Reviewerly/exHarmony huggingface dataset:
Multi-Modal Code Summarization with Retrieved Summary

A high-quality code summary describes the functionality and purpose of a code snippet concisely, which is key to program comprehension. Automatic code summarization aims to generate natural language summaries from code snippets automatically, which can save developers time and improve efficiency in development and maintenance. Recently, researchers mainly use neural machine translation (NMT) based approaches to fill this task. They apply a neural model to translate code snippets into natural language summaries. However, the performance of existing NMT-based approaches is limited. Although a summary and a code snippet are semantically related, they may not share common lexical tokens or language structures. Such a semantic gap between codes and summaries hinders the effect of NMT-based models. Only using code tokens to represent a code snippet cannot help NMT-based models overcome this gap. To solve this problem, in this paper, we propose a code summarization approach that incorporates lexical, syntactic and semantic modalities of codes. We treat code tokens as the lexical modality and the abstract syntax tree (AST) as the syntactic modality. To obtain the semantic modality, inspired by translation memory (TM) in NMT, we use the information retrieval (IR) technique to retrieve a relevant summary for a code snippet to describe its functionality. We propose a novel approach based on contrastive learning to build a retrieval model to retrieve semantically similar summaries. Our approach learns and fuses those different modalities using Transformer. We evaluate our approach on a large Java dataset, experiment results show that our approach outperforms the state-of-the-art approaches on automatic evaluation metrics BLEU, ROUGE and METEOR by 10%, 8% and 9%.
Sentiment Analysis of Online Catering User Comments Based on Random Forest Feature Extraction

With the development of the Internet economy, online ordering modes have gradually been widely accepted, and even become an indispensable part of the lives of office workers. But, the online catering platform has many difficulties, such as low entry threshold for merchants, no restriction of online catering industry standards, and unable to effectively and longterm supervise a large number of merchants. In order to realize the effective supervision of online catering and take into account the characteristics of Chinese, this paper first obtains the user generated content of catering businesses through the Internet and uses the Jieba word segmentation tool for Chinese word segmentation, then carries out data cleaning and uses the Random Forest model to screen out five features that have great impact, and finally uses the SVM, Decision Tree and AdaBoost algorithm for data modeling, prediction and analysis. The experimental results show the better performance of our method in online catering user comments data.
PCB Image Registration Based on Improved SURF Algorithm

In order to solve the problems of time-consuming and low matching accuracy in PCB image registration process, an improved speeded-up robust features (SURF) algorithm is proposed in this paper. The Shi-Tomasi algorithm was used to replace the SURF algorithm in order to extract the feature points in image overlapping area, and the feature points are described using the SURF descriptor. After matching, the progressive sampling consensus (PROSAC) algorithm was used to replace the random sample consensus (RANSAC) algorithm to refine the matched point pairs. The experimental results show that the total time of image registration is reduced by 34% compared with the traditional SURF algorithm, and the registration efficiency is significantly improved.
Vision Transformer and Its Application in Penguin Classification

Attention, also known as the attention mechanism, is a resource allocation scheme to solve the problem of information overload in the abstract. Transformer is a Seq-to-Seq model, which can also be seen as composed of encoder and decoder, but it is not RNN. It is completely based on attention mechanism and full connection layer on large data sets and has higher accuracy than RNN. This paper also chooses one popular model Vision Transformer to classify different penguin’s genus. There are still exists incorrect classification after applying this transformer. The problem might be on the image’s type, for image which contains too many penguins and underwater image, the predict accuracy was reduced. To solve this problem, three measures might be useful, including applying target detection algorithm, applying special processing of underwater images and more datasets.
DM-YOLOv5 for ABUS Detection

Automatic breast ultrasound (ABUS) technology provides help for the diagnosis of breast cancer. Developing an accurate computer-aided diagnosis system is important to improve the efficiency and accuracy of diagnosis. In this paper, based on YOLOv5, we develop an ABUS tumor detection network assisted by dense FPN and weakly supervised segmentation. Specifically, the feature fusion strategy in YOLOv5 is replaced by a densely connected feature pyramid network, which can enhance the feature representation. To suppress the false positive results, the channel attention module is introduced. Besides, to further improve the detection performance, a segment branch is added, forming a multi-task learning network. The proposed method achieves 89.8% Recall and 0.163 FPs/S. Experimental results show that the proposed method outperforms the vanilla YOLOv5 model and other detectors.
Comparing Performance And Computational Efficiency Of Face Recognition Approaches

