Reviewerly/exHarmony huggingface dataset:
Global Reasoning Network for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation(WSSS) method with image-level labels has been obtaining tremendous attention in recent years.Current WSSS methods utilize Class Activation Map(CAM) produced by classification network, which focus on local small discriminative regions and contain useless background regions.To tackle these problems,we propose the Global Reasoning Network(GRN) to acquire dense object attention maps in this work.More specifically,the network consists of multi-feature fusion module(MFFM) and attention hybrid pooling module(AHPM).The MFFM can get enhanced feature maps by combining with multi-scale feature maps by classification network,which are essential for the generation of CAM.The AHHP,composed of spatial attention,channel attention and hybrid pooling,replaces the traditional Global Average Pooling(GAP),which the problem of useless background regions is alleviated to some extent without increasing parameters.We evaluated our method on the PASCAL VOC 2012,and the results shows improvements overâ€¨existing methods,achieving state-of-the-art performance.
Sampling Over Riemannian Manifolds Using Kernel Herding

Kernel herding is a deterministic sampling algorithm designed to draw ‘super samples' from probability distributions when provided with their kernel mean embeddings in a reproducing kernel Hilbert space (RKHS). Empirical expectations of functions in the RKHS formed using these super samples tend to converge even faster than random sampling from the true distribution itself. Standard implementations of kernel herding have been restricted to sampling over flat Euclidean spaces, which is not ideal for applications such as robotics where more general Riemannian manifolds may be appropriate. We propose to adapt kernel herding to Riemannian manifolds by (1) using geometry-aware kernels that incorporate the appropriate distance metric for the manifold and (2) using Riemannian optimization to constrain herded samples to lie on the manifold. We evaluate our approach on problems involving various manifolds commonly used in robotics including the SO(3) manifold of rotation matrices, the spherical manifold used to encode unit quaternions, and the manifold of symmetric positive definite matrices. We demonstrate that our approach outperforms existing alternatives on the task of resampling from empirical distributions of weighted particles, a problem encountered in applications such as particle filtering. We also demonstrate how Riemannian kernel herding can be used as part of the kernel recursive approximate Bayesian computation algorithm to estimate parameters of black-box simulators, including inertia matrices of an Adroit robot hand simulator. Our results confirm that exploiting geometric information through our approach to kernel herding yields better results than alternatives including standard kernel herding with heuristic projections.
RhythmCount: A Python package to analyse the rhythmicity in count data

Analysis of rhythmicity in count data has become an important aspect in different fields from science and engineering to economy and process planning. Several methods have recently been implemented to investigate the rhythmicity of continuous data. However, in most cases, these need to be manually adapted to work with count data as well. Namely, non-negative integer data that are usually obtained by counting of specific events. Herein, we describe the implementation of RhythmCount, an open-source Python module specifically devoted to rhythmicity analysis of count data. RhythmCount combines the cosinor regression model with different count data models. The proposed implementation allows automatic identification of the most suitable model for a given dataset, assessment of different measures and parameters of the rhythmicity of the dataset, and production of publication-ready figures that can be used for a straightforward interpretation of the obtained results. We demonstrate an application of the proposed module in the analysis and comparison of the daily traffic trends during the COVID-19 epidemic with the daily traffic trends in normal (non-epidemic) conditions. RhythmCount is available at https://github.com/ninavelikajne/RhythmCount under the MIT license. The implementation reported in this paper corresponds to the software release v1.1.
Preserving privacy in mobile spatial computing

Mapping and localization are the key components in mobile spatial computing to facilitate interactions between users and the digital model of the physical world. To enable localization, mobile devices keep capturing images of the real-world surroundings and uploading them to a server with spatial maps for localization. This leads to privacy concerns on the potential leakage of sensitive information in both spatial maps and localization images (e.g., when used in confidential industrial settings or our homes). Motivated by the above issues, we present a holistic research agenda in this paper for designing principled approaches to preserve privacy in spatial mapping and localization. We introduce our ongoing research, including learning-assisted noise generation to shield spatial maps, distributed architecture with intelligent aggregation to protect localization images, and end-to-end privacy preservation with fully homomorphic encryption. We also discuss the technical challenges, our preliminary results, and open research problems in those areas.
Prototype Machine for Traditional and Technological Ophthalmic Tests, Using Convergence Analysis

