Reviewerly/exHarmony huggingface dataset:
Medi-Alerta: Design and Implementation of a Medical Device for Remote Monitoring and Operating

Telemedicine is one of the emerging technology for remote monitoring and operating on patients, many hospitals such as government hospitals face difficulties while monitoring patients as most of them are understaffed. To reduce problems faced by healthcare professionals. We came up with the idea of Medi-Alerta an alarming system to know the status of patients in the critical ward. Medi-Alerta is a technology developed based on telemedicine it acts as a monitoring as well as alerting system for notifying doctors about the patient's details in the critical ward or to call or alert the nurse using the caller button. It is integrated with IoT technology for stable, secure, and remote monitoring purposes and blockchain technology for secure storing of data.
Mobile-Based Active Learning can Enhance Engagement in Classroom

Educators in a large classroom environment often face difficulties in carrying out in-class activities in a synchronized manner and use multiple platforms or resources to conduct these activities. They suffer from lack of student engagement as a significant concern. ALT (Active Learning and Teaching Application) is a minimalistic mobile-based application that uses Active Learning strategies to promote student engagement in class through live short quizzes, minute papers, and feedback. In this paper, we conduct an experiment using ALT's in-class quizzes and feedback to statistically gauge how the application could improve a class's engagement and understanding levels and validate the same.
Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space

&lt;abstract&gt;&lt;p&gt;Collaborative filtering (CF) algorithm is one of the most widely used recommendation algorithms in recommender systems. However, there is a data sparsity problem in the traditional CF algorithm, which may reduce the recommended efficiency of recommender systems. This paper proposes an improved collaborative filtering personalized recommendation (ICF) algorithm, which can effectively improve the data sparsity problem by reducing item space. By using the k-means clustering method to secondarily extract the similarity information, ICF algorithm can obtain the similarity information of users more accurately, thus improving the accuracy of recommender systems. The experiments using MovieLens and Netflix data set show that the ICF algorithm has a significant improvement in the accuracy and quality of recommendation.&lt;/p&gt;&lt;/abstract&gt;
Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling

Training deep neural network (DNN) with noisy labels is practically challenging since inaccurate labels severely degrade the generalization ability of DNN. Previous efforts tend to handle part or full data in a unified denoising flow via identifying noisy data with a coarse small-loss criterion to mitigate the interference from noisy labels, ignoring the fact that the difficulties of noisy samples are different, thus a rigid and unified data selection pipeline cannot tackle this problem well . In this paper, we first propose a coarse-to-fine robust learning method called CREMA, to handle noisy data in a divide-and-conquer manner. In coarse-level, clean and noisy sets are firstly separated in terms of credibility in a statistical sense. Since it is practically impossible to categorize all noisy samples correctly, we further process them in a fine-grained manner via modeling the credibility of each sample. Specifically, for the clean set, we deliberately design a memory-based modulation scheme to dynamically adjust the contribution of each sample in terms of its historical credibility sequence during training, thus alleviating the effect from noisy samples incorrectly grouped into the clean set. Meanwhile, for samples categorized into the noisy set, a selective label update strategy is proposed to correct noisy labels while mitigating the problem of correction error. Extensive experiments are conducted on benchmarks of different modality, including image classification (CIFAR, Clothing1M etc.) and text recognition (IMDB), with either synthetic or natural semantic noises, demonstrating the superiority and generality of CREMA.
Reversing Image Signal Processors by Reverse Style Transferring

RAW image datasets are more suitable than the standard RGB image datasets for the ill-posed inverse problems in low-level vision, but not common in the literature. There are also a few studies to focus on mapping sRGB images to RAW format. Mapping from sRGB to RAW format could be a relevant domain for reverse style transferring since the task is an ill-posed reversing problem. In this study, we seek an answer to the question: Can the ISP operations be modeled as the style factor in an end-to-end learning pipeline? To investigate this idea, we propose a novel architecture, namely RST-ISP-Net, for learning to reverse the ISP operations with the help of adaptive feature normalization. We formulate this problem as a reverse style transferring and mostly follow the practice used in the prior work. We have participated in the AIM Reversed ISP challenge with our proposed architecture. Results indicate that the idea of modeling disruptive or modifying factors as style is still valid, but further improvements are required to be competitive in such a challenge.
RISPNet: A Network for Reversed Image Signal Processing

