Reviewerly/exHarmony huggingface dataset:
Multiscale-VR: Multiscale Gigapixel 3D Panoramic Videography for Virtual Reality

Creating virtual reality (VR) content with effective imaging systems has attracted significant attention worldwide following the broad applications of VR in various fields, including entertainment, surveillance, sports, etc. However, due to the inherent trade-off between field-of-view and resolution of the imaging system as well as the prohibitive computational cost, live capturing and generating multiscale 360° 3D video content at an eye-limited resolution to provide immersive VR experiences confront significant challenges. In this work, we propose Multiscale-VR, a multiscale unstructured camera array computational imaging system for high-quality gigapixel 3D panoramic videography that creates the six-degree-of-freedom multiscale interactive VR content. The Multiscale-VR imaging system comprises scalable cylindrical-distributed global and local cameras, where global stereo cameras are stitched to cover 360° field-of-view, and unstructured local monocular cameras are adapted to the global camera for flexible high-resolution video streaming arrangement. We demonstrate that a high-quality gigapixel depth video can be faithfully reconstructed by our deep neural network-based algorithm pipeline where the global depth via stereo matching and the local depth via high-resolution RGB-guided refinement are associated. To generate the immersive 3D VR content, we present a three-layer rendering framework that includes an original layer for scene rendering, a diffusion layer for handling occlusion regions, and a dynamic layer for efficient dynamic foreground rendering. Our multiscale reconstruction architecture enables the proposed prototype system for rendering highly effective 3D, 360° gigapixel live VR video at 30 fps from the captured high-throughput multiscale video sequences. The proposed multiscale interactive VR content generation approach by using a heterogeneous camera system design, in contrast to the existing single-scale VR imaging systems with structured homogeneous cameras, will open up new avenues of research in VR and provide an unprecedented immersive experience benefiting various novel applications.
Advances in verification of ReLU neural networks

Abstract We consider the problem of verifying linear properties of neural networks. Despite their success in many classification and prediction tasks, neural networks may return unexpected results for certain inputs. This is highly problematic with respect to the application of neural networks for safety-critical tasks, e.g. in autonomous driving. We provide an overview of algorithmic approaches that aim to provide formal guarantees on the behaviour of neural networks. Moreover, we present new theoretical results with respect to the approximation of ReLU neural networks. On the other hand, we implement a solver for verification of ReLU neural networks which combines mixed integer programming with specialized branching and approximation techniques. To evaluate its performance, we conduct an extensive computational study. For that we use test instances based on the ACAS Xu system and the MNIST handwritten digit data set. The results indicate that our approach is very competitive with others, i.e. it outperforms the solvers of Bunel et al. (in: Bengio, Wallach, Larochelle, Grauman, Cesa-Bianchi, Garnett (eds) Advances in neural information processing systems (NIPS 2018), 2018) and Reluplex (Katz et al. in: Computer aided verification—29th international conference, CAV 2017, Heidelberg, Germany, July 24–28, 2017, Proceedings, 2017). In comparison to the solvers ReluVal (Wang et al. in: 27th USENIX security symposium (USENIX Security 18), USENIX Association, Baltimore, 2018a) and Neurify (Wang et al. in: 32nd Conference on neural information processing systems (NIPS), Montreal, 2018b), the number of necessary branchings is much smaller. Our solver is publicly available and able to solve the verification problem for instances which do not have independent bounds for each input neuron.
Hand gesture recognition via image processing techniques and deep CNN

In the present study, we sought to enable instant tracking of the hand region as a region of interest (ROI) within the image range of a webcam, while also identifying specific hand gestures to facilitate the control of home appliances in smart homes or issuing of commands to human-computer interaction fields. To accomplish this objective, we first applied skin color detection and noise processing to remove unnecessary background information from the captured image, before applying background subtraction for detection of the ROI. Then, to prevent background objects or noise from influencing the ROI, we utilized the kernelized correlation filters (KCF) algorithm to implement tracking of the detected ROI. Next, the size of the ROI image was resized to 100×120 and input into a deep convolutional neural network (CNN) to enable the identification of various hand gestures. In the present study, two deep CNN architectures modified from the AlexNet CNN and VGGNet CNN, respectively, were developed by substantially reducing the number of network parameters used and appropriately adjusting internal network configuration settings. Then, the tracking and recognition process described above was continuously repeated to achieve immediate effect, with the execution of the system continuing until the hand is removed from the camera range. The results indicated excellent performance by both of the proposed deep CNN architectures. In particular, the modified version of the VGGNet CNN achieved better performance with a recognition rate of 99.90% for the utilized training data set and a recognition rate of 95.61% for the utilized test data set, which indicate the good feasibility of the system for practical applications.
Mutually Informed Correlation Coefficient (MICC) - a New Filter Based Feature Selection Method

