Reviewerly/exHarmony huggingface dataset:
Self-Supervised Consistency Based on Joint Learning for Unsupervised Person Re-identification

Recently, unsupervised domain adaptive person re-identification (Re-ID) methods have been extensively studied thanks to not requiring annotations, and they have achieved excellent performance. Most of the existing methods aim to train the Re-ID model for learning a discriminative feature representation. However, they usually only consider training the model to learn a global feature of a pedestrian image, but neglecting the local feature, which restricts further improvement of model performance. To address this problem, two local branches are added to the networks, aiming to allow the model to focus on the local feature containing identity information. Furthermore, we propose a self-supervised consistency constraint to further improve robustness of the model. Specifically, the self-supervised consistency constraint uses the basic data augmentation operations without other auxiliary networks, which can improve performance of the model effectively. Then, a learnable memory matrix is designed to store the mapping vectors that maps person features into probability distributions. Finally, extensive experiments are conducted on multiple commonly used person Re-ID datasets to verify the effectiveness of the proposed generative adversarial networks fusing global and local features. Experimental results reveal that our method achieves results comparable to state-of-the-art methods.
An Approach to Extract Information from Academic Transcripts of HUST

In many Vietnamese schools, grades are still being inputted into the database manually, which is not only inefficient but also prone to human error. Thus, the automation of this process is highly necessary, which can only be achieved if we can extract information from academic transcripts. In this paper, we test our improved CRNN model in extracting information from 126 transcripts, with 1008 vertical lines, 3859 horizontal lines, and 2139 handwritten test scores. Then, this model is compared to the Baseline model. The results show that our model significantly outperforms the Baseline model with an accuracy of 99.6% in recognizing vertical lines, 100% in recognizing horizontal lines, and 96.11% in recognizing handwritten test scores.
Log4j Vulnerability

Log4j vulnerability was publicly disclosed on December 10, 2021, and it is considered the most severe security breach of all time. Hundreds of millions of devices were vulnerable to attackers executing malicious code remotely on any target device. For that reason, the name Log4jShell was given to the vulnerability. This article is going to talk about the impact that has on the world. Black-Hat Hackers leveraging log4jShell for personal gain. How federal governments, security researchers, security companies responded, and organizations protect themselves. A practical section that shows how a Log4j vulnerability can be discovered and exploited. Showing a methodology to look for log4jShell on a large scope to then explain how to exploit by manual testing in one application.
Predicting the Number of Pedestrians per Street Section: A Detailed Step-by-step Example

There are currently software solutions for estimating pedestrian flow by street section, but they are proprietary and expensive. Moreover, they rely mainly on cell phone technology, or even wifi, and are only applicable in areas on land with good network coverage. The goal of this application article is to show that reasonable results can be obtained for free from a limited number of manual pedestrian flow surveys, taking advantage of OpenStreetMaps data and advances in artificial intelligence. We will show concretely such a realization, step by step, in a neighborhood of Paris, France, and we will discuss the advantages and disadvantages of the approach, as well as future developments.
Research progress of intelligent design of on-chip optical interconnection devices

&lt;sec&gt;Compared with traditional communication technologies such as electrical interconnection, optical interconnection technology has the advantages of large bandwidth, low energy consumption, anti-interference, etc. Therefore, optical interconnection is becoming an important approach and development trend of short distance and very short distance data terminal communication. As the chip level optical interconnection is implemented, silicon on insulator (SOI) based on-chip optical interconnection has been widely utilized with the support of a series of multiplexing technologies. In recent decades, many on-chip optical interconnection devices have been developed by using conventional design methods such as coupled-mode, multimode interference, and transmission line theories. However, when used in device design, these conventional methods often face the problems such as complex theoretical calculations and high labor costs. Many of the designed devices also encounter the problems of insufficient compactness and integration, and single function.&lt;/sec&gt;&lt;sec&gt;Intelligent design method has the advantages such as pellucid principle, high freedom of optimization, and good material compatibility, which can solve the problems of conventional design methods to a large extent. With the widespread use of intelligent design methods in the design of on-chip optical interconnection devices, three main trends have emerged. Firstly, the size of on-chip optical interconnect device is gradually developing towards ultra compact size. Secondly, the number of intelligently designed controllable on-chip optical interconnect devices is increasing. Thirdly, on-chip optical interconnect devices are gradually developing towards integration and systematization. This paper summarizes the most commonly used intelligent design methods of photonic devices, including intelligent algorithms based intelligent design methods and neural networks based intelligent design methods. Then, the above three important research advances and trends of intelligently designed on-chip optical interconnection devices are analyzed in detail. At the same time, the applications of phase change materials in the design of controllable photonic devices are also reviewed. Finally, the future development of intelligently designed on-chip optical interconnection devices is discussed.&lt;/sec&gt;
Multilabel Classification for News Article Using Long Short-Term Memory

