Reviewerly/exHarmony huggingface dataset:
Cross-Modal Semantic Fusion Video Emotion Analysis Based on Attention Mechanism

In view of the semantic gap problem existing in the existing multimodal video emotion analysis model due to the insufficient fusion process of different modes, which affects the performance of emotion analysis, a multimodal video emotion analysis model based on multi-head attention mechanism and multimodal cross fusion is proposed. The model first extracts the single modal features of text, voice, vision (image) and so on in the video; Then, the GRU network is used to extract the temporal characteristics of each single mode context; Then, the multi-head attention mechanism is used to fuse text-voice, text-video and voice-video, and then the single-mode features and the features after the fusion are cross-mode fused; Finally, through the attention mechanism, the fused features are input into the emotion classification network for classification. The experimental results show that compared with the existing models, this model can better fuse the attribute features within and between modes, and has a certain improvement in the accuracy of emotion recognition and F1 value.
Tourism Event Knowledge Graph for Attractions Recommendation

Attractions recommendation faces a severe data sparsity problem. Introducing the knowledge graph into recommendation can alleviate the sparsity effectively. Existing general KGs focus on static knowledge of attractions, but these public attributes provide less value for users’ decision making in tourism. To improve the recommendation performance, it is necessary to introduce more meaningful information which is of greater interest to tourists. We present a novel knowledge graph construction paradigm—Travel Event Knowledge Graph (TEKG), which focuses on describing tourists’ behaviors and experiences in tourism events. Taking a travelogue as a travel event, we set the travelogue as the main entity instead of attractions, and introduce the "4W1H" event description framework to organize the information in travelogues effectively. We conduct extensive experiments on a real-world rating dataset of attractions. Experimental results prove that the application of TEKG to recommendation methods can significantly improve the model performance.
Coded Distributed Computing Schemes with Fewer Files

Coded distributed computing (CDC) is an efficient method to reduce the communication load in general distributed computing frameworks such as MapReduce. In these systems, one usually needs to split the data set into disjoint files to complete a computational task. Li et al. provided some CDC schemes achieving optimal communication load. However, as the number of computing nodes increases, the number of files grows too fast to be applied in practice. In this paper, we first propose a new construction of CDC schemes. Then we show that the communication load in the new scheme is slightly larger than that of the scheme obtained by Li et al, while the number of files in the new scheme is much smaller than that of the scheme obtained by Li et al.
Blockchain-Based Distributed Service Registry and Discovery in 6G Core Network

Compared with previous generations of traditional network, the 5G core network uses a service-based architecture, in which the communication between network functions (NF) depends on a centralized multi-level service registry and discovery mechanism. In the 6G era, the core network will be further distributed and the participants in the network ecosystem will be more diverse. Therefore, a more flexible and efficient way is needed to achieve rapid addressing while avoiding the trust problem between different participants. As a “decentralized” information storage and management technology, Blockchain is naturally suitable for solving this problem. In this paper, we propose an effective service registry and discovery method. This method uses blockchain technology to record the information of the core network NF, then other processes can be realized through it. The application scenario and scheme of this method are introduced, as well as the feasibility and functional integrity are verified.
Comparison of cuFHE vs TFHE on Arithmetic Circuit Computation

This is an extension of the ongoing research project on Fully Homomorphic Encryption. Arithmetic Circuit Homomorphic Encryption (ACHE) was implemented based on the Fast Fully Homomorphic Encryption over the Torus (TFHE). This paper focuses on not just comparing the latency of the digital gates between cuFHE and TFHE, but the differences for the Public/Private key generations, the security bits size, the actual default generated key size and transmission latency. At the same time, new Arithmetic Circuits on Addition, Subtraction and Multiplication will be constructed on cuFHE. The new Circuits are compared and analysed against earlier implementation in TFHE with and without enhancements.
Study on The Time Evolution Pattern of Mineral Exploration Activities Correlated with Resource-Based Cities

The geological data catalog is a summarized archive of the historical records of geological exploration, which records the knowledge and structure of all geological data and the number of geological data entities, and can provide an index for quickly locating relevant geological and mineral knowledge. In this paper, we analyzed the relevant mineral exploration activities in the geological data catalog, by using the global autocorrelation Moran's I statistic and General G statistic to reveal the spatial aggregation of mineral exploration sites; and studied the correlation between the spatial and temporal distribution patterns of mineral exploration activities and the development of resource-based cities. The result showed that the exploration time of mineral exploration areas (points) for coal, gold, and iron indicated clustering characteristics around recent exploration points, which correspond to resource growing and regenerating cities or regions, suggesting the possibility of emerging resources. The exploration time of the copper mineral exploration area (point) shows the clustering of old exploration points, and the center of the clustering area corresponds to resource-mature cities or regions, suggesting the possibility of resource exhaustion. This study is an extension of the application of the geological data catalog, and the mineral exploration time evolution pattern derived from the analysis can provide a reference for mineral exploration planning and spatial planning, etc.
Post-Quantum Lightweight Encryption Algorithm for Internet of Things Devices

