title: A System for Assisting the Visually Impaired in Localization and Grasp of Desired Objects

abstract: A prototype wearable visual aid for helping visually impaired people find desired objects in their environment is described. The system is comprised of a head-worn camera to capture the scene, an Android phone interface to specify a desired object, and an attention-biasing-enhanced object recognition algorithm to identify three most likely object candidate regions, select the best-matching one, and pass its location to an object tracking algorithm. The object is tracked as the user’s head moves, and auditory feedback is provided to help the user maintain the object in the field of view, enabling easy reach and grasp. The implementation and integration of the system leading to testing of the working prototype with visually-impaired subjects at the Braille Institute in Los Angeles (demonstration in the accompanying video) is described. Results indicate that this system has clear potential to help visually-impaired users in achieving near-real-time object localization and grasp.


title: Mutant-Based Model-Checking to Ensure Accessibility and Safety Aspects of Human Computer Interfaces.

abstract: Network computers have a plurality of network-level interfaces associated with overlapping portions of the network-level address space, such as those represented by multiple static entries with the same address range, or multiple default entries in a network-level routing table. The computers distribute outgoing messages address to the overlapping portions of the address space between the multiple network interfaces associated with those overlapping portions. In the TCP/IP environment, the distribution of messages can be performed on the basis of the outgoing message's source port, the amount of traffic associated with a TCP/IP service indicated by the message's destination port, or by the relative amount of traffic going though each of the respective network interfaces. In some embodiments, the computers are multi-homed end systems which pick the source address of outgoing messages and their associated socket. In some embodiments, the network interfaces between which messages are distributed are dial-up modems having network-level addresses dynamically allocated to them by the computer to which they are connected. Some systems can automatically connect or disconnect such dial-up connections in response to changes communications demands. In some embodiments of the invention, a multi-homed end system distributes messages addressed to a given destination address between multiple network interfaces for the purpose of load testing.


title: Natural Language Requirements Processing: A 4D Vision

abstract: Natural language processing (NLP) and requirements engineering (RE) have had a long relationship, yet their combined use isn’t well established in industrial practice. This situation should soon change. The future evolution of the application of NLP technologies in RE can be viewed from four dimensions: discipline, dynamism, domain knowledge, and datasets.


title: Progress in the gut microbiota in exploring shrimp disease pathogenesis and incidence

abstract: It is now recognized that gut microbiota contributes indispensable roles in safeguarding host health. Shrimp is being threatened by newly emerging diseases globally; thus, understanding the driving factors that govern its gut microbiota would facilitate an initial step to reestablish and maintain a “healthy” gut microbiota. This review summarizes the factors that assemble the shrimp gut microbiota, which focuses on the current progresses of knowledge linking the gut microbiota and shrimp health status. In particular, I propose the exploration of shrimp disease pathogenesis and incidence based on the interplay between dysbiosis in the gut microbiota and disease severity. An updated research on shrimp disease toward an ecological perspective is discussed, including host–bacterial colonization, identification of polymicrobial pathogens and diagnosing disease incidence. Further, a simple conceptual model is offered to summarize the interplay among the gut microbiota, external factors, and shrimp disease. Finally, based on the review, current limitations are raised and future studies directed at solving these concerns are proposed. This review is timely given the increased interest in the role of gut microbiota in disease pathogenesis and the advent of novel diagnosis strategies.


title: Implementation of Training Convolutional Neural Networks

abstract: Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we gave a detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied the particular convolutional neural network to implement the typical face recognition problem by java. Then, a parallel strategy was proposed in section4. In addition, by measuring the actual time of forward and backward computing, we analysed the maximal speed up and parallel efficiency theoretically.


title: Detection of image sharpening based on histogram aberration and ringing artifacts

abstract: With the wide use of sophisticated photo manipulation, capability of digital images for recording authentic scene information has been addressed with public suspicion. So it is urgent to develop forensics techniques for verifying photographs' originality and authenticity. In this paper, a blind forensic algorithm is proposed to detect sharpening manipulation in digital images. Gradient aberration of the gray histogram generated from unsaturated luminance regions of an image, is measured and employed to capture trails of sharpening operation. Ringing artifacts around step edges are exploited to provide another complementary clue, especially useful when the histogram-based features are not available. Tests on plenty of photo images show the effecttiveness of our proposed sharpening detection scheme.


title: Peer Lending Risk Predictor

abstract: Warren Buffett famously stated two rules for investing: Rule #1. Never lose money, and Rule #2. Never forget Rule #1. Recent Peer Lending opportunities provide the individual investor to earn an interest rate significantly higher than that of a savings account. However, a default on the loan by the borrower means the investor will lose her entire principal. In this paper, we will use Machine Learning algorithms to classify and optimize peer lending risk.


title: Robust control of bicycle model with CMG

abstract: This paper is an extension of previous work, where authors developed a nonlinear model and its linear approximation of bicycle (or single track vehicle generally) with controlled moment gyro (CMG) used for stability algorithm in vertical direction. Also, simple stability was proposed. This extension focuses on robustness of control.


title: Low-Power Supply-Regulation Techniques for Ring Oscillators in Phase-Locked Loops Using a Split-Tuned Architecture

abstract: A supply-regulated phase-locked loop (PLL) employs a split-tuned architecture to decouple the tradeoff between supply-noise rejection performance and power consumption. By placing the regulator in the low-bandwidth coarse loop, the proposed PLL architecture allows us to maximize its bandwidth to suppress the oscillator phase noise with neither the power supply-noise rejection nor the power dissipation of the regulator being affected. A replica-based regulator introduces a low-frequency pole in its supply-noise transfer function and avoids degradation of supply-noise rejection beyond the regulator-loop's dominant pole frequency. The prototype PLL fabricated in a 0.18 mum digital CMOS process operates from 0.5 to 2.5 GHz. At 1.5 GHz, the proposed PLL achieves 1.9 ps long-term rms jitter and a worst case supply-noise sensitivity of -28 dB (0.5 rad/V), an improvement of 20 dB over conventional solutions, while consuming 2.2 mA from a 1.8 V supply.


title: Learning task-parametrized assistive strategies for exoskeleton robots by multi-task reinforcement learning

abstract: Recent studies suggest that reinforcement learning has great potential for generating assistive strategies in exoskeletons through physical interactions between a user and a robot. Previous methods focused on a task-specific assistive strategy, where for every single task (situation/context), the user needs to interact with a robot to learn an appropriate assistive strategy. Therefore, the learned strategies cannot be generalized for a new task. Since the sampling cost is expensive for such human-in-the-loop systems as exoskeletons, generalization must be enabled. In this paper, we propose to learn task-parametrized assistive strategies for exoskeleton robots. Our method employs an assistive strategy, which depends on the task parameter and the state variable, that can be learned from multiple sets of human-robot interaction data across different tasks and generalized even for an unseen task, given the task parameter without additional learning. To alleviate the user's burden in the learning process across multiple tasks, we exploit a data-efficient multi-task reinforcement learning framework. To verify the effectiveness of our method, we developed an experimental platform with an exoskeleton robot. We conducted a series of experiments whose experimental results show that our method can learn such a task-parametrized assistive strategy and be generalized for unseen tasks to reduce the user's electromyography signals (EMGs) during tasks.


title: State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF

abstract: This paper presents a method for modeling and estimation of the state of charge (SOC) of lithium-ion (Li-Ion) batteries using neural networks (NNs) and the extended Kalman filter (EKF). The NN is trained offline using the data collected from the battery-charging process. This network finds the model needed in the state-space equations of the EKF, where the state variables are the battery terminal voltage at the previous sample and the SOC at the present sample. Furthermore, the covariance matrix for the process noise in the EKF is estimated adaptively. The proposed method is implemented on a Li-Ion battery to estimate online the actual SOC of the battery. Experimental results show a good estimation of the SOC and fast convergence of the EKF state variables.


title: General simulation platform for vision based UAV testing

abstract: The main purpose of this article is to build a general platform for Unmanned Aerial Vehicle (UAV) simulation especially for vision algorithms. This general simulation platform can also connect to realistic UAVs for further tests. The platform enables UAVs controlled by a variety of advanced vision algorithms to accomplish certain tasks. Unlike the previous simulation systems, which can only achieve tasks with certain drones, we are committed to build a platform to support a variety of vision based UAVs simulation platform. And if the simulation test is successful, we can apply vision algorithms to realistic UAVs' tests based on our platform with little adaption. We use open source Gazebo as a simulator and combined with ROS (Robot Operating System) to implement UAVs simulation. Besides, we build a general UAVs link interface to connect realistic UAVs with ROS. All these works construct the general simulation platform.


