Reviewerly/exHarmony huggingface dataset:
An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids

In the context of rapid advancements in artificial intelligence (AI) technology, new technologies, such as federated learning and edge computing, have been widely applied in the power Internet of Things (PIoT). When compared to the traditional centralized training approach, conventional federated learning (FL) significantly enhances privacy protection. Nonetheless, the approach poses privacy concerns, such as inferring other users’ training data through the global model or user-transferred parameters. In light of these challenges, this research paper introduces a novel privacy-preserving data aggregation scheme for the smart grid, bolstered by an improved FL technique. The secure multi-party computation (SMC) and differential privacy (DP) are skillfully combined with FL to combat inference attacks during both the learning process and output inference stages, thus furnishing robust privacy assurances. Through this approach, a trusted third party can securely acquire model parameters from power data holders and securely update the global model in an aggregated way. Moreover, the proposed secure aggregation scheme, as demonstrated through security analysis, achieves secure and reliable data aggregation in the electric PIoT environment. Finally, the experimental analysis shows that the proposed scheme effectively performs federated learning tasks, achieving good model accuracy and shorter execution times.
Mapping and Autonomous Obstacle Avoidance of Mobile Robot Based on ROS Platform

With the progress of science and technology and the continuous development of robot technology, the performance and intelligence of robots are also constantly improving. It has been widely used in many fields such as life service, military, industrial production and so on. Among them, autonomous mobile is an important embodiment of intelligence. Therefore, it is necessary to solve the problem of robot real-time positioning and map building (SLAM). SLAM is the abbreviation of Simultaneous localization and mapping, which means "synchronous localization and mapping". It is mainly used to solve the problem of localization and mapping when robots move in unknown environments. This paper designs and implements a positioning and navigation system for mobile robots based on lidar in the environment of robot operating system (ROS). The system is based on the gamping algorithm of particle filter, so that robots can perform self-positioning and map building in strange environments. By studying the Rao-Blackwelized particle filter algorithm and enlarging the bandwidth of Kalman filter to increase its estimation accuracy, the filter was optimized. In the process of robot implementation of map construction and autonomous obstacle avoidance, the robot can conduct self-positioning and map building in unfamiliar environments by using the algorithm provided by the open source Gampping function package in the robot operating system (ROS).The navigation function package allows the robot to navigate independently and avoid obstacles with known maps of the environment. Finally, the simulation tool gazebo of the robot operating system (ROS) is used to build the simulation environment required for the experiment and simulate the real environment of the robot. Finally, the robot is equipped with lidar sensors to carry out experimental simulation, so that it can achieve the functions of self-positioning, map building, self-navigation and obstacle avoidance.
Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection

With the advent of the digital age, traditional lifestyle activities, such as reading books, referencing recipes, and enjoying music, have progressively transitioned from offline to online. However, numerous issues plague the conventional approach to digital copyright protection. This is especially true in the realm of recipe protection, where the rights and interests of original creators are inadequately safeguarded due to the widespread dissemination of a large number of recipes on the Internet. This primarily stems from the high costs of gathering evidence, incomplete coverage of evidence collection, and the inability to identify and halt infringement activities in a timely manner during the process of traditional digital copyright protection. Therefore, this study designs and implements a blockchain-based digital recipe copyright protection scheme to address the issues of insufficient legal evidence and cumbersome processes in traditional digital copyright protection. First, we enhance standard short text similarity calculation method SimHash, boosting the accuracy of text similarity detection. We then utilize the decentralization, immutability, time-stamping, traceability, and smart contract features of blockchain technology for data privacy protection. We employ the Interplanetary File System (IPFS) to store raw data, thereby ensuring user privacy and security. Lastly, we improve the proxy voting node selection in the existing delegated proof of stake (DPOS) consensus mechanism. According thorough evaluation and empirical analysis, the scheme effectively improves the accuracy of text similarity detection. Simultaneously, the enhanced DPOS mechanism effectively rewards nodes with excellent performance and penalizes nodes exhibiting malicious behavior. In this study, we successfully designed and implemented an innovative digital recipe copyright protection scheme. This scheme effectively enhances the accuracy of text similarity detection; ensures the privacy and security of user data; and, through an enhanced DPOS mechanism, rewards well-performing nodes while penalizing those exhibiting malicious behavior.
Drivable Area Detection in Unstructured Environments based on Lightweight Convolutional Neural Network for Autonomous Driving Car

