Reviewerly/exHarmony huggingface dataset:
Sentinel-3B OLCI satellite imagery and advanced electron microscopy of nanoparticle analysis in a marine estuary and watershed: Robust multi-analytics and geospatial assessment of key contaminants

Globally, hydrographic basins are routinely contaminated by anthropogenic contaminants originating from coal mining, swine farming, urban sewage, pesticide use, and other various sources. These contaminants not only spread pollution but also nanoparticles (NPs) capable of contaminating the fauna and flora of estuaries in addition to being harmful to human health. The use of images from the Sentinel-3B OLCI (Ocean Land Color Instrument) satellite makes it possible to obtain spectral data aimed at identifying the presence of possible contamination in large regions, which can be complemented by advanced electron microscopies. The general objective of this study is to analyze the dispersion of NPs and ultrafine particles (UFPs) in a region that contains multiple contaminants through the use of images from the Sentinel-3B OLCI satellite. The authors examined images taken after two particularly intense tropical rain events in December 2018 and 2021, in the region of the Laguna estuary located in the Brazilian state of Santa Catarina. Suspended sediments (SSs) were collected from seventeen points on the Tubarão River in 2018. These samples were then submitted to an analytical laboratory for various analyses such as X-ray Diffraction (XRD), and transmission and scanning electron microscopes. Additionally, images from the Sentinel-3B satellite OLCI in the Laguna estuary region from 2018 and 2021 were analyzed based on the following variables: water turbidity (TSM_NN), chlorophyll-a (CHL_NN) and suspended pollution potential (ADG_443_NN). The SSs demonstrated the presence of massive crystals of ultrafine pyrite in a pseudomorph crystal of jarosite containing nanospheres with hazardous elements (e.g. As, Hg, Cd and Pb), which represent important pollutants. The Sentinel-3B OLCI images in relation to the variables TSM_NN, CHL_NN and ADG_443_NN (2018 and 2021), identified high turbidity in the waters of the Tubarão River. Analysis of SSs and satellite images are suitable for identifying contaminants toxic to the marine ecosystem and human health on a global scale.
Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning

Human activity recognition has grown in popularity with its increase of applications within daily lifestyles and medical environments. The goal of having efficient and reliable human activity recognition brings benefits such as accessible use and better allocation of resources; especially in the medical industry. Activity recognition and classification can be obtained using many sophisticated data recording setups, but there is also a need in observing how performance varies among models that are strictly limited to using sensor data from easily accessible devices: smartphones and smartwatches. This paper presents the findings of different models that are limited to train using such sensors. The models are trained using either the k-Nearest Neighbor, Support Vector Machine, or Random Forest classifier algorithms. Performance and evaluations are done by comparing various model performances using different combinations of mobile sensors and how they affect recognitive performances of models. Results show promise for models trained strictly using limited sensor data collected from only smartphones and smartwatches coupled with traditional machine learning concepts and algorithms.
Recommendation algorithm for agricultural products based on attention factor decomposer and knowledge graph

To alleviate the distress of data sparsity and cold start in agricultural products e-commerce platforms, this paper proposes an agricultural products recommendation algorithm based on the combination of attention factor decomposer and knowledge graph. The algorithm constructs a knowledge graph for the produce dataset, models the higher-order connectivity of the produce knowledge graph in an end-to-end manner under the space of the knowledge graph, recursively propagates embeddings from the neighbors of the nodes, and extracts the potential feature vectors of the produce by using the attention factor decomposer as the message aggregation of the neighboring nodes. Using MLP, the agricultural product feature vectors and user embedding vectors are integrated and sent to the prediction module, and user click-through rate prediction is obtained by vector inner product operation. Experimenting on an agricultural e-commerce dataset, the ACC and AUC are improved by 1.60&#x0025; and 1.14&#x0025;, respectively, compared with the optimal baseline model KGCN. Thus, it verifies the effectiveness as well as feasibility of the improved algorithm on agricultural products data, which can provide a new idea and method for agricultural products e-commerce platform.
Backdoor Federated Learning-Based mmWave Beam Selection

