Reviewerly/exHarmony huggingface dataset:
Movement and mineralization of the Kanggur-Huangshan shear zone in the East Tianshan: Implications for regional exploration

中亚造山带在显生宙期间经历了强烈的陆壳增生与改造作用, 其地壳增生过程和成矿作用复杂多样。晚古生代的天山以大规模走滑剪切变形、同构造岩浆和多金属成矿作用为显著特征, 是中亚造山带增生汇聚作用的典型地区。前人的研究主要关注于剪切带起源与演化时限、变形特征或独立矿床的典例剖析, 缺乏针对剪切带演化与成矿时空联系的综合研究。本文选取天山康古尔-黄山剪切带出露的代表性矿床——康古尔金矿和红山南金矿, 开展金矿石中绢云母<sup>40</sup>Ar/<sup>39</sup>Ar定年研究, 并结合前人报道的年代学数据来探讨金成矿与大规模剪切活动的时空联系, 从而揭示区域差异性隆升与成矿作用的耦合关系。定年结果显示, 康古尔金矿的成矿时代为264.8±1.7Ma, 而红山南金矿的成矿时代为261.5±1.5Ma, 与康古尔-黄山剪切带右行走滑运动时间(约279~249Ma)一致。基于康古尔-黄山剪切带西段和东段在区域地质、动力学背景、岩浆作用时间、右行走滑运动的相似性和矿床空间分布的差异性, 本文认为康古尔-黄山剪切带中西段的岩浆镍铜硫化物矿床和(中成至深成)造山型金矿床可能尚未出露, 深部找矿潜力大, 而东段出露深度大, 岩浆铜岩浆镍铜硫化物矿床分布广, 但中-浅成造山带金矿剥蚀殆尽, 深部潜力不足。综合区域构造演化历史, 本文认为康古尔-黄山剪切带的东-西差异是天山-北山三叠纪斜向汇聚所引起的区域差异性隆升导致, 而差异性隆升则是区域内现今构造变形和矿床空间分布分布巨大差异的主要原因。
Enhancing Image Classification and Explainability with Object Isolation and Background Randomization

In this paper, we address the problem of training a model on an image dataset that contains multiple objects that can introduce noise during the training of an image classification model. We propose a method for separating individual objects from the images and synthesizing these separated objects with a random background dataset to generate a new dataset in which each image contains a single, clearly defined object. We use the Attribution mask Compress-Semantic Input Sampling for Explanation (AC-SISE) method, a perturbation-based explainable artificial intelligence (XAI) model, to analyze the explainability of models trained on the previously generated dataset and the original dataset. The experimental results show that the ResNet50 model does not improve the explainability, but the VGG16 model improves the explainability somewhat.
Fusing Multi-scale Residual Network for Skeleton Detection

The skeleton is an important topological description of the object’s geometric form. As an advanced feature, the object skeleton information constitutes an abstract representation of the original shape. Skeleton detection helps further understanding of the object detection and recognition tasks. When processing natural images with complex backgrounds, which often blurred skeleton pixel scale or inaccurate classification. In this paper, we propose a Fusing Multi-scale Residual Network (FMRN) to improve the accuracy of skeleton detection, driven by pre-training the backbone network and adding multi-scale side output in its different stages, we also add the residual module to solve the computational redundancy problem. The atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales and ensure good resolution in feature maps. The experiments were conducted on five open datasets, where the datasets SK-LARGE, SK-SMALL (SK506), and WH-SYMMAX are commonly used for the skeleton detection task. The F-measure score obtained for these three datasets are 0.789, 0.751, and 0.865, respectively. The effectiveness of the method in this paper can be verified by ablation study, and the evaluation protocol are represented by F-measure and P-R curve. The test results showed that our approach has positive extraction accuracy and generalization ability.
W2VC: WavLM representation based one-shot voice conversion with gradient reversal distillation and CTC supervision

