Reviewerly/exHarmony huggingface dataset:
Optical character recognition on engineering drawings to achieve automation in production quality control

Introduction: Digitization is a crucial step towards achieving automation in production quality control for mechanical products. Engineering drawings are essential carriers of information for production, but their complexity poses a challenge for computer vision. To enable automated quality control, seamless data transfer between analog drawings and CAD/CAM software is necessary. Methods: This paper focuses on autonomous text detection and recognition in engineering drawings. The methodology is divided into five stages. First, image processing techniques are used to classify and identify key elements in the drawing. The output is divided into three elements: information blocks and tables, feature control frames, and the rest of the image. For each element, an OCR pipeline is proposed. The last stage is output generation of the information in table format. Results: The proposed tool, called eDOCr, achieved a precision and recall of 90% in detection, an F1-score of 94% in recognition, and a character error rate of 8%. The tool enables seamless integration between engineering drawings and quality control. Discussion: Most OCR algorithms have limitations when applied to mechanical drawings due to their inherent complexity, including measurements, orientation, tolerances, and special symbols such as geometric dimensioning and tolerancing (GD&amp;amp;T). The eDOCr tool overcomes these limitations and provides a solution for automated quality control. Conclusion: The eDOCr tool provides an effective solution for automated text detection and recognition in engineering drawings. The tool's success demonstrates that automated quality control for mechanical products can be achieved through digitization. The tool is shared with the research community through Github .
THE EFFECT OF THE USE OF VOLUME CONSERVATION TOOLS ON STUDENT'S LEARNING OUTCOMES

Education is the main factor that must be improved in quality, the progress of the civilization of a society or nation will lie in the level of education. To improve the quality of education in this case it is necessary to increase student learning outcomes, in studying science to achieve these goals, in the teaching and learning process there must be adequate situations and conditions as well as appropriate methods in learning that can have a positive effect on learning effectiveness in achieving goals. education. The method used in this study is a quantitative method, where the final score of the variable is in the form of numbers which are then processed using a statistical formula to produce a value. The population in this study were all students of class VIII at SMP Negeri 1 Padangcermin and the samples in this study were 40 students of class VIII A and 40 students of class VIII B of SMP Negeri 1 Padangcermin. To find out the results of this study, it was done by giving an essay test. From the results of the study, it can be concluded that there is an effect of the use of conservation of volume teaching aids on learning outcomes in class VIII even semester of SMP Negeri 1 Padangcermin where the average learning outcomes of students who use conservation of volume teaching aids are higher than the average learning outcomes of students who do not use props. volume conservation prop. Thus, learning that is accompanied by the use of conservation of volume props has a positive effect or can improve student learning outcomes.
UnTiCk: Unsupervised Type-Aware Complex Logical Queries Reasoning over Knowledge Graphs

For the task of answering complex logical queries on large-scale incomplete knowledge graphs, the promising approach is to embed the knowledge graph and complex logical queries into a low-dimensional space and perform iterative reasoning to find the final answers. The general problem is that these models do not include entity types as an important feature, which reduces the reasoning potential. However, explicit type information is not always available on large-scale knowledge graphs. We innovatively propose an embedding-based framework for Unsupervised Type-Aware Complex Logical Queries (UnTiCk). Our approach implements unsupervised type constraints on multi-hop complex logical query processing. Moreover, it can capture the different representations of type features when entities are in different locations in the logical path. We designed type compatibility measurement meta-operators combined with popular Existential Positive First-Order (EPFO) neural logical operators to achieve type-aware EPFO complex query embedding. We validated the effectiveness of our framework on popular large-scale knowledge graphs by using the same embedding dimensionality as complex logical embedding methods. The results showed an average relative improvement of 1.9–12.8% on Hit@3 and up to 42.1% on the certain logical pattern.
Consistency and stability of gaze behavior when reading manga

Abstract Manga, a Japanese comic, conveys contextual information about underlying stories based on the expression of a mixture of textual and pictorial elements. Two experiments were designed to assess whether individuals' eye movements when reading manga were consistent, independent of the specific materials, and stable over time. Experiment 1 examined the consistency of eye‐movement parameters when participants read two distinct manga works and a narrative text and demonstrated that the parameters were consistent among the different reading materials. Experiment 2 provided evidence of the stability of parameters over time (i.e., an average interval of approximately 3 months). In summary, eye‐movement parameters observed during manga reading were reliable stability indices over time and consistency among diverse materials. These findings imply that gaze behavior during manga reading could be an effective methodology for investigating individual differences in various aspects of cognitive processes, such as face perception and story comprehension.
BIBLIOMETRIC COMPUTATIONAL MAPPING ANALYSIS OF PUBLICATION ON Bi2O3 NANOPARTICLES USING VOSVIEWER