title: Self-Supervised Learning of Depth and Camera Motion from 360{\deg} Videos

abstract: As 360{\deg} cameras become prevalent in many autonomous systems (e.g., self-driving cars and drones), efficient 360{\deg} perception becomes more and more important. We propose a novel self-supervised learning approach for predicting the omnidirectional depth and camera motion from a 360{\deg} video. In particular, starting from the SfMLearner, which is designed for cameras with normal field-of-view, we introduce three key features to process 360{\deg} images efficiently. Firstly, we convert each image from equirectangular projection to cubic projection in order to avoid image distortion. In each network layer, we use Cube Padding (CP), which pads intermediate features from adjacent faces, to avoid image boundaries. Secondly, we propose a novel"spherical"photometric consistency constraint on the whole viewing sphere. In this way, no pixel will be projected outside the image boundary which typically happens in images with normal field-of-view. Finally, rather than naively estimating six independent camera motions (i.e., naively applying SfM-Learner to each face on a cube), we propose a novel camera pose consistency loss to ensure the estimated camera motions reaching consensus. To train and evaluate our approach, we collect a new PanoSUNCG dataset containing a large amount of 360{\deg} videos with groundtruth depth and camera motion. Our approach achieves state-of-the-art depth prediction and camera motion estimation on PanoSUNCG with faster inference speed comparing to equirectangular. In real-world indoor videos, our approach can also achieve qualitatively reasonable depth prediction by acquiring model pre-trained on PanoSUNCG.


title: LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering

abstract: Motivated by augmented and virtual reality applications such as telepresence, there has been a recent focus in real-time performance capture of humans under motion. However, given the real-time constraint, these systems often suffer from artifacts in geometry and texture such as holes and noise in the final rendering, poor lighting, and low-resolution textures. We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time. We call this approach neural (re-)rendering, and our live system "LookinGood". Our deep architecture is trained to produce high resolution and high quality images from a coarse rendering in real-time. First, we propose a self-supervised training method that does not require manual ground-truth annotation. We contribute a specialized reconstruction error that uses semantic information to focus on relevant parts of the subject, e.g. the face. We also introduce a salient reweighing scheme of the loss function that is able to discard outliers. We specifically design the system for virtual and augmented reality headsets where the consistency between the left and right eye plays a crucial role in the final user experience. Finally, we generate temporally stable results by explicitly minimizing the difference between two consecutive frames. We tested the proposed system in two different scenarios: one involving a single RGB-D sensor, and upper body reconstruction of an actor, the second consisting of full body 360° capture. Through extensive experimentation, we demonstrate how our system generalizes across unseen sequences and subjects.


Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods


title: The relationship between spoilage and Listeria monocytogenes contamination in bagged and wrapped big bale silage

abstract: A farm-scale trial to compare the effectiveness of polythene bags and wrapping of big bale silage was monitored for Listeria monocytogenes contamination. There was no significant difference between the level of L. monocytogenes found in wrapped and bagged big bale silage, but the work confirmed that big bale silage is prone to contamination by Listeria organisms, and that the degree of Listeria contamination could be significantly reduced by the removal of obviously spoiled material prior to feeding.


Investigating Active Learning for Concept Prerequisite Learning.


title: A Practical Switching Loss Model for Buck Voltage Regulators

abstract: In this paper, a review of switching loss mechanisms for synchronous buck voltage regulators (VRs) is presented. Following the review, a new simple and accurate analytical switching loss model is proposed for synchronous buck VRs. The model includes the impact of common source inductance and switch parasitic inductances on switching loss. The proposed model uses simple equations to calculate the rise and fall times and piecewise linear approximations of the high-side MOSFET voltage and current waveforms to allow quick and accurate calculation of switching loss in a synchronous buck VR. A simulation program with integrated circuit emphasis (Spice) simulations are used to demonstrate the accuracy of the voltage source driver model operating in a 1-MHz synchronous buck VR at 12-V input, 1.3-V output. Switching loss was estimated with the proposed model and compared to Spice measurements. Experimental results are presented to demonstrate the accuracy of the proposed model.


title: STEGANOGRAPHY IN PERSIAN AND ARABIC UNICODE TEXTS USING PSEUDO-SPACE AND PSEUDO CONNECTION CHARACTERS

abstract: Sending information secretly and communicating covertly have been of great interest for ages. On the other hand, text documents have been widely used and consequently various methods for hiding information in texts (Text Steganography) have been developed so far. In this paper a new method is proposed for hiding information in Persian and Arabic Unicode texts. In Persian and Arabic, some letters are connected together in a word. But there are two characters, zero width non joiner (ZWNJ) and zero width joiner (ZWJ) characters, which are respectively prevents the Persian and Arabic letters from joining or forces them to join together. In this method by using these two special characters, the information is hidden in Persian and Arabic Unicode text documents. This method has a high hiding capacity because it hides one bit in each letter. Also this method does not make any apparent changes in the original text and have a perfect perceptual transparency.


title: Multi-resonant LCC converter - comparison of different methods for the steady-state analysis

abstract: Several different approaches for the investigation of the steady-state properties of resonant converters have been published. In addition to solving the occurring equations in the time domain, which turns out to be quite cumbersome, the designer can make use of approximate approaches in the frequency domain. Two of these methods are compared to the exact solution with regard to their accuracy and mathematical complexity within this paper for the multi-resonant LCC converter with inductive output filter. Additionally, a minimized set of nonlinear equations is derived in case of the exact solution based upon the state-plane technique.


title: HP-GAN: Probabilistic 3D Human Motion Prediction via GAN

abstract: Predicting and understanding human motion dynamics has many applications, such as motion synthesis, augmented reality, security, and autonomous vehicles. Due to the recent success of generative adversarial networks (GAN), there has been much interest in probabilistic estimation and synthetic data generation using deep neural network architectures and learning algorithms. ::: We propose a novel sequence-to-sequence model for probabilistic human motion prediction, trained with a modified version of improved Wasserstein generative adversarial networks (WGAN-GP), in which we use a custom loss function designed for human motion prediction. Our model, which we call HP-GAN, learns a probability density function of future human poses conditioned on previous poses. It predicts multiple sequences of possible future human poses, each from the same input sequence but a different vector z drawn from a random distribution. Furthermore, to quantify the quality of the non-deterministic predictions, we simultaneously train a motion-quality-assessment model that learns the probability that a given skeleton sequence is a real human motion. ::: We test our algorithm on two of the largest skeleton datasets: NTURGB-D and Human3.6M. We train our model on both single and multiple action types. Its predictive power for long-term motion estimation is demonstrated by generating multiple plausible futures of more than 30 frames from just 10 frames of input. We show that most sequences generated from the same input have more than 50\% probabilities of being judged as a real human sequence. We will release all the code used in this paper to Github.


A Novel Data Representation for Effective Learning in Class Imbalanced Scenarios


title: Structured AutoEncoders for Subspace Clustering

abstract: Existing subspace clustering methods typically employ shallow models to estimate underlying subspaces of unlabeled data points and cluster them into corresponding groups. However, due to the limited representative capacity of the employed shallow models, those methods may fail in handling realistic data without the linear subspace structure. To address this issue, we propose a novel subspace clustering approach by introducing a new deep model - Structured AutoEncoder (StructAE). The StructAE learns a set of explicit transformations to progressively map input data points into nonlinear latent spaces while preserving the local and global subspace structure. In particular, to preserve local structure, the StructAE learns representations for each data point by minimizing reconstruction error with respect to itself. To preserve global structure, the StructAE incorporates a prior structured information by encouraging the learned representation to preserve specified reconstruction patterns over the entire data set. To the best of our knowledge, StructAE is one of the first deep subspace clustering approaches. Extensive experiments show that the proposed StructAE significantly outperforms 15 state-of-the-art subspace clustering approaches in terms of five evaluation metrics.


title: Distributed Agile Software Development: A Review

abstract: Distribution of software development is becoming more and more common in order to save the production cost and reduce the time to market. Large geographical distance, different time zones and cultural differences in distributed software development (DSD) leads to weak communication which adversely affects the project. Using agile practices for distributed development is also gaining momentum in various organizations to increase the quality and performance of the project. This paper explores the intersection of these two significant trends for software development i.e. DSD and agile. We discuss the challenges faced by geographically distributed agile teams and proven practices to address these issues, which will help in building a successful distributed team.


title: Reading behavior in the digital environment : Changes in reading behavior over the past ten years

abstract: Purpose – This study attempts to investigate reading behavior in the digital environment by analyzing how people's reading behavior has changed over the past ten years.Design/methodology/approach – Survey and analysis methods are employed.Findings – With an increasing amount of time spent reading electronic documents, a screen‐based reading behavior is emerging. The screen‐based reading behavior is characterized by more time spent on browsing and scanning, keyword spotting, one‐time reading, non‐linear reading, and reading more selectively, while less time is spent on in‐depth reading, and concentrated reading. Decreasing sustained attention is also noted. Annotating and highlighting while reading is a common activity in the printed environment. However, this “traditional” pattern has not yet migrated to the digital environment when people read electronic documents.Originality/value – Implications for the changes in reading behavior are discussed, and directions for future research are suggested.


