Reviewerly/exHarmony huggingface dataset:
Autonomous Path Planning Logistics Sorting Robot Experimental Teaching Platform Based on Aruco Marker

In order to optimize the teaching content of “Engineering Training II” for Automation Specialty, the curriculum teaching system has been reshaped with project-based teaching as the center based on the characteristics of the discipline. We have designed a logistics robot experimental teaching platform that combines intelligent vehicles and robotic arms based on ARUCO Marker positioning. The system uses STM32 as the main control chip, Quanzhi H6 as the computing power platform, L298N and DC deceleration motor as running drivers, and the Yuejiang robotic arm as the cargo grabbing device. At the same time, combining ARUCO Marker recognition, Dijkstra optimal path planning, and incremental PID as intelligent algorithms, it achieves functions such as navigation control, cargo handling, and fixed point stacking for the logistics sorting robot system. The experimental results show that the system has fast recognition speed, high positioning accuracy, low price, and is easy to expand. It can effectively improve students' ability to comprehensively apply basic theoretical knowledge to solve practical problems and enhance their engineering literacy.
Research on Enhanced Multi-Task Traffic Scene Object Detection and Classification Method Based on YOLOv5

A YOLOv5 based enhanced multitask traffic scene object detection and classification method, is proposed to address the issues of weak ability for multi-scale small object detection in complex traffic scenes. By improving the feature fusion network, the model can fully utilize feature information and increase the accuracy of target localization and boundary regression; Design multiple detection head structures and perform structural lightweighting, can preserve the original feature information and fuse multi-scale feature information. Enhancing the global perception ability of the model, to achieve simultaneous learning and detection classification of multi-scale and different category attribute samples. The experimental results show that, the improved method achieved the highest mAP of 97.2 % and inference speed of 105.62 frames/s on the Chinese urban traffic signal dataset, which were 2.1 % and 40.9 frames/s higher than the original YOLOv5. The model has significantly improved the detection ability and robustness in various complex environments, effectively improving the real-time detection accuracy of multi-scale targets in general complex scenarios.
An Image-based Digital Forensic Investigation Framework for Crime Analysis

Digital Forensic Investigation (DFI) is a big field in the technological era. Technology is constantly improving and being advanced. As technology advances, criminals are also changing their crime strategies. The real criminal is brutal to be caught. DFI is a process by which we can find out the criminal by making a detailed report based on the crime scene investigations. As a result, crime is increasing day by day. Several types of research have been conducted, and a lot of research is running on this topic. However, still have some confinements in this field. In this paper, we propose an image-based DFI model for crime analysis. The main goal of this paper is to analyze the crime scene images, detect suspicious objects efficiently and reduce crimes using the DFI process. Moreover, we use object detection algorithms (e.g., Faster R-CNN, R-CNN, and SSD), image enhancement techniques, and sharpening filters for low-quality images. Furthermore, the authors assess the performance of the framework using various metrics ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$e$</tex> . <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$g$</tex> ., Accuracy, Precision, Recall, and F1 Score).
Design and Implementation of a Crawler-Based Bad Web Page Discovery System

With the advent of the era of big data and 5G., people's demand for browsing the web is increasing., and the quality of web content is crucial to the user's browsing experience., but some web sites still have bad web content., such as pornography., violence., gambling., etc., which brings security risks to social security. In order to effectively identify and filter bad web content., this paper proposes a crawler-based bad web page discovery system. This paper first introduces the functional design., process design., neural network design and other aspects of the bad web page detection system., and elaborates on the implementation of the core functions. The final test results show that the crawler-based bad web page discovery system can effectively improve the discovery and filtering ability of bad web pages and meet the actual needs of current users.
Multi-Layer Open-Domain Bangla Conversational Chatbot with a Hybrid approach

Bangla is one of the most spoken language globally, with approximately 228 million native speakers. But there isn't much research done in the Bangla chatbot field. There are a few closed domain retrievals based on Bangla chatbots like “Golpo”, “Doly”, and“ Anirudha”. But the author is not aware of any other work where an open-domain Bengali chatbot is implemented. This scientific research paper proposes “Kothok”, an open-domain three-layer Bangla chatbot with a hybrid mechanism consisting of both generative and retrieval approaches that can converse with users in Bangla. The first layer with a retrieval mechanism provides a quick controlled response, and if the question is informative, the second layer comes to play and retrieves the response from the web and acts as the knowledge base of the bot. The last layer with a deep learning transformer-based model uses a generative approach and acts as the brain of the system and responds to empathic and any type of conversation outside the first two layer's capabilities. We deployed our model in the browser, where users can interact directly with the bot. We tested it in both open domains and closed domain situations using independent Human raters to evaluate the quality of conversation and analysis the effect of three layers in open domain situations, revealing this approach's strength, limitations, and potential for future work. We also used an evaluation metric called SSA, which evaluation method was proposed in a research paper for the ‘Meena’ bot from google research in 2018 to evaluate the human likeliness. Our bot scored an SSA of 70.8% in the open domain scenario and an extraordinary score of 87.5% in the domain-specific application on a multi-turn evaluation.
Cross-age face recognition for face images in digital archives