Road detection technology is an important part of the automatic driving environment perception system. With the development of technology, the situations that automatic driving needs to consider will become broader and more complex. This paper contributes a lightweight convolutional neural network model, incorporating novel convolution and parallel pooling modules, an improved network activation function, and comprehensive training and verification with multiple datasets. The proposed model achieves high accuracy in detecting drivable areas in complex autonomous driving situations while significantly improving real-time performance. In addition, we collect data in the field and create small datasets as reference datasets for testing algorithms. This paper designs relevant experimental scenarios based on the datasets and experimental platforms and conducts simulations and real-world vehicle experiments to verify the effectiveness and stability of the algorithm models and technical solutions. The method achieves an MIoU of 90.19 and a single batch time of 340 ms with a batch size of 8, which substantially reduces the runtime relative to a typical deep network structure like ResNet50.
Super-Resolution Reconstruction of Remote Sensing Images of the China–Myanmar Pipeline Based on Generative Adversarial Network

The safety monitoring and early warning of overseas oil and gas pipelines are essential for China, as these pipelines serve as significant energy channels. The route of the China–Myanmar pipeline in Myanmar is mountainous and covered with vegetation, changeable climate, and abundant rain, and is prone to disasters. Therefore, artificial route inspection is dangerous and inefficient. Satellite remote sensing technology has an advantage over a traditional ground survey due to its large range and long-distance capabilities, thus can aid in monitoring the safety of oil and gas transportation. To improve the resolution of remote sensing data, in this paper, we propose a Nonlocal dense receptive field generative adversarial network, using remote sensing images of the Muse section of the China–Myanmar pipeline as data. Based on super-resolution generative adversarial network (SRGAN), we use a dense residual structure to improve the network depth and introduce the Softsign activation function to optimize the problem of easy saturation and gradient disappearance. To extract deep features of the image, we proposed a residual-in-residual nonlocal (RRNL) dense block, followed by the addition of the receptive field block (RFB) mechanism at the end to extract global features. Four loss functions are combined to improve the stability of model training and the quality of reconstructed image. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of the reconstructed remote sensing image of Muse section reach 30.20 dB and 0.84. Compared to conventional methods and generic deep neural networks, the proposed approach achieved an improvement of 8.33 dB and 1.41 dB in terms of PSNR and an improvement of 21.7% and 5.9% in terms of SSIM. The reconstructed images exhibit improved texture clarity and are more visible to the human eye. This method successfully achieves super-resolution reconstruction of remote sensing images for the Muse section of the China–Myanmar pipeline, enhances the details of the image, and significantly improves the efficiency of satellite remote sensing monitoring.
Keep It Simple: Using README Files to Advance Standardization in Chronobiology

Standardization plays a crucial role in ensuring the reliability, reproducibility, and interoperability of research data in the biomedical sciences. Metadata standards are one foundation for the FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data management. They facilitate data discovery, understanding, and reuse. However, the adoption of metadata standards in biological research lags in practice. Barriers such as complexity, lack of incentives, technical challenges, resource constraints, and resistance to change hinder widespread adoption. In the field of chronobiology, standardization is essential but faces particular challenges due to the longitudinal nature of experimental data, diverse model organisms, and varied measurement techniques. To address these challenges, we propose an approach that emphasizes simplicity and practicality: the development of README templates tailored for particular data types and species. Through this opinion article, our intention is to initiate a dialogue and commence a community-driven standardization process by engaging potential contributors and collaborators.
The Posthuman University: A Sign of Our Times

In the spring of the year when bird flu or SARS broke out in China and spread across the globe, the research-intensive university at which I taught at the time issued an edict that all teaching materials and detailed session plans were to be put online for the September of that year so that in case members of staff fell ill (and died?), the courses could proceed anyway. Staff complied. Online materials also of course obviate the need to be present at lectures or seminars. In many UK universities ‘lecture capture’, the recording of lectures so that those unable to attend can watch them at their leisure, has become commonplace. And since this is automated, it requires precise start and finish times—the automation, in a sense, of the teaching process itself. The drive here is potentially towards the people-less, posthuman university, an evacuated dispersed space in which all connection is mediated and made via the traceable internet. This contrasts interestingly with recent eruptions around the globe—in Hong Kong, in Chile, in India—where students and staff have come together, organized and demonstrated to protest contemporary socio-political, economic and climate conditions and policies. This chapter explores questions of the posthuman university in an age when dispersal of community (e.g. academic community) as a function of technologization and the retrenchment of the public sector is challenged by political, economic and climate developments that require the re-formation of community.
Locating Areas of Crime Against Children with k-means Cluster Algorithm

