Reviewerly/exHarmony huggingface dataset:
A YOLO-based Method for Improper Behavior Predictions

Deep learning (DL) algorithms have found extensive applications in the realm of intelligent policy contexts. In specific environments like waiting rooms and interrogation rooms, where actions such as physical assault and confrontations can pose serious risks, identifying and addressing improper behaviors is crucial. YOLO stands out as a prominent network architecture commonly employed for object detection tasks. In addition, SLTM-GCN represents an innovative DL model designed for predictive analysis utilizing unstructured data. To identify instances of inappropriate behavior in videos, this research leveraged both the YOLO and SLTM-GCN DL models. Consequently, this study makes a significant contribution by establishing the relevance of DL in the context of smart policing.
Underwater Image Processing with Normalized AttUNet

The goal is enhancement of images with different water types and to achieve visual quality improvement of the underwater images. Poor visibility in underwater images is a result of color casts, low contrast, and particle dispersion, all of which are caused by wavelength-dependent and distance-dependent attenuation. To address these degradation issues and to learn water type desensitized features, normalization is applied along both spatial and channel dimensions. In the spatial dimension, instance whitening is applied, to decorrelate activations. Moreover, channel-wise normalization is used to manipulate the first two moments of the channel activations. Then, attention is used to provide weights to certain features based on their importance. We proposed a lightweight model named Normalised AttUNet to provide fast processing time for diverse water type images, while still maintaining comparable PSNR.
Deep Learning Recurrent Attention Optical Character Recognition Network with Data Augmentation for Cheque Data Extraction

Bank cheques are often used for monetary transactions in many different fields. Before a check is cashed, it undergoes rigorous scrutiny. The normal verification process includes checking the date, signature, legal information, and cheque amount. This research uses a recurrent attention optical character recognition network (RA_OCRN) to extract legal information from a taken picture of a check in order to address these problems. In this work, we have utilized the recommended deep learning model to do data pretreatment and data augmentation, and we have compared the results to those obtained by using the already available methods. We run the model on the Kaggle cheque detection dataset, and we find that data augmentation improves the algorithm's detection accuracy. Results from the research show that RA_OCRN with data augmentation has the highest recognition accuracy and so has the greatest performance".
CT Image-based Breast Cancer Risk Detection Leveraging ResNet

Breast epithelial tissue can become a malignant tumour known as breast cancer. The second most lethal illness in women, it is a malignancy that affects many women. The breast cancer is now the number one malignant tumour in women, with an incidence rate of 100,000 per 100,000 in developed countries in Europe and the USA. It seriously affects women’s physical and mental health. Early detection, treatment and diagnosis are crucial for patients since they can increase their survival chances. In order to achieve recognition of breast cancer images, a neural network-based approach was chosen to investigate breast cancer images. Also compared with the traditional machine learning models. The image pre-processing process cropped a large number of blank areas and data enhancement was performed on positive data due to uneven data distribution. Finally, it was shown that ResNet can recognize breast cancer images well and the experimental results improved significantly (from 70.3% to 79.4%) after image processing and data enhancement.
Federated Learning for Beginners: Types, Simulation Environments, and Open Challenges

Federated Learning (FL) is a <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> decentralized machine learning (DML) technique. It was introduced by Google research team in 2017, where several clients can train a model collaboratively without sharing their raw data. Unlike centralized machine learning techniques, FL is able to preserve privacy. Since its inception, FL has gained widespread attention and has been the focus of extensive research, resulting in the development of various novel protocols, frameworks, and techniques to address its challenges. Despite these advancements, several research challenges in FL remain unexplored. While there are numerous research articles and tutorials available on FL, a beginner’s guide to this domain is lacking in the literature. Therefore, this paper targets to motivate novice researchers by presenting a simplified and systematic introduction to FL. In addition to introducing FL fundamentals, this paper also highlights some open research areas in the field.
Overview of Distributed Task Scheduling

Under the background of big data how to more efficiently solve the problem of concurrency has always been a hot research problem. In this case, the distributed scheduling technology produces many new ideas and new structures. In this context, this paper introduces the task architecture and three scheduling techniques in big data environment.
Dendritic Learning-Based DenseNet for Classification

