Reviewerly/exHarmony huggingface dataset:
Real Time Face Mask Detection and Monitoring System (RFMDM)

Face masks have played a critical role in limiting the spread of COVID-19, reducing the transmission of respiratory droplets containing the virus. As vaccination efforts ramp up and the pandemic subsides, wearing face masks will continue to be important in certain scenarios such as during flu season or in crowded indoor spaces. Although COVID-19 cases are on the decline, the risk of any new contagious diseases is simply around the corner. The enforcement of face mask policies, however, can be challenging. To address this issue, computer vision techniques have been applied to develop face mask detection algorithms. These algorithms utilize deep learning models that are capable of accurately detecting individuals who are not wearing face masks in real-time. Face mask detection algorithms have been integrated into surveillance systems to ensure compliance with face mask policies in various settings, including public transportation, schools, and workplaces. The development of such algorithms provides a novel approach to enforce public health policies and mitigate the spread of respiratory illnesses. The proposed Real Time Face Mask Detection and Monitoring System (RFMDM) is primarily concerned with using object identification algorithm for face mask recognition and merging the algorithm with a Raspberry PI to build RFMDM using standard laser tripwire technology.
Design and Improvement of Firefly Algorithm Based on Ternary Optical Computer

The firefly algorithm is used in the fields of image processing and task scheduling. The traditional design of this algorithm suffers from defects such as too slow convergence speeds and an easy tendency to fall into local optimal solutions. To this end, this paper designs a modified firefly algorithm based on the ternary optical computer, which has the characteristics of megabits, parallelism, and assignability by bit, and improves the classical firefly algorithm, mainly by using chaotic mapping for population initialization, introducing an adaptive dynamic step size, and making out-of-bounds adjustments to equilibrium. The experimental results show that the modified Firefly algorithm based on the ternary optical computer has enhanced merit-seeking ability and increased convergence speed.
Intelligent Storage Innovation of Intelligent Supply Chain Based on Internet of Things and Real-Time Database System

Intelligent storage applications Artificial Intelligence (AI) is continuing to learn more about good management to provide the data; it is a storage system or service to adapt to the hybrid cloud environment. Data deduplication there is no excessive copy processing of data. It will significantly reduce the need for storage capacity. After being written to the disk as data is written to the storage system and a background process, the data deduplication can be performed as an Internet of Things (IoT) process. As a virtual device and cloud services can be deployed as local hardware. Supply chain IoT device has a combination of monitoring and recording and storing real-time data IoT devices in smart contract networks. If the given data has to avoid such repetition, the proposed system is validated by analyzing the generated numbers Improved Knut Morris Pratt (IKMP) algorithm. Each time it's happening during the match, the system itself is to remove the new price list digital code in a random code generator in linear time algorithm that misunderstanding observers have sufficient information to determine the number. IoT data storage real-time monitoring result in data de duplicate. Data deduplication is avoiding the duplicate data and proposed IKMP algorithm is filter the deduplicate data.
An Innovation of Multichip Dynamic Industrial Network Development with The Flooding Network Resources

The multichip dynamic industrial network is a powerful tool for industrial applications. It enables communication between multiple devices, such as sensors, actuators, controllers, and other industrial components. It is an efficient means of connecting and controlling a variety of industrial systems in a single network. The multichip dynamic industrial network is a distributed control system consisting of several interconnected nodes. Each node contains a processor, memory, and other components that are essential for communication. The nodes are connected to one another through a network of cables and connectors. The nodes can be located anywhere in the world and can communicate with each other in real-time. The multichip dynamic industrial network is used in many industrial applications such as manufacturing, process control, and machine automation. It can be used to monitor, control, and modify the machines in a factory. It can also be used to monitor and control the supply chain from the raw material supplier to the finished product.
Prediction of Human Age and Gender Using Deep Learning For Smart Security Systems

The demand for accurate identification and verification of a person has increased as the number of smart security systems has grown. In recent years, data from a human face has been used in numerous real-world applications, including social networking, security monitoring, advertising, and entertainment. Computer vision researchers have long been interested in this topic because automatic age and gender prediction from facial images is crucial for interpersonal communication. This work predicts that the gender will be either ‘Male’ or ‘Female,’ and that the age will be one of the following ranges: (0-5), (6-10), (11-17), (18-25), (25-32), (33-45), (46-55), (55-70). In the proposed system the images are preprocessed and then the convolutional neural networks are used to extract age and gender-related features and classified the images using the appropriate classifiers. The face images are taken from the UTK dataset and the proposed method achieved a training accuracy of 92.5% and a validation accuracy of 90%.
The Asymmetric Two Branch Network for Low Illumination Image Enhancement

