Reviewerly/exHarmony huggingface dataset:
Design and implementation of intelligent question and answer electronic dictionary in the context of big data

In the current era of the Internet, social life is inseparable from data, and people also live in a network. For people, network technology and smartphones are like swimming fish without water. The swimming fish, a pond obviously cannot meet the demand, people need the sea, and the development of science and technology is as broad as the sea and has unlimited future development space [1]-[4]. The Android system is the main part of the mobile smart phone system. For this, I have developed an English-Chinese electronic dictionary app based on android. It realizes the functions of registration, login, vocabulary query, input translation, and new words of dictionary on the platform. In order to take into account various factors and user needs, the software interface design is concise and clear, and the use and operation are convenient and easy to understand. All functions are operated according to the preset plan, and the project functions are fully realized, except for some functions that cannot be completed due to technical reasons or other force majeure factors [5]-[6]. The software platform can run normally on the Android system smart terminal. After installing the software, users can use it to query English vocabulary in a normal network environment. On this basis, speech recognition translation and photo recognition translation are also added. The basic knowledge and technology of android can be used to quickly complete the production and design of the ui. The Android electronic dictionary app is based on the Android electronic dictionary app. After running the test work on various Android mobile devices and emulators, the requirements design is debugged, and the concept of the software is explained. The main significance of the design and its specific implementation process is to allow more Android users to fully enjoy and understand the software running under the android platform.
Few-shot Classification Network Based on Feature Differentiation

Aiming at the existing problems in the few-shot learning methods which treat samples in an isolated perspective and ignore the difference information between samples, we propose a few-shot learning classification network based on feature differentiation. A new feature adaptive fusion module and a feature conversion module form our network, where the former is proposed to fuse global information and detailed features, and the latter one marks the semantic features which have high recognition, so as to narrow the semantics within the same category and widen the semantic gap between different categories. CUB dataset and mini-ImageNet dataset were used in the experiment, and the accuracy of 5way-lshot and 5way-5shot tasks respectively achieved 57.63%, 76.54% and 54.39%, 73.19%. Experimental results show that our method can further learn how to distinguish different category concepts through differentiated features, thus the proposed few-shot learning model has higher accuracy and robustness.
The analysis of DASH manifest optimizations

In live video streaming, the size of Dynamic Adaptive Streaming over HTTP (DASH) manifest grows as the number of periods increases and/or the overall time duration of DASH manifest increases. The bigger the manifest size in bytes is, the more computation for manifest generation, manifest storage and parsing and network traffic there will be on service side, the more data to be downloaded, manifest refresh latency, manifest parsing and storage in memory there will be on device side. In this paper, we analyze the techniques and algorithms available to optimize and reduce DASH manifest size with their pros and cons, and limitations of each technique in different scenarios, and propose further optimizations and the adoption of technique(s) in each video streaming scenario according to product requirements, operational cost, system complexity and the requirement of quality of video playback experiences.
DA-YOLOv5: Improved YOLOv5 based on Dual Attention for Object Detection on Coal Chemical Industry

The wearing inspection of personnel’s safety protective clothing has important practical significance in the safety production of coal chemical plants. Manual detection or traditional target detection methods are utilized in coal chemical plants for personnel’s safety detection at the moment. However, the clothing detection accuracy is seriously reduced due to the installation position of cameras and the change of light intensity in coal chemical plants. An dual attention based on YOLOv5 is proposed on coal chemical for object detection. Two attention modules, including Efficient Channel Attention (ECA) and Pyramid Split Attention (PSA) module, are integrated into the Spatial Pyramid Pooling (SPP) module and Bottleneck module of this YOLOv5 network. Thus, more global context information is obtained to make up for the lack of global convolution, and the ability to extract features and learn multi-scale information is enhanced. Safety helmet wearing detect data set (SHWD) and self-made data set in our work are utilized to display the improved method’s effectiveness. Compared with the original YOLOv5 algorithm, the improved method achieved an average accuracy increase of 2.7% at different thresholds. Numerous comparative experiments further verify the feasibility of the improved method.
An Efficient Recommendation Algorithm Integrating Knowledge Graph with Graph Convolutional Networks