The increasing use of Internet of Things (IoT) devices in various applications has led to a growing concern about their security. Many IoT devices have limited resources such as processing power, memory, and energy, which makes them vulnerable to attacks. Encryption is a fundamental security mechanism that can be used to protect data in transit and at rest. However, traditional encryption algorithms are often too complex and resource-intensive for IoT devices. In this paper, we propose a lightweight encryption algorithm for IoT devices that is designed to provide a balance between security and resource efficiency. The Sym-BRLE (Binary Ring-Learning encryption) algorithm, based on the binary ring-learning with an error's encryption algorithm, has been proposed to improve random number selection and polynomial multiplication calculations to meet IoT communication requirements. In addition, the algorithm adds encryption security measures to achieve high security and efficiency for lightweight IoT devices. The Sym-BRLE algorithm has high communication efficiency and a small key size, and it can reduce total encryption time by 30% to 40% compared to other BRLE-based encryption algorithms. In addition, security analysis shows that Sym- BRLE can resist grid attacks, timing attacks, simple energy, and differential energy analyses.
Ensemble Deep Learning Classifier for Optimal Detection of Melanoma Cancer

The widespread adoption of electronic health record (EHR) systems in response to a diverse array of requirements for primary and secondary healthcare, there is now an abundance of clinical data that can be accessed with relative ease. This is a significant improvement over the situation that existed previously. The widespread implementation of EHR systems is directly responsible for this effect. Unstructured clinical reports that were either transcribed or dictated by clinicians make up a sizeable percentage of these data, and they were collected in that format. In this paper, we develop an ensemble model to classify cancer disease from EHR using several convolutional neural network (CNN). The simulation is conducted to test the efficacy of the model and the results show that the proposed method achieves higher classification rate than other methods.
Human Recognition and Action Analysis in Thermal Image with Deep Learning Techniques

Recognizing human activity in a camera-based surveillance system is still a difficult issue. When a human is unseen in an image taken in a dark area, it is difficult to identify the action. Thermal cameras have been utilized in earlier investigations to address this problem. Frequently, for heat transfer inference, Infrared thermal imaging is a technique of passive imaging that captures the discharged radiation from an object to calculate the temperature of the surface. Infrared thermal imaging provides the ability to detect movement without the shadows, glare, or lighting modifications affiliated with imaging digital visualization or active infrared challenges. The identification and analysis of human action recognition in thermal images are discussed in this work using a hybrid approach. This paper proposes a hybrid strategy for human detection in thermal images that combines Faster Region-Based Convolutional Neural Network (R-CNN) with You Only Look Once, Version 3 (YOLOV3). This approach bridges the gap between single-shot and two-shot detectors. In this study, a long short-term memory (LSTM) and convolutional neural network (CNN) is used to analyze human behavior. The LTIR dataset has been used to test the proposed technique, and the findings support its efficacy. A proportionate analysis using various state-of-the-art approaches shows that the proposed method performs consistently well in terms of Accuracy, Precision, Recall, and Computational Time.
Research on the Construction of Medical Insurance Intelligent Monitoring System Based on Blockchain

The current health care monitoring system in China, the lack of real-time monitoring of medical services, the lack of public information, the lack of rigorous use of health care funds, and the insured behavior is difficult to achieve supervision, all these problems require that China’s current health care monitoring system must be transformed. Blockchain technology through decentralization, open and transparent operation, makes the Medicare intelligent monitoring system based on blockchain technology save medical records and other public information, promotes the two-way exchange of information and share information, realize the real-time reimbursement of medical expenses, improves the efficiency of service, standardize the behavior of service, ensure the quality of service, etc.
Shear Detection and Key Frame Extraction of Sports Video Based on Machine Learning

