal ratiometric output and electrode-bound DNAzyme assembly

abstract: • A novel one-step triggered dual-signal ratiometric electrochemical Pb 2+ biosensor is developed based on the electrode-bound DNAzyme assembly.


title: Development of mobile application for autistic children using augmentative and alternative communication technique

abstract: Communication is essentially a perfect approach to start and end everyday life routines. It is exceedingly critical to maintain one’s life ::: whether it is verbal or nonverbal communication. As the number of autistic children is increasing, the children experience in having problems in communication. Furthermore, there is a lack of mobile learning application in Malay on speech and language.The objective of this paper includes reporting on the development and evaluation of user acceptance of an Android based application using Augmentative and Alternative Communication ::: technique.Suara Saya is a developed mobile application which will act as a learning-based and communication-based application.Rapid Application ::: Development methodology has been applied in the development.The tools used include Android Studio, Unified Modeling Language (UML) and Adobe.User acceptance testing has been conducted to 13 participants which consist of speech therapists, teachers and parents.A survey has been conducted ::: after they have gone through the application. Results have shown that the teachers and parents are willing to use the app for their children and ::: they believed that the usage of mobile phones will benefit children with autism.


title: Bangladeshi road sign recognition based on DtBs vector and artificial neural network

abstract: Road sign recognition from road image is a crucial area of intelligent transportation system which role is to raise awareness of drivers, pedestrians and automated driving system. In this regard, a framework has been proposed in this paper for recognizing Bangladeshi road sign (BRS). For detecting the road sign (RS), two natural properties of a BRS is utilized, they are — border color rim and shape of the RS. Secondly distance to borders (DtBs) vector is used for shape verification of BRS. For recognizing those candidate regions artificial neural network (ANN) has performed. Based on these ideas initially, YCbCr color model is used to eliminate the illumination sensitiveness and statistical threshold value is used for color segmentation. Next, labeling and filtering is used to extract the shapes of candidate region. DtBs vector is used to verify the region of interest (ROI) of BRS. Affine transformation is used for triangular and quadrangular region to avoid sheared road sign (RS) blob. Finally, ANN is used to recognize the BRS blob. Various road signs are utilized to test the proposed framework under various conditions and results are presented to verify its efficiency.


From Efficient Markets Theory to Behavioral Finance


title: Evaluating Fairness Metrics in the Presence of Dataset Bias

abstract: Data-driven algorithms play a large role in decision making across a variety of industries. Increasingly, these algorithms are being used to make decisions that have significant ramifications for people's social and economic well-being, e.g. in sentencing, loan approval, and policing. Amid the proliferation of such systems there is a growing concern about their potential discriminatory impact. In particular, machine learning systems which are trained on biased data have the potential to learn and perpetuate those biases. A central challenge for practitioners is thus to determine whether their models display discriminatory bias. Here we present a case study in which we frame the issue of bias detection as a causal inference problem with observational data. We enumerate two main causes of bias, sampling bias and label bias, and we investigate the abilities of six different fairness metrics to detect each bias type. Based on these investigations, we propose a set of best practice guidelines to select the fairness metric that is most likely to detect bias if it is present. Additionally, we aim to identify the conditions in which certain fairness metrics may fail to detect bias and instead give practitioners a false belief that their biased model is making fair decisions.


title: Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing

abstract: Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network provides a principled, practical way to train an RL agent. As an application, on 10 different ATARI games, we demonstrate real-time end-to-end game playing on low-power neuromorphic hardware by converting a sequence of game frames into discrete actions.


title: Mani-Pull-Action: Hand-based Digital Puppetry

abstract: The human hand dexterity provides a natural and rich interface for computer interaction. Technologies for tracking hand and finger motion are now accessible and can be used with a minimum computation power for the manipulation of virtual puppets in real-time. However creating an intuitive hand-based motion con- trol interface presents problems such as, mapping the hand to an object that demands more degrees of freedom or to assign gestures that are difficult to memorize or to execute. We propose an ergonomic hand- mapping model for digital puppetry, based on the human hand anatomy and biomechanics, adapting tradi- tional puppetry methods. A cinematic virtual puppetry application was developed supporting distinct in- teraction styles based on the hand dexterity skills. An experiment using the Leap Motion controller was conducted to evaluate the hand mapping feasibility. The participants considered that the proposed interface provides a good level of directness. Furthermore, a custom interface for smartphones was combined with the application to extend the puppet manipulation and show control operations.


title: An isolated three-phase three-switch buck rectifier with inrush current limiting

abstract: In this paper a single stage structure is proposed for realizing a three-phase unity power factor rectifier. This topology utilizes a forward/flyback transformer to allow the high-frequency isolation. The principle of the operation and the applied control concept is discussed. Space vector modulation is used in order to achieve unity power factor and to have the output voltage be regulated. Furthermore, the system transient is analyzed during turn-on and a novel method is applied to the control system in order to limit inrush current during startup. The proper operation of the soft-start method is confirmed by numerical simulation of a 6KW, 380V/48Vdc. The stability of the closed-loop as well as the converter performance is verified by measurements on a low-power prototype.


title: Crime, Punishment, and the Market for Offenses

abstract: Crime is a subject of intense emotions, conflicting ideologies. However, economists have generally explained it as a reflection of individual choice and equilibrating market forces. Two major themes of the literature are outlined: the evolution of a 'market model' to explain the diversity of crime across time and space, and the debate about the usefulness of 'positive' versus 'negative' incentives. Systematic analyses generally indicate that crime is affected on the margin by both positive and negative incentives; there are serious limitations to the effectiveness of incapacitation and rehabilitation; and optimal enforcement strategies involve trade-offs between narrow efficiency and equity considerations.


title: EMT-activating transcription factors in cancer: beyond EMT and tumor invasiveness

abstract: Cancer is a complex multistep process involving genetic and epigenetic changes that eventually result in the activation of oncogenic pathways and/or inactivation of tumor suppressor signals. During cancer progression, cancer cells acquire a number of hallmarks that promote tumor growth and invasion. A crucial mechanism by which carcinoma cells enhance their invasive capacity is the dissolution of intercellular adhesions and the acquisition of a more motile mesenchymal phenotype as part of an epithelial-to-mesenchymal transition (EMT). Although many transcription factors can trigger it, the full molecular reprogramming occurring during an EMT is mainly orchestrated by three major groups of transcription factors: the ZEB, Snail and Twist families. Upregulated expression of these EMT-activating transcription factors (EMT-ATFs) promotes tumor invasiveness in cell lines and xenograft mice models and has been associated with poor clinical prognosis in human cancers. Evidence accumulated in the last few years indicates that EMT-ATFs also regulate an expanding set of cancer cell capabilities beyond tumor invasion. Thus, EMT-ATFs have been shown to cooperate in oncogenic transformation, regulate cancer cell stemness, override safeguard programs against cancer like apoptosis and senescence, determine resistance to chemotherapy and promote tumor angiogenesis. This article reviews the expanding portfolio of functions played by EMT-ATFs in cancer progression.