Reviewerly/exHarmony huggingface dataset:
Face attribute editing network based on style-content disentanglement and convolutional attention

Face attribute editing is a research hotspot in the field of computer vision, which aims to modify a certain attribute of a face image to generate a new face image. The current methods based on Generative Adversarial Networks (GAN) have attribute entanglement problems and the implementation process is relatively complicated. To this end, this paper proposes a face attribute editing network based on style-content disentanglement and convolutional attention. Adding convolutional attention (CAT) module to the StyleGAN generator makes the network's control of content features no longer affected by the overall style of the image, and realizes the separation of spatial content and style from coarse to fine. In addition, the hierarchical CAT modules control different levels of attribute features, and changing the input of any layer of CAT can change the corresponding attribute features. The experimental results on the CelebA-HQ dataset show that the method in this paper can achieve disentangled editing of face attributes, and the scores of various indicators are better than the existing models.
Streaming Machine Learning for Supporting Data Prefetching in Modern Data Storage Systems

Modern data storage systems optimize data access by distributing data across multiple storage tiers and caches, based on numerous tiering and caching policies. The policies' decisions, and in particular the ones related to data prefetching, can severely impact the performance of the entire storage system. In recent years, various machine learning algorithms have been employed to model access patterns in complex data storage workloads. Even though data storage systems handle a constantly changing stream of file requests, current approaches continue to train their models offline in a batch-based approach. In this paper, we investigate the use of streaming machine learning to support data prefetching decisions in data storage systems as it introduces various advantages such as high training efficiency, high prediction accuracy, and high adaptability to changing workload patterns. After extracting a representative set of features in an online fashion, streaming machine learning models can be trained and tested while the system is running. To validate our methodology, we present one streaming classification model to predict the next file offset to be read in a file. We assess the model's performance using production traces provided by Huawei Technologies and demonstrate that streaming machine learning is a feasible approach with low memory consumption and minimal training delay, facilitating accurate predictions in real-time.
Scene text image correction based on transformer

In this work, we propose a new algorithm framework based on Transformer to address the distortion problem of scene text images caused by shooting angles or lighting conditions. This new algorithm framework is called Scene text image transformer (StTr). Specifically, we use a feature extractor containing six convolution modules for feature extraction. Then, we flatten the obtained feature maps and input them into the Transformer encoder and decoder. After obtaining the displacement matrix, we use it to perform geometric correction on the input image. We also use Transformer to eliminate the lighting problem and improve the accuracy of OCR recognition. We evaluated our StTr on different datasets and achieved a 16.73% improvement in accuracy compared to the state-of-the-art methods. Additionally, our model has the advantages of being lightweight and efficient.
Recognition of expiry data on food packages based on deep learning

There is a great deal of information on food packaging, including the name of the food, the expiry date and the ingredients. This information, especially the expiration date, needs to be printed correctly before the product is brought to market. Failure to print the correct expiration date can lead to public health problems and recalled products causing financial losses to the company. In this work, we propose an automatic detection and identification of validity areas that can be achieved efficiently and accurately. First, the DBNet network-based approach is applied to detect the expiration date region on food packages. Then the detected expiration date area is intercepted and input to the character recognition network CRNN for character recognition. Finally, the proposed model is deployed on Jetson Xavier NX to implement edge computing of the algorithm, while inference acceleration of the model is performed using TensorRT and FP16 or INT8 quantization. The experimental results show that the method achieves good performance in the detection and identification of expiration dates on different types of food packages, and the method has good real-time and portability.
Improving fragment information recommendation via enhanced knowledge graph

Fragment information refers to irregular, unorganized and unstructured data scattered across various sources such as texts or images in public newspapers, reports, and other online articles. Due to its small units, large quantity and low value density, it is not easy to discover the related fragments for queries from a specific domain. To make better use of domain knowledges, this paper utilizes the entity co-occurrence relationship in domain to enhance base knowledge graph, followed by fusing the enhanced graph representations to obtain the entity-related knowledge representation via attention mechanism. By enhancing the base knowledge graph with frequency statistics of entity co-occurrence, the neighborhood information can be encoded into entity representations, thereby providing entity-related domain knowledge. We then linearly combine the entity representation with the pretrained representation of whole fragment texts and calculate the similarity between the resulted vectors to retrieve top-k related items for fragment information recommendation. Finally we demonstrate the effectiveness of the proposed method through experiments on the MIND dataset.
Pixel-level feature enhancement and weighted fusion for visual relationship detection