Abstract Non-parallel data voice conversion (VC) has achieved considerable breakthroughs due to self-supervised pre-trained representation (SSPR) being used in recent years. Features extracted by the pre-trained model are expected to contain more content information. However, in common VC with SSPR, there is no special implementation to remove speaker information in the content representation extraction by SSPR, which prevents further purification of the speaker information from SSPR representation. Moreover, in conventional VC, Mel-spectrogram is often selected as the reconstructed acoustic feature, which is not consistent with the input of the content encoder and results in some information lost. Motivated by the above, we proposed W2VC to settle the issues. W2VC consists of three parts: (1) We reconstruct feature from WavLM representation (WLMR) that is more consistent with the input of content encoder; (2) Connectionist temporal classification (CTC) is used to align content representation and text context from phoneme level, content encoder plus gradient reversal layer (GRL) based speaker classifier are used to remove speaker information in the content representation extraction; (3) WLMR-based HiFi-GAN is trained to convert WLMR to waveform speech. VC experimental results show that GRL can purify well the content information of the self-supervised model. The GRL purification and CTC supervision on the content encoder are complementary in improving the VC performance. Moreover, the synthesized speech using the WLMR retrained vocoder achieves better results in both subjective and objective evaluation. The proposed method is evaluated on the VCTK and CMU databases. It is shown the method achieves 8.901 in objective MCD, 4.45 in speech naturalness, and 3.62 in speaker similarity of subjective MOS score, which is superior to the baseline.
Wavelet Knowledge Distillation via Decoupled Target for Scene Text Detection

In this paper, we investigate the knowledge distillation strategy for training a compact student model for scene text detection, using a cumbersome teacher model that is too computational to apply on resource-constrained devices. We observed that the frequency domain information of the response map is different between the teacher and student models obviously, which can effectively guide the student model to learn more effective knowledge. Furtherly, we propose a wavelet knowledge distillation method via decoupled target for training accurate compact scene text detection networks. Specifically, we first use discrete wavelet transformation to decompose the probability map into different frequency bands which contain different characteristic components, transferring knowledge in the high-frequency band and low-frequency band respectively. In addition, we decouple the target to enhance the distillation effect of the corresponding region, by separating text and background regions through the ground truth mask. Extensive experiments demonstrate that our method consistently improves the F-measure of the student model and outperforms the other mainstream distillation methods.
Achieving Sustainability Through Smart Home Optimization

The importance of sustainability is increasing in many areas, including our homes. Although the adoption rate of smart home technologies is still lower than anticipated, smart home technologies are making an increasingly significant contribution to energy management, economics, and people’s quality of life. This research investigates the sustainable benefits of smart home technologies on individuals’ perception and behavior by proposing a theoretical model that is based on Technology Acceptance Model (TAM) and the extended unified theory of acceptance and use of technology (UTAUT2) model to determine the key determinants (performance expectancy, effort expectancy, social influence, facilitating conditions and hedonic motivation).
BRMR: TAL Based on Boundary Refinement and Multi-scale Regression

Transformer based networks have been widely used in Temporal Action Localization (TAL). An example of this is the previous state of the art method ActionFormer. By analyzing the predicted results of these transformer based models, we find that though these models are lightweight and powerful, they still have two drawbacks: inaccurate boundary predictions and unreliable confidence in results. Therefore, we propose the Boundary Refinement and Multi-scale Regression (BRMR) model to solve these two weaknesses and improve model performance. The core of BRMR is the RCE Head and MTDR Module. The RCE Head supervises the quality of the predicted boundaries of the action segments during training, allowing the model to have objective evaluation indicators for the quality of the action segments during testing. The MTDR Module synthesizes and extracts the information of Encoders to obtain aggregated feature pyramids with different temporal scales, which is beneficial for the model to perceive the information of actions at different scales. BRMR achieves state of the art on THUMOS14 and EPIC-Kitchens 100. Besides, BRMR has comparable performance on ActivityNet 1.3.
Joint Memory Propagation and Rectification for Video Object Segmentation

