Reviewerly/exHarmony huggingface dataset:
Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch

Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people’s activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F1-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.
Emojis Are Comprehended Better than Facial Expressions, by Male Participants

Emojis are colorful ideograms resembling stylized faces commonly used for expressing emotions in instant messaging, on social network sites, and in email communication. Notwithstanding their increasing and pervasive use in electronic communication, they are not much investigated in terms of their psychological properties and communicative efficacy. Here, we presented 112 different human facial expressions and emojis (expressing neutrality, happiness, surprise, sadness, anger, fear, and disgust) to a group of 96 female and male university students engaged in the recognition of their emotional meaning. Analyses of variance showed that male participants were significantly better than female participants at recognizing emojis (especially negative ones) while the latter were better than male participants at recognizing human facial expressions. Quite interestingly, male participants were better at recognizing emojis than human facial expressions per se. These findings are in line with more recent evidence suggesting that male individuals may be more competent and inclined to use emojis to express their emotions in messaging (especially sarcasm, teasing, and love) than previously thought. Finally, the data indicate that emojis are less ambiguous than facial expressions (except for neutral and surprise emotions), possibly because of the limited number of fine-grained details and the lack of morphological features conveying facial identity.
A novel algorithm for small object detection based on YOLOv4

Small object detection is one of the difficulties in the development of computer vision, especially in the case of complex image backgrounds, and the accuracy of small object detection still needs to be improved. In this article, we present a small object detection network based on YOLOv4, which solves some obstacles that hinder the performance of traditional methods in small object detection tasks in complex road environments, such as few effective features, the influence of image noise, and occlusion by large objects, and improves the detection of small objects in complex background situations such as drone aerial survey images. The improved network architecture reduces the computation and GPU memory consumption of the network by including the cross-stage partial network (CSPNet) structure into the spatial pyramid pool (SPP) structure in the YOLOv4 network and convolutional layers after concatenation operation. Secondly, the accuracy of the model on the small object detection task is improved by adding a more suitable small object detection head and removing one used for large object detection. Then, a new branch is added to extract feature information at a shallow location in the backbone part, and the feature information extracted from this branch is fused in the neck part to enrich the small object location information extracted by the model; when fusing feature information from different levels in the backbone, the fusion weight of useful information is increased by adding a weighting mechanism to improve detection performance at each scale. Finally, a coordinated attention (CA) module is embedded at a suitable location in the neck part, which enables the model to focus on spatial location relationships and inter-channel relationships and enhances feature representation capability. The proposed model has been tested to detect 10 different target objects in aerial images from drones and five different road traffic signal signs in images taken from vehicles in a complex road environment. The detection speed of the model meets the criteria of real-time detection, the model has better performance in terms of accuracy compared to the existing state-of-the-art detection models, and the model has only 44M parameters. On the drone aerial photography dataset, the average accuracy of YOLOv4 and YOLOv5L is 42.79% and 42.10%, respectively, while our model achieves an average accuracy (mAP) of 52.76%; on the urban road traffic light dataset, the proposed model achieves an average accuracy of 96.98%, which is also better than YOLOv4 (95.32%), YOLOv5L (94.79%) and other advanced models. The current work provides an efficient method for small object detection in complex road environments, which can be extended to scenarios involving small object detection, such as drone cruising and autonomous driving.
Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Abstract The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors , computer vision , and wireless and mobile‐based applications . Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics. This article is categorized under: Technologies &gt; Machine Learning Technologies &gt; Artificial Intelligence Technologies &gt; Internet of Things
Active Learning Based on Transfer Learning Techniques for Text Classification

Text preprocessing is a common task in machine learning applications that involves hand-labeling sets. Although automatic and semi-automatic annotation of text data is a growing field, researchers need to develop models that use resources as efficiently as possible for a learning task. The goal of this work was to learn faster with fewer resources. In this paper, the combination of active and transfer learning was examined with the purpose of developing an effective text categorization method. These two forms of learning have proven their efficiency and capacity to train correct models with substantially less training data. We considered three types of criteria for selecting training points: random selection, uncertainty sampling criterion and active transfer selection. Experimental evaluation was performed on five data sets from different domains. The findings of the experiments suggest that by combining active and transfer learning, the algorithm performs better with fewer labels than random selection of training points.
Perceptual Quality Assessment of Enhanced Colonoscopy Images: A Benchmark Dataset and an Objective Method