In the traditional visual relationship detection model, the entity representation of visual features tends to focus on coarsegrained and ignore fine-grained features. Moreover, the entity representation of spatial features does not fully reflect the prominent role of relative position. In some specific cases, it is impossible to generate a unique spatial feature representation vector. The final feature fusion did not take into account the characteristic that visual features are primary, while semantic and spatial features are secondary. In order to address the above issues, we propose a visual relationship detection model with pixel-level feature enhancement and weighted fusion. Specifically, we embed a fine-grained information block to capture pixel-level context information in the feature map, providing richer visual features for the prediction of relationships. We adopt a coordinate encoding method to encode the respective and relative positions of entity pairs to bounding boxes to obtain more accurate spatial feature representations. We construct a feature fusion method based on feature weighting to obtain a more improved fusion vector. We conducted extensive experiments on the mainstream visual relationship detection dataset. The results show that our proposed model performs better.
Chinese text summarization generation based on transformer and temporal convolutional network

The recurrent neural network model based on attention mechanism has achieved good results in the text summarization generation task, but such models have problems such as insufficient parallelism and exposure bias. In order to solve the above problems, this paper proposes a two-stage Chinese text summarization generation method based on Transformer and temporal convolutional network. The first stage uses a summary generation model that fuses Transformer and a temporal convolutional network, and generates multiple candidate summaries through beam search at the decoding end. In the second stage, contrastive learning is introduced, and the candidate summaries are sorted and scored using the Roberta model to select the final summary. Through experiments on the Chinese short text summarization dataset LCSTS, ROUGE was used as the evaluation method to verify the effectiveness of the proposed method on Chinese text summarization.
KBMQA: medical question and answering model based on Knowledge Graph and BERT

The emergence of BERT and Knowledge Graph (KG) has promoted the development of Question Answering (QA), however, existing QA systems are still inadequate in terms of the accuracy of relational reasoning and the interpretability of answers. In this paper, we combine BERT and KG, following and optimizing existing methods to build a medical QA system with better performance - KBMQA. The final experimental results show that KBMQA performs better on both MedQA and MedNLI datasets compared with previous biomedical baseline models and MOP models.
A construction method for communication APP security portrait based on flawed API

The main service mode of communication protocol or communication business support software for upper application is API (Application Programming Interface). This paper focuses on whether the defective API is abused. Based on the method of extracting the feature set of defective communication API, and the defective communication APIs called in the APPs are analyzed and the communication security portrait of the APPs is builded. 20 typical open source communication software are selected to extract the feature set of defective APIs, and 1000 candidate APPs that may have security risks are screened to further evaluate the risks to obtain their communication security profiles. The experimental results show that the methods proposed in this paper can effectively detect the functional defects and privacy disclosure defects in communication APPs. This method not only provides a new technology for communication software security testing, but also provides a new idea for communication software security protection.
Design of wind turbine oil level recognition system based on YOLOv5

In order to replenish the oil in the oil tank in time and reduce the loss caused by insufficient oil volume, we use wind turbines as an example to build a 1,300 oil tank picture dataset containing different oil levels. And the YOLOv5 network model based on the PyTorch framework trains the relevant dataset, and the oil tank and oil are detected through the training model to identify the oil volume of the oil tank, the model effect can meet industrial applications. The experimental results show that the YOLOv5 model established in this paper is 96% of the average accuracy of oil level recognition, which effectively solves the problem that the oil deficiency of oil tanks cannot be found in time.
PPT: pocket yoga personal training system

The full coverage of electronic products promotes the prevalence of human-computer interaction systems, especially in sports and fitness. Excellent human-computer interaction technology can bring high-quality experiences to human beings. However, the previous research focuses on the tracking of the wearable device and the radio frequency signal to the body part. This article will launch a lightweight, portable yoga private education system that can be mainly in line with coaches. A smartphone can realize real-time tracking of user body motion, combining the evaluation model feedback and valuable guidance. We predict the composition of the limb occlusion by the algorithm and use the segmentation to normalize the accurate calibration of the bone node. Improve the accuracy and robustness of the system while helping users achieve freedom and practical yoga training. The experimental results show that the system can provide meaningful feedback to the user in real-time by accurately assessing the user’s yoga action, high accuracy.
Research and implementation of modularization of embedded software for data chains