Graph convolutional neural networks have difficulties in model training takes a lot of time and lack of universality in meta-path design when processing knowledge graphs. To solve these issues, this paper proposes an efficient recommendation algorithm integrating knowledge graphs with graph convolutional networks(EKG-GCN). The algorithm is mainly based on the graph convolution algorithm. Firstly, the relationship between users and entities is standardized and scored, then a new neighborhood aggregation method is proposed. Some nodes in the knowledge graph are set as central nodes. A distance-influence function is set up according to the distance from other nodes to the central node, and the feature extraction of information in the knowledge graph is completed according to the distance-influence function. Finally, the knowledge graph features are used as an auxiliary to complete the recommendation task. In the experiment, three data sets were selected to compare with other advanced models, and it was found that the model is in an advantageous position in terms of training time and recommendation effect, and has a certain degree of universality.
Classification of Cell Diffraction Images Based on Deep Neural Network

With the continuous advancement of the life sciences and the advancement of ultra-high resolution technology, people can observe biological information such as organelles and molecules, and can understand their internal structure and interaction from the acquired cell diffraction images. However, cells contain various types of organelles, which have high heterogeneity, and the cell structures of different types of cells have certain differences. Therefore, the study of cell diffraction image classification is of great significance in many fields such as cell morphology and cell biology. The research task of image classification is to extract useful feature information from the image, and then distinguish the images of different attributes, and finally divide the image targets of different categories. Deep learning techniques are used in a variety of industries, including picture categorization, as a result of the development of deep learning. Among these, there has been a notable improvement in the accuracy of picture classification using deep neural networks. The classification accuracy can be further increased in the real cell diffraction image classification procedure, though. This study proposes a deep neural network-based classification strategy for cell diffraction pictures. For the cell diffraction images, some interference pictures are created by cell debris or impurities. In this work, the produced diffraction pictures are preprocessed using a clustering approach and a support vector machine (SVM).After that, a Gray Level Co-occurrence Matrix(GLCM) is used to extract the texture features of the diffraction image. This research proposes an enhanced deep neural network-based picture classification algorithm. Max-margin Minimum Classification Error (M3CE) is introduced during the training of deep neural networks, and cross-entropy is used to build the loss function. Finally, the Ramos and Jurkat cell experiments' findings support the high precision of the categorization approach.
Sigma-UAP: An Invisible Semi-Universal Adversarial Attack Against Deep Neural Networks

Although deep neural networks (DNNs) have achieved exceptional performance, they are shown to be fragile to universal adversarial perturbations (UAP), which can be applied to any images to fool a well-trained DNN. Several methods have been proposed to design universal perturbations. However, these methods often leave visible traces in natural images. In this paper, we propose Sigma-UAP, a semi-universal adversarial attack, to enhance the quasi-imperceptibility of universal adversarial perturbations, in which the Sigma-map algorithm is leveraged to hide perturbations by identifying the low-frequency region of the image and eliminating the perturbations in that region. Then, we use a simple matrix calculation to augment the perturbation in the high-frequency region to ensure the attack effectiveness of the perturbation. The extensive empirical experiments show that, compared with the state-of-the-art universal adversarial attacks, Sigma-UAP method obtains excellent attack capabilities in visual effect and attack success rate.
Zero-Shot Learning based on Vision Transformer

Zero-Shot Learning (ZSL) simulates human’s transfer learning mechanism, which can recognize samples or categories that have not appeared during the training phase. However, the current ZSL still has a domain shift issue. To solved the domain shift issue, we propose a new ZSL method that combines Vision Transformer (ViT) and the encoder-decoder mechanism. This method refers to ViT’s Multi-Head Self-Attention (MSA) to extract more detailed visual features. The encoder-decoder mechanism can make the semantic information extracted from the image features accurately express its visual features and enhance recognition accuracy. We implemented it on three data sets of CUB, SUN and AWA2, and the experimental results proved that the method suggested in this study performs better than the current available methods. It shows that our new method is an effective ZSL method.
Sensitive Data Classification of Imbalanced Short Text Based on Probability Distribution BERT in Electric power industry

The exploitation of big data in industrial fields faces several challenges, such as data privacy and security, data integration and interoperability, and data analysis and visualization. Data privacy and security is a major concern, as the data collected from industrial fields often contain sensitive information. Due to the particularity of the industrial field, there are challenges in the utilization of big data. 1. The distribution of different categories data is extremely uneven; 2. There are a large number of industry terms in the short texts that constitute the metadata, which makes semantic representation difficult. These two challenges have a large impact on the application performance of existing models. In order to resolve the problems above, this paper proposes a pre-training model based on probability distribution, which for the classification of sensitive data in the power industry. The model consists of three modules: 1. The data enhancement module adopts the technology of synonym expansion and noise introduction, so that the model can extract the classification features of sensitive data with a small proportion; 2. The pre-training module adopts the BERT model, which can obtain the semantics of industry terms in short texts; 3. The probability prediction module is used to regularize the distribution of test data to meet the training data. Compared with the traditional classification model and the classification model based on deep learning, the F1-score can be improved by 36.68% and 6.39%.
Decoupling Multimodal Transformers for Referring Video Object Segmentation