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited when there is small labeled data and leads to the difficulty of capturing semantic relationships. In this case, it requires a multi-label text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multi-label text classification techniques. By comparing the seven proposed Long Short-Term Memory (LSTM) models with large-scale datasets by dividing 4 LSTM models with 1 layer, 2 layer and 3-layer LSTM and Bidirectional LSTM to show that LSTM can achieve good performance in multi-label text classification. The results show that the evaluation of the performance of the 2-layer LSTM model in the training process obtained an accuracy of 96 with the highest testing accuracy of all models at 94.3. The performance results for model 3 with 1-layer LSTM obtained the average value of precision, recall, and f1-score equal to the 94 training process accuracy. This states that model 3 with 1-layer LSTM both training and testing process is better. The comparison among seven proposed LSTM models shows that model 3 with 1 layer LSTM is the best model.
State transfer and entanglement between two- and four-level atoms in a cavity

Qudits with a large Hilbert space to host quantum information are widely utilized in various applications, such as quantum simulation and quantum computation, but the manipulation and scalability of qudits still face challenges. Here, we propose a scheme to directly and locally transfer quantum information from multiple atomic qubits to a single qudit and vice versa in an optical cavity. With the qubit–qudit interaction induced by the cavity, our scheme can transfer quantum states efficiently and measurement-independently. In addition, this scheme can robustly generate a high-dimensional maximal entangled state with asymmetric particle numbers, showing its potential in realizing an entanglement channel. Such an information interface for qubits and qudit may have enlightening significance for future research on quantum systems in hybrid dimensions.
The Phenomenon of Hatred and Counter-discourse in Contemporary Japan: From the Perspective of Multiple Discrimination and Intersectionality

This paper aims to examine the phenomenon of hatred in contemporary Japan from the perspectives of ‘multiple discrimination’ and ‘intersectionality’ and to consider the possibility of counter-discourses to resist the spread of hate. Since the 2010s, Japan has witnessed an eruption of compounded hatred against racial and ethnic minorities, women and sexual minorities, and socially vulnerable people. Hate-related research and discourse in Japan have developed in response to this phenomenon, and anti-hate discourse requires a transversal approach to respond to the complexity of the phenomenon. Anti-hate discourses and movements in Japan have been accumulating based on the concept of ‘multiple discrimination’ and are currently connected to discussions on ‘intersectionality’. However, the potential of the intersectionality debate in Japan is ambivalent, as it does not fully reflect the experiences and voices of minorities in Japan.
The Influence of ArcGIS Story Maps Learning Media on Students' Understanding of the Geographical Location of ASEAN Countries (Quasi Experiment Grade 8 at SMPN 29 Bandung)

In the current era of ICT (Information and Communication Technology) development, learning media is undergoing transformation in its application. Teachers can use ICT in the application of varied and interactive media to help increase students' understanding of social studies materials such as ArcGIS Story Maps which can provide concrete visualization so that students will understand social studies material better. The purpose of the research was to analyze the influence of the application of ArcGIS Story Maps media about the Geographical Location of ASEAN Countries on the understanding of grade 8 students of SMPN 29 Bandung. This research uses quantitative approaches and experimental methods with quasi-experimental design, precisely non-equivalent control group design. Based on the results of the study, it was found that both in the experimental and control classes there were differences in students' understanding in the pre-test and post-test. If the two classes are compared, then in the pre-test there is no difference in understanding between the experimental and control classes with Asymp.Sig. (2- tailed) of 0.939 &gt; 0.05, but in the post-test there is a difference in understanding with Asymp.Sig. (2- tailed) of 0.045 &lt; 0.05. The N-Gain value of student comprehension is 1,622 &gt; 1, indicating that there is a difference in understanding where learning using ArcGIS Story Maps media is more able to increase student understanding than learning with interactive Power Point. Suggestions from this study, social studies teachers can develop ArcGIS Story Maps media in the social studies learning process that is tailored to learning needs.
A Bibliometric Analysis of the Last Ten Years of Fuzzy Min-Max Neural Networks