A crime against a child under 18 years of age is a heinous act that violates the moral fabric of a society and refers to the behavior that causes emotional and mental harm as well as physical harm like abuse, rape and sexual exploitation, child trafficking, child neglect, child pornography, and child begging among others. Innocent children are vulnerable, and need protection and love of their parents, friends and relatives as well as government. The data of crime against children released by India's National Criminal Records Bureau (NCRB) is used to analyze and locate hot spot areas of crime against children using k-means cluster algorithm in KNIME platform. This study analysis and findings can help the government take additional preventive measures and develop effective strategy plan in high crime hot spot states to encounter crime against children.
PEMEROLEHAN FONOLOGI BAHASA PERTAMA PADA ANAK USIA 2 TAHUN 3 BULAN STUDI KASUS “MUHAMAD SAEPUDIN”

Every child has their own level of acquiring their first language, these levels range from slow to fast. The purpose of this study was to describe the acquisition of first language phonology in children aged 2 years and 3 months. This study used a qualitative descriptive approach with the observation method. The data collection technique in this study is the technique of observing, involving, taking notes with a data source named Muhamad Saepudin. The data taken is the phonological acquisition of the first language in the data source. The results of the study stated that the acquisition of vowel phonemes namely /a/, /i/, /u/, /e/, /ə/, and /o/ in Muhamad Saepudin can be said to be consistently mastered while the acquisition of mastered consonant phonemes includes / b/, /g/, /h/, /m/, /n/, /p/, /t/, /w/, and /y/, so that data sources are stated to have not been consistently mastered. Therefore, the data source is classified as being able to pronounce vocabulary in everyday life but there are still some utterances that are not yet perfect to pronounce.
EMITS-F: A Model of Embodied Fuzzy Intelligent Tutoring System

The aim of educators is to facilitate learning among learners in cognitive, affective and psychomotor domains and there-by formulating a much more holistic form of education that can address the learning styles of learners and increase their retention capabilities. The notion is to evolve the interest among learners and give them the platform where they can build their own cognitive mental models by reconstructing on their prior knowledge. Identifying a technique to make the learning process more adaptable and motivating for students is a crucial requirement. In this research work, a model of Embodied Fuzzy Intelligent Tutoring System is proposed. The proposed model provides an individualized learning environment for slow learners by incorporating embodied agent Rachel (tutor) having fuzzy evaluation characteristics. The experimental analysis was conducted on slow learners using the pre-assessment and post-assessment test mechanism and learners have shown significant improvement in their performance.
Foreign Object Debris Detection in Aerodromes Using Deep Learning Approaches

Foreign Object Debris is an unwanted object found in the crucial locations of the aerodromes which will cause damage to the aviation equipment and also injure individuals. These objects are a key concern in the maintenance sector of the aviation industry, as it reduces aircraft safety. According to Boeing the cost of damages caused due to FOD is $ 4 billion a year. To prevent this effective detection and prevention programs are being implemented in aerodromes. Regular methods like radar-based techniques, Double-D coil detectors are used more often but their accuracies will be affected by weather conditions and can be often used to detect metal objects. For better FOD detection image processing is being used nowadays. In this work, we will develop a deep-learning detection model that can classify the FOD in an aerodrome with high optical accuracy. An image dataset containing 15,713 images of size 300 × 300 will be used for training the algorithm. Roboflow software will be used to label the images. Deep Learning models like YOLO R, YOLO v8, YOLO v7, YOLO v6, YOLO v5, and YOLO v4 will be used for classification and their accuracies (mAP) were compared. YOLO-v8 produces effective results in detecting the defects in aerodromes having mAP@0.5 of 98.4% and mAP@0.5:0.95 of 86% and can be used for FOD detection.
Block Based Self-Secured LSB Embedding Scheme for Reversible Steganography

Reversible Steganography had been a crucial topic when perceptual quality and security is concerned. There is a trade-off between these two parameters since when high security is obtained the perceptual quality of the stego image is decreased. The block based self-secured LSB embedding scheme (BBSS-ES) proposed in this work is able to maintain both the metrics with very low computational cost. The scheme similar to pseudo random and chaotic LSB embedding scheme provides better security to secret message while maintaining better stego quality. It uses the center pixel to find the position in 3 × 3 neighbourhoods from where the LSB embedding would start. The index in the neighbourhood is simply calculated using natural logarithm over the magnitude of the center pixel. The embedding process continues in clockwise direction in the neighbourhood. The immunity of the proposed scheme to unwanted noise or attack in stego image is high at values of center pixel greater than equal to 12 since the offset in band after value 12 goes on increasing and any unwanted noise or attack would not be able to shift the value beyond the limit of the band. The system is evaluated over four cover and four secret images and the performance is measured in terms of peak signal to noise ratio with perceptual quality of the stego image.
Task Allocation in the Edge Cloud Scenario Incorporating Priority: A Simulation-based Study