title: FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals

abstract: We present a novel approach to detect synthetic content in portrait videos, as a preventive solution for the emerging threat of deep fakes. In other words, we introduce a deep fake detector. We observe that detectors blindly utilizing deep learning are not effective in catching fake content, as generative models produce formidably realistic results. Our key assertion follows that biological signals hidden in portrait videos can be used as an implicit descriptor of authenticity, because they are neither spatially nor temporally preserved in fake content. To prove and exploit this assertion, we first exhibit several unary and binary signal transformations for the pairwise separation problem, achieving 99.39% accuracy. Second, we utilize those findings to formulate a generalized classifier for fake content, by analyzing proposed signal transformations and corresponding feature sets. Third, we generate novel signal maps and employ a CNN to improve our traditional classifier for detecting synthetic content. Lastly, we release an "in the wild" dataset of fake portrait videos that we collected as a part of our evaluation process. We evaluate FakeCatcher both on Face Forensics dataset and on our new Deep Fakes dataset, performing with 96% and 91.07% accuracies respectively. In addition, our approach produces a significantly superior detection rate against baselines, and does not depend on the source, generator, or properties of the fake content. We also analyze signals from various facial regions, with varying segment durations, and under several dimensionality reduction techniques.


title: Max K-armed bandit: On the ExtremeHunter algorithm and beyond

abstract: This paper is devoted to the study of the max K-armed bandit problem, which consists in sequentially allocating resources in order to detect extreme values. Our contribution is twofold. We first significantly refine the analysis of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and next propose an alternative approach, showing that, remarkably, Extreme Bandits can be reduced to a classical version of the bandit problem to a certain extent. Beyond the formal analysis, these two approaches are compared through numerical experiments.


title: Barriers and facilitators to the integration of mental health services into primary health care: a systematic review protocol

abstract: Mental health is an integral part of health and well-being and yet health systems have not adequately responded to the burden of mental disorders. Integrating mental health services into primary health care (PHC) is the most viable way of closing the treatment gap and ensuring that people get the mental health care they need. PHC was formally adapted by the World Health Organization (WHO), and they have since invested enormous amounts of resources across the globe to ensure that integration of mental health services into PHC works. This review will use the SPIDER (Sample, Phenomenon of Interest, Design, Evaluation, Research type) framework approach to identify experiences of mental health integration into PHC; the findings will be reported using the “Best fit” framework synthesis. PubMed, EMBASE, PsycINFO, and Cochrane Central Register of Controlled trials (CENTRAL) will be searched including other sources like the WHO website and OpenGrey database. Assessment of bias and quality will be done at study level using two separate tools to check for the quality of evidence presented. Data synthesis will take on two synergistic approaches (qualitative and quantitative studies). Synthesizing evidence from countries across the globe will provide useful insights into the experiences of integrating mental health services into PHC and how the barriers and challenges have been handled. The findings will be useful to a wide array of stakeholders involved in the implementation of the mental health integration into PHC. The SPIDER framework has been chosen for this review because of its suitable application to qualitative and mixed methods research and will be used as a guide when selecting articles for inclusion. Data extracted will be synthesized using the “Best fit” framework because it has been used before and proved its suitability in producing new conceptual models for explaining decision-making and possible behaviors. Synthesizing evidence from countries across the globe will provide useful insights into the experiences of integrating mental health services into PHC and how the barriers and challenges have been handled. PROSPERO CRD42016052000


title: Efficient sparse matrix-vector multiplication using cache oblivious extension quadtree storage format

abstract: In this paper, we elaborate on improving the sparse matrix storage format to optimize the data locality of sparse matrix-vector multiplication ( S p M V M ) algorithm, and its parallel performance. First of all, we propose a cache oblivious extension quadtree storage structure ( C O E Q T ), in which the sparse matrix is recursively divided into sub-regions that can well fit into cache to improve the data locality. Later on, we present a C O E Q T based S p M V M algorithm and optimize its performance through manual vectorization. With this storage format, the original S p M V M is divided into computations of relatively independent small matrices. In addition, this region-based computation framework is also suitable for high performance computing in distributed computing environment. So, we finally present a parallel S p M V M algorithm based on the proposed C O E Q T . Extensive and comprehensive experiments show that the sparse matrix-vector multiplication using the C O E Q T storage format achieves on average 1.1-1.5 × speedup compared with C S R format and further higher performance through instruction level optimization techniques. The experiment in Lenovo Deepcomp 7000 demonstrates that this method achieves on average 1.63 × speedup compared with the Intel Cluster Math Kernel Library implementation. A cache oblivious extension quadtree storage structure ( C O E Q T ) is proposed.Present the converting algorithm from C S R storage format to the C O E Q T storage format.Implement the sparse matrix-vector multiplication algorithm based on the C O E Q T .Optimize the performance of the implemented algorithm through manual vectorization.Implement the parallel sparse matrix-vector multiplication in distributed system.


title: Characterization of porcine leptin receptor polymorphisms and their association with reproduction and production traits.

abstract: Fatness in pigs is of prime economic importance due to market incentives for production of lean pork and elevated fatness increasing the feed costs. Leptin (LEP) action, mediated through its specific receptors (LEPR), was reported to be involved in the regulation of fatness via feed intake, energy expenditure, and whole-body energy balance in pigs. In this study, we have designed 17 primer sets based on the human and mouse LEPR sequences and successfully amplified coding regions of 15 porcine LEPR exon fragments by polymerase chain reactions (PCR). Four single nucleotide polymorphisms (SNP) of Intron 2, Exons 2, 6, and 18 were found in Landrace, Yorkshire, and Duroc by mutagenetically separated-PCR (MS-PCR) and PCR-restriction fragment length polymorphisms (PCR-RFLP). Chi-square statistics was used to calculate homogeneity of genotypic frequencies of 4 gene polymorphisms for three breeds of animals. Effects of Intron 2, Exon 2, and Exon 18 polymorphisms on the reproduction trait such as litter sizes of sows were evident (p < 0.05) in Duroc and Yorkshire. There was no (p > 0.05) significant influence on the production trait of average daily gain due to four candidate gene polymorphisms in three porcine breeds. However, effects of Exon 6 and 18 polymorphisms on the production trait of backfat thickness were significant (p < 0.05) in Landrace and Yorkshire, respectively. Effects of Exon 18 polymorphisms on feed efficiency were also evident (p < 0.05) in Duroc.


title: DEVELOPING SPECIES SPECIFIC VEGETATION MAPS USING MULTI-SPECTRAL HYPERSPATIAL IMAGERY FROM UNMANNED AERIAL VEHICLES

abstract: In remote, rugged or sensitive environments ground based mapping for condition assessment of species is both time consuming and potentially destructive. The application of photogrammetric methods to generate multispectral imagery and surface models based on UAV imagery at appropriate temporal and spatial resolutions is described. This paper describes a novel method to combine processing of NIR and visible image sets to produce multiband orthoimages and DEM models from UAV imagery with traditional image location and orientation uncertainties. This work extends the capabilities of recently developed commercial software (Pix4UAV from Pix4D) to show that image sets of different modalities (visible and NIR) can be automatically combined to generate a 4 band orthoimage. Reconstruction initially uses all imagery sets (NIR and visible) to ensure all images are in the same reference frame such that a 4-band orthoimage can be created. We analyse the accuracy of this automatic process by using ground control points and an evaluation on the matching performance between images of different modalities is shown. By combining sub-decimetre multispectral imagery with high spatial resolution surface models and ground based observation it is possible to generate detailed maps of vegetation assemblages at the species level. Potential uses with other conservation monitoring are discussed.


title: An orthotopic mouse model of small cell lung cancer reflects the clinical course in patients

abstract: Small cell lung cancer (SCLC) is a highly aggressive subtype of lung cancer with very poor prognosis due to early metastatic spread and development of chemoresistance. In the last 30 years the study of SCLC has been constrained by a lack of primary human tumor specimen thus highlighting the need of a suitable mouse model. In this article we present the establishment of an orthotopic xenograft mouse model which accurately reproduced the clinical course of SCLC. Orthotopic implantation enabled engraftment of primary lung tumors in all injected mice. Furthermore, immunodeficiency of mice allowed formation of spontaneous metastases in characteristic organs. Bioluminescence Imaging, Magnetic Resonance Imaging and Positron emission tomography were applied to monitor engraftment, metabolism and the exact growth of tumors over time. In order to mimic the extensive disease stage, mice were injected with aggressive human chemoresistant cells leading to development of chemoresistant tumors and early metastatic spread. As a proof of concept treatment of tumor-bearing mice with conventional chemotherapeutics reduced tumor volumes, but a complete regression of tumors was not achieved. By mimicking the extensive disease stage our mouse model can facilitate the study of mechanisms contributing to chemoresistance and metastasis formation, as well as drug screening and evaluation of new treatment strategies for SCLC patients.


title: Accurate Light Field Depth Estimation with Superpixel Regularization over Partially Occluded Regions

abstract: Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel-based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than the state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features.


