Reviewerly/exHarmony huggingface dataset:
Understanding Integrity of Time Series IoT Datasets through Local Outlier Detection with Steep Peak and Valley

With substantial advances in emerging and enabling technologies in IoT sensors, a vast amount of IoT-based environmental data allows preparation for adverse impacts by providing helpful information for predictive and precise services. However, data acquired by IoT sensors can be corrupted by external environmental factors, which can negatively affect the integrity of data interpretation. To address this problem, a prior study proposed outlier detection techniques using transform-based sparse profiles. However, it would lose its worth without an evaluation methodology for data integrity after probing datasets by outlier detection. In addition, it did not consider data with steep peaks or data that is dependent on other data, which is common in real-world scenarios such as soil moisture data used in this paper. Therefore, we propose a process of preprocessing defective soil moisture sensor data using local pattern-based outlier detection (LPOD) and evaluating the integrity of data after outlier detection. Our paper specifically aims to: 1) detect outliers of original soil IoT datasets to eliminate fault data possibly giving wrong decisions using local and global outlier detection (OD); 2) exploit the results of statistical evaluation to determine whether the outliers have been well eliminated; and 3) find the ground truth pattern of soil IoT datasets considering precipitation. Experiments using real-world soil moisture datasets show that the LPOD method outperforms other statistical outlier detection methods, suggesting that the preprocessed data can improve the integrity of IoT datasets.
Research on personalized news recommendation based on dynamic knowledge subgraph perception

Aiming at the problem of how to effectively construct the interpretability of time-sensitive personalized news recommendation, a dynamic knowledge subgraph perception research method is proposed. Firstly, the dynamic knowledge subgraph is extracted by using the time neighborhood perception technology. For each news, a dynamic knowledge subgraph covering the core entities in the news and the neighbor entities related to the news in the knowledge graph is extracted. Secondly, the CNN and Transformer technology sets are used to form a semantic collaborative encoder. The user 's personalized features and news content are encoded by the user news collaborative encoder. Finally, the matching score is calculated by the correlation model and model training. The larger the score, the more interested in that type of news. Compared with the independent modeling of user interest and candidate news, experiments are carried out on the data sets MIMD and Feeds, and four indicators of AUC, MRR, nDGG @ 5 and nDGG @ 10 are used for evaluation. The experimental results show that the proposed network model has great advantages over other benchmark models, making the recommended results more interpretable.
HermHD: Enhancing smart contract security based on code obfuscation

Abstract. Due to the transparent nature of blockchain, all transaction information and smart contract code is recorded on the public blockchain. It is easy for existing static analysis tools to analyze and exploit vulnerabilities in smart contract code. To mitigate this risk, we propose HermHD, an automated security enhancement tool that protects smart contracts on the Ethereum network. HermHD employs six obfuscation patterns that can rewrite the bytecode of a smart contract without affecting its functionality. By applying these obfuscation techniques, we aim to prevent reverse static analysis tools from cracking the contract and thereby enhance the security of smart contracts. To validate the effectiveness of HermHD, we conducted experiments on 121 smart contracts from a public dataset. 54The evaluation results demonstrate that all the protected smart contracts are resistant to two popular reverse engineering tools, and the additional gas cost incurred is minimal.
An object detection network for locating and recognizing cluttered garbage

The research on garbage classification have made remarkable achievements in the latest work. However, due to the complexity of the location elements of the garbage background, there are still many difficulties in the accuracy of garbage location detection. For complex backgrounds, it is easy to cause false detection and missed detection. In response to the above problems, we explored the improved YOLOv5n network , added the ContextAggregation module and replaced GIOU with Focal-EIOU, and replaced C3 with C3SAC to improve feature extraction for contextual information. The improved algorithm has been proved by experiments to increase mAP by 2.3%. The accuracy rate of Plastic bowl, Power banks, Cardboard boxes, Tea leaves, Plastic hangers and so on has reached more than 80%.
Leveraging Deep Learning for Improving Real Time Stolen Vehicle Tracking in Smart Cities

Efficiently detecting and tracking stolen vehicles is a crucial aspect of law enforcement and public safety. Traditional methods, reliant on limited data sources, are time-consuming and labor-intensive due to manual efforts. The emergence of deep learning technology offers the potential to streamline these processes, reducing manual work and saving valuable time. This study proposes a deep learning-based system for the surveillance of lost or stolen vehicles, utilizing the YOLOv7 object detection model and the DeepSORT tracking algorithm. The proposed system automates the process of data generation, detection, and tracking across multiple camera feeds, resulting in an interactive map that displays the vehicle trajectory. Using a publicly available dataset containing a set of camera recordings of vehicles, the dataset for the experiment is generated and enhanced by augmentation techniques. The experimental dataset encompasses a video segment highlighting the target vehicle of interest. This footage is processed in detail to yield image-label pairs, encapsulating the target vehicle and its distinctive features, thereby serving as the training foundation. Further, the resultant model is tested on varied video sets within the public dataset and the detection results are recorded for performance analysis. The proposed system's performance, evaluated through various metrics, effectively detects and tracks stolen vehicles in real-time scenarios. This research enhances public safety by empowering law enforcement agencies with a powerful tool for rapid identification and recovery of stolen vehicles.
Robust Deep Learning Approaches for Wireless Communication Systems

Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless transmission systems. But if the peak-to-average power ratio (PAPR) is too high, OFDM symbols can be distorted at the MIMO OFDM transmitter. It will make it harder for the MIMO OFDM receiver in the channel estimation and signal detection phase. To explore the possibilities of Deep Learning (DL) in particular and machine learning in general in the MIMO OFDM system and to serve as a foundation for future research, we develop a DL-based MIMO OFDM receiver using DL in this work. From there, DL models can help filter out the noise caused by the high PAPR problem and change some parts at the receiver to improve the receiver's performance in the point-to-point MIMO OFDM system. The simulations show that the suggested DL-based receivers have a lower bit error rate (BER) than conventional receivers.
Server-Aided Revocable Identity-Based Signature over Lattices

Revocable Identity-Based Signatures (RIBS) incorporate revocation mechanisms into traditional identity-based signatures, allowing for the revocation of signing capabilities for users. Specifically, RIBS periodically assigns update keys to non-revoked users. When a user needs to be revoked, a trusted Key Generation Center (KGC) ceases to assign new update keys to that user, rendering the revoked user unable to sign any message effectively within future time periods. However, previous approaches have imposed significant burdens on users, requiring extensive computational efforts and continuous communication with the KGC. These limitations pose challenges for users with limited computing, communication, and storage capabilities. To alleviate these issues, this paper proposes a Server-Aided Revocable Identity-Based Signature (SR-IBS) scheme with signing key exposure resistance. In this scheme, almost all of the user's workload can be delegated to a third-party server, which manages user public keys and update keys. Additionally, users do not need to communicate with the KGC during the key update phase, and they only need to keep a fixed-sized key. Finally, we prove that the proposed scheme is strongly unforgeable against selective-ID and adaptive chosen-message attacks in the random oracle model.
Pluralistic Face Completion of Masked Face based on 3D priors

The pluralistic face completion system is developed as a web application that generates multiple face images for a face which is covered under a face mask. The web application consists of five modules where it deals with, 1) Applying a face mask to the person's image, 2) Removing face mask in the masked face image by generating the covered part of the face corresponding to rest of the face part with multiple outputs, 3) Checking similarity between resultant images and input images given by user, 4) Querying a person's availability in group image and 5) Face aging module where a person of any age is given along with the desired age number, where it generates the face image of the required age of a person. The found similar person can be checked for his outlook on various angles, by rotating the person's face. Face generation algorithms are prone to generate differentiating outputs then the ground truth image. As these algorithms generate only single output, there is a high scope these outputs not being closely matched with the original image. Hence, in this project multiple diverse output images are generated, which increases the probability of achieving the highest similarity with the original image. Masking the face is attained by using Dlib library while the rendering of the face is achieved by using Generative Adversarial Networks (GAN). The proposed project is designed such that, it solves the dependency of manually labelling missing regions of the face (i.e., mask region on the face), identifying the best matching similar face for the generated face image from the former network and identifying the person of interest in a given group image.
Real-Time Dress Code Detection using MobileNetV2 Transfer Learning on NVIDIA Jetson Nano

The proposed research intends to develop a model for automatically identifying the dress code in companies and educational institutions where appropriate apparel needs to be frequently maintained. The concept focuses particularly on the dress code for school children. Convolutional Neural Networks (CNN), MobileNetV2, and an object detection algorithm with a high level of accuracy are examples of deep learning models. In comparison to manual checks, using an automated system for dress code detection has various advantages. It reduces the number of mistakes and faults that could happen during manual inspections while also saving time. Organizations and institutions can make sure that their staff constantly complies with the dress code rules thanks to the automated system. The Dress code detection model is designed, trained, and implemented using the Edge impulse API. The proposed Transfer learning MobileNetV2 and CNN TINY ML models were implemented on an NVIDIA Jetson Nano edge device for real-time Dress code classification. F1 Score, precision, recall, latency, and storage space are common evaluation metrics for object detection models. The goal of this project is to create a model for automatically identifying dress rules in businesses and educational institutions that must maintain suitable attire on a regular basis. The proposed model offers a practical approach to tracking workplace and educational dress code observance. A disciplined environment is created and the professionalism of the individuals inside the organization is reflected by the accuracy of the MobilenetV2 and CNN models in detecting business formal clothes. The Proposed transfer learning MobileNetV2 model performs better results compared to all other CNN models in accurately detecting business and school children's formal attire.
Exploring Interoperability and Decentralized Applications for Personal Health Data using FHIR Standards

