Reviewerly/exHarmony huggingface dataset:
A Few-Shot Speech Keyword Spotting Method Based on Self-Supervise Learning

Keyword spotting (KWS) plays a crucial role in enabling voice-based user interactions on smart devices. However, conventional KWS methods require a large number of predefined keywords to achieve acceptable detection accuracy, which users may find challenging to provide. In recent years, self-supervised training and large models have excelled in various audio tasks. Their general audio feature extraction capabilities align well with the low-resource nature and scalability requirements of KWS tasks. In this paper, we integrate self-supervised models with keyword transformers to tailor them for KWS tasks. Experiments show that our approach significantly outperforms previous supervised methods. Moreover, our method’s advantages become even more pronounced under extremely limited resource conditions, which is of great importance for the rapid deployment of KWS systems.
A Novel Multi-Target Tracker Using GNN and SPRT for Anti-Decoy Interference

In battlefield environments such as electronic warfare, countering deceptive interference from the enemy is a challenging task. Among various forms of interference, the use of decoys is a common and effective method. In such situations, the tracking side needs to employ suitable methods for data association and track management to deal with potential decoy interference. This paper adopts the global nearest neighbor (GNN) algorithm as the data association and filtering framework, and the sequential probability ratio test (SPRT) as the track management method. To address the degradation in tracking performance caused by range ambiguity in pulse Doppler radar, this paper introduces the concept of range ambiguity gating. Experimental results demonstrate that the proposed range ambiguity gating technique effectively mitigates data association errors and plays a significant role in electronic warfare environments.
DeepLabV3_FP : a novel deep learning network for hurricane damage assessment of buildings in UAV imagery

Hurricane disasters are destructive. It seriously threaten people's lives and property safety. It is an important task to assess hurricane damage effectively in emergency rescue mission. With the development of UAV remote sensing technology and deep learning technology, it has become possible to use convolutional neural networks to locate hurricane-affected areas and estimate losses. However, hurricane often cause buildings to collapse, destroy trees and result in a large amount of debris on the ground. The appearance of different ground objects overed with debris may have strong similarities, resulting in low detection accuracy. In this paper, we propose an encoder-decoder network, named DeeplabV3_FP to improve accuracy. The encoder uses ResNet50 with atrous convolution to extract features and output shallow and deep feature maps. The decoder fuses shallow and deep feature maps and restores the original resolution of the image. In the shallow branch, the Dimensionality Reduction Filter (DRF) Module is added to blur out small fragment information. In the deep branch, the Pyramid Pooling Attention (PPA) Module is added to introduce more global context information and enhance important dependencies related to the target. Experimental results on the RescueNet dataset demonstrate that our method has achieved good results and Pixel Accuracy reaches 85.7%.
A Incorporates Random Walk Strategy and Enhanced GNN Model for Link Prediction

The link prediction problem has been extensively studied in graph neural networks. However, there are still many problems to be solved in link prediction. when generating node embeddings, using some methods in unsupervised learning can lead to inefficiency, lack of accuracy, and failure to reflect the structural features of the network, which has a significant impact on the accuracy of later link predictions. Therefore, we incorporate a random walk strategy to generate the initial node embeddings in the model, which improves the richness and quality of the node embeddings. Moreover, we add nonlinear feedforward neural networks to the GNN model to increase the nonlinear modeling capability of node features, as well as better representation and learning capability of the network structure. Experimental results on several benchmark datasets show that the proposed method consistently outperforms previous models.
Generative Adversarial Network (GAN) in Social Network: Introduction, Applications, Challenges and Future Directions

Generative adversarial networks are now a hot topic in the field of artificial intelligence. GANs take their cue by including a generator and a discriminator learned using the adversarial learning concept. Specifically, GANs seek to anticipate the distribution of the information samples from the real world and then generate a new response based on this prediction. Since their inception, GANs have been the subject of extensive research in different fields of social networks, such as image, vision, speech processing, and language processing. This survey paper summarises GAN-based models' theoretic concept, applications, advantages, and disadvantages with development trends and potential future directions. We elaborate on the topic by discussing the applications of GAN and the challenges that must be addressed. In particular, we discuss speech and language processing, image processing, face detection, texture-transferring, and communication-based GAN applications. We conclude our findings with a discussion of potential future directions.
Supervised Relation Extraction Based on Labels Contrastive Learning