Some of the simplest tasks for a human to accomplish are the most difficult for a machine to solve. Face recognition is an example of this type of problem. Researchers have been attempting to solve face recognition since the 1960s. It's come a long way, and there have been many potential solutions offered. Most of these solutions are provided based on machine learning techniques. The utilisation of edge devices such as smartphones, smartwatches, automotive devices, and other smart home products has skyrocketed the use and development of face recognition in recent years. The main goal of this research paper is to do a thorough investigation and then choose the finest potential solutions for a face recognition application. This study's main objective was to evaluate various solutions theoretically and experimentally. It is not possible to experimentally evaluate every face recognition method available in literature. So, we conducted a literature survey and chose 3 of the most accurate models, namely FaceNet, MobileNet and InceptionResNet. The famous five celebrity faces dataset is used to train and test the models on Google Colab platform. Among all the three models, it was found that FaceNet provided the most reliable results in terms of accuracy while being computationally efficient. In the future, other methods and models for face recognition can be investigated and applied. Finding a model that is even more accurate than FaceNet will be an interesting task.
A Novel Distributed CA System Based on Blockchain

In the PKI-CA system with a traditional trust model based on trust chain and centralized private key management, there are some problems with issuing certificates illegally, denying issued certificates, tampering with issuance log, and leaking certificate private key due to the excessive power of a single CA. A novel distributed CA system based on blockchain was constructed to solve the problems. The system applied blockchain and smart contract to coordinate the certificate issuing process, and stored the issuing process logs and information used to verify certificates on the blockchain. It guaranteed the non-tamperability and non-repudiation of logs and information. Aiming at the disadvantage of easy leakage of private keys in centralized management mode, the system used the homomorphism of elliptic encryption algorithm, CPK and transformation matrix to generate and store user private keys safely and distributively. Experimental analysis showed that the system can not only overcome the drawbacks of the traditional PKI-CA system, but also issue certificates quickly and save as much storage as possible to store certificate private keys.
Research on Target Detection Algorithm Based on Improved YOLO

Aiming at the problem that the target detection algorithm has poor real-time performance and high complexity for small-size object detection, this paper creates a model that can effectively improve YOLOv4-tiny deep learning. The detection idea of merging different features is adopted in the model, and the convolution module is added to the model, each parameter is adjusted to ensure the optimization of the final network structure, and then the $52 \times 52$ target prediction scale output is added, which helps to improve the recognition of small-sized targets. accuracy. Based on the original network structure, the K-means++ clustering method is used to conduct experiments on the data set of small target objects to determine a priori frame size that is more suitable for small-sized object recognition, so that the network can more quickly and accurately identify small-sized objects. The target position of the object. The test set is trained on the network before and after the improvement, and the model is deployed on the platform based on Jetson AGX Xavier for testing. The results show that the improved YOLOv4tiny model has an increase of 3.56% in the accuracy rate and 7.08% in the recall rate. The average The accuracy is improved by 2.83%.
Night Pedestrian Detection Using Thermal Image Feature Extraction Enhanced YOLOv3 (TIFEEY)

False identification of pedestrian detection in autonomous driving can potentially lead to traffic accidents. The traditional visible light detection method is difficult to detect accurately under low illumination and fog conditions. To address these issues, a nighttime pedestrian detection model with infrared thermal imaging technology combined with deep learning algorithms is proposed, which is called Thermal Image Feature Extraction Enhanced YOLOv3(TIFEEY). Specifically, for nighttime infrared pedestrian images, the pedestrian candidate regions are divided, then the images are enhanced by contrast stretching. In addition, a novel downsampling structure containing convolution and maximum pooling is used to obtain feature layers, then a convolutional layer containing multiple asymmetric convolutions is used to extract pedestrian features, and finally YOLOv3 is used to regress and classify the features. However, the network complexity limits the detection speed, so the number of feature layers is pruned by channel pruning. The FLIR dataset is constructed and divided into a training set and a test set, and the model achieved a detection accuracy of 90.94% on the test set, with an inference time of only 22ms per image. In summary, it demonstrates that the proposed TIFEEY model can quickly and accurately detect pedestrians at night. Therefore, it can reduce the risk of using solely visible light detection at nighttime.
Transformer with Convolution for Irregular Image Inpainting

