Reviewerly/exHarmony huggingface dataset:
Interactive Dairy Goat Image Segmentation for Precision Livestock Farming

Semantic segmentation and instance segmentation based on deep learning play a significant role in intelligent dairy goat farming. However, these algorithms require a large amount of pixel-level dairy goat image annotations for model training. At present, users mainly use Labelme for pixel-level annotation of images, which makes it quite inefficient and time-consuming to obtain a high-quality annotation result. To reduce the annotation workload of dairy goat images, we propose a novel interactive segmentation model called UA-MHFF-DeepLabv3+, which employs layer-by-layer multi-head feature fusion (MHFF) and upsampling attention (UA) to improve the segmentation accuracy of the DeepLabv3+ on object boundaries and small objects. Experimental results show that our proposed model achieved state-of-the-art segmentation accuracy on the validation set of DGImgs compared with four previous state-of-the-art interactive segmentation models, and obtained 1.87 and 4.11 on mNoC@85 and mNoC@90, which are significantly lower than the best performance of the previous models of 3 and 5. Furthermore, to promote the implementation of our proposed algorithm, we design and develop a dairy goat image-annotation system named DGAnnotation for pixel-level annotation of dairy goat images. After the test, we found that it just takes 7.12 s to annotate a dairy goat instance with our developed DGAnnotation, which is five times faster than Labelme.
The Role of Digital Literacy and Generic Skills on Student Physics Learning Outcomes

Digital literacy is a form of literacy that is very important for students to master along with technological developments. This digital literacy can be applied in learning, one of which is in learning physics. The urgency in this study is that it is hoped that the application of digital literacy in physics learning can improve student learning outcomes and generic skills. The purpose of this study was to determine the effect of applying digital literacy in learning physics on the learning outcomes and generic skills of class A and class B students. This study used a mixed methods research type. Then the data in this study were processed using SPSS 26 software to find values from the regression analysis. The results of the regression test function to determine the effect of implementing digital literacy on student physics learning outcomes. Based on the regression test, it is known that sig. 0.05, namely 0.001, which means that there is an effect of applying digital literacy in learning physics on student learning outcomes and 0.05, namely 0.000, which means that there is an effect of digital literacy on generic skills. The conclusion obtained is that there is an effect of applying digital literacy in physics learning to student learning outcomes and generic skills, where in class B the effect is greater in terms of grades or student learning outcomes
IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification

Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are the phenomenon of noise, small disjunct samples, and overfitting due to a high imbalance ratio in a dataset. A high level of imbalance ratio and low variance conditions cause the results of synthetic data generation to be collected in narrow areas and conflicting regions among classes and make them susceptible to overfitting during the learning process by machine learning methods. Therefore, this research proposes a combination between Radius-SMOTE and Bagging Algorithm called the IRS-BAG Model. For each sub-sample generated by bootstrapping, oversampling was done using Radius SMOTE. Oversampling on the sub-sample was likely to overcome overfitting problems that might occur. Experiments were carried out by comparing the performance of the IRS-BAG model with various previous oversampling methods using the imbalanced public dataset. The experiment results using three different classifiers proved that all classifiers had gained a notable improvement when combined with the proposed IRS-BAG model compared with the previous state-of-the-art oversampling methods. Doi: 10.28991/ESJ-2023-07-05-04 Full Text: PDF
Student’s agency in English as Foreign Language learning in a Virtual Environment: An Interaction Analysis

The COVID-19 pandemic brought about new challenges for class interaction and for English as a Foreign Language teaching instruction in general, and opened a whole world of opportunities to learn about virtual-mediated instruction and about learning altogether. As a reaction to the new social conditions, alternative spaces, and ways of mediating instruction through the use of technology and digital resources were created and promoted in the quest for teaching and learning. As such, this multimodal interaction analysis inquired into the qualities of interaction taking place in an online course of advanced English at a teaching credential undergraduate program at a private university in Bogotá, and the affordances and limitations that such interactions presented for agency, engagement, and ultimately, learning as an effect of communication. To such an end, class observations, student focus groups, and interviews on teachers were conducted. The findings offered insights on the multiple ways students and the teacher resorted to their semiotic resources and communicative modes to configure meaning, construct knowledge, and relate to one another in a virtual environment. Mainly, however, it encourages a debate about the role that teachers play in designing and implementing communicative environments to enrich the teaching and learning experience.
The Model of Crowdwork System Based on Blockchain Technology in the Creative Industry