Vision is one of the human senses that influences the way we perceive the world. There are traditional and technological ophthalmic tests used to identify visual deficiencies. Two visual tests using virtual reality were implemented in our project. The results of both studies showed correlation with the traditional tests. To improve the accuracy of all tests, we proposed to build an ophthalmic testing machine that allows traditional and technological tests to be integrated. This paper presents the prototype design of an ophthalmic tests machine that will allow to integrate different devices and tests. The general method of development of Ulrich products was used for the design stages of the machine. An adaptive mesh analysis was used to verify the chosen materials and components with more forces applied on them. This obtained a smaller divergence of 2.5% on all elements.
Value-mapping transitions into the Pluriverse

research-article Share on Value-mapping transitions into the Pluriverse: Design notes on Participatory Methods, Traditional Ecological Knowledge and Emergency Community Resilience within the Ring of Fire Authors: Maria Alejandra Lujan Escalante School of Design, London College of Communication. University of the Arts London, UK School of Design, London College of Communication. University of the Arts London, UKSearch about this author , Christine Mortimer Organisation, Work and Technology/Management School, Lancaster University, UK Organisation, Work and Technology/Management School, Lancaster University, UKSearch about this author Authors Info & Claims PDC '22: Proceedings of the Participatory Design Conference 2022 - Volume 1August 2022 Pages 50–62https://doi.org/10.1145/3536169.3537779Published:19 August 2022Publication History 0citation47DownloadsMetricsTotal Citations0Total Downloads47Last 12 Months47Last 6 weeks6 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 AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
An Execution Cost Model for Aggregate Queries over Knowledge Graphs

Recent influx of knowledge graphs (KGs) focuses on managing large-scale and real-world facts as a big graph. Querying KGs is essential for a wide range of applications and has been made remarkable progress. One of the most most important query forms for KGs is aggregate query (e.g., "what is the average price of cars produced in Germany?"). Users can leverage aggregate query on KGs to obtain potential statistical information of interest. The latest efficient and effective solution for answering aggregate queries on KGs is Approximate aggregate Queries with semantic-aware Sampling (AQS), which can achieve a trade-off between query time and result accuracy. The basic idea is concluded as: estimate an approximate aggregate result based on a random sample collected from a KG and guarantee that the relative error of approximate result is bounded by a pre-defined error bound. This solution performs well for simple aggregate queries but suffers from the complex aggregate queries having multiple sub-queries, such as star queries. This is because it&#x2019;s non-trivial to configure an appropriate pre-defined error bound for each sub-query. If we set a large error bound for each sub-query, then we can quickly obtain the complex query&#x2019;s result but having low quality. Otherwise, we can obtain the high quality of the complex query&#x2019;s result but consuming long time for small pre-defined error bound. Obviously, it has a tradeoff between the error bound and query time. In this paper, we propose an execution cost model for AQS, which can accurately describe the functional relationship between query time and the pre-defined error bound, and help to determine an appropriate pre-defined error bound for each sub-query of a complex query. Extensive experiments over real KGs confirm the effectiveness and efficiency of our cost model.
Refactoring ISP for High-Level Vision Tasks

The image signal processing (ISP) pipeline, which transforms raw sensor measurement to a color image, is composed of a sequence of processing modules. Traditionally, the ISP pipeline is manually tuned by experts for human perception. The resulting handcrafted ISP configuration does not necessarily benefit the downstream high-level vision tasks. To mitigate these problems, this paper presents a simple yet effective framework based on Evolutionary Algorithm to search for a set of compact ISP configurations for high-level vision tasks. In particular, we encode ISP structure into a binary string and ISP parameters into a set of float numbers. Then we jointly optimize them with task-specific loss and ISP computation budgets (e.g., running time) through solving a nonlinear multi-objective optimization problem. By mutating the configurations of the ISP pipeline, we are able to remove redundant modules and design an ISP with both low cost and high accuracy. We validate the proposed method on extreme noisy and low-light raw images, and experimental results show that our framework can help find effective and efficient ISP configurations for both object detection and semantic segmentation tasks. We further provide a detailed analysis on the importance of different modules in the ISP configurations, which benefits the design of ISP for downstream tasks in the future.
Protecting Children in Social Media