In recent years, image inpainting is a challenging task in the field of computer vision. Many methods based on deep learning have been proposed to deal with the problem of image inpainting, but most of these methods are based on u-net. And recently, the transformer has demonstrated its powerful capabilities in the field of computer vision. In this paper, we propose a transformer-based image inpainting model to solve the problem of image inpainting with irregular missing regions. In particular, we utilize partial convolution to generate patch embedding, avoiding the influence of missing region pixels. The proposed model combines transformer and convolution to simultaneously focus on global and local features. Finally, experimental results on public datasets show that our model outperforms traditional convolutional inpainting models.
An Identity Based Key Update Scheme for Energy Internet Edge Devices

In the existing identity-based cryptosystem, the private key of the edge device is fixed once it is generated, and long-term use will increase the risk of leaking the private key of the device. This paper proposes an identity-based update key distribution scheme suitable for resource constrained devices in the energy Internet. The RTC clock information is embedded in the generation process of the updated private key. PKG uses the T-Update protocol to update the private key of the edge devices within a specified time period to complete the private key update process. Compared with the traditional identity-based solutions, in our scheme the private key is updated regularly, which improves the security of the entire system and effectively reduces the risk of leakage.
Footballer Detection on Position Based Classification Recognition using Deep Learning Approach

In the current state of the development of healthy project of mass entertainment, sports events are more often presented in the forms of videos, which are concerned and discussed by public. However, the accuracy of the information is inadequate as it is beyond the human brain. Therefore, this is where computer technology will be getting involved as it has been increasingly applied to each sector of human society, extending the human brain and perception abilities including detection and tracking a right position of a target in sport videos. Hence, automated analysis technology of sports videos is such a demand especially for football game videos with extremely wide audience. This paper proposed an automatic approach to identify players on position based for post-match evaluation for both coaches and players. We adopt a publicly available dataset, IAUFD to train and evaluate the proposed framework. First, a video or image will be input to a split feature extraction and matching by implementing ResNet50. This purposely for extracting a player from occlusion scenarios. Next, it will be further input into a deep learning algorithm, VGG-M CNN to accurately recognise a player. This approach perhaps will improve a football team’s tactical and increase individual performance. As a results, we achieve validation accuracy of 97.14%. In future, a details evaluation of players performance can be achieved with an automatic visualisation which is convenient to the users.
E-Food: Success Factors for Establishing Online Food Retailing: A Case Study from Jordan

Food industry has a significant impact on human life. However, this industry has faced many challenges in Jordan in the last decade. These challenges might be globally or locally and effecting its supply chain. Nowadays, the term e-food retailing services have become viral. These services can organize the procedure of the food sector and its supply chain. Also, it will save time and effort for consumers and provides privacy, security, reliability, and service availability. In the study, we investigated the factors that might encourage or concern people to use e-food retailing services instead of traditional food retailing services to help the conventional food retailing service operator pay attention to these factors when transferring to online service. Therefore, we developed a survey to discover these factors. Based on 78 responses, we found that delivery time and cost, food quality, food freshness, service availability, and reliability are the top factors that concern people using e-food retailing services. However, adding some to the e-food applications such as quickly reordering their previous orders, easily reviewing their previous invoices, getting a competitive price, providing special offers, providing loyalty points, privacy, security, friendliness of the user interface, and quickly finding information about the food, can encourage people to use e-food retailing applications. In addition, most participants emphasized that COVID-19 encouraged them to use e-food retailing services.
Expanding Indian Sign Language Recognition System using Class Incremental Learning

Sign language provides a visual means of communication for hearing impaired people through hand gestures, movements and facial expressions. In literature, several Sign Language Recognition (SLR) systems have been developed using deep learning algorithms. However, the performance of the SLR systems have been reported for a finite number of signs. In this work, class incremental learning is proposed to enable convolutional neural network (CNN) for recognizing new set of signs, given its knowledge about previously available set of signs. The network is trained in steps by introducing a few new classes in each step. The network is trained on a given set of classes and then retrained on training data consist of labelled samples of new classes and a few exemplars of previously learned classes. Experimental results are presented for incremental learning of 100 sign gestures from the self-recorded multi-sensor dataset of Indian Sign Language (ISL). The proposed model could classify new signs with a best average accuracy of 92.6%, while avoiding catastrophic forgetting and maintaining an accuracy of 80.81% for previously learned set of signs.
Speech-Driven 3D Facial Animation with Mesh Convolution

