Reviewerly/exHarmony huggingface dataset:
Dance Movement Recognition Technology Based on Multi Feature Fusion

The research on dance movement recognition technology based on multi feature fusion is a research aimed at developing a system that can recognize human movements in real time. The study will help people with disabilities and others who have difficulty moving their bodies perform daily tasks, such as walking, dancing or sports. Researchers are trying to build a computer program that can recognize the movement patterns of people with dyskinesia. The goal is to enable the software to detect and analyze specific movements so that it can help guide someone through activities they may not be able to move freely. It also allows them to better control their behavior.
Jarvis - AI Based Virtual Mouse

AI Based Virtual Mouse is an AI-based project that allows users to access the mouse with hand gestures, without having to physically touch the mouse. This solution integrates a chatbot with a basic camera, rather than a traditional mouse, to handle mouse operations using user-friendly voice instructions. This solution cuts down on the need for hardware such as a wireless mouse or a Bluetooth mouse by capturing hand motions and fingertip recognition using computer vision and a webcam or built-in camera. The user can perform various actions such as clicking, left-clicking, right-clicking, and dragging with different hand gestures. This system necessitates the use of a webcam, microphone, and speaker.The camera’s output will be shown on the screen with the intention that the user may fine-tune it. As a result, the suggested solution eliminates the requirement for human involvement and the computer’s reliance on external devices.
Research on the Design of Omni-Directional Mobile Robot for Sound Seeking, Positioning and Navigation

As we all know, wheeled robots generally have two independent driving wheels. Because such robots have only two degrees of freedom of motion, they can only do arc motion or rotate at a certain place, but can not complete the complete motion including lateral movement. The research on the design of omni-directional mobile robot for sound seeking, positioning and navigation is a kind of research focusing on the development of omni-directional mobile robot with high performance in mobility, stability and robustness. The main goal is to develop a new generation of omnidirectional mobile robots as a platform for developing future intelligent systems. In addition, it will provide useful information about application areas, such as security systems or medical assistance systems. The main part of the project focuses on the impact of different parameters on performance and reliability. This research is about designing an omni-directional mobile robot for sound search, positioning and navigation. The main goal is to develop a robot that can locate its position in the room by using only ultrasonic sensors. This will enable the robot to be used as a mobile sensor platform for many applications, such as environmental monitoring, surveillance or search and rescue operations. In this paper, a dynamic model of DC motor is established, and this model is used to replace the unreasonable assumption in the original optimization algorithm. The new method greatly improves the path tracking ability. Finally, the rationality of hardware design and the validity of theoretical research results are verified.
Human Activity Recognition for Office Surveillance

Human activity surveillance video systems are gaining popularity in the field of computer vision due to user demands for security as well as their growing importance in many applications such as elder care, home nursing, and unusual event alarming. Automatic activity recognition is the key to video surveillance. This paper presents a method for human activity recognition in office surveillance videos using machine learning models including convLSTM, GRCNN and LRCN with three main steps: pre-processing, feature extraction and activity classification. The main targeted activities are walking, sleeping on desk, handshaking, typing, opening or closing door. Experimental results demonstrate the effectiveness of the proposed LRCN approach in accurately recognizing human activities in office surveillance videos with acceptable training and testing accuracy.
Enhancing Data Privacy of IoT Healthcare with Keylogger Attack Mitigation

The healthcare industry has been revolutionized by the Internet of Things (IoT), which has made it possible to develop various applications to monitor patients' health conditions and provide customized care. One of the ways in which IoT is being used in healthcare is through remote patient monitoring. This involves collecting real-time data from IoT-enabled devices such as blood pressure monitors, thermometers, and heart rate monitors, which can help healthcare professionals detect and respond to changes in a patient's health condition before they become critical. Despite the numerous benefits of IoT healthcare applications, there are critical security concerns that need to be addressed. One such concern is data privacy, as IoT devices collect a significant amount of sensitive patient information that needs to be protected from unauthorized access, hacking, and breaches. Another issue is the vulnerability of IoT devices to malware and hacking attacks due to inadequate security protections and outdated software. IoT devices can be utilized by cyber attackers to remotely get the patent’s data by causing keylogger attacks. The harm caused by keylogger attacks is significant, as they compromise private information such as patients’ private details, leading to identity theft and other crimes. These attacks can also cause operational problems such as degraded response time of IoT healthcare, system crashes, and corrupted files. Keyloggers can be difficult to detect as they run covertly in the background. In this paper, a methodology is proposed for early detection of keylogger attacks in IoT healthcare to preserve the patient’s identity from cyber attackers using the machine learning-based approach. The proposed framework is experimented on IoT healthcare dataset for comparing the performance of LightGBM, CNN, and ANN machine learning models.
Solar irradiation prediction model based on cloud and aerosol parameters