Relation extraction is an important task in natural language processing. Existing relation extraction tasks usually use data augmentation to construct positive and negative samples for contrastive learning training. And previous works lack the accurate selection of positive and negative samples and the full use of relation labels. To overcome these defects, for supervised relational extraction, we propose a Labels Contrastive Learning framework (LabelsCL) that considers both global and local perspectives, which makes full use of labels for design. Specifically, from a global perspective, we use label and cosine similarity to select the most easily misclassified samples as positive and negative examples for training. From a local perspective, positive and negative samples are selected according to batch and label, and this way of sample selection increases the randomness of sample selection. The contrastive learning framework LabelsCL proposed by us makes full use of labels and synthesizes two perspectives to construct contrastive learning LabelsCL, which increases the tolerance of the framework. Finally, the experimental results demonstrate the effectiveness of our proposed framework, which is significantly improved on the Semeval-2010 Task 8 dataset compared with the baselines.
A MT-Oriented Approach to Entailment Recognition

Entailment is a relation between different expressions of the same meaning. If a hypothesis can be inferred from a text T, then T entails H(T→H). Entailment between two English sentences with light verb construction is studied from the viewpoint of their internal dependency structure. Entailment rule "take a break→break" is taken as an example, and one hundred dependent nouns for "take" and "break" are taken from the Corpus of Contemporary American English. Features are selected from the dependent nouns to compute similarity between "take" and "break". Ten examples are computed in total and their values fall within [1.00,1.50]. This result is used to recognize entailment between two English sentences for automatic translation or the online chat robot "Xialiao" which can return to users an answer which is entailed in the question.
SwinDeepLab:Swin Transformer-based DeepLab v3+ for Gland Segmentation

Gland is an important tissue structure in the human body, but its epithelial layer has a high regeneration frequency, which makes it susceptible to glandular cancer. The morphological characteristics of glands are often used by doctor to judge the degree of malignancy in glandular cancer. Accurate glandular segmentation in histological images is an essential step for obtaining morphological data. However, the significant variations in glandular appearance at different levels of malignancy and the low contrast of background staining make it challenging to segment glands precisely. Additionally, the process of creating pathological images is time-consuming and laborious. In this paper, we collected a dataset of pathological images of gastric mucosa and annotated the gland (GMG). We proposed a gland segmentation model based on the DeepLab framework and the Swin Transformer to solve this problems. We tested the proposed method on our own glandular dataset GMG and a public dataset GlaS. The experimental results demonstrated that our approach achieves high accuracy and efficiency in the field of glandular segmentation.
Heurísticas computacionais para extração de conhecimento em problemas de competições de programação

The purpose of this work is to apply some natural language processing (NLP) techniques over a set of statements typical of programming contests, so as to provide a concrete basis to the future development of a recommendation system. In addition to the possibility of generating co-occurrence networks, which presented an accurate representation of the semantic similarity between the statements, it was also possible to perform an analysis of the spatial distribution of the challenges in a bi-dimensional space, which made it possible to indentify clusters based on additionally available attributes such as category, difficulty level and correct answers ratio. Ultimately, it was observed that the term-document vectorization method presented better results when generating cooccurrence networks, while the TF-IDF performed better when searching for cluster formations.
An Algorithm for Tiny Object Detection in Aerial Images Based on YOLOv5-HSA

Addressing the challenge of diminished detection performance in general object detectors when confronted with densely distributed and tiny objects in aerial images, we present a series of innovative enhancements to the YOLOv5 framework. Firstly, we devise a feature pyramid network with high-resolution feature maps (HRFM-FPN). This approach enhances the precision of detecting tiny objects by directly incorporating finer-grained feature information from high-resolution feature maps. Secondly, we devise the sparse query self-attention mechanism (SQSM) for the feature extraction module. SQSM maximizes the utilization of features and information associated with tiny objects, thereby bolstering the model's capacity to effectively extract features for tiny objects. Finally, we propose the aviation non-maximum suppression (Aviation-NMS) algorithm to improve the model's predictive capability when dealing with densely distributed tiny objects. Combining the above work, we devise the YOLOv5-HSA (YOLOv5 combined with HRFM-FPN, SQSM, and Aviation-NMS) algorithm for tiny object detection in aerial images. Our experiments conducted on the VisDrone2019-DET dataset demonstrate that the YOLOv5-HSA algorithm achieves superior detection accuracy compared to existing detection algorithms.
Dual Self-Attention Transformer for Graph Node Classification