In colonoscopy, the captured images are usually with low-quality appearance, such as non-uniform illumination, low contrast, etc., due to the specialized imaging environment, which may provide poor visual feedback and bring challenges to subsequent disease analysis. Many low-light image enhancement (LIE) algorithms have recently proposed to improve the perceptual quality. However, how to fairly evaluate the quality of enhanced colonoscopy images (ECIs) generated by different LIE algorithms remains a rarely-mentioned and challenging problem. In this study, we carry out a pioneering investigation on perceptual quality assessment of ECIs. Firstly, considering the lack of specific datasets, we collect 300 low-light images with diverse contents during the real-world colonoscopy and conduct rigorous subjective studies to compare the performance of 8 popular LIE methods, resulting in a benchmark dataset (named ECIQAD) for ECIs. Secondly, in view of the distinctive distortion characteristics of ECIs, we propose an effective no-reference Enhanced Colonoscopy Image Quality (ECIQ) method to automatically evaluate the perceptual quality of ECIs via analysis of brightness, contrast, colorfulness, naturalness, and noise. Extensive experiments on ECIQAD demonstrate the superiority of our proposed ECIQ method over 14 mainstream no-reference image quality assessment methods.
DRIP: Domain Refinement Iteration With Polytopes for Backward Reachability Analysis of Neural Feedback Loops

Safety certification of data-driven control techniques remains a major open problem. This work investigates backward reachability as a framework for providing collision avoidance guarantees for systems controlled by neural network (NN) policies. Because NNs are typically not invertible, existing methods conservatively assume a domain over which to relax the NN, which causes loose over-approximations of the set of states that could lead the system into the obstacle (i.e., backprojection (BP) sets). To address this issue, we introduce DRIP, an algorithm with a refinement loop on the relaxation domain, which substantially tightens the BP set bounds. Furthermore, we introduce a formulation that enables directly obtaining closed-form representations of polytopes to bound the BP sets tighter than prior work, which required solving linear programs and using hyper-rectangles. Furthermore, this work extends the NN relaxation algorithm to handle polytope domains, which further tightens the bounds on BP sets. DRIP is demonstrated in numerical experiments on control systems, including a ground robot controlled by a learned NN obstacle avoidance policy.
A Hybrid Cryptography Algorithm to Improve Cloud Computing Security

Cloud computing is the use of a virtual repository of resources dispersed throughout the Internet. It is a development of computer technology that is based on the Internet. Depending on their demands, several levels of users can access cloud computing services. However, data security on the Internet is the most crucial concern for cloud computing. Confidentiality, security, anonymity, and authenticity are common problems experienced by both cloud service providers and customers. Cloud service users do not have complete access to or control over these services. However, the cloud service provider must guarantee that the client’s login information is safe. To strengthen cloud security, we were able to recognize and look at a wide range of encryption problems. A hybrid framework for cloud computing security is taken into consideration based on these techniques.
Classification Rules based Breast Cancer Detection using Machine Learning Approach

One of the medical field’s most researched issues is cancer diagnosis. Many researchers have concentrated on performance enhancement and achieving successful outcomes. One of the most lethal forms of cancer is breast cancer. A significant issue in cancer diagnosis research is the diagnosis of this cancer. A kind of artificial intelligence called machine learning allows a machine to grow over time. In bio informatics, machine learning is frequently employed, notably in the detection of breast cancer. supervised learning method known as K-nearest neighbors’ approach, is one well-liked techniques. It’s really intriguing to use the K-NN in medical diagnostics. The value of parameter “k” & distance have a significant impact on the findings’ quality. This indicates how many neighbors are in proximity. In this paper, we assess the performance of various K-NN algorithmic distances. Additionally, we investigate this distance using various “k” parameter values and classification algorithms (the formula used to determine a sample’s classification).
Prediction of Insufficient Accuracy for Human Activity Recognition using Convolutional Neural Network in Compared with Support Vector Machine