With the increasing number of internet users, the opportunity for the crowdwork system will be wide open for freelancers looking for work and for job providers looking for freelancers for their projects, especially in the creative industry. Trust is an issue in the existing crowdwork system. The users still do not know each other, so malicious actions can arise between users. For example, a job provider rejects work results from workers, even though the work results meet the job provider's expectations. As a result, the job provider receives the results of the work for free. Blockchain technology is here to solve the problem of trust in the crowdwork system. This research produces a model of a crowdwork system based on blockchain technology. The initial stage for building a blockchain-based crowdwork model is determining the components. The seven components found are trust, autonomy, actor, user reputation, management, technology, and business processes. After the components are identified, the next step is to build a value chain model along with business processes based on the components identified in the previous stage. After the value chain is built, the next step is to build a prototype of the blockchain-based crowdwork model. This research is expected to be a reference for other researchers to describe the model design of crowdwork system based on blockchain technology, so that trust issues can be resolved in the crowdwork system.
Analysis of Emotional Changes in Twitter Users by Personality Type Before and After the Outbreak of the COVID-19 Pandemic

Three years have passed since the outbreak of the novel coronavirus pandemic at the end of 2019, and the situation continues to change daily as non-pharmaceutical interventions and associated restrictions are gradually being eased. Even now, drastic changes in aspects of daily living continue to have significant impacts in terms of physical and mental stress on many people. In this study, we analyzed changes in Twitter users’ comments, thoughts, and actions by conducting sentiment analysis of the content of tweets posted before and after the outbreak of the pandemic. We extracted characteristic expressions and conducted a comparative analysis. In addition, we examined changes in emotions according to users’ personality types.
Electromagnetic Situation Awareness Based on Improved STFrFT

How to effectively perceive the distribution of electromagnetic radar radiation sources on the battlefield is of great practical significance for radar jamming systems. To this end, a radar emitter signal type classification technology based on improved Short-term Fractional Fourier Transform is studied. Firstly, establish a multifunctional radar complex modulation signal model. Time frequency structural model. Secondly, an improved STFrFT method is adopted, considered with the comprehensive consideration of the restricts of traditional TFT (time frequency transform) kernel functions, parameters of signal types, Cross-terms of multi-component signals, and distorted signal characteristics. Simulation experiments have shown that the recognition performance of radar complex modulation types with lower signal-to-jam ratio (SJR) using the proposed method is superior to conventional time-frequency analysis methods, verifying the technical rationality of the proposed technique.
Understanding the Clinical Context of Medication Change Events in Clinical Narratives using Pre-trained Clinical Language Models

The ability to understand medication events in clinical narratives is crucial to gaining a comprehensive picture of the patient's medication history. There has been some prior research on identifying medication changes in clinical notes. However, because clinical documentation is longitudinal and narrative, capturing medication changes without the necessary clinical context is not sufficient in real-world applications, such as medication reconciliation and medication timeline generation. In this research, we propose a framework to use multiple clinical-based Bidirectional Encoder Representations from Transformers (BERT) for Contextualized Medication Event Extraction, which is a task to capture the multi-dimensional context of medication changes documented in clinical notes. In addition, the BERT models in the proposed framework infused clinical context-sensitive features into the method to learn the text information around the descriptions of medication. The experiments are conducted by using Contextualized Medication Event Dataset, and the results demonstrate that the proposed method outperforms ClinicalBERT, the state-of-the-art BERT model in the previous study.
Map Classification and Layout

This chapter provides instructions on utilizing various tools in ArcGIS Pro, including data classification, quantitative and qualitative analysis, and the creation of visually appealing maps and layouts. The chapter focuses on applying these techniques to real-world geological and chemical data from the Azraq basin in Jordan. The geological data will be classified using styles imported from ArcGIS desktop and ArcGIS online, resulting in a classified geology feature layer. Additionally, the salinity data in the attribute table will be used to classify the well layer, employing methods such as Natural Break, Quantile, Equal Interval, and Manual classification to produce a salinity map of the basin. The chapter also covers sharing the project using the package project feature, along with instructions on unpacking the project package. Furthermore, the chapter guides the creation of a page layout for the four maps generated through Natural Break, Quantile, Equal Interval, and Manual classification, with the addition of a title, legend, scale bar, and North arrow. Finally, the layout is exported as a PDF file.
Design of Correlated Photonic Time Disorder for Unidirectional Scattering

We propose a theoretical approach for the realization of unidirectional light scattering without spatial patterning, enabled by correlated photonic disorder in time domain. Our study enables novel photonic devices such as spatially pattern-free color filters.
Evolution of Global Digital Forensics Laws and Emergent Challenges

