Reviewerly/exHarmony huggingface dataset:
Multiple change point clustering of count processes with application to California COVID data

In this paper, a model-based clustering algorithm relying on a finite mixture of negative binomial Lévy processes is proposed. The algorithm models heterogeneous stochastic count process data and automatically estimates multiple change points upon fitting the mixture model. Such change point estimation identifies time points when deviation from the standard process has occurred and serves as an important diagnostic tool for analyzing temporal data. The proposed model is applied to the COVID-positive ICU cases in the state of California with very interesting results.
Pragmatic Implementation of Reinforcement Algorithms For Path Finding On Raspberry Pi

In this paper, pragmatic implementation of an indoor autonomous delivery system that exploits Reinforcement Learning algorithms for path planning and collision avoidance is audited. The proposed system is a cost-efficient approach that is implemented to facilitate a Raspberry Pi controlled four-wheel drive non-holonomic robot map a grid. This approach computes and navigates the shortest path from a source key point to a destination key point to carry out the desired delivery. Q learning and Deep-Q learning are used to find the optimal path while avoiding collision with static obstacles. This work defines an approach to deploy these two algorithms on a robot. A novel algorithm to decode an array of directions into accurate movements in a certain action space is also proposed. The procedure followed to dispatch this system with the said requirements is described, ergo presenting our proof of concept for indoor autonomous delivery vehicles.
Automatic metadata information extraction from scientific literature using deep neural networks

We present a novel computer vision-based deep learning approach for metadata extraction as both a central component of and an ancillary aid to structured information extraction from scientific literature which has various formats. The number of scientific publications is growing rapidly, but existing methods cannot combine the techniques of layout extraction and text recognition efficiently because of the various formats used by scientific literature publishers. In this paper, we introduce an end-to-end trainable neural network for segmenting and labeling the main regions of scientific documents, while simultaneously recognizing text from the detected regions. The proposed framework combines object detection techniques based on Recurrent Convolutional Neural Network (RCNN) for scientific document layout detection with Convolutional Recurrent Neural Network (CRNN) for text recognition. We also contribute a novel data set of main region annotations for scientific literature metadata information extraction to complement the limited availability of high-quality data set. The final outputs of the network are the text content (payload) and the corresponding labels of the major regions. Our results show that our model outperforms state-of-the-field baselines.
Efficient Image Embedding for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) is a task belonging to multiple sub-categories classification. It is a challenging task due to high intraclass variation and inter-class similarity. Most exiting methods pay attention to capture discriminative semantic parts to address those problems. In this paper, we introduce a two-level network which consists of raw-level and object-level networks, and we name it “Efficient Image Embedding”. Its training procedure has two stages which the raw-level is for localization by the aggregation of feature maps, and the last is for classification. The two-level use Adaptive Angular Margin loss (AAM-loss), which improve an intra-class compactness and inter-class variety of image embedding. Our approach is to identify object regions without any hand-crafted bounding-box, and can be trained in an end-to-end manner. It has achieved better accuracy on two datasets compared to the existing work, which are 89.0% for CUB200-2011 and 93.3% for FGVC-Aircraft.
Comparison of Object Detection in Head-Mounted and Desktop Displays for Congruent and Incongruent Environments

Virtual reality technologies, including head-mounted displays (HMD), can provide benefits to psychological research by combining high degrees of experimental control with improved ecological validity. This is due to the strong feeling of being in the displayed environment (presence) experienced by VR users. As of yet, it is not fully explored how using HMDs impacts basic perceptual tasks, such as object perception. In traditional display setups, the congruency between background environment and object category has been shown to impact response times in object perception tasks. In this study, we investigated whether this well-established effect is comparable when using desktop and HMD devices. In the study, 21 participants used both desktop and HMD setups to perform an object identification task and, subsequently, their subjective presence while experiencing two-distinct virtual environments (a beach and a home environment) was evaluated. Participants were quicker to identify objects in the HMD condition, independent of object-environment congruency, while congruency effects were not impacted. Furthermore, participants reported significantly higher presence in the HMD condition.
Algorithm 1022: Efficient Algorithms for Computing a Rank-Revealing UTV Factorization on Parallel Computing Architectures