This work seeks to anticipate human action detection and assess its health in machine learning algorithms in order to increase accuracy and detect human activity. The framework categories Support Vector Machines (SVM) and Novel Convolutional Neural Networks (CNN) to achieve these goals (SVM). A wholly unique Convolutional Neural Network and a Support Vector Machine were used in this analytical investigation to finish the processes and give the best accurate human action recognition for neighboring video frames. The analysis study collected forty seven samples with 2 teams of calculation with an eighty p.c G-power and their activities were no heritable from varied on-line sources with the foremost recent study findings and 0.05 p.c threshold, confidence interval ninety five p.c mean and variance. To forecast the activity analysis, as a result of the novel Convolutional Neural Network formula discovered 93.83 p.c accuracy and also the native invariant approaches, this study needs bigger accuracy for activity prediction with the Support Vector Machine formula in machine learning. This study discovered 90.12 p.c accuracy for human action analysis utilizing the Novel Convolutional Neural Network technique, with a major price of 2 caudate tests of 0.013 (p0.05) and a ninety five p.c confidence interval. This study shows that the Convolutional Neural Network technique performs far better on human action analysis than the Support Vector Machine technique.
Analysis of blockchain technology based on digital management systems and data mining technology

As information technology and wireless technology have developed, digital archive management systems have grown in popularity. Electronic files and data are mostly stated based on the access database, whereas old paper repositories, have inbuilt individuality and powerful tamperproof alteration. With the use of the Internet, marketers can now connect with their current clients on a deeper level, create new online markets, and generate new desires. This active market participation targets clients more successfully using current technology. This study examines how blockchain technology might affect a company's marketing initiatives. This study combines distributed ledgers, consensus procedures, encryption methods, and blockchain technology. Businesses that desire to engage in green products have access to a number of incentives. Electronic information management is therefore necessary because of its ability to securely retain and access large volumes of data while keeping the confidentiality of computer system functionality.
Visual Appearance and Soft Biometrics Fusion for Person Re-Identification Using Deep Learning

Learning descriptions of individual pedestrian is a common goal of both person re-identification (P-ReID) and attribute recognition methods, which are typically differentiated only in terms of their granularity. However, existing P-ReID methods only consider identification labels for individual pedestrian. In this article, we present a multi-scale pyramid attention ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MSPA</i> ) model for P-ReID that jointly manipulates the complementarity between semantic attributes and visual appearance to address this limitation. The proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MSPA</i> method mainly comprises three steps. Initially, a backbone model followed by appearance and attribute networks is individually trained to perform P-ReID and pedestrian attribute classification tasks. The attribute network primarily focuses on suppressed image areas associated with soft biometric data while retaining the semantic context among attributes using a convolutional long short-term memory architecture. Additionally, the identification network extracts rich contextual features from an image at varying scales using a residual pyramid module. In the second step, the dual network features are fused, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MSPA</i> is re-trained for the P-ReID task to further improve its complementary capabilities. Finally, we experimentally evaluated the proposed model on the two benchmark datasets Market-1501 and DukeMTMC-reID, and the results show that our approach achieved state-of-the-art performance.
Public Blockchain-Envisioned Security Scheme Using Post Quantum Lattice-Based Aggregate Signature for Internet of Drones Applications

Due to high effectiveness and robust security protocols, lattice-based cryptography becomes a very broadly applicable optimistic post-quantum technique that is recently used in public key cryptosystem. An aggregate signature scheme enables a party to bundle a set of signatures together into a single short cryptographic signature, which can be verified by any verifier using the public information. In this paper, we provide a lattice-based aggregate signature scheme where the security depends on the difficulty of the Ring Learning-with-Error (Ring-LWE) problem. Next, we use the basic scheme in Internet of Drones (IoD) applications using the blockchain technology for secure and transparent data storage. The detailed security analysis and comparative study show that the proposed scheme provides superior security including resistance to quantum attacks and is efficient as compared to the existing state of art approaches. The testbed experimental results and the blockchain simulation demonstrate that the proposed scheme can be applied in real-life drones applications.
Students’ Motivation in Learning English