The Transformer has achieved great success in natural language processing and computer vision, but its use in the graph is limited due to its emphasis on node location relationships instead of structural relationships. We propose the Dual Self-Attention Transformer (DSAT) to address this issue. By incorporating a dual self-attention mechanism, DSAT combines the global feature learning capabilities of the Transformer model with the local feature learning capabilities of CNNs. Our approach utilizes the GAT module to learn the local neighborhood structure around each node, while the Transformer learns the graph data's global information. We also use RWPE for absolute position coding and RMSNorm to improve model training speed and feature distribution stability, which leads to better generalization performance. Our experiments on the CLUSTER and PATTERN datasets show that DSAT outperforms GNNs and graph transformer architectures, achieving the best result in graph node classification tasks.
End-to-End Recognition Algorithm for Degraded Invoice Text

With the improvement of social informatization and computer technology, the effective realization of electronic management and application of a large number of paper invoices has become a new challenge in industrial practice. Text detection and recognition technology is an important means to realize the structured information extraction of paper invoices. However, the straight lines, symbols and other elements in paper invoices easily interfere with text detection, and the drawings and characters are prone to adhesion, intersection, overlap and other degradation during the electronic process. In addition, the detection and recognition process of invoice text is relatively relevant, and the existing methods based on deep network cannot be applied to the detection and recognition of it. Based on this, this paper proposes a new end-to-end recognition framework Invoice Text Recognizer (ITR) for complex invoice texts. Specifically, the ITR uses the conversion encoder with self attention mechanism as the detector, and uses the fusion recognition module to coordinate the detection and recognition of the two task features. It does not require additional correction modules, nor does it need to make character level annotation on any shaped text. It can directly guide the text location through the loss in the recognition phase. The experimental results on three open text datasets (ICDAR2015, Total Text, CTW1500) and the paper-based invoices of a company prove that the proposed ITR has a high detection and recognition rate.
A Virtual Staining Method for Immunohistochemical Images of Breast Cancer

Breast cancer is a common malignant cancer. Detection of human epidermal growth factor receptor 2 (HER2) status based on immunohistochemistry (IHC) is an effective method for breast cancer diagnosis. Early diagnosis and treatment in breast cancer can greatly reduce the mortality rate, but the immunohistochemistry-based staining method is time-consuming and expensive. For the standpoint of saving labor, resources and time costs, direct generation of virtual IHC images based on hematoxylin and eosin (HE) slices is an efficient and accurate method. We proposed an IHC image generation method based on generative adversarial network (GAN). A content encoder, a style encode and a discriminator were used in our method, the first encoder of which extracted unstained images in the generator, the second encoder which obtained a stained style to stain unstained images to generate IHC images, and the discriminator of which is designed to judge the authenticity and the classification of HER2 expression at the same time. Experiments showed that our method outperforms other methods in PSNR and SSIM metrics in BCI dataset.
Awareness and Perception of Mobile-Learning Apps as E-learning Platforms in ODL Institutions, South-West Nigeria

The paper examined Distance Learners' awareness and perception of Mobile Learning Apps as e-learning platforms. The population for the study comprised of all Mathematics and Science Distance Learners in South-West Nigeria. The study employed survey research design with two hundred and eighty (280) learners involved. The sample for the study was selected using purposive sampling techniques. The instrument for data collection was a researcher designed questionnaire. The data collected were analyzed using frequency counts, percentages, mean and standard deviation, while hypothesis was tested using t-test. The study revealed that most of the learners are aware of the availability of the mobile learning Apps. However, the study showed that the learners exhibit a negative attitude towards usage of mobile learning Apps as e-learning platforms. The study recommended among others that the distance learning institutions should use different medium to sensitize the learners on the benefit of the use of mobile learning Apps. Also, learners should be properly guided on how to easily make use of different mobile learning Apps as this can encourage them to be fully integrated into the on-line learning platforms.
Vectors, Knowledge, Attitudes, and Practices in Relation to Malaria Transmission in Bauchi State, Nigeria

