Reviewerly/exHarmony huggingface dataset:
A Positioning Optimization System for Intelligent Pharmaceutical Production Line Based on Visual Imaging

On the pharmaceutical automation production line, the application of artificial intelligence technologies such as machine vision can make pharmaceutical production automated and intelligent, improve the quality and efficiency of pharmaceutical production. It can be said that machine vision is a major core technology for the automation and intelligence of pharmaceutical production equipment. This article focuses on the visual positioning technology for the filling and sealing process of traditional Chinese medicine in pharmaceutical automation production lines. The research content mainly includes two aspects: The design of visual imaging systems and the research on visual positioning algorithms for bottle mouths, stoppers, and caps. The visual imaging system uses an industrial camera combined with a circular light source, and then uses a hand eye calibration installation method to capture high-quality images of bottle mouth, cork, and cap on a movable robotic arm. Next, a series of image preprocessing operations are carried out on the original image, including Gaussian filtering and smoothing of the image to remove noise, gamma transform image enhancement and edge detection preprocessing operations, and the use of Canny edge detection to obtain images with good foreground and background contrast, appropriate brightness, and prominent edges. Finally, a circle fitting algorithm based on KMeans++ was used to locate the extracted edge images of bottle mouth, bottle stopper, and bottle cap, and the experimental results were preliminarily analyzed.
Research and Application of Information Privacy Protection Scheme Based on GRUAC

In recent years, the Internet has been extended to various terminal equipment, and gradually realized the interconnection between people, people and things, and things and things. The emergence and application of edge computing technology has accelerated the process, which can provide computing services in the edge network near the terminal equipment. However, the resources available to edge nodes are often limited, and they cannot provide a large amount of computing power like cloud servers to perform security defense activities such as network intrusion detection or access control, and they cannot effectively identify various new network attacks. Therefore, it is necessary to study a timely, lightweight and highly adaptive intrusion detection strategy. Intrusion detection strategy based on packet sampling detection. In the context of the need to detect a large amount of data, it has been pointed out that the computational complexity can be reduced by reducing the feature space dimension of network traffic. This paper proposes that intrusion detection systems can detect the most relevant data instead of completely detecting all data, so as to reduce the consumption of computing resources. Based on this, the RL-DL algorithm is proposed in this paper, which uses the autonomous decision-making behavior in reinforcement learning to make intrusion detection system decide to select specific data by itself. Finally, the reliability of the model is verified by experiments.
Video Visual Feature Extraction and Analysis Based on DL-SLAM

Visual feature extraction plays an important role in video analysis and computer vision. Due to the large amount of video data, it is difficult to conduct direct data processing and analysis, so it is necessary to improve the efficiency of data analysis and processing through feature extraction of video. Visual feature extraction is a process of extracting some representative and robust low dimensional feature vectors from video data. However, the existing video feature extraction is mostly based on traditional methods, which reduces the quality and accuracy of extraction. Therefore, the fusion of deep learning feature points and traditional visual SLAM algorithm is studied in this paper. A DL-SLAM system is constructed by integrating SP neural network with ORB-SLAM2 algorithm. The system uses SP neural network to extract image feature points and descriptors for pose estimation and subsequent function modules of DL-SLAM. A comparative experiment was carried out on TUM data set, and satisfactory results were obtained.
Spark-based collaborative filtering recommendation system and improvement of cold start problem

Abstract:In this era, the rapid development of the Internet and information exchange technology has produced a large amount of data. This has greatly increased the amount of information people are exposed to every day, but there is a lot of information that people are not interested in when it comes to obtaining information. It's getting harder and harder to find useful information in a mass of information. Therefore, only the right way to provide users with the data they need has become the focus of manufacturers. But every user has different preferences. In particular, with the increase of data volume and data depth, it is difficult to find valuable information in a large amount of data when users are unable to clarify their own needs . In this case, the recommendation system was born. Recommendation system is based on the user's buying habits, determine the user's interest. Explore user behaviors, recommend appropriate information or products to users, meet the needs of each user, and help users find the products they need in a large amount of data.
Efficient Inference Task Deployment for Enhanced Accuracy at the Edge