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue JAMA Pediatrics HomeNew OnlineCurrent IssueFor Authors Podcast Publications JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry (1919-1959) JN Learning / CMESubscribeJobsInstitutions / LibrariansReprints & Permissions Terms of Use | Privacy Policy | Accessibility Statement 2023 American Medical Association. All Rights Reserved Search All JAMA JAMA Network Open JAMA Cardiology JAMA Dermatology JAMA Forum Archive JAMA Health Forum JAMA Internal Medicine JAMA Neurology JAMA Oncology JAMA Ophthalmology JAMA Otolaryngology–Head & Neck Surgery JAMA Pediatrics JAMA Psychiatry JAMA Surgery Archives of Neurology & Psychiatry Input Search Term Sign In Individual Sign In Sign inCreate an Account Access through your institution Sign In Purchase Options: Buy this article Rent this article Subscribe to the JAMA Pediatrics journal
Understanding Robustness and Generalization of Artificial Neural Networks Through Fourier Masks

Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests that robust networks with good generalization properties tend to be biased toward processing low frequencies in images. To explore the frequency bias hypothesis further, we develop an algorithm that allows us to learn modulatory masks highlighting the essential input frequencies needed for preserving a trained network's performance. We achieve this by imposing invariance in the loss with respect to such modulations in the input frequencies. We first use our method to test the low-frequency preference hypothesis of adversarially trained or data-augmented networks. Our results suggest that adversarially robust networks indeed exhibit a low-frequency bias but we find this bias is also dependent on directions in frequency space. However, this is not necessarily true for other types of data augmentation. Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place. Surprisingly, images seen through these modulatory masks are not recognizable and resemble texture-like patterns.
Face Detection Using Convolutional Neural Network

In this paper, a face detection system based on neural network has been reported. It is a novel hybrid face-recognition approach which is based on a convolutional neural network architecture, and it is designed to robustly detect the highly variable face patterns. Automatic recognitions of the human faces are a bit concern in the development and application of face pattern recognition, where in this paper, human faces are identified in cluttered scenes which are generally based on neural nets. Face detection is complex and a challenging task since human faces may appear different in different orientations and in various head poses. And even become complex due to various changing parameters like light intensity, facial expressions, face shadows, etc.
Sound emission in vocals

All educating activities of a teacher of a higher educational institution in the field of vocals are primarily aimed at the formation of the student's professional competencies, the totality of which will contribute to the fulfillment of the creative potential of the individual, the development of high musical intelligence, spiritual culture, as well as artistic and singing skills and abilities. The article focuses on such an important pedagogical problem in the framework of education in the area of "Vocal Art" as sound emission.
“Balance Wheel” for teaching students of pedagogical direction

We consider the pedagogical technique “Balance Wheel” as a tool for teaching students of pedagogical directions, including project methods of teaching. We also analyze the key possibilities of using the “Balance Wheel” scheme for various forms of control over the cognitive activity of students and for determining the results of the educational process that must be achieved by the end of the semester in a particular discipline. For teachers, the “Balance Wheel” can become a tool for evaluating and planning educational activities. Students’ education is based on the principle of being independent and fully immersive in the development of a pedagogical tool at the time of project training, which will further simplify the task for future teachers in their own professional activities. The advantage of this tool is its adaptability to various disciplines, types of work, as well as the goals and objectives of both the teacher and student. The “Balance Wheel” can also be used as a scheme for writing and implementing a pedagogical project with specifically developed steps and deadlines for implementation, to track the dynamics and effectiveness relative to the stated goal.
Motion Correction of MR Images Using Cross-Guided Bilateral Filter

Motion artifact has been the foremost concern in the field of medical imaging (MI). These artifacts compromise image quality and interfere in image interpretation, especially in MRI applications with low signal-to-noise ratio. In motion artifact removal process, the most demanding problem is to protect the data bearing structures such as edges and surfaces to get good visual quality while enhancing peak signal-to-noise ratio (PSNR). Application of filters on these motion artifact affected MRI signals yields better performance in terms of removal of artifact and consequently correct interpretation by the radiologist. The paper presents the analysis on the use of various filters/techniques for motion artifact removal. Motion artifact removal using cross-guided bilateral filter (CGBF) is experimented, and the results are discussed with various evaluation parameters. Motion correction using CGBF is compared with the existing techniques, and the performance of CGBF is found to be superior.
C-BERTT:A BERT-based Model For Extractive Summarization Of Chinese Online Judge Questions