Randomized singular value decomposition (RSVD) is by now a well-established technique for efficiently computing an approximate singular value decomposition of a matrix. Building on the ideas that underpin RSVD, the recently proposed algorithm “randUTV” computes a full factorization of a given matrix that provides low-rank approximations with near-optimal error. Because the bulk of randUTV is cast in terms of communication-efficient operations such as matrix-matrix multiplication and unpivoted QR factorizations, it is faster than competing rank-revealing factorization methods such as column-pivoted QR in most high-performance computational settings. In this article, optimized randUTV implementations are presented for both shared-memory and distributed-memory computing environments. For shared memory, randUTV is redesigned in terms of an algorithm-by-blocks that, together with a runtime task scheduler, eliminates bottlenecks from data synchronization points to achieve acceleration over the standard blocked algorithm based on a purely fork-join approach. The distributed-memory implementation is based on the ScaLAPACK library. The performance of our new codes compares favorably with competing factorizations available on both shared-memory and distributed-memory architectures.
Research on Motion Path Planning Method of Live Working Robot Manipulator Based on Reverse Splitting Calculation

Abstract In the dual manipulator motion planning of live working robot, the traditional motion planning algorithm has the problems of too many turning points and non-smoothness. Therefore, this paper proposes a method of reverse splitting and local interpolation to remove redundant path nodes and optimize the motion trajectory. The working scene of overhead distribution line live working robot is simulated in MATLAB robot toolbox. The model includes manipulator R, manipulator L and overhead conductor to verify the effect of the method proposed in this paper. The results show that, the proposed method ensures that the redundant path nodes are removed on the premise of no collision between the two manipulators, which significantly improves the quality of the path.
Rule-Enhanced Active Learning for Semi-Automated Weak Supervision

A major bottleneck preventing the extension of deep learning systems to new domains is the prohibitive cost of acquiring sufficient training labels. Alternatives such as weak supervision, active learning, and fine-tuning of pretrained models reduce this burden but require substantial human input to select a highly informative subset of instances or to curate labeling functions. REGAL (Rule-Enhanced Generative Active Learning) is an improved framework for weakly supervised text classification that performs active learning over labeling functions rather than individual instances. REGAL interactively creates high-quality labeling patterns from raw text, enabling a single annotator to accurately label an entire dataset after initialization with three keywords for each class. Experiments demonstrate that REGAL extracts up to 3 times as many high-accuracy labeling functions from text as current state-of-the-art methods for interactive weak supervision, enabling REGAL to dramatically reduce the annotation burden of writing labeling functions for weak supervision. Statistical analysis reveals REGAL performs equal or significantly better than interactive weak supervision for five of six commonly used natural language processing (NLP) baseline datasets.
Detecting Latent Heterogeneity

chapter Share on Detecting Latent Heterogeneity Author: Judea Pearl Search about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 483–506https://doi.org/10.1145/3501714.3501742Online:04 March 2022Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Software quality prediction using machine learning

Background The demand for software is increasing every day in various fields. Software developers put more effort to develop and test the quality of the software and verify its reliability before it is released. High-quality software modules were developed to allow others to reuse the components. Purpose : This paper provides information to researchers in the software quality prediction field based on machine learning algorithms. Methodology : Most of the machine learning techniques and the relevant software metrics used in many high-quality papers published between 2010 and the end of 2021 have been analysed. Findings : Machine learning techniques are the most suitable for predicting software quality. Most of the researchers' interest was to predict the reliability of software. The detailed analysis enables researchers to choose the best way to plan their research and to make a good contribution in the field of software quality prediction.
Public key cryptosystem and binary Edwards curves on the ring F<sub>2</sub> <sub>n</sub>[e], e<sup>2</sup> =e for data management

Let <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{n}}[e]$</tex> be a finite ring of characteristic 2, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$e^{2}=e$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> is a positive integer. Let <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(a, d) \in\left(\mathbb{F}_{2^{n}}[e]\right)^{2}$</tex> , sach that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d+a^{2}+a$</tex> are invertible in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{n}}[e]$</tex> . In the present paper, we study <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$E_{B, a, d}\left(\mathbb{F}_{2^{n}}[e]\right)$</tex> , the binary Edwards curve over the ring <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{n}}[e]$</tex> . More precisely, we give a bijection between this curve and the product of two binary Edwards curves defined on the finite field <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{n}}$</tex> . Afterthat we study the addition law of binary Edwards curves over the ring <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbb{F}_{2^{n}}[e]$</tex> . We end this work with cryptography applications, ElGamal binary Edwards curve cryptosystem and Cramer-Shoup binary Edwards curve cryptosystem.
Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, (SGD)^2