ABSTRACT Background &amp; objectives: Malaria remains a major public health concern in Nigeria and the vectors ( Anopheles species) can only be controlled effectively by having good knowledge of its composition and potential for disease transmission. This work aimed at surveying the indoor malaria vectors, Knowledge, Attitudes, and Practices (KAP) in relation to the disease transmission in Toro LGA, Bauchi State, Nigeria. Methods: Mosquitoes were collected by Pyrethrum Spray Catch (PSC) between October and December 2019 and were morphologically identified using standard keys. Blood samples were also collected from individuals who slept in the rooms where PSC was conducted. Thick and thin blood smears were made for malaria parasite examination. Questionnaires were administered to 120 participants for the KAP studies. Results: Ninety-seven (97) Anopheles mosquitoes were collected and identified as Anopheles gambiae 76(78.35%), An. funestus 20(20.62%) and An. coustani 1(1.03%). The overall malaria prevalence was 15.8%. The KAP studies revealed that measures and treatment-seeking behaviours against malaria varied significantly (P&lt;0.05) among the respondents. Sleeping under net 55(45.8%) and use of insecticides 24(20.0%) were some of the preventive measures highlighted while treatment-seeking behaviours included visit to pharmacy 74(61.7%) and use of local herbs 11(9.2%). Interpretation &amp; conclusion: This work revealed that An . gambiae and An . funestus are predominant malaria vectors in the area. The KAP information demonstrated fair knowledge about the disease by the respondents. Therefore, public enlightenment about malaria prevention, control and treatment is recommended to address the few but highly negative impact knowledge gaps about malaria.
The Effects of Adult learners’ Motivation to Enter University on Convergence Major Perception: Focusing on the mediating effect of Educational Satisfaction

This study aimed to analyze the influence of adult learners' motivation for attending college and their satisfaction with education on the recognition of convergence majors, as well as how the motivation for college attendance impacts the recognition of convergence majors through educational satisfaction. The study focused on adult learners enrolled in a regular degree program at K University, which is implementing the LiFE (University Lifelong Education System Support Project) initiative. Empirical analysis was conducted based on responses from a total of 205 surveys.&#x0D; The analysis yielded several key findings. First, the study revealed that only the learning-oriented motivation, which pertains to the desire for higher education, significantly affected educational satisfaction. Second, it was observed that goal-oriented motivation, activity-oriented motivation, and learning-oriented motivation, all of which contribute to one's decision to enter university, had a substantial impact on the recognition of convergence majors. Third, the study established a significant correlation between educational satisfaction and recognition of convergence majors. Lastly, the fourth finding highlighted that educational satisfaction partially mediates the relationship between adult learners' motivation for attending college and the recognition of convergence majors.&#x0D; These findings carry important implications. The study results offer fundamental insights for shaping a lifelong education system in response to the evolving landscape of creative convergence. Furthermore, they provide guidance for defining educational directions regarding convergence majors for adult learners and structuring a relevant curriculum.
Understanding Disaster Literacy Level in Indonesia: How can students understand natural disasters?

This study aims to determine the understanding of disaster literacy in preparing disaster-resilient students. This study used a quantitative descriptive method with a quasi-experimental design. The type of research used in this research is one group pretest-posttest. The research population is students of a public junior high school in Lembang, grade 7, for the odd semester of the 2022/2023 academic year. The research sample consisted of 33 students in class 7A. Data was collected through tests and documentation for two months, from November to December 2022. Then, the data were analyzed using descriptive statistics. Data analysis used paired t-test with the result that there was a significant difference between the pretest and posttest scores of 33 students with p 0.000. Based on these results, disaster literacy is very effective in improving disaster preparedness, especially in preparing aspects of disaster literacy knowledge, attitudes, and skills. The recommendations in this study are that only a few researchers apply disaster literacy in schools so that disaster literacy activities can become routine habits carried out by students and schools.
Design and Development of a Web-Based Plastic Waste Recycling Information System case study: Bali Pet Collection Center

A plastic recycling company is an industry that specializes in recycling plastic waste into processable plastic pellets. The current business process still relies on conventional methods for recording activities within the company. The objective of this research is to design and develop a web-based information system for plastic waste recycling. The waterfall methodology employed as the software development method. The system is tested using black box testing and user acceptance testing (UAT) and measured using Likert's summated Rating (LSR) method. The results of the black box testing indicate that the system functions well. Furthermore, the results of the UAT demonstrate that the system as a whole receives highly positive responses and is considered successful.
Text Classification System Using Text Mining with XGBoost Method

Large data nowadays can be used for analysis; thus, it can obtain important/valuable knowledge in various domains. Text analysis can be carried out by utilizing text mining using computational methods so that knowledge extraction can be carried out on large text data, including processing related to unstructured text data, which is written in natural language. Classification in text mining is a type of work with the searching process for a set of models or functions that describe and differentiate text data classes with the aim that the model can be used to predict the class of an object (text data) whose class is unknown. Text mining was carried out in this research to analyze text data through the Text Classification System using a classification method, namely the XGBoost (eXtreme Gradient Boosting) Method. A text classification system was developed to classify text in the form of articles. The highest accuracy obtained from the test is 77%.