This study explores the integration of Fast Healthcare Interoperability Resources (FHIR) standards in a system designed for secure and efficient management of Personal Health Records (PHRs). PHRs are crucial for patients to have control over their healthcare information and make informed decisions. Ensuring the security of this sensitive data is vital, and blockchain technology offers robust protection. However, the high cost of storing PHRs on the blockchain has led us to investigate the use of the Interplanetary File System (IPFS) alongside blockchain. Our proposed system comprises a PHR Decentralized Application (DAPPS), IPFS, Metamask, and the Ethereum Blockchain. Users input their PHR data through the DAPPS, which is stored in IPFS, and its location is registered on the Ethereum Blockchain via Metamask. Comprehensive testing validates the system's performance, with success across all test cases, confirming the feasibility of utilizing the Interplanetary File System and Ethereum Blockchain for secure PHR management while adhering to FHIR standards, fostering interoperability in healthcare data exchange.
Tracking Drones with Manual Operation Representation

The Global Positioning System (GPS) is a system that provides position information such as latitude, longitude, and altitude anywhere on the earth. If this system is used, however, it may cause (1) position acquisition interruptions in places where GPS signals do not reach, (2) increased power consumption due to the constant processing of numerous satellite signals, and (3) cyber-attacks targeting GPS receivers. Until now, many methods have been proposed to overcome these challenges individually. This study, in contrast, addresses these challenges simultaneously for the quadcopter (abbreviated as drone) case, where the use of GPS is a prerequisite.
Graph attention neural network social recommendation based on dynamic representation

Although Few methods of deep learning have been proposed to deal with graphs that are dynamic in nature. Most of the existing recommendation systems based on social trust ignore the heterogeneous trust relationship between users and the heterogeneous interaction between users and products. And the appeal of a project can change over time, just as the dynamic interests of users are rarely considered, so is the relevance between projects. In order to overcome these limitations, a graph attention neural network Social recommendation (GNN-DR) model based on dynamic representation is proposed. Firstly, it considers the dynamic representation of users and items, and combines the influence of their relationship to model the short-term dynamic and long-term static interactive representation of item attractiveness. Second, the architecture systematically models user-user social diagrams and user-project diagrams, integrating heterogeneous trust and interactions. Finally, the attention mechanism is used to learn the potential factors of users and projects. Experiments on two real recommendation system datasets verify the validity of GNN-DR.
Analysis of the Impact of Artificial Intelligence on Digital Marketing

In today's fast-paced technological landscape, the synergy between Artificial Intelligence (AI) and digital marketing stands out, transforming business operations and influencing customer relationships. Digital marketing, having evolved from the Internet era, is on the brink of another significant evolution, propelled by AI's unmatched data analysis, prediction, and personalization capabilities. The study focuses on exploring the less studied connection between AI's technical aspects and individual digital marketing strategies. By investigating this area, the research discovers that AI can significantly enhance predictive analytics, improve customer segmentation, and help create more personalized content in digital marketing. However, merging AI with digital marketing also presents ethical and practical challenges that must be addressed. Venturing into this intersection reveals AI's vast potential in redefining critical elements of digital marketing. Notable impact areas include predictive analytics, accurate customer segmentation, and crafting tailored content. Beyond the promising opportunities, the integration of AI in digital marketing highlights the transformative role AI could play in honing and amplifying strategic outreach. Yet, this potential comes with its set of challenges. Careful consideration of the ethical and practical ramifications arising from the fusion of AI and digital marketing is imperative.
Analysis of JD.com's Acquisition of Deppon

Nowadays, logistics industry is a valid role in the economic growth. In the dissertation, JD.com’s and Deppon acquisition will be the typical example in the company acquisition. The aim of this dissertation is to analyse whether the acquisition is successful through the comparison of financial data and historical data before and after the acquisition. This will help the acquisition of companies in the future. This dissertation will provide the background of two of the companies. That will make a better understanding of the two companies and the acquisition process. Then, according to the financial data and historical data to analyse the acquisition of JD.com’s and Deppon. Some following suggestions will be given which focus on the future acquisitions, the aim of the dissertation is to help the future similar acquisitions, give them some relative suggestions.
Analysis on the Way of Short Video Platform Guiding Media Users to Carry out Self-marketing: Taking Kuaishou as an Example

