Reviewerly/exHarmony huggingface dataset:
An evaluation method with convolutional white-box models for image attribution explainers

Abstract Research in the explanation of models has developed rapidly in recent years. However, research in the evaluation of explainers is still limited, with most evaluation strategies centered around perturbation. This approach may produce incorrect evaluations when the relationship between features gets complicated. In this research, we present a ground-truth-based evaluation method for feature attribution explainers on image tasks. We design three perspectives to evaluate. The input perspective evaluates whether the explainers accurately represent the inputs that the model detects. The feature perspective evaluates whether the explainers capture the features important in decisions. The user perspective evaluates the reliability that the user derives from the data. Then, using the traditional white-box model, we extract the ground truth corresponding to the three perspectives and provide an example to demonstrate the procedure. To acquire the results of the image attribution explainer, we also reconstruct the traditional white-box model into the convolutional network white-box model. Our method provides an a priori benchmark that is not affected by the explainer. The experiments show that we may use the evaluation method for different tasks and extend it to natural datasets, which offers a flexible and low-cost evaluation strategy.
YOLOv5 UAV Detection Algorithm Based on Attention Mechanism

Abstract To address the poor accuracy issue with tiny target recognition by UAVs, this study provides an improved YOLOv5 detection method with an attention mechanism. Firstly, CBAM is integrated into Backbone to suppress irrelevant features and enhance the network’s attention to space and channels. This can help the network learn more discriminative representations of objects in the image. Then, the introduction to Biformer in Neck removes redundant information on the algorithm structure, endows the network with dynamic query-aware sparsity, and enhances its ability to detect small targets. The experimental findings demonstrate that the suggested algorithm model has a detection accuracy of 84.6% on average. in the self-built UAV dataset, and can accurately complete the detection task of small UAV targets.
Algorithm Research for Bubble Detection in Capacitive Liquid Level Sensor of Fully Automated Immunoassay Analyzer

Abstract Fully automated immunoassay analyzers are integral to clinical testing, with liquid-level detection crucial to mitigate false measurements and cross-contamination. This study introduces a bubble detection algorithm based on the Random Forest machine learning model to address bubble detection in analyzers using capacitive sensors. The algorithm processes collected capacitance data, accurately identifying bubbles and ensuring sampling accuracy. Data preprocessing, feature extraction, and classification enable precise bubble detection. Compared to the traditional CART decision tree algorithm, this algorithm demonstrates superior bubble detection performance, indicating its significant research value and potential for liquid-level detection in fully automated immunoassay analyzers.
Improved Faster R-CNN Traffic Sign Detection Algorithm Based on Transformer

Abstract In view of the fact that traffic sign detection is becoming more and more important in assisted driving, but there are situations where the target is small, occluded and the background is complex, this paper proposes a Transformer-based improved Faster R-CNN algorithm for traffic sign detection. The algorithm in this paper uses the Transformer network based on the shift window as the backbone. The multi-level feature map is fused across layers through the cascade fusion module to obtain the fusion feature map. Moreover, the model’s feature extraction ability is improved. The detection head module eliminates rounding through RoI Align quantization error. Based on experiments in the TT100K dataset, it can be known that the mAP and detection speed of the algorithm in this paper has been improved. The effectiveness of the improved model in this paper is proven and it is applicable to real scenarios.
Multi target tracking algorithm based on anchor free detection

Abstract In order to alleviate the problems of frequent identity label switching caused by mutual occlusion between targets and missed detection caused by small target sizes in multi-target tracking, this paper proposes a multi-target tracking algorithm based on anchor-free detection. This algorithm first uses YOLOX for detection and embeds the ReID algorithm to enhance pedestrian recognition. Then, it uses the DeepSORT algorithm for tracking and correcting abnormal trajectory algorithms for occluded targets. Finally, experiments are conducted on both the MOT16 dataset and the self-made dataset. This algorithm has higher tracking accuracy than other algorithms, and its tracking performance for small targets has also been effectively improved.
Dynamic Object Tracking Based on Triplet Relationship Guided Sampling Consensus Algorithm

Abstract Compared with the traditional methods based on the prediction of moving object trajectory, the method based on image registration has the advantage of not relying on the object motion equation to track the object. However, in the case of a large number of outliers, the image matching method has the defects of inadequate filtering of outliers or erroneous filtering of incomers due to the limitations of spatial constraints of feature matching pairs, which will make the tracking of flying objects inaccurate or impossible. During the flight, the object may be in a similar background and the image shooting angle is different, which makes the object imaging angle change, resulting in a large number of outlier interference. Because of the above situation, this paper adopted an improved TRESAC algorithm based on RANSAC, which used a feature matching method based on a triplet relationship to effectively filter outliers and then adopted an initial data subset selection strategy to increase its robustness. Experimental results show that the TRESAC algorithm can filter outliers quickly and accurately, and the object is still tracked effectively under the condition of similar background and pose change.
Multi-level Salient Feature Mining Network for Person Re-identification