Nowadays, the Online Judge system has been an increasingly important tool for promoting users&#x2019; algorithm skills. However, the questions of Online Judge systems are usually described with much redundant information, which is inconvenient for users to understand the description of questions quickly. Therefore, we propose an extractive summarization model to help users understand the questions. We first design a Chinese sentence segmentation method that considers the priorities of symbols, based on which we conduct a summarization dataset by utilizing the lexrank method with manual interventions. After that, we propose a BERT-based model to extract summarizations for questions. Our model considers both sentence-level and word-level features of question descriptions. Experiments on our dataset show that the proposed model achieves a better performance than baselines.
Trajectory Planning of UAVs with Fault Tolerance Based on Monte Carlo Sampling

Due to the limitation of system positioning accuracy, it is necessary to correct an unmanned aerial vehicle’s (UAV) error by some correct points arranged in advance, in order to successfully accomplish their tasks. Planning the optimal trajectory of UAVs considering error correction with fault tolerance is a great challenge that almost has not been broken out because corrections may fail by these points. To address the issue, this paper proposes two trajectory planning methods based on “the definite rule” and Monte Carlo sampling. By constructing five calculation models of the two methods in detail, designing the algorithms appropriately, and developing computational programs based on MATLAB, the results of trajectory planning of UAVs from a practical problem are obtained. The results achieve the optimization objectives that the total trajectory length is as short as possible and that the correction points UAV passes are as few as possible, and meanwhile, the optimal trajectory satisfies all the constraints, which illustrate the feasibility and reasonability of trajectory planning based on the definite rule and Monte Carlo sampling. The comparable results show that computation time is 104 and 195 times that of this paper when the Dijkstra algorithm with ant colony algorithm and the greedy algorithm with tabu search algorithm are respectively used, which illustrate the high efficiency of the proposed method. This work provides a feasible solution for UAVs’ trajectory planning that considers the error corrections and failure probability on certain correction points and achieves trajectory planning of UAVs with fault tolerance.
Comparative Study of Various Crowd Detection and Classification Methods for Safety Control System

A crowd is a distinct collection of people or anything that is involved in a community or society. The phenomenon of a crowd is fairly well known in a wide range of academic fields, including sociology, civil engineering, and physics, amongst others. At this point in time, it has developed into the most active-oriented research and fashionable issue in the field of computer vision. Pre-processing, object detection, and event or behavior identification are the three stages of processing that are traditionally included in crowd analysis. These stages are pre-processing, object detection, and event recognition. Pre-processing, object detection, and event or behaviour identification are the three stages of processing that are traditionally included in crowd analysis. These stages are pre-processing, object detection, and event recognition. This study gives a model of crowd analysis as well as a taxonomy of the most prevalent method to crowd analysis. It may be helpful to researchers and would serve as a good introduction connected to the area of work that has been conducted.
A Cultural Resource Recommendation Model Based On Graph Neural Networks

Recommender systems provide an effective solution to the problems of information overload and information insufficiency. The traditional collaborative filtering methods, however, suffer from cold start and sparsity problems in the practical application of recommender systems. Therefore, scholars have introduced auxiliary information to improve the performance of recommender systems. In some existing models, the feature vectors of user-item interaction information are not accurate enough. In this paper, we propose MKR-Bine, a graph neural network based recommendation model, which fully exploits user and item features by extracting user and item feature vectors using a bipartite graph neural network. Moreover, we use the knowledge graph as auxiliary information to improve the accuracy and interpretability of the recommender systems by learning higher-order interaction information between items and knowledge graph entities through cross-compression units. Finally, the performance of our proposed model is tested on the publicly available datasets of recommendation scenarios. The experimental results show that our model achieves significant benefits on real datasets, and the performance improvement is more obvious on sparse datasets.
Weight-Based Dynamic Hybrid Recommendation System for Web Application Content

This paper presents a prototype for a Web application recommendation system’s content applied to movies’ recommendations. It learns the pattern of user content consumption, predicting what he will consume in future based on similar items to those he has shown interest. It considers similarity with neighbor users, thus creating a user model. Content-based filtering, collaborative filtering, and memory-based on hybrid filtering techniques are used. Content-based filtering allows to extract the fundamental features or attributes of the items and select similar items. Moreover, it proposes predicted classifications for the items of interest not yet classified by the active user. Collaborative filtering allows applying the KNN methodology to identify the similarity between the active user located in the neighborhood and propose predicted classifications for items of interest not yet classified. Hybrid filtering combines the two methodologies to overcome their drawbacks. A weighted approach is applied, allowing a dynamic linear combination of collaborative and content-based filtering. The results obtained were empirically relevant in the experimental evaluation, matching with the results presented in similar studies validated with RMSE metrics.