This study examines the development of research on bismuth oxide (Bi2O3) nanoparticles through a bibliometric approach with computational mapping analysis using VOSviewer. Journal article data was obtained from the Google Scholar database using the Publish or Perish application. The keywords used to search related journals are Bi2O3, nanoparticles, chemistry, bismuth oxide. There were 998 journal articles related to keywords. The range of journal articles searched from the Google Scholar database is the last 10 years (2012 – 2022). The results show that research Bi2O3 nanoparticles can be separated into 3 terms: nanoparticle, Bi2O3, bismuth oxide. Nanoparticle term which is included in cluster 3 with 186 links total, 812 total link strength, and 163 occurrences. The second term is Bi2O3 which belongs to cluster 7 with a total of 213 links, a total link strength of 1550, and occurrences of 189 and a bismuth oxide term which belongs to cluster 4 with a total of 230 links, a total link strength of 2080, and 401 occurrences. The results of the analysis of research developments in the last 10 years show a slight fluctuation. In 2012 – 2013 there was an increase in the number of studies from 34 to 62 (28 studies). However, in 2013 – 2016 it decreased from 62 to 53 (9 studies). In 2016 – 2022 again experienced a significant increase from 53 to 152 (99 studies). With the results of this study, the development of research on Bi2O3 nanoparticles shows that this research has great opportunities that can be explored again.
ANALISA KEGIATAN BERHITUNG PADA ANAK DENGAN MEDIA SPINDLE BOX DI TK AISYIYAH 18 ASEMJAJAR

Early childhood education is useful as a stimulation for children in accordance with the growth characteristics of children at their age. Age 4-5 years is the age when children begin to recognize symbols in the form of numbers from one to ten. Therefore, early childhood education, especially in formal education such as in Aisyiyah 18 Asemjajar Kindergarten, must be able to improve the learning of arithmetic activities. Spindle Box is an educational game tool for children that will be implemented in Aisyiyah 18 Asemjajar Kindergarten to improve counting activities. This research was conducted on 18 children in class A1 (9 boys and 9 girls). Research activities were carried out for three days with predetermined steps. Based on the results of the study, the spindle box succeeded in increasing children's numeracy activities and the children became more enthusiastic so it can be concluded that the APE spindle box has a positive influence on children's numeracy activities in Aisyiyah 18 Kindergarten Asemjajar
An Efficient and Accurate Encrypted Image Retrieval Scheme via Ball Tree

With the rise and development of cloud computing, more and more companies try to outsource computing and storage to cloud in order to save storage and computing cost. Due to the rich information contained in images, the explosion of images is booming the image outsourcing. However, images may contain a lot of sensitive information and cloud servers are always not trusted. Directly outsourcing may lead to data breaches and incur privacy and security concerns. This has partly led to renewed interest in privacy-preserving encrypted image retrieval. However, there are still many challenges, such as low search accuracy and inefficiency due to the hundreds of high dimensional features extracted from a single image and the large scale of images. To address these challenges, in this paper, we propose an efficient, scalable and privacy-preserving image retrieval scheme via ball tree. First, the pre-trained Convolutional Neural Network (CNN) model is employed to extract image feature vectors to improve search accuracy. Next, an encrypted ball tree is constructed by using Learning With Errors(LWE)-based secure k-Nearest Neighbor (kNN) algorithm. Finally, we conduct comprehensive experiments on real-world datasets and give a brief security analysis. The results show that our scheme is practical in terms of security, accuracy, and efficiency.
Pairing based cryptography New random point exchange key protocol

Piaring based cryptography (PBC) is one of the latest cryptographic protocols, PBC is based on pairing functions that are mapping two points on an elliptic curve into a finite field, pairing is a non-degenerate bilinear map. The most practical property of PBC is bi-linearity: In this work, we will show that the bi-linearity property above is also a pairing, and we will see how to use this application with our input points <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> and Q in order to randomize these points to become more secure, and how to return to our original points <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> and Q, we will present the protocol to show how random point keys can be exchanged, and also the algorithm for calculating these keys, and finally we will give a brief comparison between our method and some other exist method.
Medication Prediction Using Memory Augmented Heterogeneous Information Fusion Network

