Reviewerly/exHarmony huggingface dataset:
TMVOS: Triplet Matching for Efficient Video Object Segmentation

Video object segmentation (VOS) is a critical yet challenging task in video analysis. Recently, many pixel-level matching VOS methods have achieved an outstanding performance without significant time consumption in fine-tuning. However, most of these methods pay little attention to (i) matching background pixels and (ii) optimizing discriminable embeddings between classes. To address these issues, we propose a new end-to-end trainable method, namely Triplet Matching for efficient semi-supervised Video Object Segmentation (TMVOS). In particular, we devise a new triplet matching strategy that considers both the foreground and background matching and pulls the nearest negative embedding further than the nearest positive one for every anchor. As a result, this method implicitly enlarges the distances between embeddings of different classes and thereby generates accurate matching maps. Additionally, a dual decoder is applied for optimizing the final segmentation so that the model better fits the complex background and relatively simple targets. Extensive experiments demonstrate that the proposed method achieves superior performance in terms of accuracy and running-time compared with the state-of-the-art methods. The source code is available at: https://github.com/CVisionProcessing/TMVOS.
Memory Uncertainty Learning for Real-World Single Image Deraining

Single image deraining has witnessed dramatic improvements by training deep neural networks on large-scale synthetic data. However, due to the discrepancy between authentic and synthetic rain images, it is challenging to directly extend existing methods to real-world scenes. To address this issue, we propose a memory-uncertainty guided semi-supervised method to learn rain properties simultaneously from synthetic and real data. The key aspect is developing a stochastic memory network that is equipped with memory modules to record prototypical rain patterns. The memory modules are updated in a self-supervised way, allowing the network to comprehensively capture rainy styles without the need for clean labels. The memory items are read stochastically according to their similarities with rain representations, leading to diverse predictions and efficient uncertainty estimation. Furthermore, we present an uncertainty-aware self-training mechanism to transfer knowledge from supervised deraining to unsupervised cases. An additional target network is adopted to produce pseudo-labels for unlabeled data, of which the incorrect ones are rectified by uncertainty estimates. Finally, we construct a new large-scale image deraining dataset of 10.2 k real rain images, significantly improving the diversity of real rain scenes. Experiments show that our method achieves more appealing results for real-world rain removal than recent state-of-the-art methods.
Mitigation of VR Sickness During Locomotion With a Motion-Based Dynamic Vision Modulator

In virtual reality, VR sickness resulting from continuous locomotion via controllers or joysticks is still a significant problem. In this article, we present a set of algorithms to mitigate VR sickness that dynamically modulate the user's field of view by modifying the contrast of the periphery based on movement, color, and depth. In contrast with previous work, this vision modulator is a shader that is triggered by specific motions known to cause VR sickness, such as acceleration, strafing, and linear velocity. Moreover, the algorithm is governed by delta velocity, delta angle, and average color of the view. We ran two experiments with different washout periods to investigate the effectiveness of dynamic modulation on the symptoms of VR sickness, in which we compared this approach against a baseline and pitch-black field-of-view restrictors. Our first experiment made use of a just-noticeable-sickness design, which can be useful for building experiments with a short washout period.
Centralized COVID-19 Contact Tracing in a Home-Rule State

Contact tracing is an evidence-based intervention to control many communicable diseases, including COVID-19. Before the COVID-19 pandemic, contact tracing in Michigan focused on HIV, sexually transmitted infections, and tuberculosis, and it was conducted by state and local health department staff. Within 2 weeks of the first reported COVID-19 cases in Michigan in March 2020, the existing public health workforce was overwhelmed by the need for contact tracing and daily symptom monitoring. This case study narrates the development of a staffing plan that included volunteers and contractual staff to conduct centralized contact tracing in a home-rule state (ie, a state in which local health departments have full authority and autonomy under public health code to conduct the functions necessary to prevent disease, including contact tracing). This case study details various training, workforce management, and technology tools that were used. During the study period (May 2020–June 2021), contact tracers called 432 218 contacts and 269 439 were successfully reached, 48 134 of whom reported developing symptoms. The most important lesson learned was the need for more automated processes to improve efficiency in processing volunteer applicants, training, and scheduling. Nonetheless, the centralized workforce was successful, was flexible, and met the changing demands in Michigan.
College Vocal Music Teaching Design Based on Internet Platform

