Reviewerly/exHarmony huggingface dataset:
A Survey on Image Classification of Lightweight Convolutional Neural Network

In recent years, deep neural networks have achieved tremendous success in image classification in both academic and industrial settings. However, the high hardware requirements imposed by their intensive and complex computations pose a challenge for deployment on low-storage devices. To address this challenge, lightweight networks provide a viable solution. This paper provides a detailed review of recent lightweight image classification algorithms, which can be categorized into low-redundancy network model design and neural network compression algorithms. The former reduces network computations by replacing traditional convolution with efficient lightweight convolution, while the latter reduces redundancy in the network by employing methods such as network pruning, knowledge distillation, and parameter quantization. We summarize the experimental results of some classical models and algorithms on ImageNet2012 and CIFAR-10 datasets, and analyze the characteristics, advantages and disadvantages of these models respectively. Finally, future research directions for lightweight algorithms in the field of image classification are identified.
MGRE: A Multi-Granularity Representation Enhancement Method in Multiple-Choice Question Answering

Multiple-choice question answering(MCQA) is one of the most challenging tasks in machine reading comprehension. MCQA task requires selecting the most appropriate answer from several relevant options for a given question. In recent years, many works have concentrated on designing models from the perspective of using the information of the question and options at a large granularity level. However, few studies have explored how the model uses the information to find the correct answer at a fine granularity level or a multi-granularity level. This paper proposed a multi-granularity representation enhancement method to use information from different granularities. The method introduces large-grained candidate option information into the question to guide the selection of fine-grained critical information and facilitate the information interaction between the answer and the question which is in line with the human reasoning processes. Experimental results show that the method proposed in this paper can effectively improve the accuracy of MCQA tasks without introducing external knowledge.
Stadium crowd counting method based on peer to peer network

The crowd base of the stadium can directly reflect the degree of congestion of the field, and counting, analyzing and evaluating the crowd through Internet artificial intelligence is helpful to improve the management performance of the stadium. The traditional stadium crowd counting is usually conducted by the administrator directly observing the number of athletes, or after the monitoring video rough counting, this kind of crowd counting is time-consuming and laborious, the accuracy is low, and it is hard to real-time accurately reflect the crowd density in the stadium and the management problems existing in the stadium in real time. Therefore, this thesis puts forward a stadium people counting method based on point-to-point network. First, Mosaic technology was used to improve the robustness of the subsequent detection model, and then yolov5 algorithm was used to obtain human head detection points, and improvements were made based on the point-to-point network model to increase the accuracy of people counting. VGG-19 network model was used to obtain the final count results for the key points of the head obtained through the point-to-point network. The experimental results on ShanghaiTech, UCF-QNRF_ECCV18, jhu_crowd_v2.0 and NWPU data sets show that compared with the previous recognition and counting models, the average accuracy of counting has improved, and MAE reaches 55.74%, which can be effectively in a position to people counting in various scenes in the stadium.
Two-dimensional Kapur Entropy Multilevel Thresholding Algorithm Based on Dynamic Programming and Spatial Contextual Information

The Kapur entropy multilevel thresholding image segmentation algorithm based on the two-dimensional histogram does not consider the spatial contextual information of image pixels, and the algorithm’s time complexity increases exponentially with the increase of the number of thresholds. To address the above problems, this paper proposes a 2D-Kapur entropy multilevel thresholding image segmentation algorithm based on an energy curve by using both the neighborhood information and spatial contextual information of pixels. To improve the computational efficiency of the multilevel thresholding segmentation algorithm, a dynamic programming computational framework of the 2D-Kapur entropy multilevel thresholding algorithm based on the energy curve is constructed. The performance of the proposed algorithm is compared with several different image thresholding algorithms on the noise and complex images using several evaluation indexes. The experimental results verify the effectiveness of the proposed algorithm.
Pre-processing of Social Media Remarks for Forensics

The Internet's rapid growth has led to a surge in social network users, resulting in an increase in extreme emotional and hate speech online. This study focuses on the security of public opinion in cyber security by analyzing Twitter data. The goal is to develop a model that can detect both sentiment and hate speech in user texts, aiding in the identification of content that may violate laws and regulations. The study involves pre-processing the acquired forensic data, including tasks like lowercasing, stop word removal, and stemming, to obtain clear and effective data. This paper contributes to the field of public opinion security by linking forensic data with machine learning techniques, showcasing the potential for detecting and analyzing Twitter text data.
Glare sign recognition based on improved Yolox algorithm

