Reviewerly/exHarmony huggingface dataset:
Style Modeling for Multi-Speaker Articulation-to-Speech

In this paper, we propose a neural articulation-to-speech (ATS) framework that synthesizes high-quality speech from articulatory signal in a multi-speaker situation. Most conventional ATS approaches only focus on modeling contextual information of speech from a single speaker’s articulatory features. To explicitly represent each speaker’s speaking style as well as the contextual information, our proposed model estimates style embeddings, guided from the essential speech style attributes such as pitch and energy. We adopt convolutional layers and transformer-based attention layers for our model to fully utilize both local and global information of articulatory signals, measured by electromagnetic articulogra-phy (EMA). Our model significantly improves the quality of synthesized speech compared to the baseline in terms of objective and subjective measurements in the Haskins dataset.
IAST: Instance Association Relying on Spatio-Temporal Features for Video Instance Segmentation

Most offline video instance segmentation (VIS) methods lack consideration for multi-scale spatio-temporal features, which leads to unstable instance association across frames. To address this problem, we propose IAST that builds Instance Association relying on Spatio-Temporal features for video instance segmentation. In detail, we design a novel Scale-to-Scale Attention Module in the encoder of IAST, which constructs stable cross-frame instance associations by completely leveraged multi-scale spatio-temporal features. In addition, we introduce a new data augmentation method called Sequential Copy-Paste, which effectively alleviates the overfitting problem caused by insufficient training data and enhances the robustness of the model. Empirically, IAST achieves the state-of-the-art VIS benchmarks with a ResNet-50 backbone: 47.4% AP, 41.6% AP on YouTube-VIS 2019 & 2021. Such achievements significantly outperform the previous state-of-the-art performance of 1.0% at the expense of fewer parameters. Code is available at https://github.com/clozureyez/IAST.
Extracting the Brain-Like Representation by an Improved Self-Organizing Map for Image Classification

Backpropagation-based supervised learning has achieved great success in computer vision tasks. However, its biological plausibility is always controversial. Recently, the bioinspired Hebbian learning rule (HLR) has received extensive attention. Self-Organizing Map (SOM) uses the competitive HLR to establish connections between neurons, obtaining visual features in an unsupervised way. Although the representation of SOM neurons shows some brain-like characteristics, it is still quite different from the neuron representation in the human visual cortex. This paper proposes an improved SOM with multi-winner, multi-code, and local receptive field, named mlSOM. We observe that the neuron representation of mlSOM is similar to the human visual cortex. Furthermore, mlSOM shows a sparse distributed representation of objects, which has also been found in the human inferior temporal area. In addition, experiments show that mlSOM achieves better classification accuracy than the original SOM and other state-of-the-art HLR-based methods. The code is accessible at https://github.com/JiaHongZ/mlSOM.
Gender-Cartoon: Image Cartoonization Method Based on Gender Classification

Qin Opera art is one of China’s intangible cultural heritage, and its influence is gradually declining. The cartoonization of Qin Opera is one of the feasible methods. However, current cartoonization methods suffer from the inability to classify and accurately cartoonize Qinqiang portraits by gender. Therefore, we propose Gender-Cartoon, which can achieve different gender portrait cartoons. The proposed method consists of four modules: gender classification, content feature extraction, gender identification style feature extraction and cartoonization. The gender classification module is used to obtain the gender labels of cartoon images, content feature module extracts content features from portraits by stacking convolutional blocks, and gender identification style extraction module uses the gender labels of cartoon images and image semantic features to obtain the corresponding gender style feature. The cartoonization module fuses content features and style features as the input of the adaptive residual block to obtain the corresponding cartoonization results. The experimental results show that the model can transform both texture and shape on the our collected Qinq Cartoon dataset Face2QinqCartoon and the public dataset Selfie2anime.
Volumetric 3D Reconstruction with Window-Wise Global Feature Aggregation

Volumetric 3D reconstruction methods have shown great performance in reconstructing indoor scenarios from monocular videos. However, as such approaches utilize discrete feature voxels to encode the observed scenes, the global feature interaction within and across different voxels is ignored, leading to imperfect reconstructions. To solve this problem, we propose a novel volumetric 3D reconstruction method named VolGARecon. The core portion of VolGARecon includes two parts: first, we use an MLP-based weighted fusion module (WFM) to unproject the extracted features to each voxel, which considers the visibility and is capable to reduce the noise caused by occlusion; second, a 3D transformer module (3DTR) is used to perform window-wise global feature interaction in a local sliding window, which strengthens the feature expression in 3D space and benefits estimating more complete and spatially coherent 3D models. In addition, we propose a multi-dimensional hybrid loss (MHL) that incorporates the 3D supervision in classical volumetric methods and the 2D supervision in novel view synthesis works. Extensive experiments show our method achieves superior performance on multiple datasets.
The Implementation of Artificial Neural Networks to measure the correlation of teacher's workload to the number of own learning media