Observation-based statistical and neural network models have been widely used for solar irradiance prediction. However, the existing models predict solar irradiance through time series or neural networks lacking a clear link to physics and predict solar irradiance which do not reflect the specific effects of influences such as clouds and aerosols on solar irradiance. In this paper, the physics process is incorporated into an observation-based model to fully consider the interaction of clouds and aerosols with solar radiation. A radiation attenuation model (CA model) is developed, in which the effects of clouds and aerosols on solar irradiation are discussed separately. Comparing the CA model with the simple model and the smart model, it is verified that the CA model has higher accuracy under both sunny and cloudy conditions.
Research on Demand Response Strategy of Data Center Considering Multiple Coupling Load Regulation Methods

With the surge of the demand for cloud computing services, the volume of high energy consumption of data centers has increased rapidly which has become one of the main loads in China. Compared with other demand response resources, data center load is a special flexible load with spatio-temporal adjustment potential and its demand response potential is huge. However, in the actual scheduling process, different data center loads have different delay requirements and can bear different scheduling distances which makes their demand response mechanism is complicated. Therefore, based on the data center operation framework that considers multiple coupling load regulation methods, this paper builds a data center demand response model considering multiple coupling load regulation methods. By analyzing the demand response of data center load, refrigeration facility and auxiliary equipment, it provides a theoretical basis for the data center load to be incorporated into power grid dispatching.
Power Safety Management and Control Based on the Risk Fusion Model of Object Detection and Power Operation

Power safety operations have always been a serious topic and a shallow research field. The issue of electricity security involves a wide variety of targets of all sizes. Therefore, this paper proposes a method for power safety management and control based on the risk fusion model of object detection and power operation scenario. Firstly, the object detection model is established, and then different image processing methods are used for the two situations that require scene recognition and scene recognition, and finally various risk detection standards are established and detected and identified. The test results verify the effectiveness and accuracy of the proposed method.
Research on Face recognition based on Grey Wolf algorithm optimization

Face recognition technology has made significant progress and has been widely applied in various fields due to its advantages of non-contact, non-intrusive, and concurrency, which ensures real-time performance while improving efficiency and accuracy. However, the classic Convolutional Neural Network (CNN) algorithm model used in face recognition lacks adaptability to facial changes, is difficult to handle interference factors, and has limited generalization ability. To address these issues, we propose a CNN prediction model based on the Grey Wolf Optimizer (GWO) algorithm. Compared with traditional optimization algorithms, GWO has faster convergence speed and better global search capability, which avoids falling into local optima and improves the model's generalization ability. Therefore, it can optimize the performance of CNN and improve the accuracy of CNN algorithm models in face recognition tasks. We conducted comparative experiments based on the public dataset CASIA-FaceV5 Dataset. Compared with the classic CNN algorithm, our optimization algorithm in this paper improved the original prediction accuracy from 79.81% to 96%.
UAV Transmission Line Inspection Algorithm Based on Cross-scale Feature Fusion and Attention Mechanism

Insulators play an important role in transmission lines and almost 81.3% of power accidents in the power transmission system are caused by insulator defects. Hence, insulator identification is an important task in the process of UAV inspection. However, due to the interference of complex environment, it is difficult to identify multiple targets or small targets with the normal detection of UAV. In this paper, a YOLOv7 insulator recognition algorithm based on cross-scale feature fusion and attention mechanism is proposed. First, the YOLOv7 model framework is built. In order to reduce the interference brought by the complex inspection environment, the attention mechanism is introduced to improve the contrast between the detection target and the background. Secondly, the FPN+PAN structure in the original framework is converted into BiFPN structure, which increases the ability of cross-scale feature fusion. Secondly, in the neck part, Focus layer is selected to replace MP module, which preserves the integrity of feature extraction. Then, the original SPPCSPC structure of the space pooling layer is replaced by SPPFCSPC structure to improve its computing speed. Finally, a case study is implemented to verify the accuracy and timeliness of the proposed algorithm. The results show that the proposed algorithm can achieve good results in insulator image recognition, with an average accuracy of 97.8%.
Dance movement recognition method based on convolutional neural network