RAW data is considered to be valuable owing to its beneficial properties for downstream tasks, such as denoising and HDR. Usually, RAW data gets rendered to RGB images by the in-camera image signal processor (ISP) and shall not be saved in most cases. In addition, RGB images produced by non-linear operations in ISP would necessitate the execution of other image processing tasks. To overcome this problem and acquire RAW data again, we propose a new reversed ISP network (RISPNet) to achieve efficient RGB to RAW image conversion. Our main proposal is a novel encoder-decoder network with a third-order attention module. Since Attention facilitates the complex trade-off between restoring spatial details and high-level contextual information, the accuracy of recovering high dynamic range RAW data is greatly improved, and the whole recovery process becomes a more manageable step. Benefiting from the design of the attention module, our RISPNet has a remarkable ability to recover RAW images. According to the AIM 2022 Reversed ISP Challenge, RISPNet achieved third place on both Track P20 and Track S7 test sets.
Hierarchical neural network with efficient selection inference

The image classification precision is vastly enhanced with the growing complexity of convolutional neural network (CNN) structures. However, the uneven visual separability between categories leads to various difficulties in classification. The hierarchical structure of categories can be leveraged to deal with it, but a few CNNs pay attention to the character of data. Besides, a network model with a hierarchical structure is promising to extract more specific features from the data than current CNNs, since, for the latter, all categories have the same fixed number of layers for feed-forward computation. In this paper, we propose to use category hierarchies to integrate ResNet-style modules to form a hierarchical network model in a top-down manner. To extract abundant discriminative features and improve the computation efficiency, we adopt residual block selection based on coarse categories to allocate different computation paths. Each residual block works as a switch to determine the JUMP or JOIN mode for an individual coarse category. Interestingly, since some categories need less feed-forward computation than others by jumping layers, the average inference time cost is reduced. Extensive experiments show that our hierarchical network achieves higher prediction accuracy with similar FLOPs on CIFAR-10 and CIFAR-100, SVHM, and Tiny-ImageNet datasets compared to original residual networks and other existing selection inference methods.
Phonon-induced transition between entangled and nonentangled photon emission in constantly driven quantum-dot–cavity systems

Entangled photon pairs are essential for many applications in quantum technologies. Recent theoretical studies demonstrated that different types of entangled Bell states can be created in a constantly driven four-level quantum emitter-cavity system. Unlike other candidates for the realization of the four-level emitter, semiconductor quantum dots unavoidably interact with their environment, resulting in carrier-phonon interactions. Surprisingly, phonons change the entanglement of emitted photon pairs in a qualitative way, already at low temperatures on the order of 4 K. While one type of Bell state can still be generated using small driving strengths, the other type is suppressed due to phonon interactions in strongly confined quantum dots. The degree of entanglement decreases with rising temperature and driving strength until it vanishes at a certain parameter value. Because it remains zero afterward, we encounter a phonon-induced transition between entangled and nonentangled photon emission that resembles a phase transition. The transition occurs at temperatures below 30 K and, independent of the driving strength, the concurrence as a function of the reduced temperature is found to obey a power law with exponent one near the transition point.
Design of a projection moiré system based on a nonuniform grating projection

In the projection moiré method based on equally spaced grating projection, the moiré pattern is often nonlinear and nonuniform because of oblique-angle projection. A new measurement method based on a newly designed nonuniform grating projection is proposed. A model of the projection moiré system based on the designed nonuniform grating projection was built, and the phase-to-height relationship of the proposed system was adjusted. High-quality periodic moiré patterns were obtained in the full measurement field in this system. Experiments demonstrated the precision and validity of the projection moiré system based on the designed nonuniform grating. The measurement resolution of the proposed system was 0.006 mm in an 800 × 600 mm2 area, i.e., one hundred thousandths of the measurement field of view.
Inferring colloidal interaction from scattering by machine learning

A machine learning solution for the potential inversion problem in elastic scattering is outlined. The inversion scheme consists of two major components, a generative network featuring a variational autoencoder which extracts the targeted static two-point correlation functions from experimentally measured scattering cross sections, and a Gaussian process framework which probabilistically infers the relevant structural parameters from the inverted correlation functions. Via a case study of charged colloidal suspensions, the feasibility of this approach for quantitative study of molecular interaction is critically benchmarked and its merit over existing deterministic approaches, in terms of numerical accuracy and computationally efficiency, is demonstrated.
BlockLearning: A Modular Framework for Blockchain-Based Vertical Federated Learning

