Reviewerly/exHarmony huggingface dataset:
Hybrid recommendation algorithm based on real-valued RBM and CNN

&lt;abstract&gt; &lt;p&gt;With the unprecedented development of big data, it is becoming hard to get the valuable information hence, the recommendation system is becoming more and more popular. When the limited Boltzmann machine is used for collaborative filtering, only the scoring matrix is considered, and the influence of the item content, the user characteristics and the user evaluation content on the predicted score is not considered. To solve this problem, the modified hybrid recommendation algorithm based on Gaussian restricted Boltzmann machine is proposed in the paper. The user text information and the item text information are input to the embedding layer to change the text information into numerical vector. The convolutional neural network is used to get the latent feature vector of the text information. The latent vector is connected to rating vector to get the item and the user vector. The user vector and the item vector are fused together to get the user-item matrix which is input to the visual layer of Gaussian restricted Boltzmann Machine to predict the ratings. Some simulation experiments have been performed on the algorithm, and the results of the experiments proved that the algorithm is feasible.&lt;/p&gt; &lt;/abstract&gt;
Exploring SVM for Federated Machine Learning Applications

The traditional machine learning algorithms focus on centralised data repository where the aggregate data used for training is stored in a common location and processed. This approach is not suitable when data is stored in different locations and owned by different entities. Many crucial machine learning applications need computationally efficient and privacy-preserving solution. Also the central data repository has the risk of single point of failure. Federated learning is an emerging field in machine learning where the centralised concept is changed to distributed. Federated learning approach helps to train a model in machine learning without really sharing the data to a common server. In this approach, training is done locally at client side. A technique called federated averaging is applied at server side, where the model parameters from clients are aggregated and the updated parameters are computed. We propose a federated SVM architecture for solving a binary supervised classification problem. Here the experiments are done for MNIST dataset and COVID-19 dataset. Also the results are compared with centralised training approach.
Distributed load-balancing for account-based sharded blockchains

Purpose Sharding of blockchains consists of partitioning a blockchain network into several sub-networks called “shards,” each shard processing and storing disjoint sets of transactions in parallel. Sharding has recently been applied to public blockchains to improve scalability through parallelism. The throughput of sharded blockchain is optimized when the workload among the shards is approximately the same. The purpose of this paper is to investigate the problem of balancing workload of account-based blockchains such as Ethereum. Design/methodology/approach Two known consensus-based distributed load-balancing algorithms have been adapted to sharded blockchains. These algorithms migrate accounts across shards to balance transaction processing times. Two methods to predict transaction processing times are proposed. Findings The authors identify some challenging aspects for solving the load-balancing problem in sharded blockchains. Experiments conducted with Ethereum transactions show that the two load-balancing algorithms are challenged by accounts often created to process a single transaction to optimize anonymity, while existing accounts sparsely generate transactions. Originality/value Tests in this work have been conducted on transactions originating from a blockchain platform rather than using artificially generated data distributions. They show the specificity of the load-balancing problem for sharded blockchains, which were hidden in artificial data sets.
Development of Image Translating Model to Counter Adversarial Attacks

Deep neural networks are DeepFakes (DFs) from adversarial examples, which are inputs that are virtually indistinguishable from actual data but are labeled inaccurately by the network. Indeed, some recent discoveries show that the existence of adversarial attacks may be a DL model’s intrinsic flaw. The chapter aims to develop a model to countermeasure the misuse of a deep generative model by utilizing adversarial attacks to create subtle alarms that would cause deep generative algorithms to fail in generating the fake image in the first place.
Detection of Power Data Outliers Using Density Peaks Clustering Algorithm Based on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>K</mi> </math>-Nearest Neighbors

As an important research branch in data mining, outlier detection has been widely used in equipment operation monitoring and system operation control. Power data outlier detection is playing an increasingly vital role in power systems. Density peak clustering (DPC) is a simple and efficient density-based clustering algorithm with a good application prospect. Nevertheless, the clustering results by the DPC algorithm can be greatly influenced by the cutoff distance, indicating that the results are highly sensitive to this parameter. To address the shortcomings of the DPC algorithm and take the characteristics of power data into consideration, we propose a DPC algorithm based on <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>K</mi> </math> -nearest neighbors for the detection of power data outliers. The proposed DPC algorithm introduces the idea of <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>K</mi> </math> -nearest neighbors and uses a unified definition of local density. In the DPC algorithm, only one parameter ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>K</mi> </math> ) needs to be determined, thus eliminating the influence of cutoff distance on the clustering result of the algorithm. The experimental results showed that the proposed algorithm can achieve accurate detection of power data outliers and has broad application prospects.
IoT technologies, applications and challenges, blockchain and its role in IoT: a survey