Neural networks have been widely used in many application areas such as power systems, weather forecasting, face recognition, behaviour analysis, fingerprint identification, healthcare, fault detection, flood monitoring system, and surveillance system, and navigation. However, classic neural network methods are insufficient for real-life applications. To tackle with this problem, using the learning capability of neural networks and deduction capability of fuzzy systems, neural networks and fuzzy sets are incorporated called as NeuroFuzzy. The Fuzzy min-max neural network (FMNN) is a special type of NeuroFuzzy. In this paper, a bibliometric analysis is conducted on FMNN literature. We consider the studies that are published in the last decade due to there is a jump in this field in the last 10 years. Social network analysis results show that Chee Peng Lim, is the most influential researcher in the network. The Neurocomputing is found to be the most influential journal, publishing 12% of all publications in this field. In addition, the International Conference on Computing, Communication, and Networking Technologies is the most influential conference on FMNN. The findings of this paper can draw a road map for researchers in the FMNNs.
Applied Large-Scale Group Decision Making Using Systemic Consensus and Fuzzy Method of Comparative Linguistic Expressions

The complexity of democratic public decision-making has augmented with the surge of volumes of information. One response is using digital tools in decision-making processes to enable more flexibility than the conventional yes or no responses. We proposed a solution for a large-scale democratic decision-making process using soft computing and fuzzy logic, based on systemic consensus and action design research. Our integrative fuzzy decision-making process was designed to allow the consideration of all the arguments, the deliberation of all the alternatives, and the assessment of each alternative using comparative linguistic expressions. The method was used to resolve a conflict-generating traffic problem in Geuensee, a municipality in the Swiss canton of Luzern. The citizens voted on two dimensions of resistance and support about each of the proposed alternatives. The results were computed using fuzzy membership functions and a fuzzy logic table, evaluated with different computational variants. The output was a ranking of the best options, as assessed by the decision-makers. We found that this method for smart participation of citizens was accepted and generated involvement, leading to an effective outcome for the decision-makers. In the last section, we discuss evaluation and ethical considerations.
The Level of Job Satisfaction among Private School Teachers at Irbid Governorate

he study aims to identify job satisfaction levels among private school teachers at Irbid governorate. The sample of the study consisted of (202) private school teachers who works in Qasabt Irbid and were selected using simple random method. To achieve the study objective, descriptive analytical method was used. The study results showed that the job satisfaction level of private school teachers was moderate. It also showed no statistically significant differences for the job satisfaction levels among private school teachers in light of qualification, years of experience and educational level. In light of the results, the study recommends to search for the appropriate methods which can be used to increase job satisfaction levels among private school teachers.
The Formation Process and Influence of the Constitution's Concept of the Nation

The nation are the subjects of enactment of the constitution and members of the national community. According to the Constitution, the nation become the subject of basic rights and participate in the political process. The Constitutional Assembly enacted the Nationality Act as a criterion for becoming a citizen to clarify the identity of the state and maintain continuity. Since citizens have various rights and obligations from their status, the criteria for determining nationality are very important. Since the Constitution of Korea had a strong national state tendency, the Nationality Act adopted ‘jus sanguinis’. However, it was intended to establish an open principle for the Korean people in foreign countries, which also appeared as a policy for overseas Koreans. In this study, we tried to normatively analyze how the term “nation” was adopted in the Constitution, what is the meaning of the term “nation,” and the process of determining the requirements for becoming citizens in the Nationality Act. Many drafts of the Constitution tended to use people's terms rather than nation. In particular, when expressing the subject matter of sovereignty or members of the national community, there was a tendency to use the entire nation, the entire people, and the Korean people. However, in the case of nationality as a component of the state, the national term was always used. In other words, it was confirmed that the people were an expansionary concept as a concept based on ethnicity, but the nation were a relatively narrow concept as a concept of the nation determined by the Nationality Act. This can be said to be the result of comprehensive consideration of the division of the two Koreas, the return of compatriots, the arrangement of family registers, and the exercise of the right to vote. Considering the forward-looking attitude toward compatriots at the time of the enactment of the Constitution, it was considered necessary to take improved measures against overseas Koreans even at this point.
Beidou Short Message Communication Encryption Scheme with Improved SM4 Algorithm