With the development of the Internet of Things and Artificial Intelligence, deploying the inference tasks at the network edge is widely adopted by operators so as to provide intelligent services. However, due to limited edge resources and the relatively high resource consumption of the model, deploying the appropriate model to ensure good inference accuracy can be challenging. In this work, we model this problem whose aim is to maximize overall efficiency and benefit by selecting AI models pre-trained on cloud servers to be deployed to edge servers. However, the benefit of a pre-trained model is unable to predict due to the dynamic stochastic inputs of users, which can only be disclosed to us after the inference task is executed. Therefore, we improve on the classic algorithm and propose an algorithm for selecting pre-trained models for edge servers based on the multi-armed bandit. With rigorous proof, we prove that regret grows sub-linearly along with time. We also conduct experiments and show the superiority of our algorithm compared to the state-of-the-art algorithms.
A Generative Adversarial Approach with Social Relationship for Recommender Systems

With the explosive growth of data, personalized recommendation systems have become an essential component in digitally-driven society. However, one of the main challenges faced by traditional recommendation algorithms is the difficulty in obtaining accurate user preferences, especially for users with limited historical interaction data., which ultimately impacts the performance of these methods. To address this challenge, the paper proposes a recommendation method, named STRGAN (Social trust relationships Generative Adversarial Network), it leverages the advantages of Generative Adversarial Networks (GANs) to tackle the data sparsity problem, by integrating user ratings and social relationships. By incorporating both types of information, STRGAN aims to improve the accuracy and quality of recommendations provided to users. Moreover, the proposed STRGAN model employs negative sampling techniques to ensure that the generated recommendations align with the real data. To evaluate the effectiveness of STRGAN, extensive experiments were conducted on the real world dataset FilmTrust. The empirical results demonstrate that STRGAN outperforms other GAN-based models in various evaluation metrics, such as precision, recall, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR). STRGAN offers a robust and efficient solution for personalized recommendation tasks. The results of experiment support the efficacy of STRGAN, indicating its potential to significantly improve recommendation accuracy in real-world applications.
Research on New Material Storage Equipment Inventory Management in Electric Power Industry Based on Machine Learning

The power industry is one of the indispensable basic industries in modern society, which provides the necessary energy support for people's production and life. The development of electric power industry is closely related to the development of national economy. In the power industry, storage equipment management is also a very important part, and reasonable storage equipment management has a crucial impact on the production efficiency of the power industry. Only under good storage facilities and management can the normal operation and smooth power supply of the power plant and its related equipment be guaranteed. However, the existing storage equipment management is mostly based on the traditional method. Faced with a large number of inventory equipment, the traditional method has shortcomings in management efficiency and accuracy. Therefore, when demand is a random variable, service quality constraints and inventory capacity constraints exist, and demand generation channels increase, in order to control and optimize the inventory cost of equipment enterprises, this paper classifies the inventory cost control model of supply chain, and uses machine learning methods to optimize the inventory cost control model of supply chain, so as to achieve the goal of reducing inventory cost.
Hospital Website Penetration Analysis and Vulnerability Reproduction Method

The medical industry often faces challenges related to poor information infrastructure and weak cybersecurity awareness among staff. The high value of medical data makes hospital websites a prime target for both domestic and international hacker attacks. Hospital websites are typically situated at the outer layer of hospital information systems, offering multiple points of vulnerability. Strengthening the penetration testing capabilities of hospital website administrators and enhancing their understanding of various types of web application vulnerabilities holds significant importance for ensuring the smooth operation of medical services.
Problems of software import substitution in HEIs

Adoption of IT innovations is a challenge for higher education organizations, and the main reason for the difficulties is not the failure innovations’ implementation, but the failure of fulfillment - the inability to get each employee to use the innovation in a skilled, consistent and committed way. It is important for HEIs that the innovation is accepted and supported by staff and that the HEI realizes some economic benefits as a result of the implementation. In order to manage the process of resistance to innovation in higher education institutions, it is first necessary to recognize the causes of resistance to changes, then to consider a possible strategic approach, formulate tactics and methods of levelling the resistance to changes. The research paper considers software import substitution in higher education institutions as an innovative project. It is noted that higher education institutions face a number of difficulties in the transition from predominantly foreign software to domestic analogues, such as the lack of experience of teachers and students in using domestic digital services and solutions, the need to transfer data from one system to another, and additional costs for the replacement of licensed software. The paper presents recommendations for the management of departments aimed at levelling the resistance in the process of transition to new software.
Unsupervised Domain Adaptation by Cross-Prototype Contrastive Learning for Medical Image Segmentation