Traffic sign recognition is one of the most critical aspects of autonomous driving. To improve detection accuracy without increasing the number of model parameters too much, this paper uses Conditionally Parameterized Convolutions (condconv) to replace the convolutional blocks in Yolox-s. To understand the transfer of feature maps within the deep learning network, a gradient mapping using Gradient-weighted Class Activation Mapping (Grad-CAM) is used to generate a heat map. As the existing traffic sign dataset does not include data for glare situations, data augmentation was used to simulate traffic signs in a glare context. Experiments show that the proposed improvements can improve detection accuracy (+2.3%) with only a small increase in model parameters $(+0.1 \mathrm{M})$. The effectiveness of the proposed data augmentation method is demonstrated by using the trained model for real-world detection.
Swin Transformer-based Image Captioning with Feature Enhancement and Multi-stage Fusion

The objective of image captioning involves empowering computers to autonomously produce human-like sentences that depict a provided image. To address the issues of insufficient accuracy in image feature extraction and underutilization of visual information, we propose a Swin Transformer-based image captioning model with feature enhancement and multi-stage fusion. First, the Swin Transformer is employed in the capacity of an encoder for the purpose of extracting image features, and feature enhancement is adopted to capture more information about image features. Then, a multi-stage image and semantic fusion module is constructed to utilize the semantic information from past time steps. Finally, LSTM is used to decode the semantic and image information and generate captions. The proposed model achieves better results in baseline tests on the public datasets Flickr8K and Flickr30K.
Improved YOLOv5s Object Algorithm Based on Decoupled Head Module

For the existing YOLOv5 object detection algorithm, there is the problem of spatial misalignment in the localization and classification tasks, in this paper, an improved YOLOv5s object detection algorithm based on Decoupled Head module is proposed. Replacing the Coupled Head module in the original network with the Decoupled Head module, which effectively reduces the number of parameters and computational complexity, while improving the accuracy of target detection, replace EIoU loss function with Focal-EIoU to have faster convergence and better localization results. Experimental results show that the improved YOLOv5s algorithm is significantly improved compared with the original algorithm in terms of detection accuracy and detection performance.
CRNet: Combining CenterNet and R-CNN for Object Detection in Traffic Scenes

Object detection is typically formulated by researchers as a bounding box regression task(the two-stage method) or an object center localization task (the one-stage method). However, the methods incorporating the above two ideas make inferior performance in terms of both inference speed and detection accuracy, with quite limited applications in real-world traffic scenes. In this paper, we propose a simple but effective method named Center-RCNN(CRNet) which incorporates the aforementioned two ideas. Specifically, we first adopt the methods of object center localization instead of typical region proposal network(RPN) to conduct proposal selection in a fast and simple way, and then use the bounding box regression method to predict the final object location. The whole network combines the advantages of the two methods to keep a balance between inference accuracy and speed. Experiments are conducted on the BDD100K dataset, and the results prove the effectiveness of our method.
A Deadline Constrained Scheduling Algorithm for Cloud Computing System based on the Timing Strategy

For the workflow scheduling question with delivery time constraints in cloud computing environments, a time-aware scheduling-driven cloud computing scheduling algorithm is proposed. This algorithm considers the impact of the workflow task path length on the overall workflow delivery time and introduces a sub-deadline allocation strategy. Additionally, a time-aware scheduling strategy is presented to address the cost reduction challenge of workflow execution under the pricing policies of cloud services. The sub-deadline allocation strategy assigns different sub-deadlines to cloud users based on the length of the workflow task path, ensuring compliance with user delivery time constraints while increasing the flexibility in selecting appropriate resource servers for workflow tasks. The time-aware strategy schedules the pre-scheduled workflow tasks at suitable idle times, considering the current workflow task scheduling situation. Experimental results demonstrate that the proposed algorithm can satisfies the delivery time constraints for cloud users and achieves a significant reduction in the execution cost of workflows.
PAPR:Periodic Aware Spatial temporal Network for POI Recommendation