Effective task allocation algorithms are crucial for the optimal utilization of resources in Edge and Cloud-based systems, particularly when tasks have different priorities and arrival times. Key parameters to consider include the number of Edge and Cloud instances to be used, the ratio of high to low-priority tasks to be preferentially allocated, and task-related preferences such as minimizing costs, time spent, and preferred time slots. Balancing cost and time efficiency while also considering task priority is essential for achieving maximum utilization and profitability. To optimize task allocation, it is necessary to consider parameters such as maximum task allocation, idle time of servers, and load balancing. Our proposed framework for effective task allocation in Edge and Cloud-based systems incorporates preferential allocation for high-priority tasks and balances cost versus time efficiency. We use a Poisson distribution of task arrival time and model each task’s completion time slot parameter. We detail the results of our simulation-based approach, considering two classes of tasks with different preferences for either minimizing time or cost and corresponding server requirements such as Cloud+Edge or only Edge. We use upper bounds on preferential allocation for high-priority tasks to maximize VM utilization and minimize waiting times. Our model suggests carefully choosing the parameters of preferential allocation and the number of each VM type to balance task waiting times, VM idle time, and utilization. We experimented with various VM ratios on Edge-Cloud servers and found that increasing VMs on both servers is better for handling unallocated tasks. We found that a 2:1 task ratio with a 2-unit upper bound time length and renting additional virtual machines at the start of the allocation process produce optimal outcomes for our framework.
Empirical Analysis of Variable Thresholding for Autoencoder Anomaly Detector in ECG

Detecting anomalies in electrocardiogram (ECG) signals is crucial for identifying abnormal heart rhythms and diagnosing cardiac conditions. This paper investigates the effectiveness of variable thresholding for an autoencoder-based anomaly detection approach to detect abnormal signals in two widely-used databases. This unsupervised approach uses a deep autoencoder to learn the underlying patterns of the ECG signals and then applies a thresholding method to detect anomalies in the reconstructed signals. We evaluate the performance using Receiver Operating Characteristics (ROC) curve, which demonstrates the trade-off between True Positive Rate (TPR) and False Positive Rate (FPR) over different thresholds. Our empirical study aims to analyze the effectiveness of using different thresholding methods in combination with the Autoencoder-based anomaly detection architecture. For this purpose, we considered two thresholding methods: Random Thresholding and Distribution based Thresholding.
Impact of Social Media on Large Scale Enterprises

Social media has revolutionised business-customer interactions and brand-building for both traditional large-scale companies and the latest online companies. Established enterprises now have unprecedented opportunities to connect with customers, gather feedback, and cultivate brand loyalty through platforms like Facebook, Twitter, and LinkedIn. At the same time, emerging online companies can leverage social media to establish their brand presence, engage with their target audience, and compete in the digital marketplace. Social media has become an essential tool for businesses of all types and sizes, revolutionising how they interact with customers and build their brands in the modern business landscape. This paper aims to explore the impact of social media on large-scale enterprises, taking into account facts and figures from some of the biggest companies in the world. This research examines how social media affects the operational efficacy of big corporations. Specifically, the study explores how social media use affects various dimensions of business performance, such as revenue growth, customer satisfaction, brand reputation and employee engagement. With the increasing popularity of social media platforms, large-scale enterprises have been leveraging these platforms to enhance their business performance. However, the existing literature on social media's impact on businesses has mostly focused on small and medium-sized enterprises, with little attention given to large-scale enterprises. Exploring New Ways for Large Scale Business Enterprises, including Business Giants and Tech Giants, to Connect with Diverse Customer Pools and what are the benefits and limitations of these new ways of connecting industry with customer?
Effects of Animation Types on Visual Search Behavior: A Study on Mobile Icon Search

Smartphones usage tremendously increased from being only calling device to a tool that facilitates all personal tasks. The number of applications increased with increased usage of smartphones. As mobile app icons are the basic way to access the applications installed, it becomes difficult to search desired app hidden across a vast number of pages of smartphone menu. This paper attempts to explore if animations have effect on visual search of icons and acceptability of animated icons. Different animated icons were created and evaluated for search behavior in the eye tracking experiment. Results of the current study showed that the vibration animation (out of the ten selected animations) the most positive effect on icon search behavior and was preferred by most of the users. These findings can be used by designers for mobile operating system interface design and in scenarios where search task is crucial in complex and enormous information cloud presented together in a single screen. Similarly, animated icons can also help in promoting apps as they are getting noticed repeatedly on mobile screen.