Internet has penetrated into people’s lives as the Internet education platform has faced many changes in the way vocal music is taught in the new situation. The teaching methods set up on the Internet have also undergone dramatic changes. A number of advanced music education concepts have emerged that require a careful review of the current state of music education in the world and in China. Current online learning environments are usually used only for formal or informal learning. The former overemphasizes the subjective role of the teacher and ignores the autonomy of the learner. Through a new modern network education method, this paper deeply analyzes the unique advantages and existing problems of a new voice education method based on the Internet and puts forward corresponding solutions. Its purpose is to encourage more ordinary vocal music teachers to better understand the new teaching methods, broaden their artistic horizons, consciously use modern teaching methods to support vocal music education in ordinary schools, and further expand the scope of music application. The experimental results show that for 4000 concurrent users, the response time of the system is less than 5 seconds, which can meet the time requirements of the system. For the service of querying some other data, the response time of the system is less than 9 seconds, so the response ability of the system to multiple users is impressive. Therefore, with the development of education, comprehensive network education platform is also the development direction of informatization in the future.
Semisupervised Manifold Regularization via a Subnetwork-Based Representation Learning Model

Semisupervised classification with a few labeled training samples is a challenging task in the area of data mining. Moore-Penrose inverse (MPI)-based manifold regularization (MR) is a widely used technique in tackling semisupervised classification. However, most of the existing MPI-based MR algorithms can only generate loosely connected feature encoding, which is generally less effective in data representation and feature learning. To alleviate this deficiency, we introduce a new semisupervised multilayer subnet neural network called SS-MSNN. The key contributions of this article are as follows: 1) a novel MPI-based MR model using the subnetwork structure is introduced. The subnet model is utilized to enrich the latent space representations iteratively; 2) a one-step training process to learn the discriminative encoding is proposed. The proposed SS-MSNN learns parameters by directly optimizing the entire network, accepting input from one end, and producing output at the other end; and 3) a new semisupervised dataset called HFSWR-RDE is built for this research. Experimental results on multiple domains show that the SS-MSNN achieves promising performance over the other semisupervised learning algorithms, demonstrating fast inference speed and better generalization ability.
Situated VR: Toward a Congruent Hybrid Reality Without Experiential Artifacts

The vision of extended reality (XR) systems is living in a world where real and virtual elements seamlessly and contextually augment experiences of ourselves and the worlds we inhabit. While this integration promises exciting opportunities for the future of XR, it comes with the risk of experiential distortions and feelings of dissociation, especially related to virtual reality (VR). When transitioning from a virtual world to the real world, users report of experiential structures that linger on, as sort of after images, causing disruptions in their daily life. In this work, we define these atypical experiences as experiential artifacts (EAs) and present preliminary results from an informal survey conducted online with 76 VR users to highlight different types of artifacts and their durations. To avoid disruptions caused by these artifacts and simultaneously increase the user's sense of presence, we propose the idea of situated VR, which blends the real and virtual in novel ways that can reduce incongruencies between the two worlds. We discuss the implications of EAs, and through examples from our own work in building hybrid experiences, we demonstrate the potential and relevance of situated VR in the design of a future, more immersive, artifact-free hybrid reality.
MUSE: Visual Analysis of Musical Semantic Sequence

Visualization has the capacity of converting auditory perceptions of music into visual perceptions, which consequently opens the door to music visualization (e.g., exploring group style transitions and analyzing performance details). Current research either focuses on low-level analysis without constructing and comparing music group characteristics, or concentrates on high-level group analysis without analyzing and exploring detailed information. To fill this gap, integrating the high-level group analysis and low-level details exploration of music, we design a musical semantic sequence visualization analytics prototype system (MUSE) that mainly combines a distribution view and a semantic detail view, assisting analysts in obtaining the group characteristics and detailed interpretation. In the MUSE, we decompose the music into note sequences for modeling and abstracting music into three progressively fine-grained pieces of information (i.e., genres, instruments and notes). The distribution view integrates a new density contour, which considers sequence distance and semantic similarity, and helps analysts quickly identify the distribution features of the music group. The semantic detail view displays the music note sequences and combines the window moving to avoid visual clutter while ensuring the presentation of complete semantic details. To prove the usefulness and effectiveness of MUSE, we perform two case studies based on real-world music MIDI data. In addition, we conduct a quantitative user study and an expert evaluation.
Computer Vision System for Image-based Automated Dial Meter Reading