Feature selection (FS) is a well-explored domain of data pre-processing and information theory. It is the process of selecting important features from a high-dimensional feature vectors possibly having many redundant and/or non-informative features. In this paper, we have proposed a score-based filter FS approach known as Mutually Informed Correlation Coefficient (MICC) by combining two popular statistical dependence measures namely Mutual Information (MI) and Pearson Correlation Coefficient (PCC). We have evaluated MICC on different variations of Local Binary Pattern (LBP) based feature vectors used for classifying the components of handwritten document images as text or non-text. We have compared the results with some popular filter methods namely Gini Index, T-test, ReliefF, along with MI and PCC individually. The results and corresponding comparisons show that our proposed method not only does FS efficiently but also enhances the recognition accuracy of the said classification problem. The code of the proposed algorithm can be found in this link: MICC.
Less is more? The impact of written corrective feedback on corpus-assisted L2 error resolution

The past decade has seen a sharp increase in research into L2 learners’ direct use of language corpora (typically known as ‘data-driven learning’, DDL) for error resolution in L2 writing. However, a crucial yet underexplored variable in this process is whether and how the form of written corrective feedback (WCF) provided on L2 writing facilitates effective corpus consultation for L2 error resolution. Focusing on L2 writers at the post-graduate level and using a short private online course for DDL training, we determine the impact of four WCF conditions (varying in their degree of directness) on students’ use of corpora for lexical and grammatical error resolution, and the appropriacy of error revisions made with/without corpora for these error types. The results suggest that ‘less (WCF) is more’ if learners are to make successful error revisions via corpus consultation, with more direct WCF conditions often resulting in students revising errors without consulting a corpus. However, less direct WCF conditions sometimes resulted in inappropriate revisions, as learners required additional information as to the nature and location of the specific error. Differences were also found in the effectiveness of corpus consultation for grammatical and lexical error types, with WCF a confounding factor. These results suggest that if corpora are to be used for L2 error resolution, teachers need to carefully consider whether their WCF allows for meaningful engagement with corpora to occur, and whether corpus consultation is suitable or desirable for resolving all error types.
Success and Failure in Software Engineering: A Followup Systematic Literature Review

Success and failure in software engineering are still among the least understood phenomena in the discipline. In a recent special journal issue on the topic, Mäntylä et al. started discussing these topics from different angles; the authors focused their contributions on offering a general overview of both topics without deeper detail. Recognizing the importance and impact of the topic, in this article we have executed a followup, more in-depth systematic literature review with additional analyses beyond what was previously provided. These new analyses offer: a grounded-theory of success and failure factors, harvesting over 500+ factors from the literature; 14 manually validated clusters of factors that provide relevant areas for success- and failure-specific measurement and risk-analysis; a quality model composed of previously unmeasured organizational structure quantities which are germane to software product, process, and community quality. We show that the topics of success and failure deserve further study as well as further automated tool support, e.g., monitoring tools and metrics able to track the factors and patterns emerging from this article. This article provides managers with risks as well as a more fine-grained analysis of the parameters that can be appraised to anticipate the risks.
A systematic mapping study of clone visualization

Knowing code clones (similar code fragments) is helpful in software maintenance and re-engineering. As clone detectors return huge numbers of clones, visualization techniques have been proposed to make cloning information more comprehensible and useful for programmers. We present a mapping study of clone visualization techniques, classifying visualizations in respect to the user goals to be achieved by means of clone visualizations and relevant clone-related information needs. Our mapping study will aid tool users in selecting clone visualization tools suitable for the task at hand, tool vendors in improving capabilities of their tools, and researchers in identifying open problems in clone visualization research.
Antibody Supervised Training of a Deep Learning Based Algorithm for Leukocyte Segmentation in Papillary Thyroid Carcinoma