Medication prediction plays an important role in the intelligent clinical decision support systems, which could not only improve the healthcare efficiency, but also ensure the treatment quality especially for intensive care unit patients. However, it is challenging to develop such an intelligent application due to the longitudinal and heterogeneous nature of electronic medical records (EMR), including dynamic data (e.g., laboratory examination results) and the static information (e.g., demographics). To that end, in this paper, we propose a novel memory augmented heterogeneous information fusion network to capture the complicated correlations of heterogeneous medical records and concurrently integrate them into patient representation according to their respective importance. Specifically, a memory augmented heterogeneous LSTM is incorporated to capture the temporal correlations of laboratory examination results by a sequential learning network, and model the interactions between the memory databases of hidden states and the memory database of medication representations via a novel co-general attention mechanism. Afterward, a heterogeneous information fusion module is presented to integrate the obtained heterogeneous information representations and explore the contribution of each heterogeneous EMR data. Finally, we conduct the experiments on a real-world MIMIC-III dataset with 11 disease conditions, and find that the proposed prediction model can achieve the superior prediction performances.
On The Understanding of Customer Purchase Behavior: A Case Study on the Computer Systems E-Commerce Website

The tremendous growth of the E-commerce industry over the last decade drew attention to the importance of understanding customers' behaviors and modeling user-system interactions to maximize customers' satisfaction by providing them the best user experience and gaining new customers to the e-commerce website. Within the scope of this study, software architecture _that can help us understand customers' purchase behaviors and then make a binary prediction based on their interactions with the system to determine whether a computer configuration will be bought_ is proposed and explained thor-oughly. For this purpose, machine learning algorithms are utilized to correctly model complex user-system interactions. A prototype of the proposed software architecture is developed using the latest technologies, and experimental studies are performed on top of this prototype in order to understand how successful our software architecture is. The conducted experimental results prove the efficiency of the proposed software architecture in real-world data, with 83% and 88% scores for the accuracy and F1-score metrics, respectively.
Into strain mapping of structural components

The capacity of a steel cross-sections is determined by the maximum stresses that develop during the load application. If a connection or a cross-section with inclined walls are the research subject, the stresses have an arduous distribution. As stresses appear correlated to strains, their measurement becomes the focus point. Strain gages are inappropriate to be used due to uncertainty of the position of maximum strains and limited local information. Strains can be conveniently monitored using Digital Image Correlation for which, if the modulus of elasticity of the materials is known, the corresponding stress can be determined. Digital Image Correlation represents a technique which is influenced by the initial settings and surface preparation. Some of the impediments that can be experienced while processing the recorded images are presented from two experimental tests, i.e. (i) an in-line connection for Rectangular Hollow Section, and (ii) a custom built-up steel cross section with inclined parts of the web. The study highlights the benefits of using DIC in monitoring specific criteria i.e. separation of the elements in the connection and different plastic deformation areas in the built-up beam cross-section, respectively. Only 40% of the maximum capacity of the connection is reached for the separation serviceability criterion. More than 50% of the theoretical capacity can be attained by the hollow flange beam due to the plastic strains developed in the beam.
Communication is Verification: IoT Identifier System Using Device Fingerprints

With the gradual development of the Internet of Things, people rely more and more on the Internet of Things devices to assist their daily lives. The permissions of IoT devices make it easy for them to rescue or destroy our lives. This paper proposes a new scalable IoT security framework (ISDF), which uses the unforgeable physical layer characteristics of IoT devices as the verification basis to identify illegal devices. This framework combined with the identifier generation algorithm to generate a unique trusted identifier. Under the verification of more than a dozen devices such as bluetooth, wifi, and voice, the identifiers we generate have high robustness and stability, the accuracy of verification is more than 93%. Compared with existing solutions, our framework has the advantages of supporting multimodality and high verification success rate.
Cloud Resource Demand Prediction Based on VMD-GRU-TCN Hybrid Model