Federated learning (FL) is an emerging paradigm for distributed machine learning that uses the data and the computational power of user devices while maintaining user privacy (e.g., position and motion track). It has been proved a promising way to help the learning-based millimeter wave (mmWave) system achieve efficient link configuration. However, FL systems have an inherent vulnerability to backdoor attacks during training, and this has not received attention in current FL-based beam selection research. The goal of a backdoor attacker is to implant a backdoor in the model such that at test time, the model will mispredict a certain family of inputs, and corrupt the performance of the trained model on specific sub-tasks. We study backdoor attacks in an FL-based beam selection system based on a deep neural network that utilizes user location information. Specifically, we propose a backdoor attack scheme that can be configured in the real world. The attacker’s trigger is an obstacle placed in certain locations. When the model encounters an input with these obstacles, the backdoor will be triggered, and the model will output the beam specified by the attacker. Through experiments, we show that the proposed attack can achieve a high attack success rate in a system without a defense mechanism. Moreover, we show that the traditional norm-clipping defense method cannot effectively defend against our attack. Furthermore, we propose a new backdoor attack defense method and verify the effectiveness of this scheme through experiments. In addition, we propose a backdoor detection method: the federated noise titration method, which can diagnose whether the model has a backdoor. Overall, our work explored backdoor attacks, defenses, and detection of the FL-based mmWave beam selection system.
The research of the optimized solutions to Raft consensus algorithm based on a weighted PageRank algorithm

To tackle the problem that the Raft consensus algorithm in blockchain has low node activity and cannot prevent malicious nodes, and thus cannot be well applied to the federated chain, a Raft consensus algorithm based on node activity and credit mechanism is proposed. First of all, through the weighted PageRank algorithm, different PR values are assigned according to the historical activity of different nodes, and then a credit mechanism is introduced to improve the PR values, giving punishment to malicious nodes and reward to normal nodes, so that each node can get the final weight value WV. Through simulation experiments, the optimized Raft consensus algorithm can better resist malicious nodes and has higher throughput and lower consensus latency than the mainstream PBFT algorithm in the federated chain
Organizational Adoption of Sentiment Analytics in Social Media Networks

Enterprise adoption and application of sentiment analytics (SA) has recently attracted significant interest from both academia and industry, as it offers exciting opportunities to generate competitive intelligence on consumer attitudes and opinions. Yet, there is limited understanding of the factors underlying successful and widespread adoption of SA in enterprises. This study presents a systematic literature review (SLR) to analyze and summarize previous research on corporate adoption of SA in social media. The SLR examines the results of 83 studies and focuses on tasks, techniques, application domains, and factors that influence enterprise adoption of SA. The findings provide insights into (i) key factors influencing SA adoption, (ii) research trends and paradigms across disciplines, and (iii) potential areas for future research on enterprise adoption of SA. These findings recommend actionable future research agendas for scholars and inform practitioners' understanding of the decision-making processes involved in enterprise adoption of SA in social media.
Domestic Research Hot Spots and Frontier Analysis of Virtual Reality Technology in the Field of Education

Virtual reality technology has been widely used due to its characteristics of immersion and interaction and has become a research hot spot both at home and abroad. In this research, the analysis technology of visual knowledge graph is adopted, and the core journals included in the CNKI database are taken as the literature source. The domestic research of virtual reality in the field of education. The researches show that the hot spots in this field include the construction of virtual experiment platform, the experiment application of medical simulation, the experiment application of simulation to improve safety, the teaching reform propelled by the application of virtual technology, the application of virtual simulation in innovation and entrepreneurship education, and the theoretical research of virtual learning community. Meanwhile, the research frontiers in this field are analyzed through the keyword burst graph, including the application of virtual reality in education, the informational construction of universities, and the integration of virtual reality and frontier technology.
A Scientometric Study of the Stylometric Research Field

Stylometry has gained great popularity in digital humanities and social sciences. Many works on stylometry have recently been reported. However, there is a research gap regarding review studies in this field from a bibliometric and evolutionary perspective. Therefore, in this paper, a bibliometric analysis of publications from the Scopus database in the stylometric research field was proposed. Then, research articles published between 1968 and 2021 were collected and analyzed using the Bibliometrix R package for bibliometric analysis via the Biblioshiny web interface. Empirical results were also presented in terms of the performance analysis and the science mapping analysis. From these results, it is concluded that there has been a strong growth in stylometry research in recent years, while the USA, Poland, and the UK are the most productive countries, and this is due to many strong research partnerships. It was also concluded that the research topics of most articles, based on author keywords, focused on two broad thematic categories: (1) the main tasks in stylometry and (2) methodological approaches (statistics and machine learning methods).
Multi-interaction English teaching platform based on internet of things

There are some problems in college English teaching, such as poor level, which increase the interactive function of the classroom. An interactive foreign language teaching software is proposed. The platform consists of platform management layer, multiple interactive English teaching module, test paper assignment module and database module. The task of managing and setting the instruction program lie with the administrators. The multiple interactive English teaching module constructs the multiple interactive English teaching model. In this paper, the exercise allocation module constructs the test paper quantitative model, and the database module specifies the information storage rules. The experimental results show that: the u-value of oral English and listening of the experimental group is less than 0.05, and the P-value of reading score is 0.059.
Person-based automation with artificial intelligence Chatbots: A driving force of Industry 4.0