Readability criteria, such as distance or neighborhood preservation, are often used to optimize node-link representations of graphs to enable the comprehension of the underlying data. With few exceptions, graph drawing algorithms typically optimize one such criterion, usually at the expense of others. We propose a layout approach, Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, (SGD)2, that can handle multiple readability criteria. (SGD)2 can optimize any criterion that can be described by a differentiable function. Our approach is flexible and can be used to optimize several criteria that have already been considered earlier (e.g., obtaining ideal edge lengths, stress, neighborhood preservation) as well as other criteria which have not yet been explicitly optimized in such fashion (e.g., node resolution, angular resolution, aspect ratio). The approach is scalable and can handle large graphs. A variation of the underlying approach can also be used to optimize many desirable properties in planar graphs, while maintaining planarity. Finally, we provide quantitative and qualitative evidence of the effectiveness of (SGD)2: we analyze the interactions between criteria, measure the quality of layouts generated from (SGD)2 as well as the runtime behavior, and analyze the impact of sample sizes. The source code is available on github and we also provide an interactive demo for small graphs.
Intelligent Localization Sampling System Based on Deep Learning and Image Processing Technology

In this paper, deep learning and image processing technologies are combined, and an automatic sampling robot is proposed that can completely replace the manual method in the three-dimensional space when used for the autonomous location of sampling points. It can also achieve good localization accuracy, which solves the problems of the high labor intensity, low efficiency, and poor scientific accuracy of the manual sampling of mineral powder. To improve localization accuracy and eliminate non-linear image distortion due to wide-angle lenses, distortion correction was applied to the captured images. We solved the problem of low detection accuracy in some scenes of Single Shot MultiBox Detector (SSD) through data augmentation. A visual localization model has been established, and the image coordinates of the sampling point have been determined through color screening, image segmentation, and connected body feature screening, while coordinate conversion has been performed to complete the spatial localization of the sampling point, guiding the robot in performing accurate sampling. Field experiments were conducted to validate the intelligent sampling robot, which showed that the maximum visual positioning error of the robot is 36 mm in the x-direction and 24 mm in the y-direction, both of which meet the error range of less than or equal to 50 mm, and could meet the technical standards and requirements of industrial sampling localization accuracy.
Sikh women’s internet sites: development, content, mission

Sikhs have had a significant presence on the Internet since the 1990s. For the most part, early web sites were clearly gendered, highlighting male-focused history, identity, and concerns, with some sites including a ‘women’s section’. Sikh women’s specific sites were few. The scenario has changed considerably with a number of new sites developed by and for women. “Kaur Life”, “Kaurista”, the “Kaur Project”, and the “Sikh Feminist Research Institute” reflect the needs, practices, and concerns of a new generation of engaged, reflective, and activist Sikh women, here identified as “i-Kaurs”. I explore the development of Sikh women’s sites, examining changes in content, presentation, and mission between the earlier and later online milieu. To what extent do Sikh women’s sites offer opportunities to network, to provide safe spaces for women to exchange ideas and commentary? How do these online spaces intersect with offline Sikh religious environments? To what extent do these sites engage and reflect wider women’s concerns?
Brain Tumor Segmentation Based on Minimum Spanning Tree

In this paper, we propose a minimum spanning tree-based method for segmenting brain tumors. The proposed method performs interactive segmentation based on the minimum spanning tree without tuning parameters. The steps involve preprocessing, making a graph, constructing a minimum spanning tree, and a newly implemented way of interactively segmenting the region of interest. In the preprocessing step, a Gaussian filter is applied to 2D images to remove the noise. Then, the pixel neighbor graph is weighted by intensity differences and the corresponding minimum spanning tree is constructed. The image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the minimum spanning tree into two trees. One of these trees represents the region of interest and the other represents the background. Finally, the segmentation given by the two trees is visualized. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The comparison between our results and the gold standard segmentation confirmed the validity of the minimum spanning tree approach. The proposed method is simple to implement and the results indicate that it is accurate and efficient.
Localized Statistical Shape Models for Large-Scale Problems With Few Training Data