Abstract Person re-identification (Re-ID) algorithms can retrieve the same pedestrian’s images from an image gallery captured by multiple cameras when given a pedestrian image. Due to changes in pedestrian postures, illuminations, and perspectives, it remains a significant challenge to improve the accuracy of person re-identification. Although the attention mechanism can alleviate some of these issues, it causes attention-based methods to pay excessive attention to features in the most salient areas of images while ignoring discriminant features outside the most salient areas, resulting in the insufficient discriminability of features extracted by attention-based methods. For this purpose, we propose a Multi-level Salient Feature Mining Network (MSFM-Net). First, by embedding attention modules in ResNet50, the model extract the most salient pedestrian feature maps. Second, the model uses two sub-salient feature mining branches to extract the second-level and third-level salient feature maps (collectively referred to as sub-salient feature maps). Third, the model uses the feature maps fusion module to combine the most salient feature maps with sub-salient feature maps to obtain the fused salient feature maps. Finally, the model pools the fused salient feature maps to produce more discriminant pedestrian representations. The results of two benchmark datasets demonstrate that MSFM-Nets performance reaches the current advanced level.
End-to-end Remote Sensing Image Aircraft Target Detection and Fine-grained Recognition Framework

Abstract In view of the practical application of target detection and recognition tasks for remote sensing, an end-to-end aircraft target detection and fine-grained recognition framework is proposed. It can accurately and quickly implement detection and recognition in an end-to-end way. The main network of the framework adopts the design ideas of target detection and fine-grained recognition methods using candidate region extract and visual attention, making sure the accuracy of detection and recognition. Then, to solve the problem of high false detection rate and missed detection rate of densely arranged targets, we propose the re-detection mechanism. To minimize the large amounts of calculations of deep networks and improve real-time performance, we introduce depthwise separable convolution to optimize networks. Finally, a weight mapping idea based on transfer learning is adopted to solve the problem of data labeling and also helps the detection and fine-grained recognition. The results prove that the proposed framework has good robustness, versatility, and efficiency in aircraft detection and fine-grained recognition tasks.
U<sup>2</sup>-MNet: An Improved Neural Network for Breast Tumor Segmentation

Abstract In recent years, UNet and its variants have achieved excellent performance. However, since the convolution kernel in UNet focuses solely on local pixels, these models struggle to model long-range dependencies. This issue has been addressed by recently proposed segmentation models based on transformer architecture. The internal self-attention mechanisms included in these systems capture global contextual information to improve segmentation effects. However, the results are often not ideal without pre-training on a large-scale dataset. Therefore, we designed a model (U 2 -MNet) to overcome the limitations of convolutional kernels, enabling the model to achieve high-accuracy segmentation without pre-training. This approach adopts a multi-layer framework, which can effectively describe multi-level channel information, employing a window-based channel MLP (WCM) block. It utilizes a sliding window and an MLP to capture information about details in local features. In addition, multi-level channel cross mixing (MCCM) block is included in each skip connection to reduce noise after aggregation of low-level and high-level features. The proposed model was trained from scratch and tested on Breast UltraSound Images (BUSI) dataset. Our model achieved 82.76%, 73.17%, and 86.24% on the Dice, IOU, and Precision indicators.
Capsule Endoscopy Image Deblurring Method Based on Multi-scale Recurrent Network

Abstract During the process of acquiring capsule endoscope images, image motion blur may result from errors made by the operating physician. A multi-scale recurrent attention network is proposed to address the issue of motion blur in collected images. Considering the issue of over-exposure during image acquisition in capsule endoscopy, which can adversely affect network generation results, an image preprocessing module has been incorporated into the network to eliminate highlights caused by over-exposure in input images. To enhance network sampling efficiency, the encoder is equipped with a Convolutional Block Attention Module (CBAM) to extract fuzzy features more effectively. Additionally, depthwise separable convolution is employed to reduce parameter count. Since the structure of images obtained through capsule endoscopy is relatively simple and stable, we have opted for the mean square error as our loss function due to its heightened sensitivity to data errors. The experimental results demonstrate that the proposed network achieves a Peak Signal-to-Noise Ratio (PSNR) of 31.096 and a Structural Similarity Index Measure (SSIM) of 0.925, indicating a significant improvement over DeblurGAN-v2 and Pix2pix.
Unmanned Aerial Vehicles Communication Interference Detection Based on SSA-BP Neural Network

Abstract This paper proposes a method for detecting communication interference in unmanned aerial vehicles (UAVs). First, we train the BP neural network with signal data from interference attacks, and then continuously optimize the BP neural network using the sparrow search algorithm (SSA). After iterations, we obtain the final interference detection model. With this detection model, we can detect whether there are malicious interference signals in the drone flight environment and evaluate the detection model using the detection rate. Finally, our detection model has an accuracy of up to 93.64%.
Research on Intelligent Recommendation of Maintenance Scheme based on Multi-modal Data Learning