Face images in digital archives have a large age span and suffer from varying degrees of image degradation over time, leading to a significant degradation in the performance of generic face recognition models. To address the above problems, this paper proposed an anti-noise cross-age face recognition model. The model combines local residual learning and soft thresholding module, then embeds them into the backbone network to remove irrelevant features and guide the network to extract valid initial facial features. In this case, the soft thresholding module adaptively sets thresholds by branching at two different scales. The initial facial features are decomposed into age-related features and identity-related features. The identity-related features are used for face recognition. Meanwhile, a benchmark test dataset based on real archives was constructed in this paper. The study shows that the model has a high degree of robustness and anti-noise interference. Also, soft thresholding has a positive impact on noise suppression.
Demonstration of Effectiveness of Nativeness in Stepwise Learning by Performing Sentiment Analysis

Nowadays, more people and entities express opinions on online platforms. With this increasing trend of online opinion mass information, it is impossible to analyze them manually. For this reason, an automatic approach to opinion and sentiment analysis is essential and useful in the real world. Several approaches based on machine learning techniques in sentiment analysis have been proposed. However, machine learning essentially needs massive data to generate a robust model. On the other hand, collecting the training data is costly, especially in low-resource language. To solve this problem, we introduce stepwise learning. Stepwise learning is a technique that improves the model capacity for a target task (i.e., main task) by exploiting auxiliary information from source tasks. It is effective when the data in the target tasks are inadequate. The target task of this paper is sentiment analysis for reviews in the Bangla language. Our proposed method consists of two stages: source and target tasks. For the source task, we train a transformers-based model, such as BanglaBERT, by a large dataset translated into the Bangla language; the original dataset is written in English. For the target task, we retrain and evaluate the model with a relatively small target dataset. We further investigate the effect of opinion expression style of writers between source and target data in stepwise learning. We call it “nativeness.” The experimental result shows that nativeness has an important role in improving performance.
Towards Developing an Intelligent Robotic System for Assisting Children in Basic Drawing

Children need assistance to learn basic drawing in school. However, it may not always be possible for a teacher to assist every individual separately, especially in a school with a large number of students in each class. This research aims to design and develop a robotic system to help children learn how to draw basic shapes. While the use of robotic arms has been explored in various fields such as industry and biomedicine, little attention has been given to assisting children in drawing basic shapes. To achieve this objective, a system was developed that consists of an android-based mobile application and a robotic arm. The application allows users to upload a picture of a shape or alphabet, convert it into line art, and then send the necessary instructions to the robotic arm. Based on these instructions, the robotic arm demonstrates the drawing steps to help children learn. The application also has the ability to evaluate the accuracy of the user's drawing. Finally, the effectiveness of the system's features was tested with a group of children in a laboratory environment, and it was found that the proposed system effectively supports children in basic sketching.
Face Mask Recognition Based on Improved YOLOv7-Tiny

A face mask recognition algorithm based on the improved YOLOv7-Tiny is proposed to address the current problems such as time-consuming and poor real-time performance of manual checking whether a mask is worn. Firstly, the Backbone of YOLOv7-Tiny is replaced by the MobileNetV3 network as a whole, making the structure more lightweight. Secondly, the edge loss function uses EIOU to improve the localization accuracy of face mask edges. Finally, the CBAM attention mechanism is added to improve the model detection performance. Experiments were conducted on the AIZOO dataset, and the improved YOLOv7-Tiny increased the mAP from 93.9% to 94.2% compared to the original algorithm, with a 28.3% decrease in the number of parameters and a 37.2% decrease in inference time. The experimental results show that the improved model is not only able to reduce the model size, but also improve the accuracy and speed of face mask detection, showing good mask recognition results.
Prompt-Calibrated Tuning: Improving Black-Box Optimization for Few-Shot Scenarios