New technologies called chatbots or virtual assistants have emerged to simplify the interaction between human beings and computer systems. For example, chatbots and virtual assistants are some of the newest technologies designed to simplify human–computer interaction in banking. As per Industry 4.0 standards, person-based automation is required, which can be provided with the help of chatbots. In this chapter, we introduce chatbots, examine their role in Industry 4.0, and demonstrate their real-life importance using case studies. We also analyze the role of recent technologies for automation. The chapter presents the background for developing chatbots, request-response model for user intention analysis, classification, and generating appropriate responses. The major challenge in chatbot development is understanding user intentions and sentiments behind the chats. The proposed Hybrid Intention Multiclass Classification Model (HI-MCM) shows how accurately user intentions can be classified for automating and speeding a Web-based search task through chatbots. This chapter will help chatbot researchers to explore the development of person-based automation based on Industry 4.0 standards, using artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) technologies.
A differentiable approach to the maximum independent set problem using dataless neural networks

The success of machine learning solutions for reasoning about discrete structures has brought attention to its adoption within combinatorial optimization algorithms. Such approaches generally rely on supervised learning by leveraging datasets of the combinatorial structures of interest drawn from some distribution of problem instances. Reinforcement learning has also been employed to find such structures. In this paper, we propose a different approach in that no data is required for training the neural networks that produce the solution. In this sense, what we present is not a machine learning solution, but rather one that is dependent on neural networks and where backpropagation is applied to a loss function defined by the structure of the neural network architecture as opposed to a training dataset. In particular, we reduce the popular combinatorial optimization problem of finding a maximum independent set to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest. Additionally, we propose a universal graph reduction procedure to handle large-scale graphs. The reduction exploits community detection for graph partitioning and is applicable to any graph type and/or density. Experimental results on both real and synthetic graphs demonstrate that our proposed method performs on par or outperforms state-of-the-art learning-based methods in terms of the size of the found set without requiring any training data.
Densely Connected U-Net With Criss-Cross Attention for Automatic Liver Tumor Segmentation in CT Images

Automatic liver tumor segmentation plays a key role in radiation therapy of hepatocellular carcinoma. In this paper, we propose a novel densely connected U-Net model with criss-cross attention (CC-DenseUNet) to segment liver tumors in computed tomography (CT) images. The dense interconnections in CC-DenseUNet ensure the maximum information flow between encoder layers when extracting intra-slice features of liver tumors. Moreover, the criss-cross attention is used in CC-DenseUNet to efficiently capture only the necessary and meaningful non-local contextual information of CT images containing liver tumors. We evaluated the proposed CC-DenseUNet on the LiTS dataset and the 3DIRCADb dataset. Experimental results show that the proposed method reaches the state-of-the-art performance for liver tumor segmentation. We further experimentally demonstrate the robustness of the proposed method on a clinical dataset comprising 20 CT volumes.
AnchorCapsule: a datastream-serving post-processor for object detection in embedded vision SoC

We present AnchorCapsule, a post-processor for object detection to relieve the workload of CPUs with minimal cost in latency. The capsule takes advantage of a &#x2018;datastream-serving&#x2019; architecture paradigm, where computing logic is built to cater for the throughput of DRAM datastreams and simultaneously minimize the usage of on-chip buffers. The implementation results show that AnchorCapsule has a tiny area of 0.038mm2 but achieves 1 order of magnitude faster than Intel Xeon and 2 orders of magnitude faster than ARM A53, resulting in an end-to-end system-level latency reduction of 46.7% and 16.7% for Yolo-v3-tiny and Yolo-v3 networks, respectively. The precision of AnchorCapsule has been validated on 1,500 samples from three prestige datasets, giving a promising result of 98%+ accuracy in the bounding box (BBox) coordinates, 99%+ in BBox sizes and 100% in object types. Compared with state-of-the-art, AnchorCapsule can filter 9,408 candidate BBoxes in a single run, which is 3.5&#x00D7; in the processing capacity of best-known published work.
Possibility Generalized Labeled Multi-Bernoulli Filter for Multi-Target Tracking Under Epistemic Uncertainty