The use of learning media in the teaching and learning process is an effort to increase the effectiveness and quality of the learning process. However, the need for learning media is not compatible with the number of learning media made by the teacher himself. One of the factors that causes it is the teacher's workload which is quite a lot so that the teacher does not have enough time to make his own learning media. This study aims to measure the extent of the correlation between the teacher's workload and the amount of instructional media that the teacher himself made. Artificial Neural Network with Backpropagation method is a tool that can be used to solve complex problems, one of which is to measure the level of correlation. The ability of an Artificial Neural Network with the Backpropagation method to adapt to changes that occur in the input and output values makes the prediction accuracy quite high. The teacher's workload variables used are the number of face-to-face hours of even and odd semesters, additional assignments (deputy principal/head of laboratory), homeroom teacher, and extracurricular coaches. The target used is the number of learning media made by the teacher himself. The data used in this study were taken from the workload of teachers at SMAN 4 Payakumbuh in 2022. The architectural patterns used are 5-4-1, 5-5-1, 5-7-1, 5-10-1, and 5- 12-1. From the test results with the Matlab R2013a software, the best pattern was obtained, namely the 5-12-1 pattern with an MSE value of 0.1001, a MAPE of 2.11, and a data accuracy of 97.89%. From the results of the training and testing, it was concluded that the correlation between the teacher's workload and the amount of self-made learning media is very low or not closely related.
Geometric Matrix Completion with Collaborative Routing Between Capsules

Collaborative filtering (CF) is the most common application of matrix completion, where the user-item rating matrix can be formulated as a bipartite graph. Due to the power of learning on graphs, graph neural networks (GNNs) have been widely adapted to the user-item graph. However, existing graph-based models mainly focus on modeling implicit feedback, while explicit feedback is not fully exploited where the geometric relationships between users and items are latent. Inspired by routing between capsules, we devise a routing algorithm Collaborative Routing for CF to capture these relationships. A novel graph-based matrix completion model based on Collaborative Routing (CRMC) is proposed, which can exploit the user-item graph from both implicit and explicit perspectives to learn better representations, and preserve the relationships between users and items latent in the user-item graph. Experimental results on three real-world datasets demonstrate the effectiveness of our model.
SQA: Strong Guidance Query with Self-Selected Attention for Human-Object Interaction Detection

The attention mechanism in Transformer-based HOI models plays important role in the comprehension of human and object interaction. However, most previous Transformer-based models ignore the guidance on the query and attention, which leads to a poor understanding of interaction behaviour. In this paper, we propose a strong guidance query model with self-selected attention called SQA. The model includes two novel modules, query feature extraction (QFE) and attention mask construction (AMC). QFE builds strong guidance query by concatenating guidance features. The strong guidance query effectively improves the ability to capture both human and object relationships. Meanwhile, AMC establishes distinctive attention masks for each query. The masks allow each query to contact self-selected particular attention regions. It facilitates directing query to obtain more accurate information during cross-attention even in the rare-sample case. We evaluate our SQA model on the mainstream HICO-DET and V-COCO datasets and it achieves a state-of-the-art result. The codes are available at https://github.com/nmbzdwss/SQA.
Recurrent Fine-Grained Self-Attention Network for Video Crowd Counting

Striking a balance between exploring the spatio-temporal correlation and controlling model complexity is vital for video-based crowd counting methods. In this paper, we propose a Recurrent Fine-Grained Self-Attention Network (RFSNet) to achieve efficient and accurate counting in video scenes via the self-attention mechanism and a recurrent fine-tuning strategy. Specifically, we design a decoder which consists of patch-wise spatial self-attention and temporal self-attention. Compared with vanilla self-attention, it effectively leverages the dependencies in spatial and temporal domain respectively, while significantly reducing computational complexity. Moreover, the RFSNet recurrently feeds the features into the decoder to enhance the spatio-temporal representations. This strategy not only simplifies the model structure and reduces the number of parameters, but also improves the quality of estimated density maps. Our RFSNet achieves state-of-the-art performance on three video crowd counting benchmarks, and outperforms other methods by more than 20% on the challenging FDST dataset.
SA-MAIS: Hybrid automatic sentiment analyser for stock market