The proliferation of digital devices in the commission of crimes has presented unique challenges to law enforcement the world over. Academia and industry have developed tools to assist law enforcement in conducting digital forensic investigations. However, inadequate awareness of the legal provisions on the part of law enforcement and the absence of updated laws have resulted in situations where, despite the availability of technological support, offenders could not be prosecuted. It would also be productive if academia and industry could become more aware of the legal provisions governing electronic evidence and the legal challenges to technological solutions. This chapter discusses the evolution of global laws pertaining to digital forensics with a focus on the admissibility of evidence derived from digital forensic procedures. The chapter also examines emergent challenges in digital forensics and efforts to enhance global cooperation.
Blockchain Transaction Analysis

Abstract: A wide range of industries, including finance, supply chain, healthcare, and more have used blockchain technology. Blockchain technology is the best platform for performing secure and immutable transactions because of its inherent transparency and security. The anonymity connected to blockchain addresses has however also made it a desirable option for illegal activity. The methodology, difficulties, and practical applications of blockchain transaction analysis are examined in detail in this study in the contexts of security, compliance, and forensics
The Potential Utility of Image Descriptions: User Identity Linkage across Social Networks Based on MultiModal Self-Attention Fusion

The task of user identity linkage across social networks aims to predict whether users from different social networks refer to the same person. This task plays a crucial role in cross-social network information dissemination and intelligent recommendations. However, existing user identity linkage tasks suffer from several challenges: 1) excessive reliance on social network topology, neglecting users’ visual modality information; 2) inadequate handling of noise in user feature data; and 3) ineffective fusion of users’ multimodal information. To address these issues, we investigated a method that utilizes heterogeneous multimodal posts, including user-generated text, images, and check-in messages, to achieve user identity linkage across social networks. We innovatively leveraged a pre-trained model for image-to-text conversion to further explore users’ image data and proposed an adversarial learning model based on the multimodal self-attention mechanism (AMSA). The AMSA model consists of four components: user feature extraction, user feature processing, user feature fusion, and adversarial learning. Specifically, AMSA initially employed advanced pre-trained models to extract features from multiple modalities of users, including images and text. Subsequently, it utilized multiple mechanisms, such as multi-head self-attention, to process data from each modality separately and then fused them into user representation vectors. Finally, AMSA employed adversarial learning to enhance the model’s learning capacity and mitigate semantic disparities in user information across different platforms. We conducted model performance evaluations on publicly available datasets, and experimental results demonstrated the superiority of the proposed AMSA model.
Analisis Biplot Pada Pengelompokan Kecamatan Di Kabupaten Tasikmalaya Berdasarkan Indikator Kemiskinan

Poverty is a social problem that continues to exist in people's lives according to Nurwati, 2008. Therefore, the problem of poverty is the center of attention of the Tasikmalaya Regency government. In the National Long-Term Development Plan (RPJPN) 2005-2025 the problem of poverty is seen in a multidimensional framework, therefore poverty is not only related to income measurement, but related to several things. This is because poverty is not only related to the size of income but involves several things. In the Tasikmalaya Regency Regional Medium-Term Development Plan (RPJMD), the target for achieving the poverty rate in 2021 is 10.23%. Based on BPS publications, there are 10.75% of the population of Tasikmalaya Regency who are categorized as poor, meaning that the Tasikmalaya Regency government's target has not been achieved. So it is necessary to make efforts to overcome the problem of poverty. This study aims to group sub-districts in Tasikmalaya Regency based on the similarity of poverty indicators owned by each sub-district by using biplot analysis. The data used is poverty indicator data for 39 sub-districts in Tasikmalaya Regency in 2021. From the research results it is known that the amount of variation that can be described is 97%, meaning that the plots formed can best describe actual conditions. data information. In addition, three clusters have the same poverty indicators. Cluster 1 contains sub districts that have an indicator in the form of a high student to school ratio in SMA/SMK/MA. Cluster 2 contains sub districts that have moderate to low indicators on all variables except the ratio of SMP/MTs students and the ratio of SMA/SMK/MA students. Meanwhile, Cluster 3 consists of sub-districts that have an indicator in the form of a high ratio of SMP/MTs students.
Video abnormal action detection based on enhanced video Swin transformer

