Reviewerly/exHarmony huggingface dataset:
Anomaly Segmentation Based on Cosine Similarity Edge Iteration Smoothing

At present, the existing anomaly segmentation algorithm directly uses the abnormal score of the trained network output, such as the cosine similarity score. However, due to the uncertainty of the edge, the edge pixels or pixels near the edge are less similar to the class prototype. It shows more false negatives at the target edge during inferring, which will greatly affect the performance of the anomalous segmentation. This paper proposed to solve the problem of more false negatives in target edges. First, we need to calculate the cosine similarity between the input pixel and the class prototype. The cosine similarity calculation image was performed using morphological edge detection to extract the false negative edges, features with high non-edge similarity were screened by the edge-aware pooling layer, the filtered features are used to update the edges part. Finally, the updated cosine similarity calculation image is treated with expansion smoothing. The experiments presented were conducted in the StreetHazards Dataset, the FPR95 was 19.6%, AUROC rate was 92.5%, AUPR was 12.5%. The method in this paper is simple and the network does not require retraining, and the precision is close to SOTA.
Train a Highly Robust Model with a Data-enhanced Wavelet Residual Network

Once the classification neural network is attacked by adversarial attack, the classification accuracy will drop significantly. Therefore, the combination of wavelet transform and ResNet18 network model, and then linear interpolation adversarial training is used to train the defense model to improve its classification accuracy. Before training the network, the samples are linearly interpolated to obtain the interpolated samples and interpolation labels, and after the image features are convolved and normalized layers, the wavelet transform is applied to downsample to help filter out the high-frequency information, and finally the interpolation loss is obtained to update the network parameters. The trained model is tested using multiple adversarial attack methods on the CIFAR-10 and SVHN datasets, and the experimental results show that the proposed method can show excellent defense performance under strong attacks, and improve robustness and training time.
CNN-BiLSTM-Attention Prediction Model for Merchandise Sales Based on Attention Mechanism

As companies continue to generate merchandise sales, the data is important for their marketing planning, market analysis, and logistics planning, so it is important to accurately predict merchandise sales. This paper combines a recently proposed attention mechanism multiple time series regression model, the CNN-BiLSTM-Attention model, to predict product sales for the next three months. The model can fix the overfitting problem, and can accurately predict the sales volume of products with high accuracy.
MLSCH: Multi-layer Semantic Constraints Hashing for Unsupervised Cross-modal Retrieval

As one of the mainstream research directions in the field of computer vision, cross-modal hash retrieval has been concerned by researchers. For real world data, unsupervised cross-modal hash retrieval is obviously more important. Aiming at the problem that some current research methods cannot convey the semantic information of high-level representation to hash code, this paper proposes a novel cross-modal hashing method, named Multi-layer Semantic Constraints Hashing for unsupervised cross-modal retrieval (MLSCH), for cross-modal retrieval. The neighbor matrix between intra-modal and inter-modal is used to guide the generation of hash codes, and the neighbor structure is applied to the feature representation of different modes, which reconstructs the cross-modal features containing structural information, and effectively improves the generation quality of hash codes. Extensive experiments show that MLSCH outperforms the current advanced cross-modal hashing methods.
Federated Learning Scheme Based on Gradient Compression and Local Differential Privacy

As a deep learning framework for distributed deployment, federated learning allows users to complete model training while retaining the original data, which meets the local data privacy protection needs of each user. Differential privacy, as a classic data desensitization technology, is often used to improve privacy protection. Many studies have applied it to machine learning, but it is accompanied by the problem of model utility. In addition, in the traditional federated learning model training, there are frequent gradient exchanges between the user and the central server, which poses a huge challenge to the communication overhead of the model and affects the training efficiency of the model. In order to solve these problems, we propose a new federated learning scheme, firstly propose a StaComp compression algorithm to compress the gradient parameters before the user uploads them, use the local differential privacy mechanism to add noise to protect the user's local data. Finally, we evaluate our scheme on the MNIST dataset. Experiments show that the scheme of this article is close to the classic FedAvg algorithm in the aspect of model accuracy, and is better than other methods based on differential privacy. At the same time, it significantly reduces the training time, improves the convergence speed of the model, and saves communication costs.
Improved adaptive image enhancement algorithm based on SSA

