Reviewerly/exHarmony huggingface dataset:
Prediction of Heart Disease at an Early Phase Using Machine Learning Techniques

Modern medicine recognizes that cardiac illness is a serious worldwide public health concern. The adage of the twenty-first century describes the culmination of an increase in life expectancy, as well as a substantial shift in the causes of heart disease-related deaths around the globe. The criticality of heart disorders is greater than ever before, and if they are not discovered early enough, they may have life-threatening effects if not treated. Techniques such as electronic health records and body area networks are being developed to continually monitor and diagnose patient’s health status via the projection of medical sensors and wearable devices over human bodies, allowing for more accurate diagnosis. Because the data created by body area networks is continuous and massive in volume, machine learning methods are used for the effective categorization of health data supplied by the networks. The categorization of health data, on the other hand, is the most difficult procedure to master since it must be done precisely in order to provide an early forecast of heart disease. As a result, the inability to diagnose cardiac disease at an earlier stage with greater precision is a key shortcoming of the current technologies. With high accuracy and performance metrics,this dissertation presents an effort to effectively categories and forecast cardiac disease at an early stage in a cost-effective manner. It is possible to separate the substantial contribution of this dissertation into two sections. It is first stated how an effective method to early diagnosis and categorization of heart disease may be used. Following that, a medical recommendation model based on the Fourier transform is offered for the early identification of cardiac problems. A variety of techniques, including naive bayes and smoothing Laplace transform models, are used in the categorization of health data for optimal results. It is determined how accurate, sensitive, precise, and particular the results are based on a number of different parameters. According to the findings of the study, the suggested method delivers a relatively higher level of accuracy and prediction measures than the already available approaches.
Review of Persuasive User Interface as Strategy for Technology Addiction in Virtual Environments

In the era of virtuality, the increasingly ubiquitous technology bears the challenge of excessive user dependency, also known as user addiction. Augmented reality (AR) and virtual reality (VR) have become increasingly integrated into daily life. Although discussions about the drawbacks of these technologies are abundant, their exploration for solutions is still rare. Thus, using the PRISMA methodology, this paper reviewed the literature on technology addiction and persuasive technology. After describing the key research trends, the paper summed up nine persuasive elements of user inter-faces (UIs) that AR and VR developers could add to their apps to make them less addictive. Furthermore, this review paper encour-ages more research into a persuasive strategy for controlling user dependency in virtual-physical blended cyberspace.
Few-Shot Image Classification Method Based on Fusion of Important Features of Different Scales

At present, most few-shot learning faces some difficulties. On the one hand, during feature extraction, the feature information extraction is insufficient due to the single extraction scale. Another problem is that it is difficult to accurately extract the important information content in the image. To this end, a few-shot learning method-MSIFA is proposed. In general, this strategy mainly utilizes the designed multi-scale feature generation module MSFGM to generate feature information about samples at multiple scales, so as to enrich the feature representation of samples. Next, the SAFAM module constructed by the self-attention mechanism extracts the important feature information of the samples at various scales. Afterwards, these important feature information is spliced and combined as a more accurate feature expression of the sample. Extensive experiments are performed on multiple standard datasets. Experimental results show that our method can not only greatly improve the classification performance of baseline methods, but also surpass most advanced few-shot learning methods.
Single Photon State

In the previous chapter, we examined the properties of the special Fock state, |0⟩. In this chapter, another special Fock state, |1⟩, called the single photon state, is presented. The quantum optics treatment of photodetection is explained. Methods of generating and detecting single photons are described.
Change-Aware Visual Question Answering

Change detection has been a hot research topic in the field of remote sensing, and it can provide information on observing changes of Earth's surface. However, segmentation-based change results are not very friendly to end users. Thus, in order to improve user experience and offer them high-level semantic information on change detection, we introduce a new task: change-aware visual question answering (VQA) on multi-temporal aerial images. Specifically, given a pair of multi-temporal aerial images and questions, this task aims to automatically provide natural language answers. By doing so, end users have better access to easy-to-understand change information through natural language. Besides, we also create a dataset made of multi-temporal image-question-answer triplets and a baseline method for this task. Experimental results offer valuable insights for the further research on this task.
Musebots and I: Collaborating with Creative Systems

This chapter describes musebots and their specific use as collaborators within systems designed by an artist, rather than a computer scientist, as well as a brief personal history describing the trajectory leading to their use. The musebots are not proof of concept, but producers of genuine art: their work has been performed in a variety of concerts and festivals throughout the world. While AI systems have been developed that offer themselves as compositional assistants (Agres et al., Computers in Entertainment (CIE) 14:1–33, 2016), I view collaboration as an equal partner in the creative process and describe the unique relationship between composer and artificial agents in the creation of artworks that exist as artworks, rather than examples of computational creativity.
HEND-FL: Accurate Federated Learning Using Homomorphic Encryption and a New Distributed Protocol