The paper describes a computer vision system that allows to recognize the measurements of dial meter devices obtained using a video camera, taking into account the convenience of using the system by the end user. The algorithm of operation is described, the results of recognition are given.
Application of Ultraviolet (UV) Radiation and Fluorescence for DIC Measurements - Quality Improvement

Digital image correlation (DIC) is a widely used method in materials science and experimental mechanics for full-field surface displacement or strain measurements. It is an optical method, which calculates displacement or strain fields from images taken during deformation of the test specimen and is able to achieve a sub-pixel accuracy in case of proper operation. However, the theoretical accuracy is strongly reduced by effects such as surface reflections or shadows resulting from a not ideal illumination or lighting system. This is especially the case for complex 3D structured specimen geometries such as for metal foams or other cellular materials. The present study investigates the usage of ultraviolet (UV) light illumination in combination with phosphorescent or fluorescent pigments used to apply the needed speckle pattern for high accuracy DIC. A method to optimise the lighting system in combination with the used background pigments is presented. Fluorescent pigments provide much better results than phosphorescent pigments. The application of UV lighting in combination with fluorescent background pigments strongly improves DIC accuracy not only for normal tensile specimens but also for complex 3D shaped metal foams by avoiding surface reflections and shadows.
An Efficient Method for Brain Image Preprocessing with Anisotropic Diffusion Filter &amp; Tumor Segmentation

The brain is central controlling system in human body. The internal structure and functioning of brain change due to any kind of enlargement of the brain cells which is diagnosed as a brain tumor. Early detection of tumor is critical for better treatment options. In this paper, brain magnetic resonance (MR) image is preprocessed and watershed algorithm is applied to separate the image objects and to segment the tumor. Image preprocessing includes anisotropic diffusion filter (ADF), skull stripping and contrast enhancement steps. ADF eliminates noise content from MR images and preserves the edges of existing objects, followed by skull stripping to remove the nonbrain tissues and contrast enhancement to improve visual quality. A morphological opening operation is performed to sharpen the tumor region. In the segmented images, the statistical features and textural features are extracted. A set of performance measure is used to evaluate the system performance and comparison is made with the existing literature. The proposed system performs efficiently with high PSNR value of 17.0195 and lesser computation time of 2.04 s.
A survey on traceability in food safety system using blockchain

By the development of living standards people are more involved in food safety and quality of product. Due to this scenario the traceability of food products becomes the need of every consumer. By the various characteristics of Blockchain such as transparency, decentralization and immutability, it is possible to address the problems existing in the supply chain. In this paper, a survey has been done on the traceability system in food supply chain management using Blockchain. Various methods used for traceability purposes in food supply chain management, issues faced by centralized systems in the fields of food safety, traceability, and security have been discussed in the current work. Moreover, the survey focussed on the platforms namely smart contract and Ethereum which helps in implementing the food supply chain management using blockchain.
Feature recommendation strategy for graph convolutional network

Graph Convolutional Network (GCN) is a new method for extracting, learning, and inferencing graph data that builds an embedded representation of the target node by aggregating information from neighbouring nodes. GCN is decisive for node classification and link prediction tasks in recent research. Although the existing GCN performs well, we argue that the current design ignores the potential features of the node. In addition, the presence of features with low correlation to nodes can likewise limit the learning ability of the model. Due to the above two problems, we propose Feature Recommendation Strategy (FRS) for Graph Convolutional Network in this paper. The core of FRS is to employ a principled approach to capture both node-to-node and node-to-feature relationships for encoding, then recommending the maximum possible features of nodes and replacing low-correlation features, and finally using GCN for learning of features. We perform a node clustering task on three citation network datasets and experimentally demonstrate that FRS can improve learning on challenging tasks relative to state-of-the-art (SOTA) baselines.
Image Style Transfer via Multi-Style Geometry Warping

Style transfer of an image has been receiving attention from the scientific community since its inception in 2015. This topic is characterized by an accelerated process of innovation; it has been defined by techniques that blend content and style, first covering only textural details, and subsequently incorporating compositional features. The results of such techniques has had a significant impact on our understanding of the inner workings of Convolutional Neural Networks. Recent research has shown an increasing interest in the geometric deformation of images, since it is a defining trait for different artists, and in various art styles, that previous methods failed to account for. However, current approaches are limited to matching class deformations in order to obtain adequate outputs. This paper solves these limitations by combining previous works in a framework that can perform geometric deformation on images using different styles from multiple artists by building an architecture that uses multiple style images and one content image as input. The proposed framework uses a combination of various other existing frameworks in order to obtain a more intriguing artistic result. The framework first detects objects of interest from various classes inside the image and assigns them a bounding box, before matching each detected object image found in a bounding box with a similar style image and performing warping on each of them on the basis of these similarities. Next, the algorithm blends back together all the warped images so they are placed in a similar position as the initial image, and style transfer is finally applied between the merged warped images and a different chosen image. We manage to obtain stylistically pleasing results that were possible to generate in a reasonable amount of time, compared to other existing methods.
Ground Pedestrian and Vehicle Detections Using Imaging Environment Perception Mechanisms and Deep Learning Networks