Dendritic neurons play a crucial role in information processing in neural circuits. Inspired by these neurons, researchers have developed dendritic neural models (DNM) that integrate their properties into conventional deep learning models, yielding outstanding results in various tasks. In this study, DDenseNet model is proposed. DDenseNet combines the advantages of DenseNet and DNM to better simulate brain neuron characteristics and improve deep learning model performance. Layer Normalization (LayerNorm) is added to our model to stabilize data feature distributions and increase convergence speed. Experimental results show that DDenseNet outperforms traditional DenseNet and even some established classic deep learning models in classification tasks. The study suggests that using DNM as a classifier has the potential to create more efficient deep learning models for classification tasks.
Intelligent Question and Answer System Based on Course Knowledge Graph

With the development of time, online education such as catechism is widely used, and it is difficult to meet the personalized learning needs of learners because of the difficulty of timely communication between teachers and students while breaking space and time. The intelligent question and answer system integrating subject knowledge can meet learners' knowledge retrieval and personalized learning and make up for teachers' lack of energy. Currently, intelligent question and answer systems based on knowledge graph are receiving attention from researchers in the education field, however, Chinese knowledge graphs for university courses and intelligent question and answer systems based on them are lacking. To address this problem, we propose to construct a Chinese course knowledge graph and design and implement an intelligent question and answer system based on the course knowledge graph. The main contributions of this paper are as follows. Firstly, we designed and implemented an intelligent question and answer system based on the method of building templates. Then, we have classified the knowledge of the Introduction to Database Systems course, clarified the attributes and relationships of the knowledge points, and constructed the course knowledge graph. Finally, an external evaluation method was used to collect 30 questions asked by undergraduate students for systematic testing, resulting in 80% satisfaction. The experiment results indicate that the system can answer user questions more accurately and help learners improve the efficiency of independent learning, while giving practical support to the development of knowledge graph intelligent question and answer applications in education.
Research on Biomedical Image Segmentation Method Based on Full Convolutional Neural Network

With the continuous progress of deep learning technology, the application of artificial intelligence in the field of smart medical care has been deepened, and many new artificial intelligence algorithms have been applied to the process of medical diagnosis. At present, computed tomography (CT) is the most commonly used method to examine liver tumors, and tumor resection, intervention and radiation are the main treatment methods. Accurately knowing the size, number and location of tumors before surgery can make a scientific and reasonable surgical plan, which is a necessary condition for successful surgery. Product neural network can learn the features that describe the essence of images, and these features can be better used for classification or reconstruction after nonlinear mapping, so as to deal with different medical tasks. Therefore, this paper conducts segmentation research on cell data set and blood vessel data set. Experiments show that, compared with U-Net and RU Net models, this method only uses nearly 2/3 of the training parameters and achieves better segmentation evaluation performance. Among them, Dice coefficient increases by 0.56% and 1.46%, and Jacob coefficient increases by 0.91% and 1.92%. Therefore, compared with U-Net and RU Net models, This method has higher segmentation performance and better generalization performance.
Ontology-Based Conversational Recommender System for Smartphone Domain

The development of the smartphone industry is growing very rapidly, as well as information about this product. Nowadays, smartphones have become a necessity for everyone. With so many smartphone products on the market, people need a tool to assist in buying a smartphone. Many Conversational recommender systems (CRS) have been developed to recommend smartphones. However, most of those CRS still interact in technical features. Meanwhile, most people still do not understand about the technical features. In this study, we develop CRS using the CRS framework based on high-level requirements that is proposed by previous works. In this study, we call this high-level requirements as functional requirements. An example of a functional requirement is, "a user wants a smartphone to play ultra-mode games without having to know the required specifications". The CRS framework consists of ontology and the agent for generating interaction. Ontology acts as the knowledge that maps functional requirements to technical specifications of the product. In this study, we propose the CRS for smartphone domain based on functional requirements with updated knowledge. Attributes taken from the smartphone domain contain network technology, display type, display size, display resolution, etc. For evaluation, we measure the system performance and user satisfaction. Based on the evaluation results, the system performance score is 88.87% and the user satisfaction score reaches 0.88. So that our proposed CRS recommend the product accurately and satisfying the users.
Critical Digital Skills Enhancement in Virtual Reality Environments

This study aims to explore the potential of Virtual Reality (VR) affordances in education to promote critical digital literacy skills in mixed-ability learning environments. The VR tour “A Glance at the Past” was designed to address the need for inclusive learning experience through immersion and interaction using a low-cost, user-friendly multi-user VR environment (MUVE). The design of the tour involves a two-level immersion and interaction with the content, facilitating differentiated instruction needs. The VR environment was evaluated by 30 students in eight 60-minute sessions in the context of foreign language learning through Content and Language Integrated Learning (CLIL). The preliminary results showed positive attitudes towards VR learning, while investigating the influence of guided or unguided tour exploration of artifacts and content, assessment showed that it had an impact on participants' performance and critical digital skills enhancement. Teachers' monitoring of behavior and performance revealed that there is an inconvenience to address large numbers of learners that are interacting in MUVE.
Identifying Question Intent Similarity Using Computational Techniques