Due to the continuous progress of video technology, the improvement of network transmission speed, and the continuous emergence of new and effective video compression technologies, people have realized the sharing of various video information. The explosive growth of video data makes it urgent for users to classify, browse and retrieve videos quickly and effectively. Content-based video processing and retrieval, which is dedicated to content analysis, semantic classification, index retrieval and storage management of videos by using IT, has attracted more and more attention of scientific research institutions and researchers. Sports estimation can reflect the temporal correlation between adjacent video frames. This paper adopts a simple and flexible sports estimation algorithm based on block matching. Key frame is a key image frame used to describe a shot. Based on the complexity of the shot content, one or more keyframes can be extracted from a shot. This paper studies several key technologies of semantic-based video content extraction and analysis. The research mainly focuses on how to automatically extract and analyze video content, and realize semi-automatic or automatic analysis and classification of video data to meet the needs of retrieval. Finally, this paper proposes an algorithm to extract key frames according to the content changes, and combines the shot detection and key frame extraction algorithm to form a video browsing platform based on key frames.
Improved Convolutional 3D Networks for Micro-Movements Recognition

It is of great significance for computers to recognize the actions in videos. The human body’s action recognition has been applied in many fields. The majority of action recognition methods have relatively low precision in recognizing micro-movements. In some specific scenarios, tasks such as intelligent home companionship for the elderly and early warning for dangerous driving behaviors, the micro-actions of the observed are extremely important in the recognition task. At the same time, due to the physiological characteristics of the elderly or the limitation of the environment, the amplitude of the actions is relatively small. This research suggests an action recognition method based on deep learning to better analyze micro-movements-oriented action recognition. Inspired by transformer, we split an image into fixed-size patches. The network structure of C3D is improved. The idea of image patch is introduced to reduce the receptive field of each region in the video frame. Finally, the experimental verification is performed on two action recognition datasets, UCF101 and NTU. The average accuracies on UCF101 and NTU respectively are 91.74% and 88.01%, which show that the proposed algorithm can effectively improve the recognition ability of micro-movements and obtain better results compared with other baselines.
High Efficient Compression: Model Compression Method Based on Channel Pruning and Knowledge Distillation

With the development of neural network models in the field of deep learning image processing, more complex and higher performance neural network models can be used, but with it comes greater parameter quantities and computing power requirements. Although the model compression method has been updated in recent years, it still has problems such as inefficiency and reduced accuracy. Based on this, we propose a model compression method based on channel pruning and knowledge distillation, we sort and prune the L2 regularization weights of the convolutional layer filter containing a large number of calculations in the original model and prune it according to a certain proportion, and assist in restoring the accuracy by the knowledge distillation of the pruning model through the original model. Experimental results of the VGG16 model show that our pruning method reduced the parameters of the model by half and increased its processing speed to 2.7 times that of the original model, but the accuracy was reduced by about 10%. To solve this problem, we propose a knowledge distillation method to assist the training of the pruning model, so that the problem of model accuracy degradation has been improved and maintained at about 3.3%, to achieve a balanced state of model compression and accuracy reduction, thereby realizing the efficient compression of the model.
Implementing Virtual Private Cloud Instances

In the previous chapter, you learned about Virtual Private Cloud networks (VPCs) and how to design one or more VPCs from a set of business and technical requirements for your workload. We showed how every detail of your VPC design and connectivity traces back to one or more requirements.
Various Types of Formatting with JavaScript

In the previous chapter, you looked at creating various alerts that could assist your clients in knowing when they entered incorrect information.
Working with Dropdown Lists

So far you have looked at ways to improve text boxes, buttons, radio buttons, and check boxes with JavaScript. However, there are other types of fields that can be dynamic as well.
Creating a QR Code Custom Stamp

Chapter Goal: In this chapter, you’ll create a custom QR Code stamp with the Adobe Acrobat Stamp tool.
Development of Recruitment App

The purpose of this project is to build a central recruitment process based on our company. Human resource companies create vacancies, save applicant information, initiate interview processes, schedule interviews, store candidate interview results, and finally, application settings are all integral components of this system. To use the HR Recruiting App, a report might need to be prepared. Cloud computing provides a shared pool of readily available computing resources provisioning and sharing based on user demand. In this article, universities and educational institutions can benefit from cloud computing in terms of cost, efficiency, and reliability, portability, flexibility and security. In the end, the purpose of this undertaking is to create a centralized onboarding process system for the HR Company, creating job openings, managing applicant information, conducting interviews, scheduling interviews, storing candidate interviews and results, and ultimately, handling payment and hiring processes. The application is the quality of this system. To use the HR getting newcomers telephone operations, a written statement might need to be got ready. Cloud computing provides a common set of data-processable materials that can be rapidly deployed and distributed based on user demand. This paper makes test of how organizations for higher education and deeply rooted ways of acting can get help form could computing in words of price, doing work well, level of being ready for work, ability to make ready adjustments, and security.
Analysis of Aerial Firefighting with Rotorcraft Platforms