title: Keypoint trajectory estimation using propagation based tracking

abstract: One of the major steps in visual environment perception for automotive applications is to track keypoints and to subsequently estimate egomotion and environment structure from the trajectories of these keypoints. This paper presents a propagation based tracking method to obtain the 2D trajectories of keypoints from a sequence of images in a monocular camera setup. Instead of relying on the classical RANSAC to obtain accurate keypoint correspondences, we steer the search for keypoint matches by means of propagating the estimated 3D position of the keypoint into the next frame and verifying the photometric consistency. In this process, we continuously predict, estimate and refine the frame-to-frame relative pose which induces the epipolar relation. Experiments on the KITTI dataset as well as on the synthetic COnGRATS dataset show promising results on the estimated courses and accurate keypoint trajectories.


title: A fast parallel maximum clique algorithm for large sparse graphs and temporal strong components

abstract: We propose a fast, parallel, maximum clique algorithm for large, sparse graphs that is designed to exploit characteristics of social and information networks. We observe roughly linear runtime scaling over graphs between 1000 vertices and 100M vertices. In a test with a 1.8 billion-edge social network, the algorithm finds the largest clique in about 20 minutes. For social networks, in particular, we found that using the core number of a vertex in combination with a good heuristic clique finder efficiently removes the vast majority of the search space. In addition, we parallelize the exploration of the search tree. In the algorithm, processes immediately communicate changes to upper and lower bounds on the size of maximum clique, which occasionally results in a super-linear speedup because vertices with especially large search spaces can be pruned by other processes. We use this clique finder to investigate the size of the largest temporal strong components in dynamic networks, which requires finding the largest clique in a particular temporal reachability graph.


title: Spatio-Temporal Analysis of Bandwidth Maps for Geo-Predictive Video Streaming in Mobile Environments

abstract: High quality video streaming is increasingly popular among mobile users especially with the rise of high speed LTE networks. But despite the high network capacity of LTE, streaming videos may suffer from disruptions since the quality of the video depends on the network bandwidth, which in turn depends on the location of the user with respect to their cell tower, crowd levels, and objects like buildings and trees. Maintaining a good video playback experience becomes even more challenging if the user is moving fast in a vehicle, the location is changing rapidly and the available bandwidth fluctuates. In this paper we introduce GeoStream, a video streaming system that relies on the use of geostatistics to analyze spatio-temporal bandwidth. Data measured from users' streaming videos while they are commuting is collected in order to predict future bandwidth availability in unknown locations. Our approach investigates and leverages the relationship between the separation distance between sample bandwidth points, the time they were captured, and the semivariance, expressed by a variogram plot, to finally predict the future bandwidth at unknown locations. Using the datasets from GTube our experimental results show an improved performance.


title: Ontology Based SMS Controller for Smart Phones

abstract: Text analysis includes lexical analysis of the text and has been widely studied and used in diverse applications. In the last decade, researchers have proposed many efficient solutions to analyze / classify large text dataset, however, analysis / classification of short text is still a challenge because 1) the data is very sparse 2) It contains noise words and 3) It is difficult to understand the syntactical structure of the text. Short Messaging Service (SMS) is a text messaging service for mobile/smart phone and this service is frequently used by all mobile users. Because of the popularity of SMS service, marketing companies nowadays are also using this service for direct marketing also known as SMS marketing.In this paper, we have proposed Ontology based SMS Controller which analyze the text message and classify it using ontology aslegitimate or spam. The proposed system has been tested on different scenarios and experimental results shows that the proposed solution is effective both in terms of efficiency and time.


title: An Event-based Digital Forensic Investigation framework

abstract: In this paper, we present a framework for digital forensics that includes an investigation process model based on physical crime scene procedures. In this model, each digital device is considered a digital crime scene, which is included in the physical crime scene where it is located. The investigation includes the preservation of the system, the search for digital evidence, and the reconstruction of digital events. The focus of the investigation is on the reconstruction of events using evidence so that hypotheses can be developed and tested. This paper also includes definitions and descriptions of the basic and core concepts that the framework uses.


Midwives, Women and the State: (De)Constructing Midwives and Pregnant Women in Ontario, Canada


title: BioNet: an R-Package for the functional analysis of biological networks

abstract: Motivation: Increasing quantity and quality of data in transcriptomics and interactomics create the need for integrative approaches to network analysis. Here, we present a comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. Results: The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization. Availability: The BioNet package and a tutorial are available from http://bionet.bioapps.biozentrum.uni-wuerzburg.de Contact: marcus.dittrich@biozentrum.uni-wuerzburg.de; tobias.mueller@biozentrum.uni-wuerzburg.de.


title: Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network

abstract: Accurate measurement of left ventricular volumes and Ejection Fraction from cine MRI is of paramount importance to the evaluation of cardiovascular functions, yet it usually requires laborious and tedious work of trained experts to interpret them. To facilitate this procedure, numerous computer aided diagnosis (CAD) methods and tools have been proposed, most of which focus on the left or right ventricle segmentation. However, the identification of ES and ED frames from cardiac sequences is largely ignored, which is a key procedure in the automated workflow. This seemingly easy task is quite challenging, due to the requirement of high accuracy (i.e., precisely identifying specific frames from a sequence) and subtle differences among consecutive frames. Recently, with the rapid growth of annotated data and the increasing computational power, deep learning methods have been widely exploited in medical image analysis. In this paper, we propose a novel deep learning architecture, named as temporal regression network (TempReg-Net), to accurately identify specific frames from MRI sequences, by integrating the Convolutional Neural Network (CNN) with the Recurrent Neural Network (RNN). Specifically, a CNN encodes the spatial information of a cardiac sequence, and a RNN decodes the temporal information. In addition, we design a new loss function in our network to constrain the structure of predicted labels, which further improves the performance. Our approach is extensively validated on thousands of cardiac sequences and the average difference is merely 0.4 frames, comparing favorably with previous systems.


title: Ensemble Committees for Stock Return Classification and Prediction

abstract: This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.


title: SwordDTA: A dynamic taint analysis tool for software vulnerability detection

abstract: Software vulnerabilities are the root cause of various information security incidents while dynamic taint analysis is an emerging program analysis technique. In this paper, to maximize the use of the technique to detect software vulnerabilities, we present SwordDTA, a tool that can perform dynamic taint analysis for binaries. This tool is flexible and extensible that it can work with commodity software and hardware. It can be used to detect software vulnerabilities with vulnerability modeling and taint check. We evaluate it with a number of commonly used real-world applications. The experimental results show that SwordDTA is capable of detecting at least four kinds of software vulnerabilities including buffer overflow, integer overflow, division by zero and use-after-free, and is applicable for a wide range of software.


title: Achieving high utilization with software-driven WAN

abstract: We present SWAN, a system that boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently re-configuring the network's data plane to match current traffic demand. But done simplistically, these re-configurations can also cause severe, transient congestion because different switches may apply updates at different times. We develop a novel technique that leverages a small amount of scratch capacity on links to apply updates in a provably congestion-free manner, without making any assumptions about the order and timing of updates at individual switches. Further, to scale to large networks in the face of limited forwarding table capacity, SWAN greedily selects a small set of entries that can best satisfy current demand. It updates this set without disrupting traffic by leveraging a small amount of scratch capacity in forwarding tables. Experiments using a testbed prototype and data-driven simulations of two production networks show that SWAN carries 60% more traffic than the current practice.


title: A deep learning approach to flight delay prediction

abstract: Deep learning has achieved significant improvement in various machine learning tasks including image recognition, speech recognition, machine translation and etc. Inspired by the huge success of the paradigm, there have been lots of tries to apply deep learning algorithms to data analytics problems with big data including traffic flow prediction. However, there has been no attempt to apply the deep learning algorithms to the analysis of air traffic data. This paper investigates the effectiveness of the deep learning models in the air traffic delay prediction tasks. By combining multiple models based on the deep learning paradigm, an accurate and robust prediction model has been built which enables an elaborate analysis of the patterns in air traffic delays. In particular, Recurrent Neural Networks (RNN) has shown its great accuracy in modeling sequential data. Day-to-day sequences of the departure and arrival flight delays of an individual airport have been modeled by the Long Short-Term Memory RNN architecture. It has been shown that the accuracy of RNN improves with deeper architectures. In this study, four different ways of building deep RNN architecture are also discussed. Finally, the accuracy of the proposed prediction model was measured, analyzed and compared with previous prediction methods. It shows best accuracy compared with all other methods.


title: Direct and indirect effects of sunscreen exposure for reef biota

abstract: Coral reefs are ecologically and economically important, contributing to both fishing and ecotourism economies around the world. Tourism and recreational activities have increased in coastal areas and so has the use of sunscreen. Sunscreen reduces human exposure to harmful UV rays, but washes off during aquatic recreational activities, which may negatively affect reef biota. To evaluate how sunscreen affects coral reef ecosystems, we added sunscreen at concentrations similar to previous studies to growing environments containing flatworms (Convolutriloba macropyga) with symbiotic algae, photosynthetic diatoms (Nitzschia sp.), Aiptasia anemones, and pulse corals (Xenia sp.). Using behavioral observations and estimates of population and colony growth, we show nominal concentrations of sunscreen negatively affect all of the studied species. Furthermore, we show that mobile flatworms do not avoid water which contains sunscreen and flatworms exposed to sunscreen prefer darker conditions. Based on our results, beach goers should limit use of sunscreens when near coral reefs and consider alternative protective measures, such as the utilization of sun-protective clothing.