Federated learning (FL) is the latest development of distributed machine learning(ML), in which data is acquired and processed locally on the client, and then updated ML parameters are transmitted to a central server for aggregation. However, FL also brings some challenges, since it may reveal private information by analyzing upload parameters (such as weights trained in deep neural networks) from the client. To tackle the problem, this paper introduces a decentralized system, HEND-FL, which relies on a group of computing nodes to achieve aggregate calculations of parameters. In order to ensure the data confidentiality and the privacy of the data provider, this proposed system combines an interactive protocol and homomorphic encryption, and uses the Chinese Remainder Theorem to optimize the speed of decryption. Based on the discrete logarithm problem of elliptic curves, the scheme can be proved to be secure under the defined threat model. We evaluate the effectiveness of our plan and compare it with existing related work on the MNIST dataset. The results show that our scheme has almost the same accuracy in the plaintext and encrypted state. Compared with other encryption schemes, due to our smaller ciphertext size and the assistance of multiple computing nodes, the burden on the central server is significantly reduced.
Distributed Methods with Absolute Compression and Error Compensation

Distributed optimization methods are often applied to solving huge-scale problems like training neural networks with millions and even billions of parameters. In such applications, communicating full vectors, e.g., (stochastic) gradients, iterates, is prohibitively expensive, especially when the number of workers/nodes is large. Communication compression is a powerful approach to alleviating this issue, and, in particular, methods with biased compression and error compensation are extremely popular due to their practical efficiency. Sahu et al. (2021) [30] propose a new analysis of Error Compensated SGD (EC-SGD) for the class of absolute compression operators showing that in a certain sense, this class contains optimal compressors for EC-SGD. However, the analysis was conducted only under the so-called $$(M,\sigma ^2)$$ -bounded noise assumption. In this paper, we generalize the analysis of EC-SGD with absolute compression to the arbitrary sampling strategy and propose the first analysis of Error Compensated Loopless Stochastic Variance Reduced Gradient method (EC-LSVRG) [10] with absolute compression for (strongly) convex problems. Our rates improve upon the previously known ones in this setting. Numerical experiments corroborate our theoretical findings.
Adaptive Local Context Embedding for Small Vehicle Detection from Aerial Optical Remote Sensing Images

Small vehicle detection is one of the remaining challenging task because the ambiguous appearance is against complex background interference. Consequently, in order to improve the performance of small vehicle detection from aerial optical remote sensing images, a novel adaptive local context (ALC) embedding way is designed and further introduced into an anchor free detection manner which is called ALC-Net, and in ALC-Net, it can adaptively set up the effective local context feature to improve keypoint description of small vehicles and boost the detection performance without adding extra prior information. Finally, several experiments are carried out on two widely used datasets (e.g., UCAS-AOD [1] and VEDAI [2]) and the results indicate that the proposed ALC-Net can exhibit the competitive small vehicle detection performance than other detectors.
Feature Fusion Super Resolution Network with Gradient Guidance

Single image super-resolution (SISR) is a challenging ill-posed problem due to multiple high-resolution (HR) images can degenerate into the same low-resolution (LR) image. However, existing deep learning-based super-resolution (SR) methods always have blurred edge structures in the restored images. In addition, they mainly build more profound and more complex convolutional neural networks (CNN), which leads to substantial computational overhead. To address these issues, we propose the feature fusion super-resolution network (FFSRN) that uses the gradient map of the image to guide the restoration. In FFSRN, we propose the split and shuffle concat block (SSCB), which can extract rich features while controlling the model size and computational effort. We also introduce gradient branching to provide additional structural priors for the reconstruction process to restore high-resolution gradient mapping. Experimental results show that this method has a better peak signal-to-noise ratio, computational overhead and visual quality than the existing super-resolution algorithms. Code is available at https://github.com/Qyzs/FFSRN.
Time-Series Fusion-Based Multicamera Self-Calibration for Free-View Video Generation in Low-Texture Sports Scene