Sentiment analysis of stock-related tweets is a challenging task, not only due to the specificity of the domain but also because of the short nature of the texts. This work proposes SA-MAIS, a two-step lightweight methodology, specially adapted to perform sentiment analysis in domain-constrained short-text messages. To tackle the issue of domain specificity, based on word frequency, the most relevant words are automatically extracted from the new domain and then manually tagged to update an existing domain-specific sentiment lexicon. The sentiment classification is then performed by combining the updated domain-specific lexicon with VADER sentiment analysis, a well-known and widely used sentiment analysis tool. The proposed method is compared with other well-known and widely used sentiment analysis tools, including transformer-based models, such as BERTweet, Twitter-roBERTa and FinBERT, on a domain-specific corpus of stock market-related tweets comprising 1 million messages. The experimental results show that the proposed approach largely surpasses the performance of the other sentiment analysis tools, reaching an overall accuracy of 72.0%. The achieved results highlight the advantage of using a hybrid method that combines domain-specific lexicons with existing generalist tools for the inference of textual sentiment in domain-specific short-text messages.
Continuing Professional Development Model in the Schools Division of Sarangani

This study explored and determined the extent of the Continuing Professional Development (CPD) implementation in government school institutions in the Sarangani Division for School Year 2022-2023. The 375 respondents from a total population of 6, 042 public school teacher were randomly selected across the 23 school districts. Based on the findings, the hypothesis on no significant relationship between the extent of the CPD implementation and the teacher’s competencies is accepted because of the Pearson’s r-value of -0.081 with negligible correlation 0.118 p-value. However, the hypothesis on no significant relationship between the contributing factors and the extent of CPD implementation is rejected and the model is a partial mediation. Sobel test yielded a z-value of -3.10600817 with a p-value of 0.00189631 that means significant at 0.05 level. Further, three major themes surfaced from the informant’s in-depth interview after an intensive analysis of the data gathered, namely: Limited Training Offered, Insufficient Execution of the Program, and Participatory Barriers to CPD Training. The indicated training need, preferred CPD modalities and pace, as well as monitoring, evaluation, and offering technical support to schools in terms of CPD may be taken into consideration by DepEd Offices. Hence, a CPD Model is recommended to take effect in the Schools Division of Sarangani to address the actual competency gap and relevant training needs for teachers’ professional growth and development.
MODIFICATION OF ELGAMAL ELLIPTIC CURVE CRYPTOSYSTEM ALGORITHM

The importance of data encryption has grown dramatically, especially in terms of personal data. The elliptic curve cryptosystem is the major solution for data security because it has become more prevalent. Security and privacy are required to ensure the data has recently generated much concern within the research community. This paper's objective is to obtain a complicated and secure ciphertext and make cryptanalysis difficult. In this paper, we modified the El-Gamal Elliptic Curve Cryptosystem (ECC) by producing new secret keys for encrypting data and embedding messages by using Discrete Logarithm Problem (DLP) behavior. This modification is to offer enhanced encryption standards and improve the security. The experiential results show that the proposed algorithm is more complex than the original method.
PEMODELAN SISTEM IDENTIFIKASI PEMBICARA DENGAN MFCC DAN SUPPORT VECTOR MACHINE

In this paper, we focus on speech recognition where speakers used text-dependent which means the text is agreed in advance and will be used next. This system uses MFCC as feature extraction&#x0D; and SVM as pattern recognition. Data were taken from 10 adult speakers with differences in gender, age, and ethnicity. Each speaker provides 50 ballots "computer" and its pronunciation is&#x0D; not controlled resulting in 500 data. Some data training is contaminated with gaussian noise with levels 80dB, 70dB, 60dB, 50dB, 40dB, 30dB, 20dB, 10dB, and 0 dB. The research uses a&#x0D; frame length: of 40 ms, overlapping frames: of 50%, and a coefficient me: of 13. Noise Cancelling was also tested in this research, although not getting optimal results. Pattern recognition SVM with&#x0D; RBF kernel functions produce 100% accurate results. The time process of the Sequential Minimal Optimization algorithm is better than Quadratic Programming algorithms. Increasing the&#x0D; number of speakers to see the performance of the system with a greater amount of data can be&#x0D; made for further research.
Effectiveness of an information booklet on knowledge regarding behavioral problems of school children among primary school teachers

Background: A behavioral problem is a departure from normal (acceptable) behavior beyond a point, to the extent behavioral problems can manifest themselves in many ways.There are interchangeable terms for behavior disorders-disruptive behavior disorder, conduct disorders, emotional disorders, and emotional disturbances.Methodology: A quantitative approach with pre experimental one group pretest posttest design was adopted for the study.The samples from the selected from primary schools of Vijaypura district by using non probability purposive sampling technique.The samples of present study comprises of 60 primary school teachers.The tools used for data collection was structured knowledge questionnaire. Results:With regard to pretest level of knowledge it shows that, maximum 40 (66.7%)respondents were having average knowledge, 16 (26.7%)respondents were having good knowledge and remaining 4(6.7%) of respondents were having poor knowledge and during post-test maximum 48 (80%) of respondents were having average knowledge and 12 (20%) of respondents were had good knowledge.The statistical paired' implies that the difference in the pretest and post-test value was found statistically significant at 5% level (p<0.05) with a paired value of 13.15.There exists a statistical significance in the difference of knowledge score indicating the positive impact of information booklet. Conclusion:The primary school teachers were had moderate level of knowledge in pretest and is improved after undergoing intervention.School teachers should be equipped with adequate knowledge for finding the behavioral problems among children at the early stage and give proper attention to them.
Perancangan Aplikasi Korespondensi Rekam Medis Berbasis Web Di Rumah Sakit Bhayangkara Tk.1 Pusdokkes Polri