title: TF-IDF method in ranking keywords of Instagram users' image captions

abstract: Instagram is one of the popular social media applications used by a wide range of people around the world. The significant growth of active Instagram users affects the size of Instagram data. The more number of users, the larger and more various Instagram data is posted. In line with its popularity, in recent years many researchers begin to study and analyze it for various purposes, such as detecting event photos based on location, clustering the photo content, advertising strategies based on user types, and so on. As of now there are three types of data available in Instagram which are text, image, and video. In this paper we propose Term-Frequency and Inverse Document Frequency (TF-IDF) method to rank keywords of top twenty most followed Instagram users based on image captions of Instagram. The objective of this research is to automatically know the main idea of Instagram users based on 50 recent image captions posted. In our experiments, TF-IDF has been successfully implemented to reveal a set of keywords with its ranking. The highest ranking of keyword is indeed the main topic of a user, indicated by the value of TF-IDF. The result of study indicates that TF-IDF method is very useful to find and rank the keywords of Instagram users image captions. In the future research, the ranking keywords are needed in solving classification and clustering tasks as feature extractions.


title: Enabling Classification and Shading for 3D Texture Mapping based Volume Rendering using OpenGL and Extensions

abstract: We present a new technique which enables direct volume rendering based on 3D texture mapping hardware, enabling shading as well as classification of the interpolated data. Our technique supports accurate lighting for a one directional light source, semi-transparent classification, and correct blending. To circumvent the limitations of one general classification, we introduce multiple classification spaces which are very valuable to understand the visualized data, and even mandatory to comprehensively grasp the 3D relationship of different materials present in the volumetric data. Furthermore, we illustrate how multiple classification spaces can be realized using existing graphics hardware. In contrast to previously reported algorithms, our technique is capable of performing all the above mentioned tasks within the graphics pipeline. Therefore, it is very efficient: The three dimensional texture needs to be stored only once and no load is put onto the CPU. Besides using standard OpenGL functionality, we exploit advanced per pixel operations and make use of available OpenGL extensions.


title: A Stochastic Approximation Method

abstract: Let M(x) denote the expected value at level x of the response to a certain experiment. M(x) is assumed to be a monotone function of x but is unknown to the experimenter, and it is desired to find the solution x = θ of the equation M(x) = α, where a is a given constant. We give a method for making successive experiments at levels x1, x2, ··· in such a way that xn will tend to θ in probability.


title: 45nm low power CMOS logic compatible embedded STT MRAM utilizing a reverse-connection 1T/1MTJ cell

abstract: This paper reports a 45nm spin-transfer-torque (STT) MRAM embedded into a standard CMOS logic platform that employs low-power (LP) transistors and Cu/low-k BEOL. We believe that this is the first-ever demonstration of embedded STT MRAM that is fully compatible with the 45nm logic technology. To ensure the switching margin, a novel Ȝreverse-connectionȝ 1T/1MT cell has been developed with a cell size of 0.1026 µm2. This cell is utilized to build embedded memory macros up to 32 Mbits in density. Device attributes and design windows have been examined by considering PVT variations to secure operating margins. Promising early reliability data on endurance, read disturb, and thermal stability have been obtained.


MLMD: Multi-Layered Visualization for Multi-Dimensional Data


title: Fairness via source throttling: a configurable and high-performance fairness substrate for multi-core memory systems

abstract: Cores in a chip-multiprocessor (CMP) system share multiple hardware resources in the memory subsystem. If resource sharing is unfair, some applications can be delayed significantly while others are unfairly prioritized. Previous research proposed separate fairness mechanisms in each individual resource. Such resource-based fairness mechanisms implemented independently in each resource can make contradictory decisions, leading to low fairness and loss of performance. Therefore, a coordinated mechanism that provides fairness in the entire shared memory system is desirable. ::: This paper proposes a new approach that provides fairness in the entire shared memory system, thereby eliminating the need for and complexity of developing fairness mechanisms for each individual resource. Our technique, Fairness via Source Throttling (FST), estimates the unfairness in the entire shared memory system. If the estimated unfairness is above a threshold set by system software, FST throttles down cores causing unfairness by limiting the number of requests they can inject into the system and the frequency at which they do. As such, our source-based fairness control ensures fairness decisions are made in tandem in the entire memory system. FST also enforces thread priorities/weights, and enables system software to enforce different fairness objectives and fairness-performance tradeoffs in the memory system. ::: Our evaluations show that FST provides the best system fairness and performance compared to four systems with no fairness control and with state-of-the-art fairness mechanisms implemented in both shared caches and memory controllers.


title: Effective training of convolutional neural networks for face-based gender and age prediction

abstract: Abstract Convolutional Neural Networks (CNNs) have been proven very effective for human demographics estimation by a number of recent studies. However, the proposed solutions significantly vary in different aspects leaving many open questions on how to choose an optimal CNN architecture and which training strategy to use. In this work, we shed light on some of these questions improving the existing CNN-based approaches for gender and age prediction and providing practical hints for future studies. In particular, we analyse four important factors of the CNN training for gender recognition and age estimation: (1) the target age encoding and loss function, (2) the CNN depth, (3) the need for pretraining, and (4) the training strategy: mono-task or multi-task. As a result, we design the state-of-the-art gender recognition and age estimation models according to three popular benchmarks: LFW, MORPH-II and FG-NET. Moreover, our best model won the ChaLearn Apparent Age Estimation Challenge 2016 significantly outperforming the solutions of other participants.


title: Anti-ferroelectric-like ZrO2 non-volatile memory: Inducing non-volatility within state-of-the-art DRAM

abstract: Even though the discovery of ferroelectricity in HfO 2 enabled further scaling of ferroelectric (FE) memories, these memories suffer from limited endurance. Recently, we showed how the non-volatility can be induced within the state-of-the-art DRAM capacitor stacks. By using an asymmetric stack, comprising electrodes with different workfunction values an internal bias field can be used to make anti-ferroelectric (AFE) ZrO 2 , which is used as a dielectric in DRAM, non-volatile (NV). In this way, an excellent endurance compared to HfO 2 based ferroelectric memories is achieved. In this work we provide an overview of theoretical considerations and different methods which can be utilized for the introduction of the internal bias field required for centering of one of the AFE hysteresis loops. Furthermore, memory performance and design limitations due to stack asymmetry are discussed. In addition, an operation scheme for this novel AFE NV memory is suggested.


title: Ethnic diversity and economic development

abstract: Abstract This paper analyzes the role that different indices and dimensions of ethnicity play in the process of economic development. Firstly, we discuss the advantages and disadvantages of alternative data sources for the construction of indices of religious and ethnic heterogeneity. Secondly, we compare the index of fractionalization and the index of polarization. We argue that an index of the family of discrete polarization measures is the adequate indicator to measure potential conflict. We find that ethnic (religious) polarization has a large and negative effect on economic development through the reduction of investment and the increase of government consumption and the probability of a civil conflict.


Multi-dimensional Topic Modeling with Determinantal Point Processes


title: Exploring the dimensions of nomophobia: Development and validation of a self-reported questionnaire

abstract: Nomophobia was investigated as a theoretical construct among young adults.Dimensions of nomophobia were identified and described.The Nomophobia Questionnaire (NMP-Q) was devised and validated.The NMP-Q can be used as a measure of nomophobia.Nomophobia can be considered a situational phobia and included in DSM-5. Nomophobia is considered a modern age phobia introduced to our lives as a byproduct of the interaction between people and mobile information and communication technologies, especially smartphones. This study sought to contribute to the nomophobia research literature by identifying and describing the dimensions of nomophobia and developing a questionnaire to measure nomophobia. Consequently, this study adopted a two-phase, exploratory sequential mixed methods design. The first phase was a qualitative exploration of nomophobia through semi-structured interviews conducted with nine undergraduate students at a large Midwestern university in the U.S. As a result of the first phase, four dimensions of nomophobia were identified: not being able to communicate, losing connectedness, not being able to access information and giving up convenience. The qualitative findings from this initial exploration were then developed into a 20-item nomophobia questionnaire (NMP-Q). In the second phase, the NMP-Q was validated with a sample of 301 undergraduate students. Exploratory factor analysis revealed a four-factor structure for the NMP-Q, corresponding to the dimensions of nomophobia. The NMP-Q was shown to produce valid and reliable scores; and thus, can be used to assess the severity of nomophobia.