The next POI recommendation plays a significant role in location-based services because it provides personalized suggestions for destinations to users. The most advanced research utilizes enhanced attention mechanisms or neural graph networks to handle temporal and spatial factors. However, existing methods mainly concentrate on simulating users’ complex transition patterns while neglecting the geographical proximity and complex periodicity of the POIs users visit. In this paper, a transformer-based model is proposed for the next POI recommendation in order to figure out periodic intervals. The multi-head attention mechanism of the transformer is used to capture the intricate interdependencies between POIs in the user check-in sequence. The periodic interval matrix is added to the attention mechanism as an inductive bias, allowing the multi-head attention mechanism to analyze the systematic relationships between all POIs under the periodicity constraint. In addition, as an auxiliary job, we offer a distance loss function that reflects the geographical distance between the predicted POI and the actual visited POI by the user. The distance loss function assures that the predicted and natural geographical spaces are consistent. The experimental results based on two classical LBSN datasets demonstrate the excellent performance of PAPR, which improves by 2.04% and 4.30% relatively compared to the state-of-the-art model.
Obstacle Avoidance Path Planning for a 6-DOF Manipulator Based on Improved RRT Algorithm

Taking the automatic handover process of parts in factory product assembly as the research object, the route of the manipulator to grasp the parts and place the parts is planned. In order to address the problems of strong path search randomness, low success rate and poor smoothness of generated trajectories in motion planning, an modified RRT algorithm is put forward for path planning combined with APF method, and a non-uniform four-order B-spline function is used for trajectory planning. Firstly, the RRT algorithm is improved, the probabilistic adaptive target bias strategy and the local minimum detachment mechanism are used to improve the planning efficiency, and then, the inhomogeneous four-degree B-spline function is applied for path smoothing, so as to realize trajectory optimization and ensure security, smooth and fast action of the robotic arm. The improved RRT algorithm aims to enable the robotic arm to successfully avoid barrier on the way to the target location while the motion trajectory of each joint has high-order continuity, and the time used for path planning running time of the robotic arm can be greatly shortened.
Deep learning knowledge tracing based on behavioral features

Knowledge tracing assesses students’ understanding of knowledge concepts and learning progress by tracking their learning behaviors and performance in answering questions. Research has shown that existing deep learning-based knowledge tracing models only consider the training and outcomes of students as inputs, ignoring the rich behavioral information generated by students during the learning process. This information can provide a clearer understanding of students’ mastery of the knowledge points. In this study, a deep learning knowledge tracing model based on a dynamic key-value memory network model with strong interpretability is selected as the foundation. The XGBoost decision tree algorithm is used to preprocess the student’s behavioral information, capturing the impact of student behavioral factors on their cognitive states, and integrating the classification results with the learning features. Experimental results demonstrate that the improved deep learning knowledge tracing model outperforms traditional deep learning knowledge tracing models in terms of predictive accuracy on educational datasets.
Research on Small Target Detection Algorithm Based on Improved YOLOv5

Accompanied by nowadays rapid development of artificial intelligence, scientific computing equipment and the upgrading of portable camera equipment, the detection of large and medium targets in videos and images can no longer satisfied the needs of people’s real life, and small target detection has become a fiery-hot spot in the range of target detection research problem. Aiming at the problems of missed detection and low precision caused by factors such as few effective features and low resolution of small targets in the picture, this paper proposes a small object detection algorithm based on improved YOLOv5(You Only Look Once). First of all, in order to solve the problems of missed detection and false detection in small target detection, the SE(Squeeze-Excitation) attention mechanism is integrated into the backbone network to improve the performance of the model. Secondly, in order to solve the unknown geometric transformation problem of the same object in different scenes and different angles, the DCNv2(Deformab1e ConvNets v2) model is added to expand the receptive field and improve the robustness of target detection. The experimental results show that the average detection accuracy (mAP) of the improved YOLOv5s model on the VisDrone dataset has reached 37.56%, which is 5.37% higher than that of YOLOv5s and can effectively detect small targets.
DyHead-YOLOv5 Based on Improved Object Detection Heads with Attentions

A survey of object detection algorithms has revealed that the low accuracy of most lightweight object detection algorithms,More and more people are working on various algorithms. In this paper, the model training is carried out by improving the YOLOv5 object detection algorithm, adding a Dynamic Head framework based on attention mechanism to the original Head layer, modifying the default normalization method Group Normalization (GN) to Batch Normalizatio (BN) in the original framework, and then conducting experimental research on the VisDrone datasets and VOC2012 datasets. The results show that compared with the YOLOv5s model, key indicators such as mean of Average Precision (mAP)@0.5 and mAP@0.5:0.95 are improved, which can achieve better recognition effect on the dataset without significantly increasing the complexity of the model.
A Graph Neural Recommendation With Role Information Enhancement