Facial animation has always been a high-profile problem in computer graphics and computer vision and has been extensively studied. But speech-driven realistic 3D face animation remains a challenging problem because of the complex geometry of 3D face models and the small size of 3D audiovisual datasets. Previous studies have focused on using fully connected layers in the last layer of the network to regress latent representations of speech to 3D face vertex offset points without considering the geometry of the 3D model. To address this limitation, we introduce mesh convolution in the decoder to better learn the high-level mapping relationship between speech latent features and 3D face models, enabling natural and realistic 3D face animations. Finally, our experimental results on public datasets verify the advantages and disadvantages of the proposed network compared with existing models.
Image Encryption and Decryption System with Clock Controlled Destruction

Image encryption is an important tool for encrypted communication. Pixel dots can carry a lot of information and are more confusing than text encryption, which cannot be recognized with the naked eye and cannot be easily analyzed for encryption laws, so it has an important position in modern encryption technology. We have improved the traditional image encryption technology by using symmetric and asymmetric encryption techniques and adding the clock parameter as a factor to participate in the synthesis to achieve the automatic destruction function. This algorithm has a much higher encryption and decryption speed than conventional encryption algorithms due to the operation at the array level and the use of xor encryption on bit operations. After an experiment of encryption and decryption of images, we proved that this method has a promising application for commercial and military encrypted communications.
Estimator performance for shallow and deep models

In recommendation system, users post-click conversion post great challenge for training a recommender because selection bias in data. Bias in recommendation system has become a severe issue for ranking performance. Currently, naïve, inverse propensity score and doubly robust estimator have been proposed to address the problem. However, the estimator have not been applied to state of the art deep learning models, which are more realistic in practical settings. In this study, to overcome those limitations, we explored the performance of those estimators on shallow models and deep models, and compared their results on same bunch of models. Extensive experiments shows that doubly robust estimator is more robust than inverse propensity score estimator in deep model.
RGB-D Salient Object Detection via Joint Learning and Multi-feature Fusion

The detection performance of RGB salient object detection (SOD) needs to be improved when faced with complex backgrounds, and depth map can provide RGB map with spatial information that clearly distinguishes foreground and background. So RGB-D SOD has advantages over RGB SOD. Most existing RGB-D SOD models are built on convolutional neural networks (CNN), besides a few models are built on transformer architecture. However, convolutional operation cannot establish long-term dependency, while the detailed information of the shallow feature extracted by a transformer-based model is insufficient that may lead to limited location information of the object. In this work, a novel RGB-D SOD network combining CNN and transformer is proposed. Salient object is detected via joint learning and multi-feature fusion. The size of the proposed network model is reduced, while the feature extraction process can be more efficient. Meanwhile, the multi-feature fusion architecture can ensure exploring the commonality of features from different modalities on different scale levels. The proposed model is tested on six popular RGB-D SOD datasets, the performance is evaluated by four metrics and compared with other state-of-the-art models. Experimental results demonstrate that the proposed model outperforms its rivals with high accuracy and fine detail preserving capability.
Real-time Vision image processing based on LabVIEW and Microcontroller controlled Parallel Robot

Manual inspections and executions in factory operations are not efficient, with many issues regarding accuracy, precision, and control. This research presents the development of camera vision to classify the different colors of the target object and works with parallel robots to pick and place objects. The efficiency of using the camera and the robot’s accuracy is studied by simulating a suitable robot workspace with MATLAB Simulink. The focus is on developing a parallel robotic arm that can be used in practice using the kinematic computational. Two key components are studied: robot kinematics for motion analysis by designing a prototype of a parallel robot that can be controlled automatically by Arduino microcontroller, and vision analysis to check and analyze the target object’s different colors by using a camera and NI vision LabVIEW. The camera vision and the robotic arm were set up together to inspect three spherical metal specimens of different colors: blue, yellow, and red. The parallel robot will pick and move the workpiece to various positions in conjunction with the vision system. The experimental result is found satisfactory and can be used effectively. The accuracy of image processing detection is 100.00%, and the kinematic of the robot actuator for picking and placing to the correct position is 100%.
Voice assistants in neurorehabilitation

Voice assistants, such as Amazon Alexa and Google Home Hub, have potential applications across a spectrum of disability and rehabilitation settings, with the visually impaired and those with marked motor disability particularly likely to reap benefits. We review some of the evidenced and potential applications, as well as our own preliminary experience in an inpatient rehabilitation setting. We conclude that voice assistants are likely to make a significant contribution to the wellbeing of a number of patients with neuropsychological conditions.