One of the activities of the medical record unit is correspondence, which is a correspondence activity related to medical records which includes medical resumes, insurance forms related to health services and medical certificates. According to the Minister of Health Regulation No. 21 of 2020, the Strategic Plan of the Ministry of Health for 2020-2024 concerning the Integration of Healtho Information Systems, it is stated that HIS is directed at strengthening health information services that are faster, valid, and resource sharing; consolidation of integrated electronic-based standard HIS; and strengthening the application of HIS in health care facilities. Related to this, it is a challenge for Bhayangkara Hospital Tk.1 Pusdokkes Polri to make changes, one of which is by making medical record correspondence applications to make it easier for users and service providers. So far, the correspondence process is still done manually so it takes a long time to complete the work. The information system that will be developed allows users to apply for correspondence anywhere and anytime without having to come directly to the Medical Record Installation of Bhayangkara Hospital Tk.1 Pusdokkes Polri. Furthermore, the officer will process the application and provide information to the user when the application is complete.
COMPARE OF MEDIAN FILTER AND WIENER FILTER TO DETECT CONCEALED WEAPONS

Many types of terror and terrorist attack aimed to finish our safety and friendly life. Therefore, detecting weapons has become a very important role in our life. Image processing and computer vision are one of the best field that targets to clarify different kinds of images, especially weapons images that can be a source of threat in many places such as metros stations, malls, airports, schools, and train stations. So that, there is a need to employ techniques for discovering different weapons, like guns, knives, and chemical explosives hidden in the clothing. In this paper, we will use different kinds of image filters Median and Wiener to detect images with different weapons. The experimental results ensure that wiener filter is best filter for eliminate noise from the concealed weapons images. Outcomes of this paper have been implemented on MATLAB.
Upaya Meningkatkan Hasil Belajar Bahasa Indonesia melalui Penggunaan Media Gambar Siswa Kelas VIII.H SMP Negeri 12 Mataram

This study aims: to find out the use of media images in learning Indonesian in class VIII.H students at SMP Negeri 12 Mataram Odd Semester in the Academic Year 2021/2022. And to find out the increase in Indonesian learning outcomes through the use of media pictures of class VIII.H students of SMP Negeri 12 Mataram Odd Semester 2021/2022 Academic Year. This research was designed using Classroom Action Research. The subjects in this study were class VIII.H students of SMP Negeri 12 Mataram, totaling 31 people, consisting of 15 female students and 16 male students. While the research procedure goes through the stages of Planning, Action Implementation, Observation, and reflection. The results showed that the results of learning Indonesian for class VIII.H students of SMP Negeri 12 Mataram in the 2021/2022 academic year, using media images could be improved with the final result of the study being the class average before the action of 69.06 or classical completeness of 61.29 %, to 73.68 or 74.19% classical completeness in cycle I, increased to an average of 74.84 or 77.55% in cycle II and increased to an average test of 80.45 or 100% in cycle III. From the results of student observations it was shown that there was an increase in student activity in cycle I obtained a score of 17 or 60.70% to 20 or 71.43% in cycle II and 24 or 85.17 in cycle III. The increase in student activity in learning Indonesian was supported by the increase in teacher activity in maintaining and improving the learning atmosphere using media images, this can be seen from the observations of teacher activities where in cycle I a score of 22 or 61.10% was obtained, becoming 26 or 72, 23% in cycle II and increased to 33 or 86.12% in cycle III.
Digital Tiffin Services Using Responsive Web Application

Abstract: The food industry has always been a profitable one, not only for manufacturers and suppliers, but also for consumers and retailers. Nowadays online food delivery systemis the need of the hour due to the recent changes in the industry and the increasing use of the internet. Our proposed system is a real-time digital Tiffin services ordering system for the customer and students. They can be easily join and ordered online from restaurants and mess services through our proposed system. Users can choose the desired meal items fromthe menu that is displayed. The user places a food order. Payment options include online and cash-on-delivery. It maintains a separate account for each user so that information provided by the user is kept private. Every user gets their own user ID and password. It offers a more secure ordering approach as an outcome. It makes use of Cloud to figure out theuser's location and then displays nearby mess service providerswho have registered with it. This allows the user to rapidly locate the mess service providers nearby and choose the appropriate mess based on their food ordering needs. The many types of mess that are offering good service can be searched by users