Unsupervised Domain Adaptation (UDA), which aligns the labeled source distribution to the unlabeled target distribution, has shown remarkable achievement in the medical image segmentation task. Previous UDA methods unilaterally consider the global distribution alignment through explicit category-based loss while good separation and discrimination of class are insufficiently explored, resulting in the sub-aligned distribution across domains. In this paper, we propose cross-prototype contrastive learning method (CPCL) for UDA segmentation through class centroid alignment. Specifically, to reduce the intra-class distance and increase the inter-class distance, we first introduce prototype-feature contrastive learning to align the pixel-level features and the same-class global prototype across domains. Secondly, we further present prototype-prototype contrastive learning to align the same class prototypes between the source domain and target domain for compact category centroid and better global domain distribution alignment. Extensive experiments on two public cardiac datasets demonstrate that the proposed CPCL achieves superior domain adaptation performance as compared with the state-of-the-art.
A Factual Aware Two-Stage Model for Medical Dialogue Summarization

Medical dialogue summarization (MDS) is commonly known as generating electronic health records (EHR) from doctor-patient dialogues to relieve doctors from trivial recordings. Because of their excellent performance on summarization tasks, it is advisable to employ pre-trained language models (PLM) for MDS. However, most of these models are not designed to handle such lengthy dialogues and struggle with domain-specific characteristics. To address this problem, we propose a two-stage summarization model that first constructs compact contexts by selecting salient utterances and then generates EHRs with delexicalization and lexicalization. A REINFORCE algorithm with a multiple reward strategy is employed to connect the two modules to increase faithfulness and adaptively control the length of the extracted context. We implement our model using publicly available PLMs without changing their nature, and we pre-train each module separately before alternately fine-tuning them with the reinforcement objective. Extensive experiments on two public datasets show that our proposed model significantly outperforms state-of-the-art comparison models w.r.t. ROUGE score and terminology matching rate.
Semantic-guided Unknown-aware Rare Disease Diagnosis System

The significant challenge posed by rare disease diagnoses has recently motivated researchers to explore computer-aided solutions. While deep learning approaches have shown potential in developing automatic diagnosis systems, their effectiveness diminishes when addressing rare diseases with limited data. Moreover, existing diagnostic models typically fail to detect unknown diseases, making them inappropriate for real-world applications. In this paper, we address the above challenges by proposing a Semantic-guided unknown-aware Rare Disease Diagnosis (SRDD) model. SRDD aims to tackle the performance degradation of classification models in low-data regimes, as well as their inability to distinguish unknown diseases. Specifically, we propose a Semantic-guided Saliency Discovery (SSD) module to explore semantic saliency information within images by aligning image regions with the semantic knowledge embedded within category labels. The image content is subsequently decomposed into semantically related information (SRI) and image instance template (IIT). Then, we design a Reciprocal samples Synthetic Strategy (RSS) to create known and unknown reciprocal points using SRI and IIT. This facilitates a compact feature space for known classes while preserving space for unknown data, promoting accurate known disease diagnosis and unknown disease detection. We validate SRDD on the public skin disease dataset SD-260. SRDD achieves state-of-the-art performance in both known disease classification and unknown disease identification.
Enhancing Medical Language Understanding: Adapting LLMs to the Medical Domain through Hybrid Granularity Mask Learning

Large Language models have made remarkable strides in natural language understanding and generation. However, their performance in specialized fields like medicine often falls short due to the lack of domain-specific knowledge during pre-training. While fine-tuning on labeled medical data is a common approach for task adaptation, it may not capture the comprehensive medical knowledge required. In this paper, we proposed a Hybrid Granularity Mask Learning (HGM) method for domain adaptation in the medical field. Our method incorporates multi-level linguistic characteries including token, entity, and subsentence to enable the model to acquire medical knowledge comprehensively. We fine-tune a medical-specific language model derived from ChatGLM-6B and Bloom-7B on downstream medical tasks and evaluate its performance. The results demonstrate a significant improvement compared to the baseline, thus affirming the effectiveness of our proposed method.
The Secondary Isolated Data Island: Isolated Data Island Caused by Blockchain in Federated Learning

Compared to other fields, data in the medical field has a higher degree of sensitivity. Therefore, in order to ensure that when using medical data for machine learning, a Federated Learning (FL) framework is usually used. However, the traditional centralized FL framework greatly increases the risk of leaking privacy of clients. For this reason, many researchers have developed and designed a Federated Learning Based on Blockchain (FLchain) to enhance the privacy of this framework. Usually, each hospital will use an independent FLchain. But the researchers overlooked a crucial issue: the application of blockchain in FLchain can create isolated data island. Different FLchain use different blockchains, so the data between the two frameworks cannot be used to train a model at the same time. We call this problem the secondary isolated data island (SIDI). The problem leads to hospitals using FLchain architecture being unable to use each other’s data for model training while ensuring information security. We first discovered and proposed the issue of SIDI, and solved it by using Federated Learning Based on Blockchain with Inter-Blockchain Technology (IBT-FLchain) framework. We designed an Hashed Timelock Contract of global model aggregation (HTLC-M) in FLchain by improving the Hashed Timelock Contract (HTLC). We have demonstrated through experiments that the IBT-FLchain framework can effectively solve SIDI problem and greatly alleviate the storage cost of clients in the blockchain.
Contextualized Large Language Model-Based Architecture for Outcome Measure Alignment in Clinical Trials