Beidou short message function is widely used in all walks of life, and the security of its data transmission has become particularly important. Three-party hijacking, resulting in compromised data confidentiality and integrity. In response to this problem, this paper proposes a Beidou short message encryption scheme that optimizes the SM4 algorithm, which can solve the security problem of Beidou short message data transmission to a certain extent.
Machine Vision for Automatic Recognition of Aircraft Engine Blade Coding

Aircraft engine blade coding plays a crucial role in various aspects such as blade production, manufacturing, and utilization. However, aircraft engine blade coding tasks heavily rely on human recognition, which is susceptible to fatigue and increases the likelihood of identification errors. this paper presents a machine vision-based system for recognizing aircraft engine blade coding. The system begins by capturing coding images using a high-resolution camera and performing pre-processing. Subsequently, a deep learning model is employed for initial character recognition, followed by the selection and matching of corresponding templates based on the predictions of the deep learning model. The recognition results are evaluated for confidence, and when the confidence falls below a predefined threshold, manual intervention is performed to provide the correct coding. This intervention also aids in expanding the training set and matching templates for the deep learning model. Trial results demonstrate that by constructing a training set comprising 582 sample characters and 349 generic characters and pre-training a character recognition model, with an initial condition of a matching template containing 1752 characters, and after approximately 30 rounds of manual intervention, an algorithm with a confidence level above 95% and an accuracy rate above 96% is achieved. The research findings indicate the significant practical value of combining deep learning and template-matching algorithms in aircraft engine blade coding recognition and provide insights for similar application scenarios. This study offers a highly practical solution for aircraft engine blade coding recognition and holds promise for providing a reliable and practical solution in this domain.
Study on Hyperspectral Remote Sensing Images of GF-5 De-Blurring Based on Sparse Representation

Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out.
Data Augmentation for Medical Image Classification based on Gaussian Laplacian Pyramid Blending with a Similarity Measure

Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new possibilities for addressing the challenges of labeled data scarcity and accurate prediction in critical applications. However, a lack of diversity, as well as unrealistic and unreliable data, have a detrimental impact on performance. Therefore, this study proposes an augmentation scheme to address the scarcity of labeled data and data imbalance in medical datasets. This approach integrates the concepts of the Gaussian-Laplacian pyramid and pyramid blending with similarity measures. In order to maintain the structural properties of images and capture inter-variability of patient images of the same category similarity-metric-based intermixing has been introduced. It helps to maintain the overall quality and integrity of the dataset. Subsequently, deep learning approach with significant modification, that leverages transfer learning through the usage of concatenated pre-trained models is applied to classify breast cancer histopathological images. The effectiveness of the proposal, including the impact of data augmentation, is demonstrated through a detailed analysis of three different medical datasets, showing significant performance improvement over baseline models. The proposal has the potential to contribute to the development of more accurate and reliable approach for breast cancer diagnosis.
Development and validation of a deep learning algorithm for pattern-based classification system of cervical cancer from pathological sections

BackgroundMulti-center research has demonstrated that adopting Silva's pattern-based classification system (SPBC) enhances the clinical prognosis and facilitates hierarchical management of patients with endocervical adenocarcinomas (EAC). However, inconsistencies in SPBC can arise due to variations in pathologists' experience levels. Thus, the implementation of standardized decision-making tools becomes crucial to enhance the practicality of SPBC in clinical diagnosis and treatment.MethodsWe enrolled a total of 90 patients with EAC in this study, of which 63 were assigned to the training group, and the remaining 27 were allocated to the validation group. To create and validate the prediction models for SPBC, we utilized a deep learning system (DLS) and calculated the area under the receiver operating characteristic curve (AUC).ResultsIn Silva pattern classification, ResNet50 achieved an average accuracy of 74.36% (63.64% for pattern A, 55.56% for pattern B, and 89.47% for pattern C respectively). Moreover, in test set, ResNet50 achieved an AUC of 0.69 for pattern A, 0.58 for pattern B, and 0.91 for pattern C.ConclusionsWe successfully established a DLS for SPBC, which holds the potential to aid pathologists in accurately classifying patients with EAC.