The IoT is the next epoch of communication. It is a very unique platform which is getting well liked for everyone. The major reason for this to happen is the advancement in technology and its potential to get connected to everything. The IoT gives us an opportunity to build effective administrations, applications for manufacturing and more. The existing and forthcoming IoT applications are favourably hopeful to increase the level of comfort, efficiency and automation. To implement such a world in an ever increasing fashion requires high security, privacy, authentication and recovery from attacks. Existing technologies are not enough to achieve that. Blockchain addresses the solutions to the above security issues of IoT. Blockchain technology attracts all industries by its decentralised architecture. The main aim of this paper is how the system becomes powerful while integrating blockchain with an IoT application. This paper explains various architectures of IoT and explains various technologies used as IoT solutions. It also explains about the IoT applications and its challenges. Finally, how the blockchain technology is used to overcome the challenges in IoT applications is discussed.
Some types of numeral systems and their modeling

In this article, the operator approach to modelling numeral systems is introduced. This approach can be useful for coding information and providing computer protection. Certain examples of such numeral systems are considered. In addition, the pseudo-binary representation is investigated. A description of further investigations of the author of this article is given.
Development of a generalizable multi-site and multi-modality clinical data cloud infrastructure for pediatric patient care

World-renowned pediatric patient care in scoliosis, craniofacial, orthopedic, and other life-altering conditions is provided at the international Shriners Children's hospital system. The impact of scoliosis can be extreme with significant curvature of the spine that often progresses during childhood periods of growth and development. Gauging the impact of treatment is vital throughout the diagnostic and treatment process and is achieved using radiographic imaging and patient reported feedback surveys. Surgeons from multiple clinical centers have amassed a wealth of patient data from more than 1,000 scoliosis patients. However, these data are difficult to access due to data heterogeneity and poor interoperability between complex hospital systems. These barriers significantly decrease the value of these data to improve patient care. To solve these challenges, we create a generalizable multi-site and multi-modality cloud infrastructure for managing the clinical data of multiple diseases. First, we establish a standardized and secure research data repository using the Fast Health Interoperability Resources (FHIR) standard to harmonize multi-modal clinical data from different hospital sites. Additionally, we develop a SMART-on-FHIR application with a user-friendly graphical user interface (GUI) to enable non-technical users to access the harmonized clinical data. We demonstrate the generalizability of our solution by expanding it to also facilitate craniofacial microsomia and pediatric bone disease imaging research. Ultimately, we present a generalized framework for multi-site, multimodal data harmonization, which can efficiently organize and store data for clinical research to improve pediatric patient care.
Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET

Shadows in drone images commonly appear in various shapes, sizes, and brightness levels, as the images capture a wide view of scenery under many conditions, such as varied flying height and weather. This property of drone images leads to a major problem when it comes to detecting shadow and causes the presence of noise in the predicted shadow mask. The purpose of this study is to improve shadow detection results by implementing post-processing methods related to automatic thresholding and binary mask refinement. The aim is to discuss how the selected automatic thresholding and two methods of binary mask refinement perform to increase the efficiency and accuracy of shadow detection. The selected automatic thresholding method is Otsu’s thresholding, and methods for binary mask refinement are morphological operation and dense CRF. The study shows that the proposed methods achieve an acceptable accuracy of 96.43%.
Reclaiming saliency: Rhythmic precision-modulated action and perception

Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action. In other words, they do not consider where to sample next, given current beliefs. Here, we reclaim salience as an active inference process that relies on two basic principles: uncertainty minimization and rhythmic scheduling. For this, we make a distinction between attention and salience. Briefly, we associate attention with precision control, i.e., the confidence with which beliefs can be updated given sampled sensory data, and salience with uncertainty minimization that underwrites the selection of future sensory data. Using this, we propose a new account of attention based on rhythmic precision-modulation and discuss its potential in robotics, providing numerical experiments that showcase its advantages for state and noise estimation, system identification and action selection for informative path planning.
Transfer Learning-based Feature Fusion of White Blood Cell Image Classification