A recognition method based on the enhanced Transformer model is proposed to solve the task of human abnormal action recognition in surveillance videos. Video Swin Transformer (VST) is used to extract video features, and the 3D Adaptive Spatial Pyramid Pooling (3DASPP) module is used to enhance video features. Human body detection is performed on the key frame in the video through the target detection algorithm, and the video features corresponding to the target are extracted. Finally, the human body action category in the video sequence is identified, and whether there is an abnormality is judged. The mean Average Precision (mAP) is used as the evaluation metric. Experimental results show that the proposed algorithm can effectively recognize abnormal human actions in videos, providing strong technical support for intelligent surveillance, intelligent security, and other related fields.
An improved method for partial occlusion face inpainting

Face with mask would lead to a reduction in the number of features that can be extracted from the face, thus severely reducing the accuracy of face recognition. Image inpainting methods can be used to recover the obscured regions. A face image inpainting model based on pix2pix generative adversarial network is proposed for the current blurring problem after face image inpainting. The generator network and discriminator network in the model are trained adversarially to achieve the restoration of obscured faces. Multi-branch residual modules and dilated convolutional layers are added to the generator network as a way to guarantee the boundary consistency of the restored images. In addition, a face dataset wearing masks is constructed on the CelebA dataset. According to the experimental findings, the inpainting result is effective, and both PSNR and SSIM are much better than those of the original pix2pix and other methods.
Reality image super-resolution algorithm based on an improved pipeline processing model

A suppression module is proposed based on pipeline super-resolution (SR) algorithm to address the issues of unstable restoration effect of real images and unsatisfactory details in perception level often generated in traditional image SR. This module segments the original image into several sub-images of the same size and categorizes them into three levels of simple, medium, and hard based on their restoration difficulty. It suppresses the generation of image artifacts and stabilizes the restoration of real details by applying different restoration processes to sub-images of different categories. An artifacts-map is generated based on the observed residual in the artifacts region to regulate and stabilize the model training process. Through experiments, by replacing the original model's simple classification module, more sub-images are processed through a smaller network, and the restoration effect of sub-images of different categories is improved by about 4%, and the overall image restoration effect is improved by 2%.
Transmon-qubit readout using an <i>in situ</i> bifurcation amplification in the mesoscopic regime

We demonstrate a transmon-qubit readout based on the nonlinear response to a drive of polaritonic meters in situ coupled to the qubit. Inside a three-dimensional readout cavity, we place a transmon molecule consisting of a transmon qubit and an ancilla mode interacting via nonperturbative cross-Kerr-coupling. The cavity couples strongly only to the ancilla mode, leading to hybridized lower and upper polaritonic meters. Both polaritons are anharmonic and dissipative, as they inherit a self-Kerr nonlinearity $U$ from the ancilla and effective decay $\ensuremath{\kappa}$ from the open cavity. Via the ancilla, the polariton meters also inherit the nonperturbative cross-Kerr-coupling to the qubit. This results in a high qubit-dependent displacement $2\ensuremath{\chi}&gt;\ensuremath{\kappa},U$ that can be read out via the cavity without causing Purcell decay. Moreover, the polariton meters, being nonlinear resonators, present bistability, and bifurcation behavior when the probing power increases. In this work, we focus on the bifurcation at low power in the few-photon regime, called the mesoscopic regime, which is accessible when the self-Kerr and decay rates of the polariton meter are similar, $U\ensuremath{\sim}\ensuremath{\kappa}$. Capitalizing on a latching mechanism by bifurcation, the readout is sensitive to transmon-qubit relaxation error only in the first tens of nanoseconds. We thus report a single-shot fidelity of 98.6% while having an integration time of 500 ns and no requirement for an external quantum-limited amplifier.
Work-Related Learning Practices of University Graduates: Scope and Determinants

The article analyzes the scale of work-related learning of recent higher education graduates, describes its main practices, determines the factors of participation in training. The empirical analysis is carried out on data from the federal survey of Russian university graduates who completed their studies in 2016-2020. The sample consists of 9325 graduates. About a quarter of recent graduates participated in job-related training in their first year of work. The probability of work-related learning is higher for full-time graduates and for those who are employed as a specialist. It does not depend on whether the graduate works in his specialty or not. Important predictors of participation in work-related learning are the lack of professional skills and the importance of personal (individual) characteristics for successful work. The results showed that work-related learning acts as a tool to overcome the gap between the knowledge and skills that graduates have and the knowledge and skills necessary for the employer, mainly professional content. It was found that participation in work-related training practices is differentiated by qualification groups and by the scarcity of different types of knowledge and skills. On the one hand, formal learning and, on the other, mentoring and self-learning are interchangeable practices. Mentoring and self-learning are in many ways complementary practices of job-related training.