This paper presents a flexible modeling framework for multi-target tracking based on the theory of Outer Probability Measures (OPMs). The notion of labeled uncertain finite set is introduced and utilized as the basis to derive a possibilistic analog of the <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-Generalized Labeled Multi-Bernoulli (<inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-GLMB) filter, in which the uncertainty in the multi-target system is represented by possibility functions instead of probability distributions. The proposed method inherits the capability of the standard probabilistic <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula>-GLMB filter to yield joint state, number, and trajectory estimates of multiple appearing and disappearing targets. Beyond that, it is capable to account for epistemic uncertainty due to ignorance or partial knowledge regarding the multi-target system, e.g., the absence of complete information on dynamical model parameters (e.g., probability of detection, birth) and initial number and state of newborn targets. The features of the developed filter are demonstrated using two simulated scenarios.
Offshore Unexploded Ordnance Detection and Data Quality Control—A Guideline

The detection of old submerged ammunition that is located on or below the seabed is important preparatory work for offshore projects.Multibeam echosounder, side scan sonar, subbottom profiler and magnetometers are the sensor types that are most commonly used for the task.Survey design decisions center around both detection campaign efficiency and sufficient data quality to find specific reference objects.The paper presents a comprehensive workflow for unexploded ordnance surveys in the sea and focuses on aspects of quantitative data quality control.For this purpose, data quality factors and corresponding threshold values were developed for each of the main sensor types.The authors designed and moderated a workshop-based stakeholder engagement process to establish expert consensus on suitable data quality factors and appropriate calculation of thresholds that define which data are sufficient and which are not.This approach was accompanied by a literature review of existing guidelines, standards, and survey recommendations.A quantitative description of data quality eases analyzing and comparing newly acquired and existing data.The paper presents the results of this process, thereby providing guidance for the planning, execution, and quality control of a technical investigation with the aim of finding unexploded ordnance in the sea.The defined data quality factors derived from the literature review and collective expert knowledge can be used in the suggested workflow.
On the Convergence of Multi-Server Federated Learning with Overlapping Area

Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical multi-server FL architecture, where the coverage areas of regional servers may overlap. The key point of this architecture is that the clients located in the overlapping areas update their local models based on the average model of all accessible regional models, which enables indirect model sharing among different regional servers. Due to the complicated network topology, the convergence analysis is much more challenging than in single-server FL. In this paper, we firstly propose a novel MS-FedAvg algorithm for this multi-server FL architecture and analyze its convergence on non-iid datasets for general non-convex settings. Since the number of clients located in each regional server is much less than single-server FL, the bandwidth of each client should be large enough to successfully communicate training models with the server, which indicates that full client participation can work in multi-server FL. Also, we provide the convergence analysis of the partial client participation scheme and develop a new biased partial participation strategy to further accelerate convergence. Our results indicate that the convergence results highly depend on the ratio of the number of clients in each area type to the total number of clients in all three strategies. The extensive experiments show remarkable performance and support our theoretical results.
Lightweight CNN and GRU Network for Real-Time Action Recognition

Artificial Intelligence explores the means for computers to think like a human. In this era of technological advancements, the unobstructed method for monitoring activities in daily life is a challenging task. Most of the recent research successfully achieved good performance at the high computational cost. With high computational costs, it is not possible to deploy action recognition methods in real-time scenarios. This research work aims to develop an action classification approach for video surveillance with scalable extension for deployment on edge devices. The input video is preprocessed extensively to extract the maximum information about the actions with all variants, which enables the network to even recognize the actions from an unseen environment. The Hybrid Video Classification (HVC) is the combination of CNN and GRU that are used for feature selection and preserving the preceding information, and in the end, SoftMax activations are used to classify the action labels. We have used HMDB51 and UT-Interaction datasets to train our proposed model. The model is tested in real-time on low end laptop. The experimentation shows that the proposed work recognizes the actions with better accuracy at a low computation cost.
H-YoLov3: High performance object detection applied to assisted driving

For the application scenarios of assisted driving, a new detection model H-YoLov3 with higher accuracy, faster speed and smaller space occupation based on the YoLov3 is proposed. First, the FM module and the method of gradually increasing feature channel number with the perceptual field are proposed to redesign the feature extraction network. Second, constructing a neck of integrating target and environment using depthwise separable convolution. On the autonomous driving dataset BDD100K, the average accuracy of H-YoLov3 is 48.5&#x0025;, the model size is 23.77MB, and the running speed is 35FPS. Compared with the YoLov3, the accuracy of the algorithm is improved by 8.4&#x0025;, the model size is reduced to 10&#x0025;, and the running speed of the model is also improved by 15FPS. This indicates that the H-YoLov3 provides better properties for the application scenarios of assisted driving and can be used in mobile or embedded scenarios.