White Blood Cells (WBCs) are a crucial part of the human immune system that helps shield the human body from foreign bodies and other infections. Any change in the count, increase or decrease of WBC can be fatal to the human body. This leads to the need for proper identification and classification of WBCs. Automating the classification of WBCs can lead to earlier predictions of abnormalities. This paper makes an effort to devise a methodology for automation by using feature fusion. For feature extraction, various fusion techniques using transfer-learning approaches such as Densely connected convoluted neural networks (DenseNet201) and VGG16 (Visual Geometry Group 2016) were proposed. The classification results are compared using various performance metrics such as Accuracy, Precision, Recall, and F1-Score. The maximum accuracy of 89.75% was obtained with the help of feature fusion combined with the Convolutional Neural Network (CNN) classifier.
Image Recognition of Sports Athletes’ High-Intensity Sports Injuries Based on Binocular Stereo Vision

Sports athletes are prone to certain injuries during high-intensity exercise training. In the process of treating an injury, images of the injury site need to be collected and identified. However, the traditional recognition method cannot effectively extract the features of the image. At the same time, it ignores the optimization of the damage image recognition results, resulting in low recognition accuracy and poor efficiency. Binocular stereo vision technology can quickly and accurately detect moving objects. Therefore, in order to more accurately identify high-intensity sports injury images, this study takes the high-intensity sports injury images as the basic research object. Several processes of image processing based on binocular stereo vision are analyzed, and the vulnerable parts of the body in high-intensity sports are also studied. Finally, the method in this study is verified. The experimental results show that the method proposed in this study reduces the average error rate by 0.19% compared with the traditional recognition method. It can effectively identify and detect injury images, thereby improving the accuracy and stability of sports injury image identification. The identification time is also shortened accordingly, which has certain practicability and feasibility. In addition, the binocular camera used in this study has high accuracy, and the obtained images of sports injuries are of good quality, which lays a foundation for image detection and recognition.
Research on Video Target Detection and Tracking in Football Matches

Computer vision is an interesting branch of artificial intelligence which is dedicated to how electronic devices can achieve the level of capabilities to perceive things just like ordinary human beings do. In order to solve the poor effect of video for the detection of target in football matches and the low accuracy of target tracking, this paper aims to make a deep exploration of the methods of video for the detection of target and tracking in football matches. The video moving for the detection of target method based on background model is used to extract the image in the background of the matching video which improves the light flow field. Secondly, the video differential image is acquired according to the difference of colors, the ghost target of the image in the video background model is scientifically determined, the ghost degree of the pixel points of the image is scientifically determined, and the flicker matrix of the target image is constructed. The number of pixels of the moving target is derived. A meanshift-based video target tracking algorithm is used in conjunction for the detection of target result to determine whether to track the target image until the overall video target tracking task is completed, move the central position of the target frame and background frame to the target position, select the best one to adapt to the target change, and determine whether to track the target image until the overall video target tracking task is completed. The simulation results suggest that the approach described in this study is capable of detecting and tracking moving objects, as well as improving target recognition and tracking accuracy.
A Study of College Teachers’ English Teaching Quality Based on Fuzzy Neural Network

In many universities and colleges, the government is now paying more attention to the quality of teaching assessment, and research on English teaching quality evaluation is becoming increasingly significant. The goal of this paper is to investigate how to use a fuzzy neural network to assess the quality of English education. The research of the model NN and the evaluation of the quality of English teaching is detailed first. A fuzzy NN is an algorithm that examines the quality of English instruction evaluation using fuzzy criteria. In the case results, the data show that the previous evaluation of quality teaching is between the intermediate and the intermediate level, while the research on the evaluation quality of English teaching based on fuzzy NN shows that the evaluation quality is between the intermediate and the advanced level. Therefore, the combination of Fuzzy Rules (FR) and NN methods can effectively improve the quality of college teachers' English teaching evaluation. In college English teaching, FRs can simplify the analysis steps of evaluation of quality teaching, accurately judge the quality of college English teaching evaluation, and provide support for the improvement of evaluation of quality teaching.
Extended Reality