Paraphrase identification is becoming an important use case in the industry. From a business context, paraphrase identification is significantly helpful to improve the user experience of the platform. It is an active research area by companies like Quora, Stack Overflow, and other question-answering websites. One of the main issues that these companies face is the problem of repeating questions. If one is successfully able to capture and interpret the semantics of the sentence and the intent of the user, this would improve the consumer experience. Quora’s official dataset was used in the present research work. To find a question that was similar, we modeled the dataset of Quora question pairs. It was a Binary classification task and various approaches were explored and experimented with. Bag of Words was used for feature extraction. A traditional machine learning approach was followed and algorithms like XGBoost, RF, and decision tree were used. These models were evaluated based on their F-1 and accuracy scores. Feature engineering was performed that significantly improved the model accuracy. The decision tree model was able to achieve an accuracy of up to 78.8% while Random Forest and XGBoost had an accuracy of 76.2% and 75.4%.
Improved TextRank Method for Streamlined Generation of Audit Work Orders

Audit work orders in the power system are extensive, with an average length of hundreds of Chinese characters and an overwhelming annual volume of tens of millions of orders. This presents a significant challenge that requires addressing. To tackle this problem, we present a novel text summarization model that utilizes an improved TextRank algorithm to simplify audit work orders. Our method involves generating sentence vectors through pre-training models and clustering them while considering the position of sentences and clue words to calculate sentence weight. Additionally, we established a dedicated synonym library for audit work orders and introduced synonym processing to compute similarity. Finally, we merged similar sentences to streamline the abstract content further. Our experimental results show that our system effectively extracts vital information from the audit work order's text, increasing the ROUGE value by approximately 20%. This offers significant support for subsequent automatic audit tasks, and we believe that our proposed model can enhance the accuracy of audit procedures.
Jelly Bot: Revolutionizing Beach Safety

Drowning is a major problem in most of the world. It is the 3rd leading cause of accidental demise globally and accounts for 7% of all injury deaths. There are approximately 236000 deaths worldwide every year due to drowning, but it is still believed that this number underestimates the actual public health problem associated with drowning. This paper proposes a solution to address this issue by introducing autonomous Jelly Bots equipped with drowning detection capabilities for deployment in high-risk areas such as beaches and ghats. The Jelly Bot offers improved rescue capabilities in uncertain situations and is computationally efficient. The proposed model utilizes a Convolutional Neural Network (CNN) for real-time video analysis to identify drowning activity with high speed and accuracy. The implementation of underwater drones, visual surveillance, and search and rescue techniques contributes to enhancing beach safety and reducing drowning incidents.
Diversified Teaching Reform of Software Requirement Engineering Based on Case

Software requirement engineering is a curriculum that stresses not only basic theories but also lots of practice. Traditional teaching methods have been unsuitable for the requirements of the course. This paper proposes a cases-based teaching reform, which employs various teaching methods and elements, such as case-based teaching design, multi-media teaching design and group-based discussion. The teaching reform connects the theory and the practice closely. It motivates the students' enthusiasm and thrust students' learning efficiency greatly.
Predict Food Recipe from Image Using CNN/Transformer &amp; Knowledge Distillation for Portability

Without a doubt, Food is a vital part of any Human's life, not only because of the nutrients provided but also the excellent taste. Subsequently, countless great people have invested their life in exchange for this enjoyment upon Human taste buds. However, even a three-Michelin-starred chef cannot remember the detailed steps to all the dishes, and ordinary citizens do not have the time or energy to create their recipes. This paper proposes an aid system that uses neural networks to achieve accurate recipe projections to facilitate life. A deep learning system based on Decoder and Encoder is implemented using CNN and Transformer. The CNN reduces the memory cost by transforming the image to a vector, achieving faster processing. The Transformer is used to determine ingredients' presence and combine the information of existing dishes to construct an instruction. Such a network can facilitate food production and research. It is making the creation of unique dishes convenient by revolutionizing the method of recording. After the model is created, the backbone network of CNN layers are manipulated to test for the best result. Backbone network tests include ResNet50, ResNet152 without attention, ResNet152 with attention, Vgg16, and AlexNet. Out of all tested, ResNet152 is resulted with the highest iou (Intersection over Union), and without-attention is chosen as it possesses less complexity than its counterpart. Then, knowledge distillation is applied to decrease the complexity and size of the model. This process used the complex model as a trainer to the student model by basing the student training on the result of the teacher model, achieving a similar level of accuracy with a significantly lower complexity and size. This decrease in complexity increases the portable capability of the model, potentially applying into applications and websites on devices in the future without consuming much memory.
A Composite Sentiment Summarizer Score for Patient Reviews: Extending RoBERTa