The article researches and implements the embedded software of data chain from the perspective of modular development. Firstly, the current development trend of data chain technology and the pain points of software development are introduced. In contrast to the challenges faced by the traditional model, the architecture of modular software design is studied, the functions of each level are analyzed, and the design scheme and advantages are explained. Finally, the specific ideas and standard interfaces of modular software implementation are illustrated with code engineering, and the implementation steps and related results in the application process are introduced.
Flight test domain knowledge graph construction and application

Aiming at the problem that the association query demand between the massive and complex relationship between flight test data in the flight test field cannot be realized at present, a knowledge graph construction method in the flight test field is proposed. Eight types of entities are defined and twelve types of relationships are designed according to the business requirements of association query, forming a complete set of flight test domain knowledge entities and relationship descriptions, and flight test data stored in the database are loaded into HugeGraph, a graph database, through data format alignment, entity schema construction, relationship schema construction and data extraction and transformation from the original database. Based on the attribute graph model, the flight test domain knowledge graph was constructed; a flight test data correlation query system was built based on the knowledge graph, providing a web query interface in graph form and displaying the query results to users through the D3 visualisation component with a graph structure. The experimental test of the flight test domain knowledge graph was conducted using the internal business system data as the source, and the results verified the completeness and reliability of the constructed knowledge graph, while the experimental verification of the constructed correlation query system showed that the query efficiency of the system was significantly improved compared with the traditional database method.
Robustness evaluation of object detection models: a gradient attack method based on spatial sensitivity

In the field of model robustness, adversarial attacks have become the most powerful way to threaten the performance of various deep models; adversarial attacks are to add adversarial noise to common samples to generate adversarial samples in order to mislead the model. The aggressiveness and imperceptibility of its noise are two main indicators to measure the adversarial attack. In this paper, we focus on attacks on object detection models. Previous gradient-sign based noise generation methods have achieved powerful attacks, however the adversarial examples they generate are usually easily perceived by our visual system. This is mainly due to the crude sign noise addition strategy adopted by the gradient sign method on the global input space. To solve this problem, the sensitivity of the deep detection model to the input sample space is analyzed, and a gradient attack method based on space sensitivity is proposed based on this clue. Specifically, inspired by the attention mechanism of the deep model, we investigate the global gradient information of the entire image, and verify the spatial regions that play a key role in detection and classification; further, we propose a method based on the key gradient information noise screening strategy to generate adversarial examples. This not only avoids the perceptibility flaw caused by directly attaching the global gradient symbol, but also provides a more powerful attack effect. Taking the Yolov3 detection model as an example, we conducted observational verification and offensive testing on the VOC detection dataset, and the experimental results confirmed the effectiveness of our method. This poses a greater challenge on how to conduct more effective model defense.
FNCSE: contrastive learning for unsupervised sentence embedding with false negative samples

In recent years, contrastive learning has gradually been applied to the field of natural language processing from the explosion of the computer vision community. Generally speaking, for a given sentence, the original sentence is used as the anchor point, the current model uses a special data enhancement method to generate positive samples, and the remaining sentences in the batch are used as negative samples for the sentence. Although this approach effectively improves sentence embedding, we believe that in the process of constructing negative samples, negative samples will also have information similar to the current anchor point, and should not be mistakenly attributed to negative samples, which will confuse the learning ability of the model, and The increase in the number of negative pairs during training is beneficial to the training of the model. In this paper, we propose methods to identify false negatives in the text domain, and two strategies to mitigate the impact of false negatives: masking false negatives and adding to the set of positives. In addition, we propose an optimized version of momentum contrast to expand the number of negative pairs. Our method is based on the improvement made on SimCSE called FNCSE. We evaluate FNCSE on multiple benchmark datasets in the semantic similarity (STS) task. The experimental results show that in the Bert base, FNCSE averages 1.29% Spearman Correlation is better than SimCSE.
Tyrannosaurus optimization algorithm: A new nature-inspired meta-heuristic algorithm for solving optimal control problems