Spatio-temporal memory matching-based methods have achieved significant breakthroughs in the task of video object segmentation. Nevertheless, they are deficient in exploiting the long-term temporal correlation of frames by performing pixel-wise memory matching with limited reference frames, and tend to segment similar objects incorrectly. In this paper, we propose two novel modules, memory propagation and rectification modules, that are able to execute the memory reading in all proceeding frames while leveraging long-term temporal smoothness. Specifically, the memory propagation module employs the lightweight ConvGRU architecture to integrate abundant temporal cues from the reference frame sequence. Afterwards, the memory rectification module guided by the similarity between frames is proposed to increase the inter-frame consistency constraint to retrieve reliable memory and transfer them to the current frame. Additionally, we adopt a tiny-capacity spatio-temporal matching module to model the pixel relationship with only two reference frames. The experimental results on DAVIS and YouTube-VOS datasets demonstrate that combining memory propagation and rectification can achieve considerable performance gain and maintain constant inference time and memory usage in the sequence segmentation. To facilitate research in this direction, we release the project code at https://github.com/GDPUCJH/MPRNet .
Fostering Digital Literacy in Higher Education: Benefits, Challenges and Implications

The perception of "literacy" has traditionally been linked to the ability to read and write. However, with the advancement of technology, the scope of literacy has expanded to encompass various other skills and abilities. One significant aspect is digital literacy, which goes beyond computer proficiency and comprises a range of skills essential for effective teaching and learning. Digital literacy plays a crucial role in making information more accessible and respecting the preferences of young learners. In today's increasingly digital world, students and teachers must master digital competence to succeed. Therefore, it becomes imperative to incorporate digital literacy into the curriculum, as it not only contributes to lifelong learning but also serves as a vital competency for future employment. This article presents a literature review on the positive impact of digital literacy and its associated learning outcomes in higher education. It seeks to address the following questions: How does digital literacy, as a powerful technological tool, enhance the teaching-learning process in higher education? Also, what challenges may hinder the successful implementation of various digital-related skills? Through exploring these aspects, this study aims to shed light on the significance of digital literacy in higher education and its potential implications for both educators and learners.
Machine Learning-Powered Identification of Source Code Vulnerabilities

These methods for identifying source code defects face limitations, including false positives and negatives, resource demands, and integration issues in modern software projects. This article explores cutting-edge research in using ML to enhance source code security. As ML gains traction in bug prediction, numerous studies investigate its potential. This research contributes to the growing interest in applying ML to source code, addressing the need for more efficient, accurate, and scalable defect detection methods. By leveraging ML techniques, software development processes can become more robust, reducing vulnerabilities and enhancing overall code quality.
Implementation of Smart Pop Up Book Media to Improve Read-Write Literacy in Children

Literacy improvement is a crucial task that is carried out in both schools and homes, or lingkungan. This is how the government was informed through the national literary festival. Using interactive media can help with literary event execution. Currently, children are becoming more interested in contemporary media or games like smartphones, tablets, and other devices of that nature. However, if children are not aware of their peers' sex, a variety of media might be harmful to them. Due to this, educators conducted media savvy pop-up book evaluations to promote early literacy in children. The purpose of this paper is to understand how to create a media smart pop-up book program for teaching children to read. Based on the mosque, this study was conducted on children aged 7 to 12 in Buntu Batu, Kecamatan Bastem, Kabupaten Luwu, and Sulawesi Selatan. The current study used a kualitatif deskriptif design for qualitative research. The study's findings are based on the development of young children's language literacy, which can improve when using media such as smart pop-up books during instruction. In light of this, it is evident that media smart pop-up books may be used to teach young children and assist them in developing their reading abilities.
Cloud technologies for the development of digital communication competence