Multicamera calibration is an important technique for generating free-view videos. By arranging multiple cameras in a scene after camera calibration and image processing, a multidimensional viewing experience can be presented to the audience. To address the problem that low texture cannot be robustly self-calibrated in common sports scenes when placing artificial markers or towers in the calibration process is impractical, this article proposes a robust multicamera calibration method based on sequence feature matching and fusion. Additionally, to validate the effectiveness of the proposed calibration algorithm, a virtual axis fast bullet-time synthesis algorithm is proposed for generating a free-view video. First, camera self-calibration is performed in low-texture situations by fusing dynamic objects in time series to enrich geometric constraints in scenes without the use of calibration panels or additional artificial markers. Second, a virtual-axis bullet-time video synthesis method based on the calibration result is proposed. In the calibrated multicamera scenario, a fast bullet-time video is generated by constructing a virtual axis. Qualitative and quantitative experiments in comparison with a state-of-the-art calibration method demonstrate the validity and robustness of the proposed calibration algorithm for free-view video synthesis tasks.
Trends in Mask Recognition Technology Based on Artificial Intelligence

The outbreak of the epidemic at the beginning of the year 2020 to now quickly swept the world, due to the strengthening of control measures. Now everyone wears a mask when going out. In Korea, the number of confirmed cases of corona is still increasing, and the wearing of a mask is mandatory to prevent infection. Therefore, when entering public places, it is necessary to determine whether or not to wear a mask. To achieve better recognition, this paper examines the mask recognition technology using MobileNetV2, Faster R-CNN, SSD-Mask Algorithm, YOLOv5, and Improved YOLOv3 algorithms. Although these mask recognition algorithms show excellent performance, it should be checked whether they work well in various exceptional situations, such as covering the mouth with a hand or a scarf. In the future, we plan to study the mask recognition algorithm that reflects the exceptional situation.
Design of Interactive Language Education Assistant System Based on Data Classification

Aiming at the poor effect of language education, this paper puts forward the design method of language interactive education assistant system based on data classification, optimizes and improves the hardware structure of the system to ensure the operation quality of the system, further combines the principle of data classification, optimizes the function of the system software and simplifies the operation process of the system, Finally, experiments show that the language interactive education assistant system based on data classification can effectively break the limitations of space and time and improve the quality of language teaching.
Stability of A Class of RNN Controllers for First-order Uncertain Systems

This paper studies the stability of a class of first-order uncertain systems controlled by a simple recurrent neural network (RNN) controller. The convergence conditions are given. The relationship between the parameters of RNN controller and its robustness is quantitatively analyzed for the linear uncertain system. Simulation examples are provided to verify the analysis results.
A 3D Interactive Visualization Program for Interconnect Capacitance Extraction of Integrated Circuits

In order to assist the development and debugging of the RWCap program package, and increase the visuality of Cap3D files, a three-dimensional (3D) interactive visualization program RWCapView is developed to show intuitively and clearly the special structure illustrated by the Cap3D files. The program reads in Cap3D files and extract 3D coordinate data from it using a LL grammar parser generated by ANTLR. Then Z-buffering algorithm and sweep line algorithm are utilized to output the image, together with the axes and scale of the conductor space to identify its position and measurement. By implementing basic affine transformation detailed multi-angle inspection of the conductor space is supported. And by constructing linkages among metal layers, medium layers and conductors, multiple ways of filtering conductors are supported. Experiments based on real life layouts show that it takes about 3 seconds in total to parse and output the image of a layout with more than 10 000 conductor blocks, with about 100 milliseconds response time when performing various transformation or filtering, proving that it can efficiently handle very large scale integrated (VLSI) circuit layouts.
Adversarial Learning to Improve Question Image Embedding in Medical Visual Question Answering

Visual Question Answering (VQA) is a computer vision task in which a system produces an accurate answer to a given image and a question that is relevant to the image. Medical VQA can be considered as a subfield of general VQA, which focuses on images and questions in the medical domain. The VQA model’s most crucial task is to learn the question-image joint representation to reflect the information related to the correct answer. Medical VQA remains a difficult task due to the ineffectiveness of question-image embeddings, despite recent research on general VQA models finding significant progress. To address this problem, we propose a new method for training VQA models that utilizes adversarial learning to improve the question-image embedding and illustrate how this embedding can be used as the ideal embedding for answer inference. For adversarial learning, we use two embedding generators (question–image embedding and a question-answer embedding generator) and a discriminator to differentiate the two embeddings. The questionanswer embedding is used as the ideal embedding and the question-image embedding is improved in reference to that. The experiment results indicate that pre-training the question-image embedding generation module using adversarial learning improves overall performance, implying the effectiveness of the proposed method.
Enhancing closures in scala 3 with spores3

The use of closures, a core language feature of functional programming languages, has become popular in the context of concurrent and distributed programming. Using closures in a concurrent or distributed setting increases safety hazards, however, due to captured variables. Previous work proposed spores, enhanced closures that increase safety by constraining their environment using types. This paper presents Spores3, a completely new library-based implementation of spores for Scala 3. It is shown how the new design is enabled by a unique combination of several new features in Scala 3. Moreover, Spores3 contributes a new, portable approach to serializing closures based on type classes. Its implementation supports the same serialization approach on both the JVM and the JavaScript backends of Scala.
Robotic Path Planning Algorithm based on Ray Tracking and Diffuseness