The inconsistency in reporting clinical trial outcome measures is a source of variability in clinical trial data which complicates comparability of results across studies. Automatic aggregation of semantically similar outcomes will greatly facilitate the harmonization, cross-trial comparison, and metadata analysis. Interpretation, comparison, and combination of results of clinical trials are reliant on the consistency of outcome reporting. This study aimed to align outcome measures by leveraging the significant capability of large language models. This study proposes a time-efficient, fully automated pipeline to find trials reporting semantically similar outcomes in order to facilitate cross-compatibility and metadata analysis.
A Weak Supervision-based Robust Pretraining Method for Medical Visual Question Answering

Medical images are complex, and the annotation of medical images requires high expertise. It would be time-consuming and costly to annotate directly for experts. As a result, one of the primary challenges currently faced by medical visual question answering (VQA) is the lack of large-scale annotated data. To address this issue, a Weak Supervision-based Robust Pretraining (WSRP) method for medical VQA is proposed. Specifically, our method builds upon a contrastive language-image pretraining framework by introducing adversarial training. However, the contrastive language-image pretraining framework, by treating each image-text pair as a separate category, may lead to class collision problems, thereby affecting the quality of image representation. Therefore, weakly supervised contrastive learning is introduced to generate weak labels, enabling the model to learn fine-grained feature representations. The proposed weak supervision-based robust pretraining method for medical VQA is empirically evaluated and experimental results on public datasets demonstrate its superior performance.
Adaptive Thresholding based on Multi-task Learning for Refining Binary Medical Image Segmentation

Binary medical image segmentation plays a pivotal role in the diagnosis and treatment of a wide range of diseases. However, the performance of the segmentation model is closely related to the choice of the binarization threshold (default 0.5), which is used to binarize the output probability mask. In this paper, we introduce an innovative multi-task learning framework featuring an Adaptive Thresholding Module (ATM) designed to predict the optimal threshold for each image. Within this multi-task learning framework, the segmentation task is divided into two distinct subtasks. The first subtask focuses on the original segmentation task to output the probabilistic mask. Simultaneously, the second subtask leverages spatial features extracted from the segmentation network, with ATM learning and applying these features in a regression task to derive the optimal threshold for each image. Subsequently, a binarization process is enacted using these optimal thresholds, leading to a marked improvement in segmentation accuracy. The crux of ATM’s contribution to enhanced segmentation accuracy lies in its ability to optimize the binarization process, striking a well-balanced equilibrium between the labels of true positive and false positive. We integrated ATM into various medical segmentation models and subjected it to evaluation on datasets encompassing diverse binarized medical image patterns. The results underscore the effectiveness of ATM in elevating the accuracy of pre-existing medical segmentation models.
Handling Imbalanced and Poorly Separated Data: a Multi-Stage Multi-Group Machine Learning Approach

Poorly separated data and imbalanced data present major challenges to classifiers which often result in lower accuracy and reliability in making predictions. In this paper, we introduce the multi-stage classification construct in which ‘difficult-to-classify’ observations are placed into a reserved judgment region for delayed future classification. Such a design is well-suited for poorly separated data that are difficult to classify without committing a high percentage of misclassification errors. The misclassification constraints within the classifier can be finetuned to allow management of imbalanced data, guiding minority entities into the reserved judgement region to avoid being misclassified into the majority groups. We wrap the classifier with a fast feature selection heuristic based on particle swarm optimization. An exact combinatorial branch-and-bound algorithm is also implemented to measure the quality of the heuristic solutions. We apply this multi-stage multi-group machine learning framework to two real-life medical problems: (a) multi-site treatment outcome prediction for best practice discovery in cardiovascular disease, and (b) uncovering patient characteristics that predict optimal response to intra-articular injections of hyaluronic acid for the treatment of knee osteoarthritis. Both problems involve poorly separated data and imbalanced groups in which traditional classifiers yield low prediction accuracy (47% - 66%). The multi-stage BB-PSO/DAMIP manages the poorly separated and imbalanced data well and returns interpretable results with 82% - 97% blind prediction accuracy.