Recently, sentiment analysis is applied in the healthcare domain to improve patient’s experiences through collecting and analyzing patient reviews. With the ascent of artificial intelligence, the Robustly Optimised Bidirectional Encoder Representations from Transformer Pretraining Approach (RoBERTa) has shown exceptional performance in the task of Natural Language Processing, including sentiment analysis. The result of sentiment analysis using RoBERTa generates three sentiment scores, representing the degree of negative, neutral, and positive sentiment in the analyzed text. Nevertheless, these sentiment scores are presented separately, making it difficult to gain a comprehensive understanding of the mixed sentiments expressed in the review text. In this paper, the concept of composite sentiment summarizer is introduced by combining the positive, negative, and neutral scores into an overall sentiment score, a more complete analysis of the mixed sentiments can be obtained from the unstructured reviews. This composite sentiment summarizer score provides a more balanced and precise representation of the sentiment expressed in the text. This paper investigated four proposed approaches by extending capabilities of RoBERTa with the composite sentiment summarizer score. The results demonstrated the potential of using the ranking approach to calculate a composite sentiment summarizer score for sentiment analysis and highlighted the importance of considering a more nuanced approach to sentiment analysis with a wider range of sentiment intensities.
The Context for Contextualizing - Design Implications for Adaptive Teacher Support Systems for More Relevant Instruction

Contextualization in education connects learning material to students' experiences and background knowledge to make it relevant and meaningful. It involves placing content in a real-world context to show students the practical applications and relevance of the concepts they are learning. Contextualizing is an important pedagogical strategy. In order to support teachers in this effort, it is necessary to understand what contexts to provide for teachers to make relevant instruction in certain situations. In this paper, we aim to explore: In what context do teachers need students' everyday life experiences to make the class more relevant to students? We conducted focus groups with teachers who shared their contextualizing experiences. The context for contextualizing refers to the information used to characterize the situation in the classroom and the experiences of the students. The results of this research include the three categories of context information, time, activity, and location, that are relevant to the lesson and the students' prior experiences related to these factors. Design implications are discussed in this paper to illustrate how adaptive teacher support systems can provide relevant and meaningful information to the teachers by considering context information and students' prior experiences.
A Case Study of Virtual Reality Simulation System Application in Inquiry-Based Instruction Learning in Primary Education

Recently, there has been a lot of interest in the use of virtual reality scenarios in educational settings. Different contexts and activities are required for inquiry-based instruction, which is challenging to implement in conventional teaching. Therefore, this study uses a virtual reality simulation system to design a virtual reality scenario for pre-service teachers to learn inquiry-based instruction. Then, the researchers collected some data to evaluate the effective of the VR system. Besides, according to the method of design-based research, some design suggestions are provided in this study. Through quantitative and qualitative data analysis, the results showed that the VR scenario could help pre-service teachers improve their teaching skills, transfer abilities, and self-efficacy. However, there were also some limitations that were discussed in this study.
Research on Power Voice Based on WaveNet+LSTM Improvement

With the rapid development of speech recognition [1] in recent years, the WaveNet model has also been discovered by more people and applied to speech recognition. In the open source LJSpeech-1.0, AISHELL and thch-30 and other general speech datasets, the model has achieved relatively good recognition results, but in the face of a large number of power voice data, because of the uniqueness and complexity of industry speech, the model is generally performed, so in this paper we have made some improvements to the model, aiming to improve the recognition of power-related speech data. First of all, in view of the shortcomings of the WaveNet network model in power speech recognition, we have improved and optimized it. The output gate structure of the LSTM network model is used to increase the nonlinearity of the model, so as to optimize the structural composition of the new model. We then introduced the core structure of The Reduced Convolution in WaveNet. According to the length of speech in the electric speech dataset, the receptive field of this structure is adjusted, and the sensory field is theoretically analyzed in order to better analyze the speech data. Finally, we experimentally verify the feasibility of applying the improved WaveNet model on the thch-30 dataset and the power-related dataset, and evaluate the trained model.