The quantity of leukocytes in papillary thyroid carcinoma (PTC) potentially have prognostic and treatment predictive value. Here, we propose a novel method for training a convolutional neural network (CNN) algorithm for segmenting leukocytes in PTCs. Tissue samples from two retrospective PTC cohort were obtained and representative tissue slides from twelve patients were stained with hematoxylin and eosin (HE) and digitized. Then, the HE slides were destained and restained immunohistochemically (IHC) with antibodies to the pan-leukocyte anti CD45 antigen and scanned again. The two stain-pairs of all representative tissue slides were registered, and image tiles of regions of interests were exported. The image tiles were processed and the 3,3'-diaminobenzidine (DAB) stained areas representing anti CD45 expression were turned into binary masks. These binary masks were applied as annotations on the HE image tiles and used in the training of a CNN algorithm. Ten whole slide images (WSIs) were used for training using a five-fold cross-validation and the remaining two slides were used as an independent test set for the trained model. For visual evaluation, the algorithm was run on all twelve WSIs, and in total 238,144 tiles sized 500 × 500 pixels were analyzed. The trained CNN algorithm had an intersection over union of 0.82 for detection of leukocytes in the HE image tiles when comparing the prediction masks to the ground truth anti CD45 mask. We conclude that this method for generating antibody supervised annotations using the destain-restain IHC guided annotations resulted in high accuracy segmentations of leukocytes in HE tissue images.
Near Real-time Crowd Counting using Deep Learning Approach

Abstract In the current digital era, at many places crowd counting mechanisms still rely on old-fashioned methods such as maintaining registers, making use of people counters and sensors based counting at entrance. These methods fail in the places where the movement of people is completely random, highly variable and dynamic. These methods are time consuming and tedious. The proposed system is developed for situations where emergency evacuations are required such as fire outbreaks, calamitous events, etc. and making informed decisions on the basis of the number of people such as food, water, detecting congestion, etc. A deep convolution neural network (DCNN) based system can be used for near real-time crowd counting. The system uses NVIDIA GPU processor to exploit the parallel computing framework to achieve swift and agile processing of the video feed taken through a camera. This work contributes towards constructing a model to detect heads captured by CCTV camera. The model is trained extensively by providing several scenarios such as overlapping heads, partial visibility of heads etc. This system provides significant accuracy in estimating the head count in dense population in reasonably less amount of time.
MagHacker

Stylus pens have been widely used with today's mobile devices to provide a convenient handwriting input method, but also bring a unique security vulnerability that may unveil the user's handwriting contents to a nearby eavesdropper. In this paper, we present MagHacker, a new sensing system that realizes such eavesdropping attack over commodity mobile devices, which monitor and analyze the magnetic field being produced by the stylus pen's internal magnet. MagHacker divides the continuous magnetometer readings into small segments that represent individual letters, and then translates these readings into writing trajectories for letter recognition. Experiment results over realistic handwritings from multiple human beings demonstrate that MagHacker can accurately eavesdrop more than 80% of handwriting with stylus pens, from a distance of 10cm. Only slight degradation in such accuracy is produced when the eavesdropping distance or the handwriting speed increases. MagHacker is highly energy efficient, and can well adapt to different stylus pen models and environmental contexts.
A Primal-Dual SGD Algorithm for Distributed Nonconvex Optimization

The distributed nonconvex optimization problem of minimizing a global cost function formed by a sum of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> local cost functions by using local information exchange is considered. This problem is an important component of many machine learning techniques with data parallelism, such as deep learning and federated learning. We propose a distributed primal-dual stochastic gradient descent (SGD) algorithm, suitable for arbitrarily connected communication networks and any smooth (possibly nonconvex) cost functions. We show that the proposed algorithm achieves the linear speedup convergence rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(1/\sqrt{nT})$</tex> for general nonconvex cost functions and the linear speedup convergence rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(1/(nT))$</tex> when the global cost function satisfies the Polyak-Łojasiewicz (P-Ł) condition, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> is the total number of iterations. We also show that the output of the proposed algorithm with constant parameters linearly converges to a neighborhood of a global optimum. We demonstrate through numerical experiments the efficiency of our algorithm in comparison with the baseline centralized SGD and recently proposed distributed SGD algorithms.
The TV<i> <sub>p</sub> </i> Regularized Mumford-Shah Model for Image Labeling and Segmentation

