Reviewerly/exHarmony huggingface dataset:
A Mobile Application for Domestic Violence Victims Support and Evidence Recorder

ccording to a study conducted in 2014 by Pusat Penyelidikan Wanita dan Jantina (KANITA) at Universiti Sains Malaysia (USM), 9% of women who have ever had a partner in Peninsular Malaysia had domestic violence at one compilation in their lives. In 2010, there were 3,173 cases of domestic violence reported. However, this number increased to 5,014 cases in 2015. According to the Corporate Communication division of Kementerian Pembangunan Wanita, Keluarga dan Masyarakat, calls to Talian Kasih have increased by 57 percent since Perintah Kawalan Pergerakan (PKP). However, calls and WhatsApp messages to the Women’s Aid Organization (WAO) helpline were reduced to 73% of calls a week during the PKP. Before PKP were enforced, WAO received an average of 77.5% of calls and message via WhatsApp in a week. This reduction does not mean that there is a reduction in cases because it is feared that the victim will not be able to make calls due to the situation where they are always with the abuser during the period of PKP. This study aims to develop a mobile application to aid and guide people facing domestic violence. The primary function of this application is to help the victim; 1) to record their evidence in audio/video recording, 2) to help the victim to alert the polis in case of emergency, 3) to get help and find the nearest shelter whenever needed. This mobile application is developed using the Flutter framework. Firebase is used as the back-end component to store all the records. The result of this study is the improvement of the existing mobile application for domestic violence in Malaysia.
Analisis Efektivitas Pengelolahan Dana Bantuan Operasional Sekolah (BOS) Di SMA Negeri 1 Telaga Kabupaten Gorontalo

The purpose of this study is to analyze the effectiveness of the manement of School Operational Assistance (BOS) funds. The methods used in this study are quantitative methods, data collection techniques with observation, interviews and documentation. The results of the study showed that the BOS funds at SMA Negeri 1 Telaga were evvetife because the school budget activity plan (RKAS) and budget realization are in accordance with what was previously budgeted so that it is said to be effective
Kemajuan Seni Suara Dalam Tradisi Bani Umayyah

&lt;p&gt;This study is entitled about the Progress of Sound Art in the Bani Umayyah Tradition, the focus of this research is the beginning of the tradition of sound art in theBani Umayyah , the initiator of the art of music and the existence of the genre of sound art and the culmination of the progress of sound art. This research is a historical research using a historical approach and data corruption data that leads to the interpretation of the interpretation of the facts as a whole. And the method used in this research is the Library Reserch method which takes data from books, journals, and other scientific works. The results obtained from this study can be concluded several kinds, namely, the art of sound or music has existed since the time of Rasullullah, and the very dominant figure in developing the art of sound is Ziryab, then the peak of the existence of the art of music during the Dynasty Umayyah was in Andalusia, precisely Umayyah II.&lt;/p&gt;&lt;p&gt; &lt;strong&gt;Kata Kunci :&lt;/strong&gt; Dynasty Umayyah , Sound Art, Music Art, Ziryab.&lt;/p&gt;
Pengaruh Self Efficacy Terhadap Kemampuan Public Speaking Mahasiswa

Public Speaking ability is an ability that everyone must have, especially for a student. Public Speaking Ability is the ability of an individual to speak well and confidently to others. This study aims to determine: (1) does Self Efficacy have an influence on public speaking ability in 2021 Communication Science students of Universitas Mercu Buana Yogyakarta, (2), How does Self Efficacy influence the public speaking ability of 2021 Communication Science students of Communication Science Faculty of UMBY. This study uses a quantitative method with the research subject, namely students of Communication Science, Universitas Mercu Buana Yogyakarta class of 2021. Quantitative research method, which looks for the relationship between the two variables. The research data were obtained from the results of distributing questionnaires to 110 communication science students. The data obtained was analyzed using SPSS software. The results showed thatn 33.8% of Public Speaking variables can be explained and influenced by independent variables X1, X2, X3, and X4, while the remaining 66.2% are influenced by other variables that have not been studied.
Visualisation and analysis method of enterprise financial expenditure data based on historical database

Under the influence of the trend of data informatisation, the storage and utilisation of enterprise financial expenditure information is more dependent on technologies such as databases. How to use enterprise information databases more effectively and make the storage and utilisation of enterprise financial expenditure data more efficient and easier is a concern of users. The study proposes a visual analysis model of enterprise financial expenditure data based on real-time historical database, which is constructed based on auto-encoder and K-mean clustering algorithm. It improves both algorithms in the design process to reduce the negative impact of their defects on the visual analysis model. The performance test of the visual analysis model of financial expenditure data shows that the loss value is as low as 0.02 and the error sum of squares is as low as 0.18. This indicates the value of the model for visual analysis of financial expenditure data of large enterprises.
Upaya Pengenalan Huruf Menggunakan Metode SAL pada Siswa Kelas Nol Besar di TK Dahlia Mataram Semester Genap Tahun Pelajaran 2021/2022