In recent years, User-Generated Content (UGC) has gradually become an important component of the internet content ecosystem. In order to improve the recommendation effect in UGC scenarios, existing work has successfully incorporated user role information into traditional recommendation models to assist in modeling different user behavior features. However, there are still three shortcomings. First, it ignores the differences in information richness when users assume different roles. Second, it fails to explicitly capture the higher-order connectivity information among nodes in the consumer-item and consumer-producer interaction graph. Third, the model parameters are optimized based on the negative sampling learning strategy. To address these issues, we propose a Dual-role Enhanced Graph Neural Recommendation (DERec) method to achieve personalized recommendation in UGC scenarios. Experimental results verify that the DERec outperforms six state-of-the-art baselines.
GreenPHABLETTM video for effective information dissemination on hermetic groundnut storage technology

Information and communication technologies (ICT) tools can facilitate dissemination of need based and farmercentric information at an affordable cost to India's rural population. One of the major constraints of groundnutproduction is aflatoxin accumulation and insect infestation during storage. In our studies conducted at ICRISAT,the Purdue Improved Crop Storage (PICS) hermetic storage technology proved effective against insect infestation and aflatoxin accumulation during storage. To facilitate visual learning of the use of hermetic storage, a five minute GreenPHABLETTM video (GPV) in the local language was developed at ICRISAT. A 3-month long experiment was conducted in collaboration with an NGO Samatha of Penugonda in Anantapur district of Andhra Pradesh, India to assess the dissemination potential of GPV. A survey conducted among 30 farmers who received the video, revealed that about 80% of farmers received the video from a fellow farmer and only 20 per cent farmers received from the extension agents. Majority of the farmers received the video on their mobile phones through "Share It" (73.3%) and 13.3 per cent received via "Bluetooth", further 10 per cent reported through "WhatsApp" and while only 3.3% received it through the computer by USB Copy. After three months, 300 farmers from40 villages received the GPV, while our 30 respondents shared the GPV with 150 farmers and screened the GPV to 200 farmers. The experiment showsthat GPV can be an effective tool for spreading information about the groundnut hermetic storage technology and other agricultural innovations.
A literature review of implementing virtual reality in hospitality education

ABSTRACTThis article provides a systematic view of the published research topics relevant to an understanding of using virtual reality (VR) in hospitality education. The structure of the literature review is based on the PRISMA reporting guidance and reviews two main elements: the status of VR embedded in hospitality education and the learning frameworks best fitting VR in education. Using the Google Scholar, Science Direct, and EBSCO databases, the present review found relevant articles. The included papers were classified with the following dimensions: year of publication, hospitality industry domain and search keywords. A total of 51 articles were deemed relevant and were reviewed. These articles focused on different aspects of the hospitality education, including virtual reality technologies (VRTs), applied learning frameworks, design and evaluation of VR. The results highlight among others that VRT can be integrated in (design-based) teaching and is seen as a valuable addition. Also, VR has a strong effect on student learning, especially the technology itself and classroom design. Fundamentally, a social constructivist learning framework fosters a good basis for including VRT in education. These results provide potential directions for hospitality educators, researchers and practitioners in future research efforts to enhance the correct use of the virtual learning environment in education and training of practical aspects in the hospitality industry. Key implications of this study include the call for determining the appropriate level of VRT most suitable for the development of virtual learning environments (VLE), the online learning programmes and how to motivate the students the most.KEYWORDS: learning frameworksocial constructivist educationVRVR training
Improving Interpretable Models based on Knowledge Distillation for ICU Mortality Prediction using Electronic Health Record

Recently, deep learning has shown good performance in various fields such as computer vision, natural language processing, and healthcare. However, deep learning-based models are processed as a black box model and has the disadvantage that it is difficult for humans to understand the reason of the results of the model. The interpretability of the model in the medical field, especially, is extremely important where decision making must be made based on strong evidence. Many studies on mortality prediction, which is a representative healthcare-related application that requires interpretability, have been solved through deep learning-based models. These deep learning show good predictive performance but have a disadvantage in that they have difficulty in providing interpretability for prediction results, while linear models in machine learning have high interpretability but low predictive performance. In this paper, we contribute to improve the performance of linear models using knowledge distillation method. We show the effects of first-order input features and second-order ones on mortality prediction with logistic regression and factorization machine, respectively. In addition, we show the interpretability of the model by visualizing the weight of the trained model. We expect that providing interpretability of the linear models through visualization could help humans intuitively to understand the reason of the results, and furthermore, it could improve the quality of healthcare provided to specific patients in hospitals.