As cognitive technologies, such as artificial intelligence (AI) and cross-reality (XR), become more advanced and ubiquitous in creative practices, the deeper integration of these technologies is inevitable for new musical applications. With the advent of smaller, lightweight devices and faster microchip processing speeds, AI and XR will uniquely impact the creative technology domain as music performance adapts and evolves. Furthermore, XR is experiencing a resurgence in interest, given the global impact of COVID-19 on live music industry. Fundamentally, the pandemic has intensified real-time telepresent performance requirements and new methods for artists to engage audiences remotely. Here we discuss the application of XR for presenting new musical interactions, specifically focusing upon the use of volumetric video (VV) for the embodiment of musicians in real-time immersive virtual environments (IVEs). We anticipate that our essay will be a starting point for exploring new platforms for musical expression and present emergent paradigms for audiences to consume music.
Gamification of education: innovative forms of teaching and education in culture and art

The article is devoted to the analysis of the concept of "gamification of education". The study aimed to determine the features of gamification in education and analysis of innovative forms of teaching and development of the educational process in the field of culture and art. Research methods: among the theoretical -analysis, synthesis and systematization and generalization of scientific and scientific-methodical literature to clarify the state of study of the research problem and specify the concepts on whichthe process is based; among the empirical -questionnaires of participants in the educational process in order to understand the place of the method of "gamification" in the educational process of higher education institutions. The results of the study highlight the features of the phenomenon of "gamification" and its possibilities in the organization of the educational process during the training of future professionals, in particular in the field of culture and art; there are three types of gamification that are used in education (light, deep and combined); the fundamental components of the method (elements, mechanisms, dynamics, game characters)
Challenges and opportunities of image and video retrieval

SPECIALTY GRAND CHALLENGE article Front. Imaging., 04 August 2022Sec. Image Retrieval https://doi.org/10.3389/fimag.2022.951934
Bright, Waveguide-based Entanglement Sources for High-rate Quantum Networking

We designed and built two polarization entanglement sources optimized for high-rate quantum networking under pump power constraints. We demonstrated entanglement swapping between the sources.
Parallel Algorithms for Adding a Collection of Sparse Matrices

We develop a family of parallel algorithms for the SpKAdd operation that adds a collection of k sparse matrices. SpKAdd is a much needed operation in many applications including distributed memory sparse matrix-matrix multiplication (SpGEMM), streaming accumulations of graphs, and algorithmic sparsification of the gradient updates in deep learning. While adding two sparse matrices is a common operation in Matlab, Python, Intel MKL, and various GraphBLAS libraries, these implementations do not perform well when adding a large collection of sparse matrices. We develop a series of algorithms using tree merging, heap, sparse accumulator, hash table, and sliding hash table data structures. Among them, hash-based algorithms attain the theoretical lower bounds both on the computational and I/O complexities and perform the best in practice. The newly-developed hash SpKAdd makes the computation of a distributed-memory SpGEMM algorithm at least 2x faster than previous state-of-the-art algorithms.
Nonblocking execution in GraphBLAS

GraphBLAS is a recent standard that allows the expression of graph algorithms in the language of linear algebra and enables automatic code parallelization and optimization. GraphBLAS operations are executed either in blocking or in non-blocking mode. Although there exist multiple implementations of GraphBLAS for efficient blocking execution on both shared-and distributed-memory systems, none of these implementations supports full nonblocking execution to improve data locality. In this paper, we present a preliminary evaluation for two algorithms, Pagerank and Conjugate Gradient, that confirms the importance of nonblocking execution, by showing promising speedups over the corresponding blocking execution.
Phonotactics of Gender in Mandarin Given Names: Patterns and Constraints

In this paper, we investigate the phonotactic patterns that correlate with gender in given names for Mandarin Chinese, a language that is phonotactically quite different from English. We find that many of the phonological predictors for gender in English trend in the same direction for Mandarin. We also compare the phonotactic grammars of Mandarin female and male given names obtained from Maximum-Entropy phonotactic learning models, and find that certain low acoustic frequency sounds that imply largeness according to the Frequency-Code Hypothesis are penalized for female names, while higher acoustic frequency sounds that imply smallness according to the Frequency-Code Hypothesis are not marked in the grammar for male names.