This research aims to determine the application of the SAL method in introducing letters to students in class zero major at Kindergarten Dahlia Mataram even semester of the 2021/2022 academic year. This research was designed using Classroom Action Research. The subjects in this study were students in class zero at TK Dahlia Mataram, totaling 15 students consisting of 6 girls and 9 boys. With research procedures: (1) planning stage, (2) action implementation stage (3) observation stage (4) reflection stage. The results of the study show that: proper use of the SAL method can introduce letters effectively to students in major zero grade at TK Dahlia Mataram even semester of the 2021/2022 academic year. Furthermore, the researcher recommends: (1) teachers who have the same difficulties can apply the SAL learning method to introduce letters to students. (2) in order to get maximum results, it is hoped that the teacher will make learning more interesting and varied
Keefektifan Pengukuran Kebutuhan Belajar Menggunakan Model Deduktif di Masa Pandemi Covid-19 di SDN Serdang Wetan

In learning activities, a teacher must understand what is needed by students. In identifying student learning needs, an appropriate learning needs measurement model is needed. One of them is the inductive model. The deductive model is the identification of learning needs in general and with broad targets. The effectiveness of the deductive model was tested by research sourced from the results of an interview with an elementary school teacher. The deductive model is often applied in a learning model. This is useful to find out how effective the learning model has been. The deductive model that conveys messages from the general to the specific and the abstract to the real turns out to have advantages and disadvantages when applied online.
Efficient online real-time video stabilization with a novel least squares formulation and parallel AC-RANSAC

A novel online real-time video stabilization algorithm (LSstab) that suppresses unwanted motion jitters based on cinematography principles is presented. LSstab features a parallel realization of the a-contrario RANSAC (AC-RANSAC) algorithm to estimate the inter-frame camera motion parameters. A novel least squares based smoothing cost function is then proposed to mitigate undesirable camera jitters according to cinematography principles. A recursive least square solver is derived to minimize the smoothing cost function with a linear computation complexity. LSstab is evaluated using a suite of publicly available videos against state-of-the-art video stabilization methods. Results show that LSstab achieves comparable or better performance, which attains real-time processing speed when a GPU is used.
SymbIoT: Towards An Extensible Blockchain Integration Testbed for IIoT

This paper presents SymbIoT, an extensible hybrid simulation-emulation testbed to investigate the integration of blockchain and distributed ledger technology (DLT) within the Industrial Internet of Things (IIoT) continuum. By adopting a joint software and hardware-based approach, we amalgamate the flexibility of software solutions and the real-world applicability offered by integrating comparable IoT hardware. The versatility of SymbIoT lies in its extensibility, offering flexibility in parameters including consensus algorithms, block size, node count and topology, throughput limitation, and use-case application deployment. SymbIoT facilitates comprehensive empirical studies of blockchain implementations within IIoT, focusing on performance, scalability, and security considerations. The testbed provides a platform for innovative and pragmatic experimentation in blockchain and IIoT integration, holding promise for shaping future applications and solutions in this cross-disciplinary field. We also present results from preliminary experimentation, indicating the applicability of the testbed for IIoT and broader IoT-to-cloud scenarios.
3D profile measurements by coaxial fringe projections and image acquisitions

A 2D fringe pattern is designed and employed to perform 3D profile measurements by means of coaxial fringe projections and image acquisitions. To enhance the reliability and systematic accuracy, a signal processing algorithm is proposed as well. Accuracy of the retrieved 3D profile can be achieved in the order of sub-millimeters.
Timbre-reserved Adversarial Attack in Speaker Identification