Referring Video Object Segmentation (RVOS) aims to segment the text-depicted object from video sequences. With excellent capabilities in long-range modelling and information interaction, transformers have been increasingly applied in existing RVOS architectures. To better leverage multimodal data, most efforts focus on the interaction between visual and textual features. However, they ignore the syntactic structures of the text during the interaction, where all textual components are intertwined, resulting in ambiguous vision-language alignment. In this paper, we improve the multimodal interaction by DECOUPLING the interweave. Specifically, we train a lightweight subject perceptron, which extracts the subject part from the input text. Then, the subject and text features are fed into two parallel branches to interact with visual features. This enables us to perform subject-aware and context-aware interactions, respectively, thus encouraging more explicit and discriminative feature embedding and alignment. Moreover, we find the decoupled architecture also facilitates incorporating the vision-language pre-trained alignment into RVOS, further improving the segmentation performance. Experimental results on all RVOS benchmark datasets demonstrate the superiority of our proposed method over the state-of-the-arts. The code of our method is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gaomingqi/dmformer</uri> .
Federated Learning in Healthcare Industry: Mammography Case Study

The paper focuses on the role of federated learning in a healthcare environment. The experimental setup involved different healthcare providers, each with their datasets. A comparison was made between training a deep learning model using traditional methods, where all the data is stored in one place, and using federated learning, where the data is distributed among the workers. The experiment aimed to identify possible challenges that could arise when training a model in a federated learning scenario, including the impact of federated learning on the obtained measures for breast density classification and examining the impact of data preprocessing and domain adaptation. The results indicate that using federated learning deep-learning models can be effectively trained on distributed healthcare data, performing similarly to the traditional approach while providing additional benefits such as improved data privacy and security. However, domain adaptation and data heterogeneity must be carefully addressed to achieve optimal performance in federated learning.
Multimodal camera for pedestrian detection with deep learning models

Road deaths are still alarmingly high. It also ranks first regarding causes of death unrelated to pre-existing health status. More than half of all road traffic deaths occur among vulnerable road users like pedestrians. The main purpose of the work was to design, implement, and evaluate pedestrian detection methods using multimodal imaging and machine learning meth-ods. Two cameras were co-calibrated using a specially designed reference chessboard. The method of superimposing multimodal images using thermal and RGB data was proposed to produce the final images. New datasets consisting of 8000 multimodal images were collected in good and limited visibility. The adapted YOLO models, which included edge device variants for TFLite, were proposed to detect pedestrians in a new representation of multimodal data. The models were trained to optimize the detection accuracy and speed performance for mobile devices compared to desktop computers in different lighting conditions. The best results obtained in tests for a subset of images related to good visibility were achieved by the adapted YOLOv5s model, reaching mAP:0.5 of 96.5% for CUDA architecture and 93.4% for edge devices. The same model achieved 87.8% and 86.8% for limited visibility. In both cases, the frame rate was approximately 7 FPS. The methods could be further improved for practical application in traditional or semi-autonomous car systems, drones, security cameras, etc.
A New Power System Data Security Monitoring System Based on Ensemble Learning

The improvement of China's economic development level has actively promoted the development of various industries, and the demand for electric energy has also gradually increased. For power supply enterprises, ensuring the safe and stable operation of the new power system is the prerequisite to meet the high demand for power supply. The new power system can be applied to electrical equipment such as electric power, with the functions of monitoring, automatic detection and maintenance. The new power system's information security related to power supply system stability. We settle this problem by strengthening the safety management strategy of power system and developing automatic risk monitoring technology, which can effectively remove the failure on the one hand, and generate fault alarm on the other hand, which will further reduce the adverse impact of the failure on the operation of the entire power supply system.
Deployment and Detection of Anti-Forensics Shredding Technique in Android Mobile Devices