Recently, the optimal control problem has gained much importance for solving practical problems. In this regard, the meta-heuristic algorithms are proven to be effective while solving these problems effectively and efficiently. However, these algorithms may not be effective for solving all the optimization problems as per the no free lunch theorem. Thus, there is always a scope of development of new meta-heuristic algorithms. This paper proposes a new hunting-based optimization algorithm called Tyrannosaurus (T-Rex) optimization algorithm (TROA). This algorithm is inspired by the hunting behavior of the T-Rex. This algorithm was tested on 12 benchmark problems and 4 practical optimal control problems. The performance of the TROA is compared with seven famous optimization techniques, i.e. Differential Evolution (DE) Algorithm, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), White Shark Optimizer (WSO), Jellyfish Search (JS), Crow Search Algorithm (CSA), Golden Eagle Optimization (GEO). The results obtained for the proposed method have given better when compared to these methods.
Similarity Measures for Person Identification with Face Mask

Facial recognition is a method of identifying or verifying a person's identification by examining their face. People can be identified in pictures, videos, or in real time using facial recognition technology. After facing more than two years of Covid-19 restrictions, face masks are optional but strongly advised for usage. As new cases are still recorded on a daily basis, wearing face mask in a social setting is still the safest way to protect ourselves and others from COVID-19. However, the usage of face mask has brought difficulties for a person or face identity recognition because half of the face features has been covered. The proposed research starts with face mask detection for determining if the subject is wearing a mask or not. The system uses MobileNetV2 for face mask detection resulted to 100% correct when identifying face mask. Secondly, to evaluate the effectiveness of various similarity coefficient formula for person identification with face mask. A dataset of 35 people wearing face masks was examined. The study involving usage of multiple similarity measures formula; Euclidean Distance, Cosine Similarity, Manhattan Distance, and Jaccard Similarity to evaluate the similarity between two images. It is identified that Jaccard Similarity is the most sensible similarity coefficient method for person identification with face mask. This study offers some insight that worth to further analyses the potential of Jaccard coefficient in measuring the person identity with a face mask.
Leveraging Robust CNN Architectures for Real-Time Object Recognition from Conveyor Belt

In the contemporary era, the problem of recognizing undesirable objects and individuals on the conveyor belt is addressed by various architectural or algorithmic approaches. Conveyor belts are an object-carrying medium by which things go in a straight line for transportation. However, sometimes some unwanted objects go through the belt mistakenly, which can be very dangerous. Hence, detection accuracy is much needed to avoid such deadly occurrences and better accuracy can be achieved by performing detection in real time. Furthermore, this upgraded system will assist individuals by lowering the danger of any accidents and provide a real-time example of an airport conveyor belt by detecting any unwanted moving objects with the help of different algorithms and methods of the neural network. Therefore, in this paper, we offer a few algorithms that comprise Convolutional Neural Network (CNN), YOLOv5, YOLOv7, and Vision Transformer (ViT) as well as some Transfer learning methods over a few pre-trained models (VGG16, ResNet50, MobileNetV2) to produce a better strategy on our customized dataset to find out if it can recognize objects and human babies. Our research indicates that our custom dataset closely fits the architecture of YOLOv5, with an accuracy of 88%.
Hashtag Recommendation based on Neural Topic Model

Hashtag recommendation for microblogs is an appealing research topic that can assist users in selecting hashtags for their microblogs. Previous hashtag recommendations have been proposed based on the topic model approach. In spite of their improvement, two issues are still unresolved. First, prior studies inferred the topic distribution of users, words, and hashtags by using a non-neural network framework, making them unable to deal with the complex topic distribution of users, words, and hashtags. Second, previous work required word and hashtag frequency to process the topic distribution inference only, ignoring not only word ordering and word context but also the relation between word and hashtag. It is one of the crucial factors that affect hashtag recommendation since some words in the target microblog can indicate the relevant hashtags. In this paper, we propose a novel hashtag recommendation model called Hashtag-NTM to infer the topic distribution of users, words, and hashtags by using the neural network framework, making the model able to deal with the complexity of distribution. Moreover, we explore the relationship between words and hashtags to model word and hashtag representation before processing topic distribution inference, making the recommended hashtags more related to words in the target microblog. Extensive experiments on the Twitter dataset demonstrate that Hashtag-NTM consistently outperforms baseline methods over hit rate metrics.