Generating virtual CT from MRI using fully convolutional neural networks with improved structure quality


title: Game Theory with Learning for Cyber Security Monitoring

abstract: Recent attacks show that threats to cyber infrastructureare not only increasing in volume, but are getting moresophisticated. The attacks may comprise multiple actions that arehard to differentiate from benign activity, and therefore commondetection techniques have to deal with high false positive rates. Because of the imperfect performance of automated detectiontechniques, responses to such attacks are highly dependent onhuman-driven decision-making processes. While game theory hasbeen applied to many problems that require rational decisionmaking, we find limitation on applying such method on securitygames when the defender has limited information about theopponent's strategies and payoffs. In this work, we propose Q-Learning to react automatically to the adversarial behavior ofa suspicious user to secure the system. This work comparesvariations of Q-Learning with a traditional stochastic game. Simulation results show the possibility of Naive Q-Learning, despite restricted information on opponents.


title: A synthetic test circuit for current switching tests of HVDC circuit breakers

abstract: High voltage direct current (HVDC) circuit breaker current switching test in a test laboratory is difficult due to the limited test power installation. Reproduction of test stresses by means of direct testing confronts huge investment. Synthetic testing, as an alternative method, could amplify the test power more than ten times with a relative low cost. A synthetic test circuit based on the most used parallel current injection method was developed for the current switching test of HVDC circuit breakers. This synthetic test circuit could reproduce the current and voltage stresses equal to or greater than those could meet in service.


title: Data Mining; A Conceptual Overview

abstract: This tutorial provides an overview of the data mining process. The tutorial also provides a basic understanding of how to plan, evaluate and successfully refine a data mining project, particularly in terms of model building and model evaluation. Methodological considerations are discussed and illustrated. After explaining the nature of data mining and its importance in business, the tutorial describes the underlying machine learning and statistical techniques involved. It describes the CRISP-DM standard now being used in industry as the standard for a technology-neutral data mining process model. The paper concludes with a major illustration of the data mining process methodology and the unsolved problems that offer opportunities for research. The approach is both practical and conceptually sound in order to be useful to both academics and practitioners.


title: Wideband Four-Way Out-of-Phase Slotline Power Dividers

abstract: Two new wideband four-way out-of-phase slotline power dividers are proposed in this paper. The half-wavelength slotlines are employed to construct the presented compact power dividers. Based on the proposed power-dividing circuit, a four-way power divider is implemented with compact size and simple structure. To obtain high isolation among the four output ports and good output impedance matching, another four-way out-of-phase slotline power divider with improved isolation performance is designed by introducing an air-bridge resistor and two slotlines with isolation resistors. The simulated and measured results of the proposed power dividers demonstrate reasonable performance of impedance matching, insertion loss, amplitude balancing, and isolation among the output ports.


title: Region based undetectable multiple image watermarking

abstract: Today detectability of Intellectual Property in the digital world is a major issue. And a watermarking is one of them process to secure intellectual rights of digital assets. In our paper we propose a method to insert multiple watermark in spatial and frequency domain to make our assets secure and undetectable by dividing our host image into two regions, called region-A and region-B. In a region-A, an owner information is inserted with the help of LSB technique and in another, region-B, a circular watermarks are inserted with additive watermarking technique in DWT-DFT domain.


title: Consistent, durable, and safe memory management for byte-addressable non volatile main memory

abstract: This paper presents three building blocks for enabling the efficient and safe design of persistent data stores for emerging non-volatile memory technologies. Taking the fullest advantage of the low latency and high bandwidths of emerging memories such as phase change memory (PCM), spin torque, and memristor necessitates a serious look at placing these persistent storage technologies on the main memory bus. Doing so, however, introduces critical challenges of not sacrificing the data reliability and consistency that users demand from storage. This paper introduces techniques for (1) robust wear-aware memory allocation, (2) preventing of erroneous writes, and (3) consistency-preserving updates that are cache-efficient. We show through our evaluation that these techniques are efficiently implementable and effective by demonstrating a B+-tree implementation modified to make full use of our toolkit.


title: Character Segmentation of Sindhi, an Arabic Style Scripting Language, using Height Profile Vector

abstract: 2 Abstract: In this p aper, a problem of su b-word segmentation of p rinted Sindhi, an Arabic style scripting language, into characters is addressed. Printed or handwritten Sindhi text is cursive in nature. In t he c ursive w riting, mostly t he s ubsequent characters in a word ar e j oined with ea ch ot her. In t he proposed s egmentation al gorithm, first of all, Height Profile V ector (HPV) of thinned primary s troke of a s ub-word i s calculated a nd ana lyzed for the segmentation i nto i ts constituent characters. The number and locations of possible se gmentation points (PSP) are determined. The number of PSPs gives a rough estimation of the number of characters in t he sub-word. The data around the last PSP is f urther a nalyzed t o de termine t he ex act number of charact ers in t he sub -word. As t he ch aracters' set of Sindhi is the s uperset set of Arabic c haracters' set hence the pr oposed s egmentation al gorithm may b e used for t he seg mentation of text written i n other Arabic scripting languages.


title: Beyond reuse distance analysis: Dynamic analysis for characterization of data locality potential

abstract: Emerging computer architectures will feature drastically decreased flops/byte (ratio of peak processing rate to memory bandwidth) as highlighted by recent studies on Exascale architectural trends. Further, flops are getting cheaper while the energy cost of data movement is increasingly dominant. The understanding and characterization of data locality properties of computations is critical in order to guide efforts to enhance data locality. Reuse distance analysis of memory address traces is a valuable tool to perform data locality characterization of programs. A single reuse distance analysis can be used to estimate the number of cache misses in a fully associative LRU cache of any size, thereby providing estimates on the minimum bandwidth requirements at different levels of the memory hierarchy to avoid being bandwidth bound. However, such an analysis only holds for the particular execution order that produced the trace. It cannot estimate potential improvement in data locality through dependence preserving transformations that change the execution schedule of the operations in the computation. In this article, we develop a novel dynamic analysis approach to characterize the inherent locality properties of a computation and thereby assess the potential for data locality enhancement via dependence preserving transformations. The execution trace of a code is analyzed to extract a computational directed acyclic graph (CDAG) of the data dependences. The CDAG is then partitioned into convex subsets, and the convex partitioning is used to reorder the operations in the execution trace to enhance data locality. The approach enables us to go beyond reuse distance analysis of a single specific order of execution of the operations of a computation in characterization of its data locality properties. It can serve a valuable role in identifying promising code regions for manual transformation, as well as assessing the effectiveness of compiler transformations for data locality enhancement. We demonstrate the effectiveness of the approach using a number of benchmarks, including case studies where the potential shown by the analysis is exploited to achieve lower data movement costs and better performance.


title: A survey on Human Mobility and its applications

abstract: Human Mobility has attracted attentions from dierent elds of studies such as epidemic modeling, trac engineering, trac prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diusion and ow in the network and makes it easier to estimate how much one part of the network inuences another by using metrics like centrality measures. We aim to study population ow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of dierent data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges.


title: The issue of LPWAN technology coexistence in IoT environment

abstract: This paper discusses Low Power Wide Area Network technologies. The purpose of this work is a presentation of these technologies in a mutual context in order to analyse their coexistence. In this work there are described Low Power Wide Area Network terms and their representatives LoRa, Sigfox and IQRF, of which characteristics, topology and some significant technics are inspected. The technologies are also compared together in a frequency spectrum in order to detect risk bands causing collisions. A potential increased risk of collisions is found around 868.2 MHz. The main contribution of this paper is a summary of characteristics, which have an influence on the resulting coexistence.


title: Pose for Action - Action for Pose

abstract: In this work we propose to utilize information about human actions to improve pose estimation in monocular videos. To this end, we present a pictorial structure model that exploits high-level information about activities to incorporate higher-order part dependencies by modeling action specific appearance models and pose priors. However, instead of using an additional expensive action recognition framework, the action priors are efficiently estimated by our pose estimation framework. This is achieved by starting with a uniform action prior and updating the action prior during pose estimation. We also show that learning the right amount of appearance sharing among action classes improves the pose estimation. Our proposed model achieves state-of-the-art performance on two challenging datasets for pose estimation and action recognition with over 80,000 test images.


title: Novel Broadband 3-dB Directional Coupler Design Method

abstract: A novel broadband 3-dB directional coupler design method utilizing HFSS ® and realization are given in this paper. It is realized in stripline, showing great agreement with the simulation and design format. The unique property of this design method is that it unnecessitates both a feedback from the realization for broadbanding and an additional smoothing of transition between coupled sections of the whole directional coupler. There is also no need for either a specialised CAD tool or a computer program.


title: Compiler mitigations for time attacks on modern x86 processors

abstract: This paper studies and evaluates the extent to which automated compiler techniques can defend against timing-based side channel attacks on modern x86 processors. We study how modern x86 processors can leak timing information through side channels that relate to data flow. We study the efficiency, effectiveness, portability, predictability and sensitivity of several mitigating code transformations that eliminate or minimize key-dependent execution time variations. Furthermore, we discuss the extent to which compiler backends are a suitable tool to provide automated support for the proposed mitigations.