Mobile device forensics faces a number of challenges and obstacles due to heterogeneity and limitations in accessing data on these devices. The main problem with the use of mobile devices is that they contain personal data stored in their memory that can be exposed to theft and destruction, and because the loss of this data poses a great risk to these users. In this study, we deployed an anti-forensic shredding technique to destroy digital evidences in android mobile devices by near permanently destroying and not being able to recover them. The efficacy of the antiforensic shredding technique was assessed by comparing the quantity of digital evidence retrieved before and after its application. The results demonstrate that the shredding technique effectively erases data almost permanently to a rate of 99%. This shows that shredding technique is one of the best method for destroying digital evidence near permanently in android mobile devices.
Research on the Application of Public Cultural Service APP Design Based on AI Technology

The Fifth Plenary Session of the 19th Party Congress emphasized the need to promote the digitalization of public culture. In order to support the improvement of public cultural service system and enhance the level of public cultural service, this paper takes the design of public cultural service apps as the theme, combines the background environment of artificial intelligence for Internet of Things and cell phone apps, and discusses three aspects of user demand analysis, app interface orderly design, human factor adaptability evaluation system and user feedback, and concludes that the design of public cultural service apps should It is concluded that the design of public culture service apps should focus on the concept of people-oriented, and adding demand analysis, orderly design of interface, and human factor adaptability evaluation system will better improve the user participation and pleasure of public culture service apps.
FSFNet: Foreground Score-Aware Fusion for 3-D Object Detector Under Unfavorable Conditions

Nowadays, various multimodal fusion-based 3-D object detectors appear to provide a potential opportunity to solve the failure cases in single-modality methods. However, current fusion approaches still face some unfavorable factors, e.g., poor illumination driving conditions and crowded traffic scenarios, which will cause unsatisfying image quality and objects’ occlusion. To this end, we present a multimodal fusion network FSFNet consisting of local graph-aware point cloud backbone (LGB), foreground score-aware fusion network (FSFN), and the proposals’ refining loss (PRL) for the 3-D object detection task in this article. Concretely, the directed graph is built to generate edgewise features for each point, and the point features are supplemented with graph information in LGB. To alleviate the defect of undesirable image quality features caused by poor illumination condition, FSFN is designed to produce an adaptive multimodal feature by taking pointwise foreground scores into consideration. Hence, levelwise point features with high confidence are fully used, and the imperfect image information is suppressed in fusion stage. We further introduce PRL to reduce the false positive and false negative cases in crowded scenes by optimizing the location and scores of predicted 3-D bounding boxes. Extensive experiments conducted on the KITTI benchmark demonstrate that FSFNet owns its superiority over state-of-the-art networks. Moreover, FSFN is verified to be robust against the image inputs under poor illumination conditions.
Research on Seismic Signal Recognition and Prejudgment Based on Deep Learning Model CNN

Traditional seismic signal detection algorithms mainly use micro-seismic signal sensors to capture seismic signal waves. In this way, the corresponding parameters and specific information of the seismic source can be obtained. However, this method has its potential disadvantages, mainly due to the harmonic noise under the micro-sensor collected during the earthquake being too large. Moreover, the sensor’s monitoring environment in the automatic signal transmission is often very harsh. Therefore, the signal-to-noise ratio of the acquired data information is extremely low, which causes the problem that microseismic events are difficult to identify. This paper uses an improved deep learning convolutional neural network model to innovate the traditional seismic signal recognition process algorithm to deal with the above problems. Focus on improving the accuracy and reliability of seismic signal identification using sensors by optimizing the processes and steps of seismic signal identification and judgment.
Speech in Parts Understanding and Modelling the Semantic Differences between Words

This thesis is about the problem of differences in lexical semantics with a special emphasis on antonymy. It explores part-of-speech as a means to formalize semantic differences computationally, enhance the performance of computational linguistic tasks and aid in the understanding of lexical semantics more broadly. The thesis begins with an overview of how antonymy has been studied within experimental psychology and the major schools of theoretical linguistics as well as a review of the semantic foundations of part-of-speech. It then turns to computational experiments that use part-of-speech as a primitive organizing principle, including a source categorization task and four automatic antonym identification experiments, which with few exceptions, show results that either meet or exceed human performance. The final chapter presents a computational analysis of semantic markedness and the sequence preferences that that antonyms often demonstrate when they co-occur. The theoretical accounts for these observations are evaluated on the basis of corpus statistics and the thesis concludes with some general observations about the usefulness of computational linguistics in the analysis of semantic theories.