Statistical shape models have been successfully used in numerous biomedical image analysis applications where prior shape information is helpful such as organ segmentation or data augmentation when training deep learning models. However, training such models requires large data sets, which are often not available and, hence, shape models frequently fail to represent local details of unseen shapes. This work introduces a kernel-based method to alleviate this problem via so-called model localization. It is specifically designed to be used in large-scale shape modeling scenarios like deep learning data augmentation and fits seamlessly into the classical shape modeling framework.Relying on recent advances in multi-level shape model localization via distance-based covariance matrix manipulations and Grassmannian-based level fusion, this work proposes a novel and computationally efficient kernel-based localization technique. Moreover, a novel way to improve the specificity of such models via normalizing flow-based density estimation is presented.The method is evaluated on the publicly available JSRT/SCR chest X-ray and IXI brain data sets. The results confirm the effectiveness of the kernelized formulation and also highlight the models' improved specificity when utilizing the proposed density estimation method.This work shows that flexible and specific shape models from few training samples can be generated in a computationally efficient way by combining ideas from kernel theory and normalizing flows.The proposed method together with its publicly available implementation allows to build shape models from few training samples directly usable for applications like data augmentation.
Accelerating Video Captioning on Heterogeneous System Architectures

Video captioning is a core technology to many important applications, such as AI-assisted medical diagnosis, video question answering, storytelling through videos, and lip-reading. Video captioning employs a hybrid CNN + RNN model. Accelerating such a hybrid model on a heterogeneous system is challenging for two reasons. First, CNN and RNN exhibit very different computing behaviors, making the mapping between computation and heterogeneous devices difficult. Second, data dependency exists between the CNN and RNN within a video frame and between adjacent RNNs across video frames. These data dependencies prohibit the full parallelization of the hybrid model. The issues also include the utilization of accelerator resources, which is critical to maximizing the performance. In this work, we propose a fine-grained scheduling scheme for mapping computation and devices within a video frame, and a pipeline scheduling scheme for exploiting maximum parallelism between the execution of the video frames. In addition, we propose two capacity-guided scheduling methods. On the server, the concurrent kernel execution mechanism is exploited for improving GPU utilization. On the edge platform, we rearrange CNN computation among the CPU and EdgeTPUs guided by the EdgeTPU’s SRAM capacity so that balanced computation is achieved and off-chip memory overhead is minimized. Experimental results show that our scheduling scheme improves video captioning performance by up to 3.24 \( \times \) with CPU + GPU collaboration over the GPU-only execution. On an edge platform with an ARM CPU and two EdgeTPUs, our CPU + EdgeTPU scheduling exhibits outstanding performance, which achieves up to 54.9 \( \times \) speedup compared to using ARM CPU only and can perform video captioning of 59 frames per second.
<scp>IFGAN</scp>: Information fusion generative adversarial network for knowledge base completion

Abstract Knowledge base completion (KBC) aims to predict missing information in a knowledge base. From a data governance perspective, KBC is an important task not only in knowledge management but also in downstream knowledge base applications. The prosperity of mobile applications and online systems enables devices to generate an enormous volume of data containing valuable knowledge. However, these data are vast and contain noise, so utilizing them in KBC requires particular skill. In this paper, we propose information fusion generative adversarial network (IFGAN) to handle heterogeneous data. We design a bidirectional learning architecture including a graph convolutional neural network and graph attention network to learn contextual embeddings that fuse knowledge from a knowledge base and data generated by an application. For efficient negative sampling, we employ different kinds of convolution structures, such as depthwise separable convolution and involution in the generator of the network. The convolution structure is known to be suitable for collaborative computing and promises great potential with the progress of technology since the structure is extensible. We demonstrate the effectiveness of the proposed model on KB4Rec dataset, the evaluation metrics MRR and H@10 were improved compared with previous models; the code is available at https://github.com/Tianchen627/IFGAN .
HARDy: Handling Arbitrary Recognition of Data in Python

HARDy is a