In order to build a robust network for the unmanned aerial vehicle (UAV)-based ground pedestrian and vehicle detection with a small number of training datasets but strong luminance environment adaptability, a system that considers both environment perception computation and a lightweight deep learning network is proposed. Because the visible light camera is sensitive to complex environmental lights, the following computational steps are designed: First, entropy-based imaging luminance descriptors are calculated; after image data are transformed from RGB to Lab color space, the mean-subtracted and contrast-normalized (MSCN) values are computed for each component in Lab color space, and then information entropies were estimated using MSCN values. Second, environment perception was performed. A support vector machine (SVM) was trained to classify the imaging luminance into excellent, ordinary, and severe luminance degrees. The inputs of SVM are information entropies; the output is the imaging luminance degree. Finally, six improved Yolov3-tiny networks were designed for robust ground pedestrian and vehicle detections. Extensive experiment results indicate that our mean average precisions (MAPs) of pedestrian and vehicle detections can be better than ~80% and ~94%, respectively, which overmatch the corresponding results of ordinary Yolov3-tiny and some other deep learning networks.
Toward Fine-grained Image Retrieval with Adaptive Deep Learning for Cultural Heritage Image

Fine-grained image classification is a challenging research topic because of the high degree of similarity among categories and the high degree of dissimilarity for a specific category caused by different poses and scales.A cultural heritage image is one of the fine-grained images because each image has the same similarity in most cases.Using the classification technique, distinguishing cultural heritage architecture may be difficult.This study proposes a cultural heritage content retrieval method using adaptive deep learning for fine-grained image retrieval.The key contribution of this research was the creation of a retrieval model that could handle incremental streams of new categories while maintaining its past performance in old categories and not losing the old categorization of a cultural heritage image.The goal of the proposed method is to perform a retrieval task for classes.Incremental learning for new classes was conducted to reduce the re-training process.In this step, the original class is not necessary for re-training which we call an adaptive deep learning technique.Cultural heritage in the case of Thai archaeological site architecture was retrieved through machine learning and image processing.We analyze the experimental results of incremental learning for fine-grained images with images of Thai archaeological site architecture from world heritage provinces in Thailand, which have a similar architecture.Using a fine-grained image retrieval technique for this group of cultural heritage images in a database can solve the problem of a high degree of similarity among categories and a high degree of dissimilarity for a specific category.The proposed method for retrieving the correct image from a database can deliver an average accuracy of 85 percent.Adaptive deep learning for fine-grained image retrieval was used to retrieve cultural heritage content, and it outperformed state-of-theart methods in fine-grained image retrieval.
On Consistent Entropy-Regularized k-Means Clustering With Feature Weight Learning: Algorithm and Statistical Analyses

Clusters in real data are often restricted to low-dimensional subspaces rather than the entire feature space. Recent approaches to circumvent this difficulty are often computationally inefficient and lack theoretical justification in terms of their large-sample behavior. This article deals with the problem by introducing an entropy incentive term to efficiently learn the feature importance within the framework of center-based clustering. A scalable block-coordinate descent algorithm, with closed-form updates, is incorporated to minimize the proposed objective function. We establish theoretical guarantees on our method by Vapnik-Chervonenkis (VC) theory to establish strong consistency along with uniform concentration bounds. The merits of our method are showcased through detailed experimental analysis on toy examples as well as real data clustering benchmarks.
An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management, effective cost estimation is one of the most crucial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However, finding the best estimation results in software development is challenging.Thus, accurate estimation of software development efforts is always a concern for many companies.In this paper, we proposed a novel software development effort estimation model based both on constructive cost model II (COCOMO II) and the artificial neural network (ANN).An artificial neural network enhances the COCOMO model, and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feedforward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort, thus enhanced the reliability and improving the software effort estimation accuracy.