When taking images under low illumination conditions, the obtained images are usually affected by low visibility. The low visibility and low brightness image not only affect the visual effect, but also causes many difficulties for subsequent use. In this paper, an improved adaptive image enhancement algorithm based on SSA is proposed, first adjusts the parameter value of the normalized incomplete beta function to fit four types of typical grayscale transformation function curves, then use the intelligent optimization algorithm with strong optimization ability and fast convergence speed, namely sparrow search algorithm, to dynamically determine the best transformation parameters α and β, thus quickly realize adaptive image enhancement. The algorithm is highly adaptive and the convergence speed is accelerated by the sparrow search algorithm. Additionally, the image gray coverage is wide and the enhancement quality evaluation is significantly improved.
Multi-objective Feature Selection Algorithm Based on Apache Spark and Particle Swarm Optimization

The algorithm for feature selection using multi- objective particle swarm optimization has been effectively implemented in certain programs. Existing algorithms face difficulties in handling large data volumes and high- dimensional data due to their high computational complexity. To tackle this problem, data parallel frameworks have been suggested with the advancement of distributed parallel computing. However, increasing parallelism could lead to the problem of uneven data distribution which would affect the effectiveness of the cluster. We have introduced a feature selection approach that utilizes the Spark-MOPSO parallel algorithm and is implemented on the Spark platform in this paper. Firstly, the entire dataset was divided into multiple partitions, distributed computing and in-memory computing. They performed out by using Apache Spark. The local fitness values of particles were iterative independently which updated based on the data parallel computation of each partition. Among the interactive process, each partition was independent and did not affect each other. It reduced the necessity to transform mass date between nodes and network communication. At the same time, the local external archives produced by the particle swarm every m iterations pulled back to the main node and broadcasted to the partitions as the global external archive, guiding the search for the optimal particle in each partition and improving the issue of imbalanced data distribution impacting the outcomes. The experimental results indicated that the algorithm could obtain a good quality and diversity Pareto frontier through multi- objective evaluation indicators, and it could also enhance the effectiveness of problem-solving by increasing the speed of finding solutions and exhibit a better capabilities on performance and parallel computing.
Knowledge Graph of Urban Firefighting with Rule-Based Entity Extraction

There is little research on entity extraction in constructing the knowledge graphs for urban firefighting. In this paper, we propose a rule-based entity extraction method for this field. The Precision of the experiment is 85.25%, while the Recall is 83.58%. In addition, we establish the relationships between entities in urban firefighting in advance with the experience of domain experts. Through the above two steps, we have initially established a knowledge graph in the field of urban firefighting, which including 13 types of entities and 12 types of relationships. This study will provide reference for the construction of knowledge graphs in the field of urban firefighting.
Multi-view Semi-supervised Learning Using Privileged Information

In this paper we propose to combine the paradigm of multi-view semi-supervised learning with that of learning using privileged information. The combination is realized by a new method that we introduce in detail. A distinctive feature of the method is that it is classifier agnostic which contracts with most of the methods for learning using privileged information. An experimental study on a real-life problem shows that using privileged information is capable of improving multi-view semi-supervised learning.
A Multi-relationship Language Acquisition Model for Predicting Child Vocabulary Growth

If we can predict the words a child is likely to learn next, it may lay the foundations for developing a tool to assist child language acquisition, especially for children experiencing language delay. Previous studies have demonstrated vocabulary predictions using neural network techniques and graph models; however, individually these models do not fully capture the complexities of language learning in infants. In this paper, we describe a multi-relationship-layer predictive model, based on a graph neural network. Our model combines vocabulary development over time with quantified connections between words calculated from fifteen different norms, incorporating an ensemble output stage to combine the predictions from each layer. We present results from each relationship layer and the most effective ensemble arrangement.
UJI KESESUAIAN PESAWAT PANORAMIC CHEPALOMETRIC DI LABORATORIUM JURUSAN TEKNIK RADIODIAGNOSTIK DAN RADIOTERAPI, POLTEKKES KEMENKES SEMARANG (UJI MENGGUNAKAN FILM GAFCHROMIC)

Background: X-ray equipment used for radiographic examination according to Bapeten regulations must undergo a compliance test, both for new installation and for relocating. The Panoramic Chepalometry in the Department of Radiodiagnostics and Radiotherapy Techniques Semarang is a new device and has undergone a relocation from the initial installation. In addition, the unique condition of the Panoramic Chepalometry is direct image processing or direct radiography, so a special method is needed for testing x-ray collimation beams, namely using gafchromic films&#x0D; Methods: The research is experimental with a one shot case study at the Laboratory of the Department of Radiodiagnostic and Radiotherapy Techniques, Poltekkes Kemenkes Semarang. The research object is a Panoramic cephalometry device. The measuring instrument used was Raysave, and the test parameters measured were: X-ray beam, voltage accuracy, time accuracy, linearity, reproducibility, HVL, and tube container leakage. The analysis of the test results is adjusted to Bapeten regulation no. 2 of 2018&#x0D; Results: Parameter test results obtained: X-ray beam is still within normal limits, voltage accuracy measurement results obtained a maximum error of 2.73% (max. limit 6%), time accuracy obtained a maximum error of 7.8% (max. limit 10 %), linearity was not measured because the mA value was fixed, reproducibility was obtained CV at a voltage of 0.0012 and radiation output of 0.0006 (CV≤ 0.05), x-ray beam quality (HVL) was obtained at 3.72 mm Al at 80 kVp (HVL limit ≥ 2.3 mm Al), and no leakage in the tube housing&#x0D; Conclusions: The results of the panoramic cephalometric compliance test at the Laboratory of the Department of Radiodiagnostic and Radiotherapy Techniques, Poltekkes Kemenkes Semarang, on 7 test parameters as a whole are still within normal limits according to Perka Bapeten no.2 of 2018
Balancing High-Dimensional Datasets with Complex Layers