The article presents the cloud technologies, especially open-source, necessary and important for the training and development of digital communication skills in pupils and students. The ways in which these technologies can be exploited for the effective development of the mentioned competence are explained. The formulated recommendations are addressed to both educational institutions and teaching staff and the main beneficiaries: pupils and students. One of the main results is the correspondence diagram between the descriptors of digital communication competence (interaction and communication, content sharing, involvement in social life, collaboration, netiquette, digital identity management, communication and collaboration security) and cloud technologies (e-mail, video conferencing, educational platforms, forums, online interactive whiteboards, express assessment applications, speech development platforms, blogs, websites, concept maps, document editing applications) that support the development of these descriptors.
The Efficacy of Using Problem Solving Approach in Teaching Speaking to Indonesian English as Foreign Language (EFL) Students

This research aims at finding out the efficacy of using problem solving approach in teaching speaking especially describing to Indonesian English as Foreign Language (EFL) students. The main question of this research: Is the use of problem-solving approach effective in teaching conditional sentences to Indonesian EFL students? The research can be an addition to the persisting teaching speaking in English through the use of problem solving approach. This research was conducted at First semester students of the English Study Program of Nusa Cendana University, Kupang. The population of this research was First semester students of the English Study Program of Nusa Cendana University, Kupang. The number population is 160 in four classes. The research used a pre-experimental method with a pre-test and post-test design. The pre-test was given to find out the basic ability of the students in mastering speaking and the post-test given to find out the students’ improvement in mastering speaking after giving the treatment by using problem solving approach. The findings show that using problem solving is effective in teaching speaking to Indonesian EFL students. It is supported by the result of the significance test through SPSS 20 program that the P was 0.00. Therefore, it is recommended to use problem solving approach in developing students’ speaking of Indonesian EFL students.
Adversarial Attack for Robust Watermark Protection Against Inpainting-based and Blind Watermark Removers

The rise of social media platforms, especially those focusing on image sharing, has made visible watermarks increasingly important in protecting image copyrights. However, multiple studies have revealed that watermarks are vulnerable to both inpainting-based removers and blind watermark removers. Though two adversarial attack methods have been proposed to defend against watermark removers, they are tailored to a particular type of removers in a white-box setting, which significantly limits their practicality and applicability. To date, there is no adversarial attack method that can protect watermarks against the two types of watermark removers simultaneously. In this paper, we propose a novel method, named Adversarial Watermark Defender with Attribution-Guided Perturbation (AWD-AGP), that defends against both inpainting-based and blind watermark removers under a black-box setting. AWD-AGP is the first watermark protection method employing adversarial location. The adversarial location is generated by a Watermark Positioning Network, which predicts an optimal location for watermark placement, making watermark removal challenging for inpainting-based removers. Since inpainting-based removers and blind watermark removers exploit information in different regions of an image to perform removal, we propose an attribution-guided scheme, which automatically assigns attack strengths to different pixels against different removers. With this design, the generated perturbation can attack the two types of watermark removers concurrently. Experiments on seven models, including four inpainting-based removers and three blind watermark removers demonstrate the effectiveness of AWD-AGP.
Modigiz Application as an Educational Media for Overweight Teenagers

Background: Android mobile applications about nutrition and health are starting to be developed, but only a few develop mobile applications with a discussion of overweight adolescents. Android media with exciting features can be an alternative to increase adolescents' awareness of the problem of obesity.Research Methods: Development of an Android mobile application named "Modigiz," which stands for mobile obesity diet nutrition. The application is developed using the agile software development method and uses its development technology, namely Android Studio, Android SDK, Kotlin, Android Jetpack Library, Github, Figma, and Canva.Research Result: The Modigiz application implements the principle of quick feedback for application users. Users can find nutritional status information about each feature display, and there is feedback for consultations between users and the team via WhatsApp no.Conclusion: The Modigiz application was developed for overweight adolescents to monitor their nutritional status and manage their diet independently. In addition, this application also provides nutrition and health information for users, so it is hoped that it can encourage healthy behavior habits and implement balanced nutrition.
Improve Students' English Vocabulary Using the Smart TTS game App