In order to improve the accuracy of dance movement recognition, we propose a dynamic adaptive video segmentation strategy. In this method, motion information statistics are carried out under the optical flow field with median compensation extracted, and video of different frame lengths are obtained by setting the threshold of frame segmentation, which is used as the input of convolutional neural network. The 3DSTPP structure is introduced into the transmission mapping of the network, and the characteristics of the traditional 3D convolutional neural network are extended by using the idea of pyramid pool. Feature description operators of the same dimension are extracted from video input sequences, and feature dimension reduction is carried out by long-term time pooling method, which is solved by two-level optimization function. The test results show that the generalization performance of the improved network is improved, and the classification accuracy of the two test data sets can reach 92.25% and 66.48%, respectively.
Visual Object Tracking Based on Improved Convolutional Neural Network

Convolutional neural network(CNN) is widely used in the field of image processing due to their powerful feature extraction function. This paper is based on the theoretical foundation of CNN and aims to solve relevant key problems in a targeted manner. Firstly, This paper combines offline tracking of CNN with particle filtering and proposes two real-time tracking methods, namely GOTURN based tracking algorithm and kernel function based tracking algorithm. Secondly, This paper evaluates the GOTURN based tracking algorithm by comparing it with eight known tracking algorithms in the OTB100 dataset. Likewise, this paper also compares the kernel function based tracking algorithm with seven different tracking algorithms and evaluates it in the VA V123 dataset. Finally, The comparative analysis of experimental results on the dataset proves that these two visual object tracking algorithms based on CNN have good performance in tracking accuracy and real-time performance.
RD-YOLO Target Detection For Night Images

Due to the problems of blurred background, dim light and less feature details in night images, the accuracy of visual object detection is reduced and the difficulty of visual detection is increased. To solve the above problems, this paper proposes a RD-YOLO target detection algorithm, which combines the feature extraction ability of ResNet network, the image generation ability of DCGAN network and the YOLOv5 target detection ability to effectively improve the night target detection effect. Experimental verification on typical night data sets shows that the RD-YOLO network proposed in this paper can achieve 92% mAP and 270FPS detection speed, which is significantly higher than the original network, and meets the actual requirements of the night scene target detection task.
HyBNN: Quantifying and Optimizing Hardware Efficiency of Binary Neural Networks

Binary neural network (BNN) has recently presented a promising opportunity for deep learning inferences on resource-constrained edge devices. Using extreme data precision, i.e., 1-bit weight and 1-bit activation, BNN not only significantly reduces the network memory footprint, but also trades massive multiply-accumulate operations for much cheaper logical XNOR and population count operations. However, our investigation reveals that, to achieve satisfactory accuracy gains, state-of-the-art (SOTA) BNNs, such as FracBNN [4] and ReActNet [1], usually have to incorporate various auxiliary floating-point ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AFP$</tex> ) components and increase the model size, which in turn degrades the hardware performance efficiency.
Anatomical and radiological evaluation of frontal lobe morphometry in healthy and dementia people and machine learning-based prediction of dementia

Purpose: This paper aimed to determine the morphometry of the frontal lobe and central brain region using magnetic resonance imaging in patients having dementia and healthy subjects. &#x0D; Materials and Methods: 243 subjects (121 subjects having dementia; 122 subjects healthy group) aged 60-90 years over for 2 years between January 2018 and 2020 were included in this study. Also, the supervised Machine learning based (ML based) detection of dementia has been studied on this obtained real world data.&#x0D; Results: The gender-related changes of frontal region measurements in dementia and healthy subjects were analyzed and, there were differences of measurements’ mean values in gender. In healthy subjects, significance differences were found in all measurements (except the distance from anterior commissure to posterior commissure and outermost of corpus callosum genu to innermost of corpus callosum genu). The means of the measurements were found higher in males than in females.&#x0D; Conclusions: We believe that the knowledge of our study will provide valuable reference data for our population and will help for a surgeon in planning an operation by considering measurements related to the frontal lobe. In addition, ML based supervised methods that were trained on the collected data for detection of dementia showed that it is required to provide as many attributes and instances as possible to train an accurate estimator. However, if this is not possible, by creating new features based on the hidden patterns between attributes and instances we could increase the success of the estimators up to 96.3% f-score value.
Trusted connections, transforming the research data landscape