Unbalanced datasets generate difficulties in designing good classification models because those classes that are represented by the most numerous training sets are harmfully preferred. For this reason, learning sets are often balanced by adding some synthetic feature vectors or by reducing the most numerous learning sets. High-dimensional learning sets give possibility to design complex layer of linear classifiers. Such layers can also be used for balancing purposes. In this approach, averaging of a small number of feature vectors is partially complemented by averaging vertices based on balanced feature subsets.
Research on Aircraft Object Detection Based on Deep Learning

Abstract At present, the hot topic in academia is how to extract information in remote sensing images quickly. According to the existing methods, to identify the aircraft targets in remote sensing images more effectively, the RSOD-Dataset annotated by Wuhan University was selected and constructed into PASCAL VOC format in this paper. Faster R-CNN and YOLOv3 were used to test the dataset respectively, and their underlying network structures are studied and the results are evaluated using four evaluation metrics. It was found that the metrics obtained with YOLOv3 were Pr=94.72%, Re=80.25%, AP=89.45%, and mAP=89.45% while the metrics obtained with Faster R-CNN were Pr=49.00%, Re=68.06%, AP=60.59%, and mAP=60.59%. For the dataset of this paper, YOLOv3 can be used to identify aircraft targets in remote sensing images more effectively.
Adaptive Enhancement of High Greyscale Weld Images Based on the New Pixel Self-transformation Method

Abstract X-ray nondestructive testing technology is widely used in the weld detection of oil and gas pipeline industry. The industrial welding manufacturing field produces 12-bit and higher high grey scale images that cannot be displayed directly on an 8-bit display. In this paper, we propose an adaptive pixel self-transformation method for high greyscale weld images. The method takes into account the characteristics of weld images and human perception. Firstly, a high dynamic range tone mapping model is designed, which converts the original RAW data into optical observable data. Secondly, in order to solve the problem that the traditional pixel self-transformation method can only adapt to the input with 256 levels of greyscale and cannot process the high-bit image data. An adaptive high-bit pixel self-transformation is proposed. Finally, the method of this paper was tested on 12-bit X-ray films. The experimental results show that the designed method, which improves the recognizability of weld images and can effectively obtain weld images with outstanding details, is an efficient weld feature enhancement algorithm.
Short Words for Writer Identification Using Neural Networks

In biometrics, it is desirable to distinguish a person using only a short sample of his handwriting. This problem is treated in the present work using only a short word with three letters. It is shown that short words can contribute to high-performance writer identification if line characteristics are extracted using morphological directional transformations. Thus, directional morphological structuring elements are used as a tool for extracting this kind of information with the morphological opening operation. The line characteristics are organized based on Markov chains so that the elements of the transition matrix are used as feature vectors for identification. The Markov chains describe the alternation in the directional line features along the word. The analysis of the feature space is carried out using the Fisher linear discriminant method. The identification performance is assessed using neural networks, where the simplest neural structures are sought. The capabilities of these simple neural structures are investigated theoretically concerning the achieved separability into the feature space. The identification capabilities of the neural networks are further assessed using the leave-one-out method. It is proved that the neural methods achieve identification performance that approaches 100%. The significance of the proposed method is that it is the only one in the literature that presents high identification performance using only one short word. Furthermore, the features used as well as the classifiers are simple and robust. The method is independent of the language used regardless of the direction of writing. The NIST database is used for extracting short-length words having only three letters each.
Exploring the Effects of a Theory-Based Mobile App on Chinese EFL Learners’ Vocabulary Learning Achievement and Memory