title: ARTICLES Transformational Leadership, Organizational Culture, and Innovativeness in Nonprofit Organizations

abstract: This is an exploratory study of leadership, organizational culture, and organizational innovativeness in a sample of nonprofit human service organizations: Associations of Retarded Citizens. Although leadership has been held out as one of the most important predictors of innovation, this study found it was not correlated with organizational innovativeness. Examination of the relationships between leadership and cultural variables provided some alternative explanations for this finding. Positive relationships among transformational leadership, organizational values, and cultural consensus (degree of agreement among employees on those values) indicate that leadership practices employed in this sample created strong cultural consensus around values that may inhibit innovation. These findings suggest that examining the link between leadership and organizational culture is important for understanding how leadership and innovation are related. This article sets out practical implications, based on the results of the study, that may help nonprofit managers create workplaces supportive of innovation.


title: Sparse learning via Boolean relaxations

abstract: We introduce novel relaxations for cardinality-constrained learning problems, including least-squares regression as a special but important case. Our approach is based on reformulating a cardinality-constrained problem exactly as a Boolean program, to which standard convex relaxations such as the Lasserre and Sherali-Adams hierarchies can be applied. We analyze the first-order relaxation in detail, deriving necessary and sufficient conditions for exactness in a unified manner. In the special case of least-squares regression, we show that these conditions are satisfied with high probability for random ensembles satisfying suitable incoherence conditions, similar to results on $$\ell _1$$l1-relaxations. In contrast to known methods, our relaxations yield lower bounds on the objective, and it can be verified whether or not the relaxation is exact. If it is not, we show that randomization based on the relaxed solution offers a principled way to generate provably good feasible solutions. This property enables us to obtain high quality estimates even if incoherence conditions are not met, as might be expected in real datasets. We numerically illustrate the performance of the relaxation-randomization strategy in both synthetic and real high-dimensional datasets, revealing substantial improvements relative to $$\ell _1$$l1-based methods and greedy selection heuristics.


title: Sparse Discrete Fractional Fourier Transform and Its Applications

abstract: The discrete fractional Fourier transform is a powerful signal processing tool with broad applications for nonstationary signals. In this paper, we propose a sparse discrete fractional Fourier transform (SDFrFT) algorithm to reduce the computational complexity when dealing with large data sets that are sparsely represented in the fractional Fourier domain. The proposed technique achieves multicomponent resolution in addition to its low computational complexity and robustness against noise. In addition, we apply the SDFrFT to the synchronization of high dynamic direct-sequence spread-spectrum signals. Furthermore, a sparse fractional cross ambiguity function (SFrCAF) is developed, and the application of SFrCAF to a passive coherent location system is presented. The experiment results confirm that the proposed approach can substantially reduce the computation complexity without degrading the precision.


title: Unsupervised Deep Hashing for Large-scale Visual Search

abstract: Learning based hashing plays a pivotal role in large-scale visual search. However, most existing hashing algorithms tend to learn shallow models that do not seek representative binary codes. In this paper, we propose a novel hashing approach based on unsupervised deep learning to hierarchically transform features into hash codes. Within the heterogeneous deep hashing framework, the autoencoder layers with specific constraints are considered to model the nonlinear mapping between features and binary codes. Then, a Restricted Boltzmann Machine (RBM) layer with constraints is utilized to reduce the dimension in the hamming space. The experiments on the problem of visual search demonstrate the competitiveness of our proposed approach compared to the state of the art.


title: Multichannel Variable-Size Convolution for Sentence Classification

abstract: We propose MVCNN, a convolution neural network (CNN) architecture for sentence classification. It (i) combines diverse versions of pretrained word embeddings and (ii) extracts features of multigranular phrases with variable-size convolution filters. We also show that pretraining MVCNN is critical for good performance. MVCNN achieves state-of-the-art performance on four tasks: on small-scale binary, small-scale multi-class and largescale Twitter sentiment prediction and on subjectivity classification.


title: A preliminary investigation of worm infections in a bluetooth environment

abstract: Over the past year, there have been several reports of malicious code exploiting vulnerabilities in the Bluetooth protocol. While the research community has started to investigate a diverse set of Bluetooth security issues, little is known about the feasibility and the propagation dynamics of a worm in a Bluetooth environment. This paper is an initial attempt to remedy this situation.We start by showing that the Bluetooth protocol design and implementation is large and complex. We gather traces and we use controlled experiments to investigate whether a large-scale Bluetooth worm outbreak is viable today. Our data shows that starting a Bluetooth worm infection is easy, once a vulnerability is discovered. Finally, we use trace-drive simulations to examine the propagation dynamics of Bluetooth worms. We find that Bluetooth worms can infect a large population of vulnerable devices relatively quickly, in just a few days.


title: EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

abstract: In this paper, we address the challenge of land use and land cover classification using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible provided in the Earth observation program Copernicus. We present a novel dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images. We provide benchmarks for this novel dataset with its spectral bands using state-of-the-art deep Convolutional Neural Network (CNNs). With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The resulting classification system opens a gate towards a number of Earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at this https URL.


title: Cirrhotic ascites review: Pathophysiology, diagnosis and management

abstract: Ascites is a pathologic accumulation of peritoneal fluidcommonly observed in decompensated cirrhotic states.Its causes are multi-factorial, but principally involve significant volume and hormonal dysregulation in the setting of portal hypertension. The diagnosis of ascites is considered in cirrhotic patients given a constellation of clinical and laboratory findings, and ultimately confirmed, with insight into etiology, by imaging and paracentesis procedures. Treatment for ascites is multi-modal including dietary sodium restriction, pharmacologic therapies, diagnostic and therapeutic paracentesis, and in certain cases transjugular intra-hepatic portosystemic shunt. Ascites is associated with numerous complications including spontaneous bacterial peritonitis, hepato-hydrothorax and hepatorenal syndrome. Given the complex nature of ascites and associatedcomplications, it is not surprising that it heralds increased morbidity and mortality in cirrhotic patients and increased cost-utilization upon the health-care system. This review will detail the pathophysiology of cirrhotic ascites, common complications derived from it, and pertinent treatment modalities.


A Multi-ring Color Fiducial System and A Rule-Based Detection Method for Scalable Fiducial-tracking Augmented Reality


Design of a Extraction System for Definitional Contexts from Biomedical Corpora


title: Real-time audio analysis tools for Pd and MSP

abstract: Two \objects," which run under Max/MSP or Pd, do di erent kinds of real-time analysis of musical sounds. Fiddle is a monophonic or polyphonic maximum-likelihood pitch detector similar to Rabiner's, which can also be used to obtain a raw list of a signal's sinusoidal components. Bonk does a bounded-Q analysis of an incoming sound to detect onsets of percussion instruments in a way which outperforms the standard envelope following technique. The outputs of both objects appear as Max-style control messages. 1 Tools for real-time audio analysis The new real-time patchable software synthesizers have nally brought audio signal processing out of the ivory tower and into the homes of working computer musicians. Now audio can be placed at the center of real-time computer music production, and MIDI, which for a decade was the backbone of the electronic music studio, can be relegated to its appropriate role as a low-bandwidth I/O solution for keyboards and other input devices. Many other sources of control \input" can be imagined than are provided by MIDI devices. This paper, for example, explores two possibilities for deriving a control stream from an incoming audio stream. First, the sound might contain quasi-sinusoidal \partials" and we might wish to know their frequencies and amplitudes. In the case that the audio stream comes from a monophonic or polyphonic pitched instrument, we would like to be able to determine the pitch(es) and loudness(es) of the components. It's clear that we'll never have a perfect pitch detector, but the fiddle object described here does fairly well in some cases. For the many sounds which don't lend themselves to sinusoidal decomposition, we can still get useful information from the overall spectral envelope. For instance, rapid changes in the spectral envelope turn out to be a much more reliable indicator of percussive attacks than are changes in the overall power reported by a classical envelope follower. The bonk object does a bounded-Q lterbank of an incoming sound and can either output the raw analysis or detect onsets which can then be compared to a collection of known spectral templates in order to guess which of several possible kinds of attack has occurred. The fiddle and bonk objects are low tech; the algorithms would be easy to re-code in another language or for other environments from the ones considered here. Our main concern is to get predictable and acceptable behavior using easy-to-understand techniques which won't place an unacceptable computational load on a late-model computer. Some e ort was taken to make fiddle and bonk available on a variety of platforms. They run under Max/MSP (Macintosh), Pd (Wintel, SGI, Linux) and fiddle also runs under FTS (available on several platforms.) Both are distributed with source code; see http://man104nfs.ucsd.edu/~mpuckett/ for details. 2 Analysis of discrete spectra Two problems are of interest here: getting the frequencies and amplitudes of the constituent partials of a sound, and then guessing the pitch. Our program follows the ideas of [Noll 69] and [Rabiner 78]. Whereas the earlier pitch~ object reported in [Puckette 95] departs substantially from the earlier approaches, the algorithmused here adhere more closely to them. First we wish to get a list of peaks with their frequencies and amplitudes. The incoming signal is broken into segments of N samples with N a power of two typically between 256 and 2048. A new analysis is made every N=2 samples. For each analysis the N samples are zero-padded to 2N samples and a rectangular-window DFT is taken. An interesting trick reduces the computation time roughly in half for this setup; see the source code to see how this is done. If we let X[k] denote the zero-padded DFT, we can do a three-point convolution in the frequency domain to get the Hanning-windowed DFT: XH [k] = X[k]=2 (X[k + 2] +X[k 2])=4 Any of the usual criteria can be applied to identify peaks in this spectrum. We then go back to the nonwindowed spectrum to nd the peak frequency using the phase vocoder with hop 1: ! = N k + re X[k 2] X[k + 2] 2X[k] X[k 2] X[k + 2] : This is a special case of a more general formula derived in [Puckette 98]. The amplitude estimate is simply the windowed peak strength at the strongest bin, which because of the zero-padding won't di er by more than about 1 dB from the true peak strength. The phase could be obtained in the same way but we won't bother with that here. 2.1 Guessing fundamental frequencies Fundamental frequencies are guessed using a scheme somewhat suggestive of the maximum-likelihood estimator. Our \likelihood function" is a non-negative function L(f) where f is frequency. The presence of peaks at or near multiples of f increases L(f) in a way which depends on the peak's amplitude and frequency as shown:


title: Recommendations as Treatments: Debiasing Learning and Evaluation

abstract: Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself. In this paper, we provide a principled approach to handle selection biases by adapting models and estimation techniques from causal inference. The approach leads to unbiased performance estimators despite biased data, and to a matrix factorization method that provides substantially improved prediction performance on real-world data. We theoretically and empirically characterize the robustness of the approach, and find that it is highly practical and scalable.


Comparison of Lidar-Based and Radar-Based Adaptive Cruise Control Systems


title: Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions

abstract: While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assignments and tests remains a weak link. In this paper, we study the problem of automatically grading the kinds of open response mathematical questions that figure prominently in STEM (science, technology, engineering, and mathematics) courses. Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors. MLP takes inspiration from the success of natural language processing for text data and comprises three main steps. First, we convert each solution to an open response mathematical question into a series of numerical features. Second, we cluster the features from several solutions to uncover the structures of correct, partially correct, and incorrect solutions. We develop two different clustering approaches, one that leverages generic clustering algorithms and one based on Bayesian nonparametrics. Third, we automatically grade the remaining (potentially large number of) solutions based on their assigned cluster and one instructor-provided grade per cluster. As a bonus, we can track the cluster assignment of each step of a multistep solution and determine when it departs from a cluster of correct solutions, which enables us to indicate the likely locations of errors to learners. We test and validate MLP on real-world MOOC data to demonstrate how it can substantially reduce the human effort required in large-scale educational platforms.


title: Human Action Recognition Using LBP-TOP as Sparse Spatio-Temporal Feature Descriptor

abstract: In this paper we apply the Local Binary Pattern on Three Orthogonal Planes (LBP-TOP) descriptor to the field of human action recognition. A video sequence is described as a collection of spatial-temporal words after the detection of space-time interest points and the description of the area around them. Our contribution has been in the description part, showing LBP-TOP to be a promising descriptor for human action classification purposes. We have also developed several extensions to the descriptor to enhance its performance in human action recognition, showing the method to be computationally efficient.


Joint Causal Inference from Multiple Datasets


title: Introducing compassion-focused therapy

abstract: Shame and self-criticism are transdiagnostic problems. People who experience them may struggle to feel relieved, reassured or safe. Research suggests that a specialised affect regulation system (or systems) underpins feelings of reassurance, safeness and well-being. It is believed to have evolved with attachment systems and, in particular, the ability to register and respond with calming and a sense of well-being to being cared for. In compassion-focused therapy it is hypothesised that this affect regulation system is poorly accessible in people with high shame and self-criticism, in whom the ‘threat’ affect regulation system dominates orientation to their inner and outer worlds. Compassion-focused therapy is an integrated and multimodal approach that draws from evolutionary, social, developmental and Buddhist psychology, and neuroscience. One of its key concerns is to use compassionate mind training to help people develop and work with experiences of inner warmth, safeness and soothing, via compassion and self-compassion.


title: NVMeDirect: A User-space I/O Framework for Application-specific Optimization on NVMe SSDs

abstract: The performance of storage devices has been increased significantly due to emerging technologies such as Solid State Drives (SSDs) and Non-Volatile Memory Express (NVMe) interface. However, the complex I/O stack of the kernel impedes utilizing the full performance of NVMe SSDs. The application-specific optimization is also difficult on the kernel because the kernel should provide generality and fairness. ::: ::: In this paper, we propose a user-level I/O framework which improves the performance by allowing user applications to access commercial NVMe SSDs directly without any hardware modification. Moreover, the proposed framework provides flexibility where user applications can select their own I/O policies including I/O completion method, caching, and I/O scheduling. Our evaluation results show that the proposed framework outperforms the kernel-based I/O by up to 30% on microbenchmarks and by up to 15% on Redis.


title: Low latency analytics for streaming traffic data with Apache Spark

abstract: Demand for new efficient methods for processing large-scale heterogeneous data in real-time is growing. Currently, one key challenge in Big Data is performing low-latency analysis with real-time data. In vehicle traffic, continuous high speed data streams generate large data volumes. Harnessing new technologies is required to benefit from all the potential this data withholds. This work studies the state-of-the-art in distributed and parallel computing, storage, query and ingestion methods, and evaluates tools for periodical and real-time analysis of heterogeneous data. We also introduce a Big Data cloud platform with ingestion, analysis, storage and data query APIs to provide programmable environment for analytics system development and evaluation.


title: Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM

abstract: Mobile banking (mBanking) enables customers to carry out their banking tasks via mobile devices. We advance the extant body of knowledge about mBanking adoption by proposing a model for understanding the importance and relationship between the user perception of mBanking, initial trust in mBanking services, and the fit between the technology and mBanking task characteristics. We synergistically combine the strengths of three IS theories - task technology fit (TTF) model, unified theory of acceptance and usage of technology (UTAUT), and initial trust model (ITM). The model was tested in a study conducted in Portugal, one of the European Union (EU) countries with the highest mobile phone adoption. Based on the sample of 194 individuals we applied partial least squares (PLS) to test the conceptual model propose. The path significance levels were estimated using the bootstrapping method (500 resamples). The study found that facilitating conditions and behavioral intentions directly influence mBanking adoption. Initial trust, performance expectancy, technology characteristics, and task technology fit have total effect on behavioral intention. The paper offers valuable insights to decision-makers involved in the implementation and deployment of mBanking services. For researchers, the paper highlights the usefulness of integrating TTF, UTAUT and ITM in the development of a decision support framework to study the adoption of new technologies.


Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks


title: Natural problems for stereoscopic depth perception in virtual environments

abstract: The use of virtual reality (VR) display systems has escalated over the last 5 yr and may have consequences for those working within vision research. This paper provides a brief review of the literature pertaining to the representation of depth in stereoscopic VR displays. Specific attention is paid to the response of the accommodation system with its cross-links to vergence eye movements, and to the spatial errors that arise when portraying three-dimensional space on a two-dimensional window. It is suggested that these factors prevent large depth intervals of three-dimensional visual space being rendered with integrity through dual two-dimensional arrays.


title: AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos

abstract: We propose a novel method for temporally pooling frames in a video for the task of human action recognition. The method is motivated by the observation that there are only a small number of frames which, together, contain sufficient information to discriminate an action class present in a video, from the rest. The proposed method learns to pool such discriminative and informative frames, while discarding a majority of the non-informative frames in a single temporal scan of the video. Our algorithm does so by continuously predicting the discriminative importance of each video frame and subsequently pooling them in a deep learning framework. We show the effectiveness of our proposed pooling method on standard benchmarks where it consistently improves on baseline pooling methods, with both RGB and optical flow based Convolutional networks. Further, in combination with complementary video representations, we show results that are competitive with respect to the state-of-the-art results on two challenging and publicly available benchmark datasets.


title: PatchShuffle Regularization

abstract: This paper focuses on regularizing the training of the convolutional neural network (CNN). We propose a new regularization approach named ``PatchShuffle`` that can be adopted in any classification-oriented CNN models. It is easy to implement: in each mini-batch, images or feature maps are randomly chosen to undergo a transformation such that pixels within each local patch are shuffled. Through generating images and feature maps with interior orderless patches, PatchShuffle creates rich local variations, reduces the risk of network overfitting, and can be viewed as a beneficial supplement to various kinds of training regularization techniques, such as weight decay, model ensemble and dropout. Experiments on four representative classification datasets show that PatchShuffle improves the generalization ability of CNN especially when the data is scarce. Moreover, we empirically illustrate that CNN models trained with PatchShuffle are more robust to noise and local changes in an image.