Federated Learning allows multiple distributed clients to collaborate on training the same Machine Learning model. Blockchain-based Federated Learning has emerged in recent years to improve its transparency, traceability, auditability, authentication, persistency, and information safety. Various Blockchain-based Horizontal Federated Learning models are to be found in the literature. However, to the best of our knowledge, no solution for Blockchain-based Vertical Federated Learning exists. In this paper, we introduce BlockLearning, an extensible and modular framework that supports Vertical Federated Learning and different types of blockchain related algorithms. We also present performance evaluation results in terms of execution time, transaction cost, transaction latency, model accuracy and convergence, as well as communication and computation costs when BlockLearning is applied to vertically partitioned data.
Video Forgery Detection and Localization with Deep Learning Using W-NET Architecture

We propose a W-Net architecture-based approach for detecting and localizing regions of video forged using copy-move forgery technique. The proposed methodology can be utilized for the detection of forged videos with a high degree of efficiency. The model is capable of detecting forgery even when manipulations are done in complex settings, such as those with a dynamic background or complex movement of the object in the video files. The portion of the video frame that has been tampered with using temporal copy and paste (TCP) video in painting techniques can also be localized by proposed model. With lossless video clips, we were able to achieve the best video results. Finally, we were able to develop an algorithm capable of performing a task that simply required a video clip as input.
Serket-SDE: A Containerized Software Development Environment for the Symbol Emergence in Robotics Toolkit

The rapid deployment of intelligent robots to perform service tasks has become an increasingly complex challenge for researchers due to the number of disciplines and skills involved. Therefore, this paper introduces Serket-SDE, a containerized Software Development Environment (SDE) for the Symbol Emergence in Robotics Toolkit (Serket) that relies on open-source technologies to build cognitive robotic systems from multimodal sensor observations. The main contribution of Serket-SDE is an integrated framework that allows users to rapidly compose, scale, and deploy probabilistic generative models with robots. The description of Serket-SDE is accompanied by demonstrations of unsupervised multimodal categorizations using a mobile robot in various simulation environments. Further extensions of the Serket-SDE framework are discussed in conclusion based on the demonstrated results.
Implicit Neural Representations With Structured Latent Codes for Human Body Modeling

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. Furthermore, we combine Neural Body with implicit surface models to improve the learned geometry. To evaluate our approach, we perform experiments on both synthetic and real-world data, which show that our approach outperforms prior works by a large margin on novel view synthesis and 3D reconstruction. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.
Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

In this study, we established a topic selection method that recommends topics that are interesting and unfamiliar to users. To achieve this aim, we used conceptual distance to identify topics that were unfamiliar to users and improved the accuracy of this method by removing conceptually similar words. Many words used in conversations are excluded in the dictionaries and thesauruses. Thus, we developed a model for conceptual distance measurement using machine learning to measure conceptual distances even for such words. By conducting the subject experiments, we confirmed that the established system recommends topics a user is interested in but unfamiliar with compared with the baseline method developed in a previous research.
Image Splicing Detection based on Deep Convolutional Neural Network and Transfer Learning

With the easy availability of numerous low-cost and ready-to-use image editing software, nowadays, the forgery of the digital image has become very common and widespread. The fraudsters can deliberately use the tampered images for various malicious activities. Among different types of digital image manipulation, probably the most popular and effective technique is the splicing of images, which makes a natural-looking composite image by cutting and combining different portions from multiple source images. The detection procedure of image splicing can be categorized into two types, which are - feature engineering and machine learning-based detection, and deep learning-based detection with automatic feature extraction. Usually, deep learning-based mechanisms are robust, and they provide high accuracy. However, the deep learning model requires a vast amount of data for training, and it is also time-consuming and costly for its structural complexity. Therefore, in this work, an effective image splicing detection scheme based on deep learning and transfer learning has been proposed that also overcomes the issues of high training time and complex modelling structure. In this work, a Deep Convolution Neural Network (CNN)-based model has been developed where the initial convolution layers are replaced by a pre-trained CNN model MobileNetV2. Instead of training the developed CNN model from the beginning, a transfer learning technique has been adopted to skip the time-consuming initialization of the network. With the help of experimental results, it is shown that our simplified model can successfully detect spliced images with state-of-the-art accuracy despite removing the requirements of very high training data and time.
Proxy Workflows for a Secure Remote Editing Future