In learning foreign languages, especially English, students often need help with several obstacles, including requiring more vocabulary mastery. This research aims to improve students' vocabulary using the Smart TTS game application. The research method used in this study is quasi-experimental. The data processing results show that this application effectively improves students' vocabulary
Rancang Bangun Rekapitulasi E-Presensi Dosen PNS-DPK Dilingkungan LLDikti Wilayah II

Higher Education Service Institutions (LLDikti) are government work units in certain areas whose function is to help improve the quality of higher education administration established by the Minister. Higher Education is an educational institution in the form of academics, high schools and universities. Universities consist of educators (lecturers) and students. One of the lecturers' duties is to record the presence and activity of each tertiary institution which affects the salary system that will be received, but the attendance system that is carried out still uses the way to fill in the presence found on the Google form. So that it is necessary to do a design that aims to monitor and design an information system for recapitulating the attendance list of PNS-DPK LLDikti lecturers in Region II and as a basis for paying salaries in accordance with the recap results that have been approved by superiors. The method used in this study is the SDLC method, because the SDLC method is able to adjust the results that the researcher wants to produce. So that the results of attendance reports submitted are appropriate and faster with the integration from PT to LLDikti Region II.
Research of information culture of Kazakhstan secondary school students (on the example of Karaganda, Temirtau and Saran)

This article discusses the results of the study on the information culture of Kazakhstan secondary school students. This study aims to assess the level of information culture of modern Kazakh youth, identify potential risks and threats, develop effective strategies and recommendations for the development of information literacy skills. The data of respondents of the categories “Students of secondary schools” (500 people), “Teachers of secondary schools” (500 people) and “Parents of students of secondary schools” (500 people) are analyzed. Based on the percentage distributions obtained by these categories of respondents, the analytical grouping of the obtained data was carried out under the hypotheses of the study and the analysis of the responses was carried out under the research program. The analysis of the systematized information made it possible to identify the positive and negative sides of the information culture of the main categories of respondents. In particular, the interviewed students have basic skills of working on the Internet, which is the most popular source of information for them. Most schoolchildren are confident in their information retrieval skills and do not demonstrate computer gaming addiction. Most teachers rate the student’s digital literacy, media literacy and information literacy skills highly. However, more than a third of the surveyed students spend over 6 hours a day on the Internet. Teenagers consider the network as an opportunity to communicate, have fun, relax and, to a lesser extent, study, but do not perceive it as a means to create financial, social capital, solidarity, and selfexpression.
BAYESIAN AND NON-BAYESIAN ESTIMATION BASED ON STEP STRESS-PARTIALLY ACCELERATED LIFE TESTING FOR ODD GENERALIZED NADRAJAH HAGHIGHI EXPONENTIAL DISTRIBUTION

Accelerated or partially accelerated life tests are particularly significant in life testing experiments since they save time and cost. Partially accelerated life tests are carried out when the data from accelerated life testing cannot be extrapolated to usual conditions. The step stress-partially accelerated life test is proposed in this study based on Type II censoring scheme and supposing that the lifetimes of units at usual conditions follow the odd generalized Nadarajah Haghighi exponential distribution. For the unknown parameters and acceleration factor, the maximum likelihood estimators are obtained. The balanced square error loss function as a symmetric loss function and the balanced linear exponential loss function as an asymmetric loss function are used to derive Bayes estimators based on informative priors. Finally, the performance of the proposed maximum likelihood and Bayes estimates is evaluated through a simulation study and an application using real data sets.
Influence of dual frequency auditory signals on FitzHugh-Nagumo neuron network

The work is devoted to the study of biology-relevant neural networks, which are called spiking neural networks (SNN), and is aimed at classifying two-frequency auditory signals. We consider an ensemble of FitzHugh-Nagumo neurons. Each neuron is connected to a radiophysical circuit tuned to a certain signal frequency. Different clusters of neurons respond to signals with different ratios of frequencies applied to all circuits simultaneously.