This article intends to investigate the effects of using a mobile app on the vocabulary learning achievement and memory of Chinese tertiary EFL learners. 108 non-English majors took part in the study. Moreover, their perceptions of the app were investigated. By analyzing data from a quasi-experiment, the results show that the experimental group (EG) learned vocabulary at an increased rate of 129.65%, while the control group (CG) did so at an increased rate of 68.4%. In addition, a significant difference was found in vocabulary learning achievement between the two groups, indicating that the app effectively improved students’ vocabulary learning. As for remembering the new words, it was discovered that retention of words declined by 11.11% in EG and 27.89% in CG, suggesting that the app was effective in preventing the EG students from rapid memory loss of the words. Among the participants, 72% held a positive attitude toward the app, and 82% expressed that they liked the app in their diaries. Results of this study indicate the usefulness of mobile technology-enhanced L2 vocabulary learning.
Focusing on potential named entities during active label acquisition

Named entity recognition (NER) aims to identify mentions of named entities in an unstructured text and classify them into predefined named entity classes. While deep learning-based pre-trained language models help to achieve good predictive performances in NER, many domain-specific NER applications still call for a substantial amount of labeled data. Active learning (AL), a general framework for the label acquisition problem, has been used for NER tasks to minimize the annotation cost without sacrificing model performance. However, the heavily imbalanced class distribution of tokens introduces challenges in designing effective AL querying methods for NER. We propose several AL sentence query evaluation functions that pay more attention to potential positive tokens, and evaluate these proposed functions with both sentence-based and token-based cost evaluation strategies. We also propose a better data-driven normalization approach to penalize sentences that are too long or too short. Our experiments on three datasets from different domains reveal that the proposed approach reduces the number of annotated tokens while achieving better or comparable prediction performance with conventional methods.
Vulnerability of Clean-Label Poisoning Attack for Object Detection in Maritime Autonomous Surface Ships

Artificial intelligence (AI) will play an important role in realizing maritime autonomous surface ships (MASSs). However, as a double-edged sword, this new technology brings forth new threats. The purpose of this study is to raise awareness among stakeholders regarding the potential security threats posed by AI in MASSs. To achieve this, we propose a hypothetical attack scenario in which a clean-label poisoning attack was executed on an object detection model, which resulted in boats being misclassified as ferries, thus preventing the detection of pirates approaching a boat. We used the poison frog algorithm to generate poisoning instances, and trained a YOLOv5 model with both clean and poisoned data. Despite the high accuracy of the model, it misclassified boats as ferries owing to the poisoning of the target instance. Although the experiment was conducted under limited conditions, we confirmed vulnerabilities in the object detection algorithm. This misclassification could lead to inaccurate AI decision making and accidents. The hypothetical scenario proposed in this study emphasizes the vulnerability of object detection models to clean-label poisoning attacks, and the need for mitigation strategies against security threats posed by AI in the maritime industry.
Effective learning strategies in the 21st century: the concept of A.A. Ozdogru

The article analyzes the section "Revising Effective Learning Strategies for 21st Century Students" of the collective monograph "Theory of Education in the 21st Century" (Springer, 2022), author A.A. Ozdogru. This section presents the author's model of the most effective educational strategies, as a result of which the skills necessary for people living in the 21st century are acquired. Strategies such as digital learning, inquiry-based learning, cooperative and collaborative learning, systems and support for learners, social and emotional learning are consistently considered. A.A. Ozdogru believes that the time has come to integrate these strategies on a global scale, since the tasks of forming new skills have been set in international documents at the global level. The value of the author's model A.A. Ozdogru lies in the fact that this model relies only on those results that have undergone serious empirical testing. The concept of Ozdogru is of particular importance due to the fact that the most effective educational strategies are considered here not separately from each other, but in integration. Thus, the scientific model of Ozdogru is also a research program of pedagogical sciences.
A New Panacea for Supply Chains?

Blockchain technology (BCT) is being actively discussed for application in business contexts to digitalize supply chains (SCs). The current nascent level of BCT adoption in businesses creates resistance for further scalability in the industry. This study explores real pilot cases and experiences of BCT pioneers from various continents and industries, revealing the intentions behind the adoption, feasible improvements, and challenges that need to be further addressed. Findings reveal the business incentives of decentralizing trust constituent and efficiency improvements of data sharing. However, the challenges remain in scaling the adoption to a broader level and guaranteeing the accurate input of data.
The New Approach to the Architecture of Smart Contracts

This research analyzes the problems of smart contracts and the whole concept of smart contracts and provides a solution that can resolve all such the issues in the current smart contract concept and additionally to this change the architecture used in smart contracts and blockchain. In this research, the author lists the issues, analyzes them, and provides the solution explaining how it will change the whole concept, performance, and what sort of positive impact it will have not only on the performance of smart contracts and their extensibility but also the impact on pollution and saving of energy.