When there is limited downstream data available, using prompts can still yield good results. With prompts, large language models(LLMs) have already achieved success in numerous NLP tasks. Therefore, it is necessary to find the optimal prompts for each task. Due to the limitation of only having access to LLMs' API without the ability to perform backpropagation, we optimize the prompts using derivative-free optimization. In this paper, we propose Prompt-Calibrated Tuning(PCT), which modifies the outputs of large language models before prompt tuning and keeps their parameters frozen. Besides, we adopt the Roberta-large model as the backbone model, which might split a word into several subwords during tokenization, resulting in insufficient masking. Therefore, we utilize whole-word mask(wwm) in this paper. In addition, we have also expanded the label words. The experimental results show that Prompt-Calibrated Tuning is more effective than existing black-box optimization.
Object Detection in Remote Sensing Images Based on Feature Fusion and Multi-Branch Attention

Object detection in remote-sensing images is an important and challenging task. With the development of deep learning technology, the method based on convolutional neural network has made considerable progress. However, due to the problems of remote-sensing images, such as dense arrangement, arbitrary direction and complex background, traditional detection networks are difficult to use adequately the semantic information in images. We design a novel single-stage detector based on feature fusion and three-branch attention. The feature map extracted by the backbone network is fully fused with the semantic information of different levels through the balanced pyramid structure, and then the critical foreground features are captured through the angle parameters decoupled three-branch attention network to improve the detection performance. Experimental results show that our method achieves better detection performance than many state-of-the-art methods.
Phrase Level Bangla Sign Language Recognition using Keypoints from Hand Gesture Video

Many people around the world are incapable of speaking. Sign language is the only means they use for conducting conversations. Phrase level sign language is the efficient way for hand gesture-based sign language communication. Hand detection and tracking is the crucial part for recognition of hand gesture-based sign. In research works conducted for hand gesture-based sign recognition, skin color segmentation, Kinect sensor-based data, specialized colored gloves for each hand palms etc. have been used. These research works had limitations. In this work, a dataset for recognition of 27 Bangla sign phrases was created. MediaPipe [1] library of Google was used to detect and track hands and extract key points from palm portion of each hand. Angle features between each key points of the hand palm were calculated. Long Short-Term Memory (LSTM) model was used for classification. Overall accuracy for Bangla sign language recognition by the proposed system was 92.07%. The system was also experimented on PkSLMNM [2] dataset, which is a Pakistan phrase level sign language dataset having 7 different sign phrases. Overall accuracy for recognition of phrases from PkSLMNM dataset by the proposed system was 90.98%, which was higher than the overall accuracy score of 82.66% [3] proposed by the authors of the PkSLMNM. Though the proposed system was tested on phrase level sign language recognition, it can be used in recognition of any hand gesture based sign and Human Computer Interaction (HCI).
CATrack: Combining Convolutional and Attentional Methods for Visual Object Tracking

The current popular tracking frameworks prioritize the modeling of global relationships while neglecting research on local feature extraction. This paper introduces CATrack, a novel approach for visual object tracking that integrates convolution and attention into a unified framework. In contrast to prior research, it constructs a tracking module using a unified framework that incorporates convolution and attention as its core components. Our method effectively bridges the gap between the two calculation methods. It improves the ability to extract fundamental features, integrates past experience in the tracking field more effectively, while balancing local and global contextual information. The proposed tracker achieves competitive performance on 5 challenging short-term and long-term benchmarks and can run at real-time speed.
Multifractal Image Texture Analysis Combined with 2D Empirical Mode Decomposition

The surface texture and microstructure of digital images have an important influence on the construction of features such as image analysis, transformation, and compression. Studies have shown that the fractal spectrum parameters of different types of subject matter will be significantly different. Multifractal spectra and scaling indices quantify the heterogeneity of structural features, demonstrating multiscaling properties. This paper proposes a multifractal spectrum algorithm combining empirical mode decomposition (EMD) and wavelet leaders, starting from the image texture classification task. This method describes the surface shape and microstructure of the image, extends the mode decomposition of the one-dimensional signal in the Hilbert-Huang transform to the two-dimensional image, and gives an image descriptor based on the fractal spectrum. Simulation results demonstrate the accuracy of the proposed method.
Analyzing The Security of e-Health Data Based on a Hybrid Federated Learning Model