Wildfires are an annual torment in many parts of the world. They can spread very quickly and destroy forests, wildlife, buildings, infrastructure, and homes, as well as risk human life before they are able to be controlled. Because of the rapid response time that is necessary to extinguish a wildfire before it becomes too large, the quantity and speed of water delivery are extremely important - helicopters are a great tool for accomplishing this goal. They can fly directly to the fire and refill their water supply from smaller, more remote water sources than other aerial platform options require. There are several water tank options to transport and drop water on the fire from a helicopter. This paper focuses on four of these options: an external rigid tank, an accordion tank, a Bambi Bucket® 1, and an internal tank in addition to the outlining some of the options for remote water sourcing. Each of these tank types have various advantages and disadvantages which are discussed using models developed for evaluating helicopter firefighting applications.
AI/ML-Based Object Detection on FPGA SoC

The latest research in the area of neural networks has shown remarkable advances in computer vision over traditional algorithms. The in-depth learning solutions for computer vision functions have transformed many industries in recent years. Deep Convolutional Neural Network (CNN) dependent models are prime solution for classification and detection of object. But they are computationally expensive due to their huge networks. So embedded systems have too many limitations to use modern configuration. CPU platforms are difficult to produce enough computational resources to run these large networks. Although GPUs are capable of processing expensive models, they consume more power, and their energy efficiency is very low. Currently, the “Field-Programmable Gate Array (FPGA)”-based “neural network accelerator” becomes the subject of research. With specially designed computer platforms, FPGA is the upcoming solution to outperform the GPU with energy efficiency and fast response. In this study, “You Only Look Once v3 (YOLO)” real-time acquisition of FPGA architecture is opted to bring down its calculation costs and improve power efficiency, while maintaining network accuracy at proper level.
A Redesigning of NER for Actor, Event for Summarization of Given Text

Stories are important part of our lives. When you share a story with someone, you initiate a connection with them. Story has been an integral part of human communication ever since the first cave paintings were discovered. Stories have been told by humans ever since they first had the ability to read and write. Everyday occurrences were turned into stories that were recounted to youngsters, stories about religious issues were told by scribes and priests, and leaders told heroic stories about their exploits. These tales have been passed down from generation to generation; some of them may be found in history books, some play an important role in culture, and yet others are ingrained in the morals of families. In this study, we have considered story domain and employed a ruled-based method to extract the relation, actor, event, and location. The relation extraction is going to be the primary focus of our efforts because it allows us to better understand the connections between the various entities. The retrieved words are brought to use in the process of collecting sentences, which is followed by the ranking of the sentences and the generation of a summary.
Promoting Class Safety Benchmarking Evaluation Based on Pearson Correlation Analysis

With the proliferation of information technology, big data analysis has found wide applications in today's safety management.Based on a Pearson correlation analysis between the incidence of unsafe student behavior and the coverage of unsafe student behavior by class safety benchmarking evaluation, this study focuses on how to reduce the incidence of unsafe student behaviors by promoting class safety benchmarking evaluation.The results show that promoting class safety benchmarking evaluation improves the rate of supervision on unsafe student behaviors and significantly reduces the incidence of unsafe student behaviors.
An Approach Towards Early Stage Detection of Lung Cancer Using Machine Learning

Cancer is the leading consequence of death around the world, accounting for more than ten million deaths in recent times. Lung cancer is the second most common type of cancer. The stage of cancer is determined by the size of the tumour and how quickly it spread. Recent breakthroughs in imaging and sequencing technology have made it possible for researchers to make methodical progress in their clinical studies of lung cancer. Approaches that are based on machine learning play an important part in the process of involving and evaluating big and complicated datasets. These datasets have comprehensively described lung cancer by using a variety of viewpoints that are derived from the accumulated data. The results suggest that our proposed approach outperforms other ML algorithms. The proposed model outperforms other machine learning techniques such as decision tree, SVM, KNN, random forest, XGBoost which are used to do comparison analysis, and their accuracy, precision, recall, and F1-score are determined. In our research, we found that our proposed method outperforms other machine learning models. According to the findings of our investigation, the approach that we have presented, ensemble model, performs better than other machine learning models. We were able to get an accuracy of 97.01%, a precision of 99.21%, a recall of 94.7%, and F1-score percentage of 96.9%.