As a type of biometric identification, a speaker identification (SID) system is confronted with various kinds of attacks. The spoofing attacks typically imitate the timbre of the target speakers, while the adversarial attacks confuse the SID system by adding a well-designed adversarial perturbation to an arbitrary speech. Although the spoofing attack copies a similar timbre as the victim, it does not exploit the vulnerability of the SID model and may not make the SID system give the attacker's desired decision. As for the adversarial attack, despite the SID system can be led to a designated decision, it cannot meet the specified text or speaker timbre requirements for the specific attack scenarios. In this study, to make the attack in SID not only leverage the vulnerability of the SID model but also reserve the timbre of the target speaker, we propose a timbre-reserved adversarial attack in the speaker identification. We generate the timbre-reserved adversarial audios by adding an adversarial constraint during the different training stages of the voice conversion (VC) model. Specifically, the adversarial constraint is using the target speaker label to optimize the adversarial perturbation added to the VC model representations and is implemented by a speaker classifier joining in the VC model training. The adversarial constraint can help to control the VC model to generate the speaker-wised audio. Eventually, the inference of the VC model is the ideal adversarial fake audio, which is timbre-reserved and can fool the SID system. Experimental results on the Audio deepfake detection (ADD) challenge dataset indicate that our proposed method improves the attack success rate significantly compare with the vanilla VC model without additionally introducing an adversarial noise to the attack speech. Objective and subjective evaluations illustrate that the quality of fake audio generated by our proposed method is better than directly adding adversarial perturbation to the VC-generated audio. Furthermore, the analysis shows that our generated adversarial fake audios also meet the specified text and target speaker timbre-reserved requirements of the attacker.
MMT: Cross Domain Few-Shot Learning via Meta-Memory Transfer

Few-shot learning aims to recognize novel categories solely relying on a few labeled samples, with existing few-shot methods primarily focusing on the categories sampled from the same distribution. Nevertheless, this assumption cannot always be ensured, and the actual domain shift problem significantly reduces the performance of few-shot learning. To remedy this problem, we investigate an interesting and challenging cross-domain few-shot learning task, where the training and testing tasks employ different domains. Specifically, we propose a Meta-Memory scheme to bridge the domain gap between source and target domains, leveraging style-memory and content-memory components. The former stores intra-domain style information from source domain instances and provides a richer feature distribution. The latter stores semantic information through exploration of knowledge of different categories. Under the contrastive learning strategy, our model effectively alleviates the cross-domain problem in few-shot learning. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on cross-domain few-shot semantic segmentation tasks on the COCO-20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{i}$</tex-math></inline-formula> , PASCAL-5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{i}$</tex-math></inline-formula> , FSS-1000, and SUIM datasets and positively affects few-shot classification tasks on Meta-Dataset.
Comparison of Reducing the Speckle Noise in Ultrasound Medical Images using Discrete Wavelet Transform

&lt;p&gt;Speckle noise in ultrasound (US) medical images is the prime factor that undermines its full utilization. This noise is added by the constructive / destructive interference of sound waves travelling through hard- and soft-tissues of a patient. It is therefore generally accepted that the noise is unavoidable. As an alternate researchers have proposed several algorithms to somewhat undermine the effect of speckle noise. The discrete wavelet transform (DWT) has been used by several researchers. However, the performance of only a few transforms has been demonstrated. This paper provides a comparison of several DWT. The algorithm comprises of a pre-processing stage using Wiener filter, and a post-processing stage using Median filter. The processed image is compared with the original image on four metrics: two are based on full-reference (FR) image quality assessment (IQA), and the remaining two are based on no-reference (NR) IQA metrics. The FR-IQA are peak signal-to-noise ratio (PSNR) and mean structurally similarity index measure (MSSIM). The two NR-IQA techniques are blind pseudo-reference image (BPRI), and blind multiple pseudo-reference images (BMPRI). It has been demonstrated that some of these wavelet transforms outperform others by a significant margin. &lt;/p&gt;
MMNet: A Mixing Module Network for Polyp Segmentation

Traditional encoder–decoder networks like U-Net have been extensively used for polyp segmentation. However, such networks have demonstrated limitations in explicitly modeling long-range dependencies. In such networks, local patterns are emphasized over the global context, as each convolutional kernel focuses on only a local subset of pixels in the entire image. Several recent transformer-based networks have been shown to overcome such limitations. Such networks encode long-range dependencies using self-attention methods and thus learn highly expressive representations. However, due to the computational complexity of modeling the whole image, self-attention is expensive to compute, as there is a quadratic increment in cost with the increase in pixels in the image. Thus, patch embedding has been utilized, which groups small regions of the image into single input features. Nevertheless, these transformers still lack inductive bias, even with the image as a 1D sequence of visual tokens. This results in the inability to generalize to local contexts due to limited low-level features. We introduce a hybrid transformer combined with a convolutional mixing network to overcome computational and long-range dependency issues. A pretrained transformer network is introduced as a feature-extracting encoder, and a mixing module network (MMNet) is introduced to capture the long-range dependencies with a reduced computational cost. Precisely, in the mixing module network, we use depth-wise and 1 × 1 convolution to model long-range dependencies to establish spatial and cross-channel correlation, respectively. The proposed approach is evaluated qualitatively and quantitatively on five challenging polyp datasets across six metrics. Our MMNet outperforms the previous best polyp segmentation methods.
ICDAR 2023 Competition on Detection and Recognition of Greek Letters on Papyri