<p class="first" id="d19729095e75">Open sharing of data underpinning research is essential to achieving the benefits of open science and encouraging trust and transparency in the scholarly record. Open data provides evidence to support research articles and enable experts to interrogate, validate, reproduce, and build upon new findings. Recent policies from the National Institutes of Health (NIH) as well as the OSTP Nelson Memo provide renewed urgency to data stewardship. The NIH asserts that data shared in compliance with its policy “should be of sufficient quality to validate and replicate research findings." Merely posting data online would fail to realize the intent of these policies. Dryad is an open data publishing platform and community. Our vision is to promote a world where research data is openly available, integrated with scholarly literature, and routinely reused to create knowledge. Supporting a community of academic and research institutions, funders, scholarly societies, publishers, and individual researchers Dryad has curated and published nearly 50,000 datasets since 2008 and linked them to their creators, funders, and related scholarly outputs such as research articles. This poster illustrates the critical process of data curation and publishing, including how datasets can be connected to other parts of the scholarly record through metadata, persistent identifiers (such as ORCID), and interoperability with other systems. Making data underlying research articles available for review, and ultimately for reuse, facilitates reproducibility and validation of results. Dryad can help journal publishers can make this a reality - increasing transparency and integrity in their publishing process.
FASEB DataWorks!: Driving Culture Change in Data Sharing and Reuse

<p class="first" id="d19783270e56">The Federation of American Societies for Experimental Biology (FASEB) is a coalition of 28 biological and biomedical societies representing more than 115,000 researchers worldwide. FASEB’s mission is to advance health and well-being by promoting research and education in the biological and biomedical sciences. Open science and research integrity are critical issues that align directly with our vision. A strong convener of biological and biomedical science voices, FASEB is committed to driving culture change, advocating, and providing critical feedback to support policy development across open science and research integrity. We demonstrate our commitment by publishing a fully open access journal, implementing a transition plan to open access for our flagship hybrid journal, requiring data citation and data availability statements, adding subject and data-specific deposition requirements, and providing tools to improve research integrity, data sharing, and reuse. As part of this broader commitment and in recognition of the January 2023 requirement for NIH-funded researchers to include a data management plan in grant funding requests, FASEB launched a new multi-pronged initiative, DataWorks! To date, three DataWorks! programs have launched: a series of conversational salons focused on open science and data sharing, a pilot help desk product, and a $500,000 Prize sponsored by the NIH to reward data sharing and reuse. We will report on our experience, results to date, key metrics, challenges encountered, and provide insight into how a federation of societies can leverage their convening power to develop and launch new programs to drive culture change around data sharing and reuse.
A Personalized Tour Recommender in Python using Decision Tree

A tourist recommendation system has been implemented based on python, Django framework and MySQL database. Firstly, crawler technology is used to crawl the ratings(reviews of customer experiences) information of TripAdvisor, and then the crawled ratings data is stored in MySQL. In the system, users can view the location, can view the opinion analytics(review packages), and collect the location information. According to the user's collection of location information, the decision tree is used to recommend locations that may be of interest to users. If a new user enter his requirements then decision tree will predict best location based on his given input. Decision tree don’t need new users past experience data. Through the design of these functional modules, the whole tourist recommendation system is realized.
Data Analysis

In this chapter we consider a problem we have examined earlier, which is how to derive information from data. This was central to Chapter 5 , when we derived interpolation formulas, and also in Chapter 8 , where we investigated ways to use linear and nonlinear regression. In this chapter, three different situations are considered. The first two are examples illustrating the usefulness of the singular value decomposition (SVD) in data analysis. The SVD is explained in Section 4.6 . All three methods make use of the regression material covered in Section 8.3 . The goal is to have a method that can be used to find connections between experimentally determined quantities, even though there is no obvious reason why they have to be connected. It is easiest to explain this using an example. In addition to introducing how the SVD can be used to find the connections, it will also show how the method overlaps with the regression material considered in Chapter 8 .
Approaches towards using ChatGPT as an open data companion

Many challenges are associated with the public’s use of open data. Often, there needs to be more skills and knowledge to develop ideas from published raw data, implement specific projects, and generate added value. The emergence of generative AI tools can help remedy this. This work-in-progress article presents approaches towards using ChatGPT as a companion for interested users who want to deal with open data and points out exemplary applications.