Owing to the lack of ambient light, the visibility of low-light image is very low, the detail information is not clear, and it is difficult to obtain effective information. It is a great challenge to restore high-quality and clear image by using a single low-light image. Therefore, this paper proposes a method of enhancing low-light image by using different exposure images with two branch networks Firstly, the gamma transform is used to process the original lowillumination image to obtain the processed image with different exposure, and then the features of the images with different exposures are extracted by two different networks. Finally, a fusion network is used to effectively fuse the feature information extracted from the two branch networks to reconstruct the high-quality enhanced image. To make the enhanced image more natural, the two-branch network and fusion network are used as generators, Experiments show that the algorithm can get high quality images with clear details, improved contrast and nature. A low-light enhancement method based on Generative Adversarial Networks is proposed in this paper. For the model based on Generative Adversarial Networks proposed in this paper, it can improve the visibility of the image and make the image meet the visual quality of human eyes at the same time.
Financial Fraud Detection Using Deep Learning Techniques

We live in a modern world where people often find it easy and are merely to go with the cashless payments. This advancement in technology is very helpful but ill effects have its ill effects as well. Due to these card payments, privacy can be a myth. Because there is a gap for fraud to take place. If a fraud takes place, it cannot be detected as there are a lot of normal transactions incoming. Segregation of the fraudulent ones from the normal transactions is the real difficulty because the fraudulent transactions are very less in number when compared to the normal transactions. A lot of techniques have been used by researchers so far. This research work focuses to detect the fewest methods with fewer false positives and minimal false negatives, to correctly classify fraudulent transactions by working on a variety of Machine learning and Deep learning techniques. The Dataset that we have chosen are namely, FraudTrain and FraudTest. Their respective data samples are 1296675 and 555719. Unsupervised ML techniques like Local Outlier Factor (LOF), IsolationvForest (IF) and Neural Networks like Autoencoders were used. In our experiment, LOF has outperformed with 99.0% accuracy, 0.8 recall, followed by Autoencoders with 96.40 accuracy, 0.22 recall, and Isolation Forest with 98.92% accuracy and 0.01 recall score.
The Innovation Development of Data Prediction and Clustered Compressive Sensing (CCS) in Environmental Application

Data prediction in environmental applications is a quickly evolving field that provides tremendous potential for improving environmental management and decision-making. By leveraging the power of data, predictive models can be developed to identify potential environmental risks, mitigate the impacts of climate change, and improve the efficiency of environmental management. Data prediction in environmental applications can be used to better understand the relationships between environmental phenomena and the environment. For instance, predictive models can be used to identify areas that are more likely to experience extreme weather events and inform the development of strategies for responding to such events. Predictive models can also be used to analyze the effects of changing climate on ecosystems and help inform decisions regarding the management of natural resources. Data prediction in environmental applications can also be used to develop more effective management strategies. Predictive models can be used to determine which areas are more likely to experience water scarcity or air pollution, and which areas are more likely to benefit from certain conservation practices. By leveraging data, predictive models can also help predict how certain management practices will affect the environment and inform decision-makers on how to best allocate resources.
Correlation Analysis between Photovoltaic Power Generation Efficiency and Meteorological Impact Factors Based on Clustering Algorithm

Cluster analysis is a multivariate statistical method, which can find similar or different types of things in the same sample data for comparison. In this paper, the correlation analysis between photovoltaic power generation (PVPG) and meteorological factors is carried out based on clustering method. In order to improve the accuracy of prediction, this paper uses Matlab software to design a cluster analysis system to do a detailed study on the correlation between PVPG efficiency and meteorological factors. This paper mainly uses qualitative and quantitative analysis, principal component analysis and data comparison to compare the positive and negative correlation between meteorological factors and PVPG. The experimental data shows that the irradiance has a stronger relationship with the efficiency of PVPG, and the correlation is more than 0.3. The analysis results of the clustering algorithm show that the temperature and sunshine are the most important factors affecting the efficiency of the photovoltaic power generation, which need to be considered and optimized in the design and operation of the photovoltaic power generation system. At the same time, factors such as wind speed and humidity also need to be included in the monitoring and control category of the photovoltaic power generation system, so as to ensure the stability and reliability of the system.
Advancements and Applications of Blind Channel Decoding Techniques