This competition investigates the performance of glyph detection and recognition on a very challenging type of historical document: Greek papyri. The detection and recognition of Greek letters on papyri is a preliminary step for computational analysis of handwriting that can lead to major steps forward in our understanding of this major source of information on Antiquity. It can be done manually by trained papyrologists. It is however a time-consuming task that would need automatising. We provide two different tasks: localization and classification. The document images are provided by several institutions and are representative of the diversity of book hands on papyri (a millennium time span, various script styles, provenance, states of preservation, means of digitization and resolution).
Evaluate of Random Undersampling Method and Majority Weighted Minority Oversampling Technique in Resolve Imabalanced Dataset

Classification is a model for making predictions based on existing data. Imbalanced data leads to misclassification or modeling errors where the data is not relevant and results in poor classification modeling. A poor classification model is caused by imbalanced data in the classification label, and there is a need for data balancing as a solution to resolve this issue. The methods used to handle data imbalance are Random Undersampling and MWMOTE. The goal is to see the implementation of Random Undersampling and MWMOTE working well in addressing the imbalanced dataset and to know the performance and accuracy in modeling. The dataset used is an open source dataset from Kaggle consisting of Diabetes data, Bank Turnover data, Stroke data, and Credit Card data with various data ratios, with the goal of addressing the problem of imbalanced data. Model evaluation was performed using the confusion matrix and decision tree algorithm by looking at the precision, recall, f-measure, and accuracy values from the Random Undersampling and MWMOTE methods. Random Undersampling can address the problem of imbalanced data with a precision of 76.28%, recall of 76.74%, f-measure of 76.48%, and accuracy of 76.21%. MWMOTE can address the problem of imbalanced data with a precision of 86.04%, recall of 87.30%, f-measure of 86.66%, and accuracy of 86.61%. It can be concluded that the MWMOTE method is better than the Random Undersampling method because the average evaluation of the confusion matrix of the Random Undersampling method is smaller than the MWMOTE method.
Research of Safety Management System for Hydropower Station Based on 5G Technology

5G technology, with the features of high speed, low latency and massive connectivity, has been an important strategic resource and new infrastructure to support the energy transformation. Integrated with various industries of energy field deeply, 5G will effectively drive innovation in energy production and consumption patterns, injecting strong momentum into the energy revolution. However, in application field of hydropower station, the disadvantages of high latency, slow response speed, inability to achieve remote control, limited security and encryption capabilities, have constrained the intelligent and digital development of hydropower station, as well as introduce potential safety hazards to the management of hydropower station. Thus, this paper proposes a safety management system of hydropower station based on 5G technology, which combines the advantages of 5G technology to address the remote control issues of personnel, machines, materials, systems and environment. Furthermore, to handle the network security problems arising from the practical application of 5G technology in hydropower station, two strategies are designed and introduced, which are network slicing security and private network isolation security.
Ensuring an Error-Free Transcription on a Full Engineering Tags Dataset Through Unsupervised Post-OCR Methods

The digital transformation of engineering documents is an ambitious research topic in the industrial world. The representation of component identifiers (tags), which are textual entities without a language model is one of the major challenges. Most of OCR use dictionary-based correction methods so they fail at recognizing hybrid entities composed by numerical and textual characters. This study aims to adapt OCR results on language-free strings with a specific semantics and requiring an efficient post-OCR correction with unsupervised approaches. We propose a two-step methodology to face the questions of post-OCR correction in engineering documents. The first step focuses on the alignment of OCR transcriptions producing a single prediction refined from all OCR predictions. The second step presents a combined incremental clustering & correction approach achieving a continuous correction of tags’ transcriptions relatively to their assigned cluster. For both steps, the dataset was produced from a set of 1,600 real technical documents and made available to the research community. When compared to the best state-of-art OCR, the post-OCR approach produced a gain of 9 % of WER.
Group, Contrast and Recognize: A Self-supervised Method for Chinese Character Recognition

Chinese character recognition has been a challenging problem in the field of computer vision, attracting significant research attention due to its widespread applications and technical complexity. However, previous methods rely heavily on manual annotations to guide model learning, without considering self-supervised representation learning. Motivated by the educational approach of teaching pupils to recognize Chinese characters through grouping and differentiation, we introduce a novel self-supervised method that employs clustering and contrastive learning to group similar characters and separate them. Our proposed objective consists of two components: intra-group and inter-group contrastive objectives. The intra-group objective distinguishes the target character from similar characters within the group, while the inter-group objective encourages the model to encode the discriminative semantic structure of each group. The experimental results demonstrate the advantages of our self-supervised representation over previous methods, as well as its superior performance on benchmark comparisons.