This research aims to provide an approach for analyzing the security of the e-health care system through the pre-processing of different deep learning models and the usage of federated learning. Infrastructural support for e-healthcare services is being gradually deployed by the health sector. This method increased the safety of patients and doctors through a protected platform. As a result, it is going to replace the current health service. Even if this technology is becoming more and more widespread, a number of data security threats need to be tackled. In this research, a CNN and MLP architecture with a classification-focused approach using a number of pre-trained feature extractors such as ResNet-50, VGG16, and Inception- v3 have been implemented. Additionally, various machine learning classification algorithms and Other methods of picture classification besides neural networks have been tried and compared, including Random Forest and Logistic Regression. Federated learning has also been incorporated to increase the security of healthcare data because it does not transmit actual data but models. The objective is to develop a hybrid federated learning model to analyze the security of e-health data. The core premise is to utilize a methodology like federated learning, which enables a technique for creating machine learning models while safeguarding user privacy and can maintain e-health data security without transferring real-world data.
Cross-Granularity Fusion Network for Fine-Grained Image Classification

Fine-grained image classification (FGIC) aims to identify subtle visual differences among subcategories, which is challenging due to the small inter-class variances. Existing methods recognize subcategories mainly by locating discriminative parts which exists in the regions with high responses in deep feature maps. However, the regions with high responses in deep feature maps correspond to large receptive fields in the input image, leading to the result that subtle visual differences among subcategories cannot be captured precisely. In this paper we propose a novel Cross-Granularity Fusion Network (CGFN), which excavates subtle yet discriminative granularity features within each part and captures potential interactions among granularity features to build powerful part feature representations. The CGFN consists of two modules: First, the Multi-Granularity Proposal (MGP) module locates diverse and discriminative parts and focuses context-complementary granularities across different hierarchies within each part. Second, a Cross-Granularity Fusion (CGF) module is developed by fusing granularity features to acquire robust part features for the final classification. We conduct a series of experiments on publicly available datasets i.e., CUB-200-2011, Stanford Cars and FGVC-Aircraft datasets and experimental results demonstrate that the CGFN achieves state-of-the-art performance.
Class-Specific Model Pruning Method Based on Dependency Graph and Reinforcement Learning

Model pruning, as a widely applied technique, aims to reduce the scale and computational complexity of deep neural networks, thereby enhancing the efficiency and performance of models, enabling their effective deployment on resource-limited devices. However, in specific class pruning, balancing the recognition accuracy of specific categories and overall recognition accuracy remains a challenging and underexplored task, with no satisfactory solutions available. To address this issue, we propose an automated specific category pruning method based on dependency graphs and reinforcement learning, which is called DGRLP. This method utilizes dependency graphs to model the connections between layers in the network and employs reinforcement learning to find suitable compression strategies. We evaluated our method on several classic models, such as ResNet56, VGG16, and MobileNet v2, and compared it with the current state of the model pruning methods. The results show that, in cases with fewer categories, our method can effectively improve the recognition accuracy of specific categories while maintaining or even enhancing overall accuracy.
Gesture Recognition Based on Flexible Data Glove Using Deep Learning Algorithms

Gesture recognition based on wearable devices helps to build an intelligent human-computer interaction. However, the sensing units of current gesture acquisition devices are mostly rigid MEMS with poor user experience. Meanwhile, most existing studies directly stack gesture sensing data, ignoring the interaction of gesture signals within the same modal sensing channel and between different modal sensor channels in terms of spatiotemporal characteristics. To address the above problems, we use flexible data glove as gesture capture devices and propose a framework named self-attention temporal-spatial feature fusion for gesture recognition (STFGes) to recognize gestures by integrating multi-sensors data. In addition, we conduct comprehensive experiments to build a dataset that can be used for training and testing. The experimental results show that STFGes achieves 97.02% recognition accuracy for 10 dynamic daily Chinese Sign Language (CSL) and outperforms other methods.
Explainable AI Based Malaria Detection Using Lightweight CNN

A customized lightweight Convolutional Neural Network (CNN) for the fastest malaria detection from RBC images with explainability is presented in this study. Accuracy, precision, recall, f1-score and number of model parameters are considered for evaluating the proposed method. It is called lightweight as it has a small number of parameters (0.17 M). A small number of model parameters can reduce the processing time generally. It's performance is compared with the well-known transfer-learning (TL) model's performance. Additionally, the proposed method is also compared with state-of-the-arts (SOTA) models. In both the cases, the proposed method performed similarly or better in every performance criterion as mentioned earlier. The proposed CNN model achieves 99.45%, 99.75%, 99.17% and 99.46% scores on accuracy, precision, recall and f1-score respectively. Explainable Artificial Intelligence (XAI) tool ‘SHAP’ (Shapley Additive Explanations) is used to explain the decision of the proposed model. The results show that the proposed lightweight CNN is an efficient model in terms of performance parameters as mentioned for malaria detection.