The Mumford-Shah model is an important tool for image labeling and segmentation, which pursues a piecewise smooth approximation of the original image and the boundaries with the shortest length. In contrast to previous efforts, which use the total variation regularization to measure the total length of the boundaries, we build up a novel piecewise smooth Mumford-Shah model by utilizing a non-convex ℓ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> regularity term for p ∈ (0,1), which can well preserve sharp edges and eliminate geometric staircasing effects. We present optimization algorithms with convergence verification, where all subproblems can be solved by either the closed-form solution or fast Fourier transform (FFT). The method is compared to piecewise constant labeling algorithm and several state-of-the-art piecewise smooth Mumford-Shah models based on image decomposition approximations. Both labeling and segmentation results on synthetic and real images confirm the robustness and efficiency of the proposed method.
Is Cryptocurrency Money?

Cryptocurrencies, such as Bitcoin, Ethereum or Ripple, are discussed as a new form of money. Typically, money fulfills three core functions: 1) medium of exchange, 2) store of value, and 3) unit of account. To examine whether individuals consider cryptocurrencies as money, we conduct three studies. Study 1 (N=57) provides valid and reliable measurement items for the three core functions of money. Study 2 (N=95) shows that the general perception about the fulfillment of the core functions is rather positive for cryptocurrencies. The results from Study 3 (N=99) furthermore reveal that Bitcoin is perceived significantly better in fulfilling all three functions than Ethereum or Ripple. The findings suggest that cryptocurrency research needs to include or at least control for the basic perceptions of core functions when examining individuals' adoption or use of cryptocurrency as money. Furthermore, the findings suggest that existing knowledge from Bitcoin use or adoption research cannot be easily transferred to the context of another cryptocurrency.
Firearm Detection and Segmentation Using an Ensemble of Semantic Neural Networks

In recent years we have seen an upsurge in terror attacks around the world. Such attacks usually happen in public places with large crowds to cause the most damage possible and get the most attention. Even though surveillance cameras are assumed to be a powerful tool, their effect in preventing crime is far from clear due to either limitation in the ability of humans to vigilantly monitor video surveillance or for the simple reason that they are operating passively. In this paper, we present a weapon detection system based on an ensemble of semantic Convolutional Neural Networks that decomposes the problem of detecting and locating a weapon into a set of smaller problems concerned with the individual component parts of a weapon. This approach has computational and practical advantages: a set of simpler neural networks dedicated to specific tasks requires less computational resources and can be trained in parallel; the overall output of the system given by the aggregation of the outputs of individual networks can be tuned by a user to trade-off false positives and false negatives; finally, according to ensemble theory, the output of the overall system will be robust and reliable even in the presence of weak individual models. We evaluated our system running simulations aimed at assessing the accuracy of individual networks and the whole system. The results on synthetic data and real-world data are promising, and they suggest that our approach may have advantages compared to the monolithic approach based on a single deep convolutional neural network.
ERG-DE: An elites regeneration framework for differential evolution

Differential evolution (DE) is one of the most popular paradigms of evolutionary algorithms. Numerous variants of basic DE have been developed in the past two decades after it was first proposed. However, very few works focused on re-exploring the neighborhood area of the elite solutions, which is definitely a promising area according to the proximate optimality principle. Here, a simple yet efficient elites regeneration (ERG) framework was designed to fill this gap. The elite population in this framework is defined as a group of individuals with better fitness values and they are regenerated after the selection procedure in DE. Specifically, a new individual is produced from the search space around each elite individual (i.e. the parent individual) by sampling Gaussian or Cauchy probability models and replaces the parent if it has better fitness value. The implementation of this procedure only introduces two parameters that need to be tuned, i.e. the standard deviation for Gaussian distribution and the scale parameter for the Cauchy distribution. In the proposed framework, the elite individuals serve as the mean or location parameters of the probability models and the standard deviation and scale parameters are tuned by experiments as a small constant value. Thus, offspring individuals are generated in areas close to their corresponding elite parents. The framework allows thorough exploitation of search neighborhoods around elite individuals and ultimately helps the elite individuals escaping from local optima. Experiments results on CEC2014 benchmark revealed that ERG framework significantly increased the optimization capacity for four original DE algorithms, four classical DE variants, and two state-of-the-art DE variants. In addition, it also demonstrated competitive performance when compared with another DE framework.