Using lower-resolution proxy files for video production has been an option for a number of years; however, advancements in technology, remote working demands, and a drive toward remote production and usage of cloud technologies have opened up a range of new possibilities. Modern proxy workflows depend on a set of underlying technologies to provide a seamless, secure, and productive user experience. The article will identify enablers for remote production and provide concrete examples to illustrate how media professionals: 1) overcome challenges of collaborating with high-resolution footage; 2) control access to valuable original footage; and 3) create unique renditions to simplify workflows and increase traceability.
Discrimination of coherent states via atom–field interaction without rotation wave approximation

Abstract Quantum state discrimination is an important part of quantum information processing. We investigate the discrimination of coherent states through a Jaynes–Cummings (JC) model interaction between the field and the ancilla without rotation wave approximation (RWA). We show that the minimum failure probability can be reduced as RWA is eliminated from the JC model and the non-RWA terms accompanied by the quantum effects of fields (e.g. the virtual-photon process in the JC model without RWA) can enhance the state discrimination. The JC model without RWA for unambiguous state discrimination is superior to ambiguous state discrimination, particularly when the number of sequential measurements increases. Unambiguous state discrimination implemented via the non-RWA JC model is beneficial to saving resource costs.
Value-Added Products Derived from 15 Years of High-Quality Surface Solar Radiation Measurements at Xianghe, a Suburban Site in the North China Plain

Abstract Surface solar radiation (SSR) is a key component of the energy budget of the Earth’s surface, and it varies at different spatial and temporal scales. Considerable knowledge of how and why SSR varies is crucial to a better understanding of climate change, which surely requires long-term measurements of high quality. The objective of this study is to introduce a value-added SSR dataset from Oct 2004 to Oct 2019 based on measurements taken at Xianghe, a suburban site in the North China Plain; two value-added products based on the 1-minute SSR measurements are developed. The first is clear sky detection by using a machine learning model. The second is cloud fraction estimation derived from an effective semi-empirical method. A “brightening” of global horizontal irradiance (GHI) was revealed and found to occur under both clear and cloudy conditions. This could likely be attributed to a reduction in aerosol loading and cloud fraction. This dataset could not only improve our knowledge of the variability and trend of SSR in the North China Plain, but also be beneficial for solar energy assessment and forecasting.
Discursive interactions shaping online brand communities’ social dynamics

Purpose Based on Landowski’s sociosemiotics theory, this paper aims to propose an alternative outlining of online brand communities’ social dynamics, not for their collective behaviors but for their discursive interactions. Design/methodology/approach Data were collected during a three-year netnographic immersion into a Disney-related Facebook group and authors’ trips to Walt Disney World. Findings The findings point to four styles of discursive interactions that shape an online brand community’s social dynamic. On the one hand, utterances based on the guiding and following styles of discursive interactions inform desired behaviors, which subsequently become tacitly routinized. On the other hand, utterances based on the adapting and venturing styles of discursive interactions mark random and unusual communicational situations. Originality/value This work expands current theoretical discussions on online brand communities by unveiling an unexplored linguistic dimension of them.
Cancer Prognosis by Using Machine Learning and Data Science: A Systematic Review

Cancer is one of the most fatal diseases in the world and the leading cause for most deaths worldwide. Diagnosing cancer early has become the need of the day for doctors and researchers as it allows them to categorize patients as high-risk and low-risk categories which will eventually help them in correct diagnosis and treatment. Machine learning is a subset of artificial intelligence that makes use of raw data to make predictions and insights. Using machine learning for cancer prognosis has been under study for a long time and several papers have been published regarding the same. Even though many papers have been published on the usage of statistical methods for cancer prognosis, it has been proved that machine learning models provide more accuracy when compared to conventional statistical methods of detection. These machines can be trained to detect abnormalities such as a tumour by looking at real-world examples. Models such as artificial neural networks, decision trees, clustering techniques, and K-Nearest-Neighbours (KNNs) are being used for cancer prediction, prognosis and also research purposes. The key aim of this article is to go through the popular key trends in using machine learning algorithms for cancer prognosis, types of input datasets to be fed, different types of cancers that can be studied and also the performance of these models.