It is of great significance to explore the survival of short media platform to guide new media users to adapt to the development of digital media marketing theory. With the development of society, such related studies as virtual society on short video platforms have begun to appear. On the basis of summarizing the previous research on digital media marketing, the author tries to make in-depth research on the way of guiding new media users to conduct self-marketing on the short video platform. At present, there has been relevant research on the responsibilities of news marketing and short video, but there is almost a blank field in the realization of users' self-value. For the first time, the author innovatively puts forward a new perspective of focusing on the connection between users' emotional regulation and self-realization, in order to provide suggestions for practitioners or related application fields for users to realize self-value in the short video platform in the future.
Derivative Works of Science, Literature and Art

The subject of the study is derivative works of science, literature and art, which in the list of types of works of science, literature and art are called derivative works as independent objects of copyright, which means that they have specific features. Various approaches to defining the subject of a derivative product are considered. The characteristics of a derivative work inherent in any work and the distinguishing features from other types of works of science, literature and art are analyzed. Based on the analysis of the elements of the work of science, literature, art the author shows which elements can be freely used in the creation of a new work and which elements are protected, and therefore their use makes the new work dependent, derivative. It is proposed to clarify the characteristics of the derivative product. The issue of respecting author’s rights, whose work was translated or had any type of text processing, is investigated. The relationship between the exercise of the rights of the author of the original work to inviolability and the right to process the work is analyzed. The conclusion is justified that the right to inviolability applies to the work created by the author personally, and the result of translation or any text processing is a new work, in respect of which the author of the translation and other processing has copyright. The conditions for the legitimate use of the derivative work are considered, namely the need to obtain the author’s consent to redo his/her work and keep the author›s idea and plan of the used work. It is noted that a clarification is required on what is considered as non-conservation of the meaning of the used work. The conclusion is argued that a violation of this condition is a misrepresentation, perversion of meaning or other change in the work, discrediting the honor, dignity or business reputation of the original work author.
MAPPING PADDY RICE EXTENT AND CROPPING PATTERN IN IADA BARAT LAUT SELANGOR USING INTEGRATION OF SENTINEL-1 AND 2 DATA

The Malaysian government, under the 12th Malaysia Plan (RMK-12), has set a goal to achieve a self-sufficient level (SSL) of 70% for rice production. Accurate and timely spatiotemporal information on harvested rice extent is required to measure progress towards achieving the SSL for rice. Remote sensing technology has been widely used to provide information on rice extent in large areas rapidly. The aim of this study is to create maps of rice extent and cropping patterns in the IADA Barat Laut Selangor (BLS) using a combination of Synthetic Aperture Radar (SAR) imagery data from Sentinel-1, optical imagery data at level-2A or Surface Reflectance (SR) from Sentinel-2, the unsupervised K-means clustering method, and the Google Earth Engine (GEE) cloud-based computing platform. The clustering results were then classified into rice and non-rice groups, and used to generate rice cropping patterns based on their representative profiles of temporal composite values of VH backscatter of Sentinel-1 data and NDVI of Sentinel-2 data. The accuracy of the map products was evaluated using a confusion matrix based on visual interpretation using very high-resolution imagery in Google Earth (GE) and compared with the existing data from IADA BLS. The rice extent map generated at 10-meter resolution exhibited excellent accuracy with an overall accuracy of 98% and a kappa coefficient of 0.95. The estimated rice parcel area in IADA BLS for 2021 was 17,864 ha, which is close to the existing data of 18,785 ha. The comparison of results in the irrigation block also indicated that the rice field area agreed well with the statistical data, with an R2 of 0.95, RMSE of 357 ha, and relative discrepancy of 4.9%. The cropping pattern also showed satisfactory results as compared to the existing data. These findings demonstrate that the proposed methodology can provide high-accuracy rice extent map products and has the potential to be applied to rice fields across Malaysia and other tropical regions to address food security issues.
Decision Support Analysis in Food-Borne Epidemics

This paper shows the modeling and analysis of processes associated with the spread of the epidemic of food-borne diseases. The aim of this study was to show how to test the characteristics of the processes associated with the spread of the epidemic of food-borne diseases. Using formal models and methods the epidemic of food-borne diseases is investigated. The activities of sanitary inspection are considered. It is shown how to obtain various characteristics related to operations of inspection operations during the epidemic of food-borne diseases. The result of the work are models of processes related to the activities of sanitary inspection during the epidemic of food-borne diseases. This allows analyzing the activities of sanitary inspection in preventing the spread of food-borne illness. Models are also formal description of the activities of sanitary inspection.