This study aims to determine students' motivation in learning English and its effect on their English learning achievement. The research method used is survey research with a quantitative approach. Data were collected using questionnaires, interviews, and documentation. The questionnaire was adapted from the Attitude Motivation Test Battery (AMTB) designed by Gardner. The results showed that 52% of students took an active role in learning English. Students are relative "highly" motivated to learn English to have a good chance in their educational goals. In conclusion, instrumental motivation is a significant factor among students of SMAN 5 Lubuklinggau in learning English. Based on data documents about students' English scores, students' motivation to learn English affects their English learning achievement.&#x0D; Keywords: English, Learning, Motivation
Boundary-oriented Network for Automatic Breast Tumor Segmentation in Ultrasound Images

Breast cancer is considered as the most prevalent cancer. Using ultrasound images is a momentous clinical diagnosis method to locate breast tumors. However, accurate segmentation of breast tumors remains an open problem due to ultrasound artifacts, low contrast, and complicated tumor shapes in ultrasound images. To address this issue, we proposed a boundary-oriented network (BO-Net) for boosting breast tumor segmentation in ultrasound images. The BO-Net boosts tumor segmentation performance from two perspectives. Firstly, a boundary-oriented module (BOM) was designed to capture the weak boundaries of breast tumors by learning additional breast tumor boundary maps. Second, we focus on enhanced feature extraction, which takes advantage of the Atrous Spatial Pyramid Pooling (ASPP) module and Squeeze-and-Excitation (SE) block to obtain multi-scale and efficient feature information. We evaluate our network on two public datasets: Dataset B and BUSI. For the Dataset B, our network achieves 0.8685 in Dice, 0.7846 in Jaccard, 0.8604 in Precision, 0.9078 in Recall, and 0.9928 in Specificity. For the BUSI dataset, our network achieves 0.7954 in Dice, 0.7033 in Jaccard, 0.8275 in Precision, 0.8251 in Recall, and 0.9814 in Specificity. Experimental results show that BO-Net outperforms the state-of-the-art segmentation methods for breast tumor segmentation in ultrasound images. It demonstrates that focusing on boundary and feature enhancement creates more efficient and robust breast tumor segmentation.
Quantum Kerr learning

Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven sciences. In our work, based on various arguments from complexity theory and physics, we demonstrate that a single Kerr mode can provide some "quantum enhancements" when dealing with kernel-based methods. Using kernel properties, neural tangent kernel theory, first-order perturbation theory of the Kerr non-linearity, and non-perturbative numerical simulations, we show that quantum enhancements could happen in terms of convergence time and generalization error. Furthermore, we make explicit indications on how higher-dimensional input data could be considered. Finally, we propose an experimental protocol, that we call \emph{quantum Kerr learning}, based on circuit QED.
How to Design Autonomous Service Level Agreements for 6G

With the growing demand for network connectivity and diversity of network applications, one primary challenge that network service providers are facing is managing the commitments for Service Level Agreements (SLAs). Service providers typically monitor SLAs for management tasks such as improving their service quality, customer billing and future network planning. Network service customers, on their side, monitor services provided to them, to optimize their network usage and apply, when required, penalties related to service failures. In future 6G networks, critical network applications such as remote surgery and connected vehicles will require these SLAs to be more dynamic, flexible, and automated to match their diverse requirements on network services. Moreover, these SLAs should be transparent to all stakeholders to address the trustworthiness on network services and service providers required by critical applications. Currently, there is no standardized method to immutably record and audit SLAs, leading to challenges in aspects such as SLA enforcement and accountability - traits essential for future network applications. This work explores new requirements for future service contracts, that is, on the evolution of SLAs. Based on those new requirements, we propose an end to end layered SLA architecture leveraging Distributed Ledger Technology (DLT) and smart contracts. Our architecture is inheritable by an existing telco-application layered architectural frameworks to support future SLAs. We also discuss some limitations of DLT and smart contracts and provide several directions of future studies.