This paper proposed a path planning algorithm based on ray tracking and diffuseness, named Ray Tracking & Illumination Algorithm, which searches for paths by simulating natural ray propagation and diffuse reflection, and uses redundant node removal and Bezier curve smoothing to improve the quality of the path. At the same time, in order to comprehensively evaluate the safety, length, smoothness and other indicators of a path, this paper puts forward the concept of “Motion Delay”, and evaluates the quality of the algorithm through the statistical motion delay of the path generated from many runs. Finally, a comparative test was conducted on the MATLAB platform with the A*, RRT, Informed RRT* and ABC-PSO algorithms. The results show that the generated path of RTI is smoother and safer. Especially, RTI is faster, more robust in the environment with U-shaped trap and less sensitive to the size of the map.
Introduction to Python

Python is an interpreted programming language that was developed by Guido van Rossum in the late 1980s and first publicly released in 1991 ( http://www.python.org ). Python is a free and open-source language that is promoted, projected, and advanced by the Python Software Foundation (PSF), which was founded in 2001 ( http://www.python.org/psf ). According to its website, the PSF is a non-profit corporation that holds the intellectual property rights to the Python programming language.
Discovering Novel Methods of Improving Middle Management Training through Text Mining

As people enter the 100-year life period, they are working for longer periods of time, and acquiring new skills to stay up-to-date is becoming increasingly important. Moreover, educational institutions for working adults are conducting course satisfaction surveys to improve their educational programs; however, extracting useful information from the comments of several students each year is not easy. Therefore, in this study, a method was analysed to efficiently extract the targets of overall dissatisfaction and demands by conducting reputation and demand analyses using text mining based on the comments of a small group. As a result, we found knowledge that certain words can be used in a tool to efficiently extract relevant information.
Microcosmos® 3.0 Perception of Teachers in Outdoor Hybrid Playing Based on Mobile Learning for Natural Sciences

Mobile learning (mlearning) is a potential option in the teaching process of Natural Sciences. Therefore, the aim of this research is to analyze the perception of high school teachers on the potential integration of mobile learning based on the use of Microcosmos® 3.0. The results show that teachers are highly interested in mobile learning, partially know how to use it, despite access limitations, and are positively willing to train and participate in this new learning methodology. They admit that there are benefits of Microcosmos® 3.0 for learning Natural Sciences, especially chemistry, biology, entomology and anatomy; however, they accept that the challenges are somehow difficult, but can be improved by its constant use. Likewise, they admit a high level of satisfaction and usefulness for the reinforcement of contents and that continuity in the coming years will depend on the fulfillment of some of the suggestions made. Future studies will seek a more exhaustive follow-up and will include students as part of the sample.
Code Quality Prediction Under Super Extreme Class Imbalance

Predicting the quality of software in the early phases of the development life cycle has various benefits to an organization's bottom line with wide applicability across industry and government. Yet, developing robust software quality prediction models in practice is a challenging task due to “super” extreme class imbalance. In this paper, we present our work on a code quality prediction framework, we call Automated Incremental Effort Investments (AIEl), to fasten the process of going from data to a performant model under super extreme class imbalance. Experiments on a large scale real-world dataset, from Meta Platforms, show that the proposed approach competes with or outperforms state-of-the art shallow and deep learning approaches. We evaluate the practical significance of the model predictions on test case prioritization efficiency, where AIEl achieves the top rank reducing code review time by 2.5 % and test case resource utilization by 9.3%.
SARAH: Semantic-Aware Representation Balance Hashing for Image Retrieval

Deep hashing is vitally important for large-scale image retrieval. Recently, central similarity based deep hashing approaches have shown great advantages for category-level image retrieval; in the existing approaches, however, categories are typically represented by a set of predefined binary vectors which are generated from Hadamard matrix or entry-wisely sampled from Bernoulli distribution. Unfortunately, such kind of category representations lack of discriminativity and semantic information. In this paper, we propose a novel Semantic-Aware Representation bAlance Hashing framework, dubbed SARAH, for category-level image retrieval. Specifically, in SARAH, the category representations are learned to preserve semantic similarities and to maximize pairwise distance; whereas the continuous code of each image is extracted by convolutional network and supervised via a central similarity loss with the corresponding semantic representation which is constructed by the learned category representations. As a consequence, the semantically similar images can be encoded to hash codes with small Hamming distance.