The recognition of channel coding methods and the estimate of channel coding parameters is important in different fields like information interception and analysis. However, the modes and parameters of coding are usually unknown in the information countermeasures and several real scenes. To solve this problem, many methods has been proposed in the last few years, so that an unauthorized receiver can get some information about the coding methods and parameters used in the intercepted codeword sequence. This paper aims to show the development venation of it against different coding modes and to present a review of the state-of-art reported by the scientific community over the last few years.
A Trustworthy Data Verification Technique for Cross-Chain Data Sharing Based on Merkle Trees

Cross-chain data sharing is a promising solution for integrating different blockchain networks and improving their interoperability. However, data security and trustworthiness are major concerns in cross-chain data sharing, as data may be altered or corrupted during the sharing process. In this paper, we propose a novel technique for data verification in cross-chain data sharing based on Merkle trees. By establishing the merkle tree relay mechanism, the Merkle tree is used to build a decentralized environment, and the cross-chain transaction data is verified safely and efficiently, so that the data can be shared between different blockchain networks while maintaining the integrity and credibility of the data. The technology ensures the integrity and credibility of the data by using the characteristics of the Merkle tree. It provides an efficient and secure way to validate data in a decentralized environment. We also describe the implementation of the technique and experimentally demonstrate its effectiveness. Experimental results show that measuring the time required to verify shared data using Merkle tree to evaluate the technology provides a reliable and efficient solution for cross-chain data sharing, which can be used to build secure and trusted decentralized applications.
Flight Path Simulation of Maneuverable Unmanned Aerial Vehicles Based on Kalman Filter

This paper introduces the basic concept of maneuvering target tracking by improving the tracking algorithm of the UAV maneuvering target. Finally, we simulate several typical UAV maneuvering models and analyze and compare the performance of each algorithm and the influence of parameter changes on the algorithm. Experiments show that the Kalman filter algorithm can filter linear systems and nonlinear systems after improvement. The filter precision, computational complexity, and storage capacity are considered, which is easy to meet the requirements of real-time calculation and has great engineering practical value.
Efficient Task Scheduling in Cloud Environment Based on Hyper Min Max Task Scheduling

The growing field of cloud computing deals with large tasks for processing resources. From the application point of view, the research on task scheduling mechanism of data transfer in large-scale cloud computing environment is relatively poor. Unbalanced scheduling leads to traffic overload, energy loss, and failure of hardware control. In addition, residential appliances do not consider delay reduction in power consumption. Hence, Internet of Things (IoT) dominates the current trends in the Internet. The large number of things (things) associated with the Internet creates a large amount of information that requires a lot of effort and work preparation to make it valuable. To resolve this problem, we propose a Hyper Min max task scheduling (HMMTS) based in cascade shrink priority (CSP) to allocate task to optimize the scheduling. With intent a Changeover Load Balancer (CLB) and The Preemptive Flow Manager (PFM) is responsible for the application of load balancing strategy based on the mixed load balancing algorithm improves the task allocation better to balance load to improve the response time. Experimental results have been demonstrated with respect to better load balancing, lower power rate, and time consumption rate in both phase and random uniform propagation. Simulated results performance of this process can reduce data processing time and achieve load neutralization.
An Improved Spatial Scalability Robust Video Broadcast Using Perceptual Driven Resource Allocation for Minimization of Perceptual Errors

Continuous multi-customer video with confined resources is a problematic issue in far off frameworks. The rule challenge is the way to satisfy the transmission requirements and multifaceted design of video content under different channel conditions. In existing systems, spatial versatility has engaged generous video conveyed structures (SSRVB) planned to oblige a wide extent of customers with heterogeneous objectives and channel conditions. In SSRFB, another spatial disintegration method subject to straight thumps was first planned for healthy RST video transmission. Next, the joint subcarrier organizing issues and transmission distortion minimization power apportioning are arranged. Iterative algorithms also aim to solve the problem of joint resource allocation. However, under the current system, PSNR may be incompatible with human eye perception and target index, and is adopted as the image quality rating. A cognitive-driven resource allocation program exists and proposes ways to solve this problem. In the proposed study, the perceptually driven resource allocation scheme in SSRFB further exploits the features and benefits of the human visual system (HVS) to improve the perceived visual quality of different users on mobile networks. In particular, these information aggregation functions such as structural information, gradient magnitude, and phase adaptation are most important for perceptual visual quality and must be extracted, perceptual error minimized, and resources allocated. In short, full use of subjective video quality assessment research can be made with a powerful video transmission design. The simulation results show that when compared to a reference plan (in-view, Ecast, MCAST, Discrete Wavelet Transform (DWT) Plan (SVC) planar PDRA-SSRVB Allocation Scalable Video Coding This demonstrates which can be completed 3 dB of gain as long as it is based on the vertical peak signal to noise ratio under different conditions.
The Path Integral Motion Planning of the Cable-Driven Segmented Redundant Manipulators