With the rapid development of cloud computing and data centers, the demand for cloud computing resources is constantly changing, and resource allocation has gradually become one of the most challenging problems in the cloud environment. Oversupply increases energy waste and costs, while undersupply, on the other hand, leads to violations of Service Level Agreements(SLAs) and reduces Quality of Service(QoS). Therefore, in order to cope with the changing cloud resource requirements, resource prediction models are required to provide reasonable accuracy. Traditional statistical models are simple in structure, and most of them can only deal with stationary series. In view of the relatively complex sequence of resource occupation in the cloud environment, which is nonlinear, non-stationary, noisy, and uncertain, it is difficult to capture its stable state. In this paper, we adopt a neural network model to solve this problem. First, the sequence is decomposed by variational mode decomposition(VMD), and multiple sub-sequences are preprocessed by sliding windows. The Gated Recurrent Unit(GRU) based on attention mechanism is used to preliminarily obtain the data characteristic information, and global features are extracted with Temporal Convolutional Network(TCN) by dilation convolution. Then, the comprehensive feature information extracted from the two networks with different structures is fused to predict the resource occupancy rate at a future time. For three datasets with different characteristics, the model reduces the value of the MAPE indicator by 0.442%, 4.764% and 2.331%. Experimental results show that the proposed method improves the accuracy of resource prediction, and is superior to existing models in MAPE, RMSE, and R2 evaluation criteria.
Adversarial Examples Detection of Electromagnetic Signal Based on GAN

In the field of signal modulation classification, deep neural networks (DNNs) perform very well and have good generalization ability. However, the DNN model is extremely vulnerable. The model gives a false output with high confidence from an input example, which formed only by deliberately adding tiny disturbances. Such elaborate examples that make the model misclassify are called adversarial examples. In this paper, for the detection of adversarial examples in electromagnetic signals, we propose a method based on generative adversarial networks (GANs), a novel strategy to defend deep neural networks against such attacks. First of all, by experiment we came to the conclusion that the confidence of the DNN model for normal examples is stable, and the confidence for adversarial examples is unstable. We trained a classifier that performs well in extracting electromagnetic signal features. Then, We feed the normal examples to the generative adversarial network, the generator generates examples with similar data distribution to the clean examples. We send such pairs of examples to the classifier to calculate a threshold T, and the same operations will be performed on the test examples. Finally, the absolute error E of each pair of test examples will be calculated and compared with the threshold T to achieve electromagnetic signal adversarial examples detection. We define the method in the paper as Detect-GAN. The experimental results show that Detect-GAN can effectively defend against common and powerful attack algorithm. Comparing with other detection methods, Detect-GAN achieves more competitive performance.
GAN-based Differential Privacy Trajectory Data Publishing with Sensitive Label

With the development of location awareness technology, trajectory data is easy to be collected and applied to urban transportation, mobile medical care, etc. However, trajectory data is usually collected with some sensitive attributes such as disease and age, which may contain a large amount of users' private information. If these attributes are directly published without any privacy preservation, there will be a risk of privacy leakage. Differential privacy as a robust privacy protection model independent of any background knowledge attack has been widely used to protect trajectory data privacy and reduce privacy leakage. However, there are still some problems to be solved. On the one hand, most existing schemes based on differential privacy only can anonymize the trajectory data without considering sensitive labels (sensitive attributes). On the other hand, unified noise is added to the trajectory data even though some trajectory points may not cause any leakage, which reduces the overall utility of published data conversely. In this paper, we aim to achieve the trajectory privacy based on differential privacy by adopting Generative Adversarial Network (GAN). In our scheme, sensitive locations including key stop points and abnormal points which may cause the privacy disclosure will be determined. We design a graph model to effectively capture the relationship between the sensitive attributes and the corresponding sensitive points. Laplace noise is added to the graph, while the privacy budget is trained by GAN to balance data privacy and data utility. Experiments on real data sets can verify that our proposed scheme can achieve more privacy while possessing better utility.
Expansion-Squeeze- Block: Linear Over-parameterization with Shortcut Connections to Train Compact Convolutional Networks

We propose a new structure called Expansion-Squeeze-Block by leveraging over-parameterization to train given compact neural networks. The structure expands the width of convolutional layers and adds shortcut connections for better performance without adding any nonlinearity. The expanded networks can be contracted back to the original format algebraically at inference time without loss of information. In addition, we introduce a new initialization method utilizing the weights of the pretrained original networks, which can further improve the accuracy. We evaluate our methods on CIFAR-100 and Tiny-ImageNet datasets to show their effectiveness. As evidenced by our experiments, Expansion-Squeeze-Block outperforms the baselines in most cases with average 0.81% improvements and 1.15% combined with our initialization method on CIFAR-100 and Tiny-ImageNet datasets. We also demonstrate the effectiveness of the partial expansion strategy by choosing the layers of the highest importance in VGG11-BN0.5x and ResNet20. We can save on average 58.82% #MACs and 38.82% #Params than expanding all layers.