The hyper-redundant manipulator has been widely used in small spaces and dangerous scenes due to its flexibility and manipulability. However, since the cable-driven segmented redundant manipulators(CSRM) with active and passive combination studied in this paper has hyper redundant degrees of freedom (DoFs), their kinematics modeling and motion planning is always tricky. This paper proposes a kinematics model based on the product of the exponential(PoE) formula and the path integral motion planner (PIMP) to solve the motion planning of CSRM. The kinematics model based on the PoE formula can improve the calculation speed of forward and inverse kinematics. For iterative optimization, the PIMP can add non-differentiable quantities, such as obstacles and motor torque, to the objective function. We conduct the experiments, and the result shows that the correct kinematic model and PIMP can effectively obtain a smooth and collision-free path.
Key Technologies in Computer Algorithm Dynamics Based on Front-end and Front-end Separation System

In the theoretical research and simulation experiment of computer, the front and rear end separation technology is widely used. In this paper, continuous media is modeled and dynamically simulated based on C # language. In order to facilitate the effective storage and management of some data, it is important to study the key technologies of the method of separating the front and back ends. In this paper, the computer dynamic algorithm and key technology of the front and back-end separation system are studied by using experimental comparison and encryption methods. The experimental data shows that the number of data packets optimized by the front and rear separation system is only 1% - 2%, and the loading time is about 3%. The system adopts the front and rear end separation architecture, the dynamic algorithm, data visualization and multi-language support and other technical means, making the algorithm development more flexible and efficient, and can meet the personalized needs of users. In addition, the system also provides a platform for algorithm researchers to communicate and share, which can promote the development and popularization of algorithm research.
A Fault Injection Framework for AI Hardware Accelerators

Deep Neural Networks (DNNs) have proven to give very good results for many complex tasks and applications, such as object recognition in images/videos and natural language processing. Some relevant applications of DNNs are defined by real-time safety-critical systems, which typically require the adoption of DNN accelerators that are usually implemented as systolic arrays. Assessing their reliability is not trivial and may depend on several factors such as the size of the array and the data precision. In this paper, we present a cross-layer framework for systolic array DNN accelerators described at RTL level allowing to inject faults at channel granularity for convolutional layers. The basic idea is to simulate the execution of the Channel Under Test (ChUT) at RTL level. Faulty outputs collected from the RTL simulation are then used at software level to complete the execution of the DNN and thus determine the impact of the injected faults at application level. Interestingly, the software execution is more than 100 times faster than the corresponding hardware simulation.
If only we had more data!: Sensor-Based Human Activity Recognition in Challenging Scenarios

With the proliferation of miniaturized movement sensing capabilities, human activity recognition using wearables and other forms of pervasive sensing (HAR) has seen an enormous boost in both research and practical deployments. Prominent application domains are health assessments, wellbeing monitoring, and novel interaction paradigms. Arguably, and especially when compared to video based activity recognition, the use of movement sensors such as inertial measurement units has many practical advantages including mobility, more direct movement capturing, and less privacy invasive sensing. However, those advantages come with a cost with regards to the design of machine learning based pipelines for the analysis of the sensor data streams, i.e., multi-variate time series data. Probably the biggest challenge lies in the absence of (very) large, labeled datasets that can readily be used for developing and training complex analysis models. In this paper I explore the challenges for deriving machine learning based systems for sensor-based human activity recognition. My focus is on: i) the role of representation learning in modern HAR systems that overcome the need for explicit feature engineering; ii) self-supervised methods for most effective use of small, labeled datasets; and iii) cross-modality transfer. I conclude with an outlook of next steps in the field that promise to lead to increased robustness and more flexible applicability of contemporary, machine-learnlng based HAR systems.
A Tutorial on Dataset Creation for Sensor-based Human Activity Recognition

Well-documented, large and diverse datasets are important to facilitate supervised and transferable deep learning methods for human activity recognition. The past years have seen the machine learning community strive to achieve best practices in dataset creation for various applications. This contribution is the first to provide a comprehensive tutorial on data creation for sensor-based human activity recognition with firsthand instructions. We introduce the dataset creation process consisting of three phases as a guideline for the community. Each phase explains common challenges researchers may face and suggest solutions. A case study demonstrates how the framework can be used by researchers for holistically planning and critically reflecting their approach of dataset creation.