Abstract In view of the unavoidable degradation and damage to the performance and stability of military, medical, transportation and industrial equipment in the process of daily use, especially after long-term operation or shutdown, and the maintenance personnel cannot be timely in place, an intelligent recommendation technology or program for remote maintenance of equipment based on multi-modal data learning is studied. Multi-modal data such as text, pictures and videos are integrated. Based on image recognition and learning of multi-modal data, intelligent recommendation of remote maintenance solutions can be realized when maintenance personnel cannot arrive in time.
Attentive Simple Recurrent Unit Knowledge Tracing Based on Learning Ability

Abstract Knowledge tracing can analyze the current knowledge level of students through the data of students’ previous learning activities. However, the existing models usually consider the features of exercises, ignoring the individual differences of students. It is difficult to accurately predict students’ mastery. In this paper, we propose an attentive simple recurrent unit knowledge tracing (SRU-MAKT) based on learning ability. The experimental results show that our model is superior to the existing models, and the AUC increases by 1.6%. We also conduct visualization experiments, which show that SRU-MAKT has interpretability.
Hardware Acceleration Schemes for Convolutional Neural Networks

Abstract This paper presents a hardware acceleration design for convolutional neural networks. Floating-point fixed-point operations, pipeline interlayer parallel acceleration, and design space exploration are the three key areas of optimization, and optimized modules can be used to build various networks with convolutions according to specifications for the application scenario, thus achieving a universal design. The experimental results show that the optimization of hardware resources improves the speed and performance of the algorithm, and can withstand larger data volumes and higher real-time requirements. The system achieves an accuracy of 95.09% and an inference speed of 0.237 ms per image, with a high processing speed. As a result, convolutional neural networks may now be used in a wider variety of application scenarios and manage larger datasets and higher real-time demands thanks to the design solutions presented in this research.
Improved YOLOv7 Intelligent Environment Perception Algorithm Based on Loss Function

Abstract To address the difficulty of environment perception in unmanned driving technology, an improved intelligent environment perception model based on YOLOv7 loss function is proposed. Simultaneously, the algorithm of deep sort target tracking is added for the sake of perceiving the surrounding environment through the camera to ensure the safety of the driver. And by modifying the loss term of the aspect ratio to the distinction between the predicted width and the target width, the fitting speed of the model can be effectively improved. Focal Loss is introduced to optimize the problem with the sample imbalance inside the target bounding box regression and reduce the weight proportion of numerous anchor boxes with less overlap between the predicted target bounding box and the true target bounding one to the overall model, such that the model centers more upon overlapping targets. The data applied to this article is BDD100K. The map of this algorithm reaches 60.3 %, which reaches 2.2 percentage points exceeding the baseline. Tests indicate that the raised algorithm attains certain advancements in the intelligent perception model of unmanned driving.
Homophobia and transphobia detection for low-resourced languages in social media comments

People are increasingly sharing and expressing their emotions using online social media platforms such as Twitter, Facebook, and YouTube. An abusive, hateful, threatening, and discriminatory act that makes discomfort targets gay, lesbian, transgender, or bisexual individuals is called Homophobia and Transphobia. Detecting these types of acts on social media is called Homophobia and Transphobia Detection. This task has recently gained interest among researchers. Identifying homophobic and transphobic content for under-resourced languages is a bit challenging task. There are no such resources for Malayalam and Hindi to categorize these types of content as far now. This paper presents a new high-quality dataset for detecting homophobia and transphobia in Malayalam and Hindi languages. Our dataset consists of 5193 comments in Malayalam and 3203 comments in Hindi. We also submitted the experiments performed with traditional machine learning and transformer-based deep learning models on the Malayalam, Hindi, English, Tamil, and Tamil-English datasets.
Resource efficient PV power forecasting: Transductive transfer learning based hybrid deep learning model for smart grid in Industry 5.0

This paper presents an innovative approach for enhancing power output forecasting of Photovoltaic (PV) power plants in dynamic environmental conditions using a Hybrid Deep Learning Model (DLM). The hybrid DLM employs a synergy of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Bidirectional LSTM (Bi-LSTM), effectively capturing spatial and temporal dependencies within weather data crucial for accurate predictions. To optimize the DLM’s performance efficiently, a unique Kepler Optimization Algorithm (KOA) is introduced for hyperparameter tuning, drawing inspiration from Kepler’s laws of planetary motion. By leveraging KOA, the DLM attains optimal hyperparameter configurations, elevating power output prediction precision. Additionally, this study integrates Transductive Transfer Learning (TTL) with the deep learning models to enhance resource efficiency. By leveraging knowledge gained from previously learned tasks, TTL enables the DLM to improve its forecasting capabilities while minimizing resource utilization. Datasets encompassing environmental parameters and PV plant-generated power across diverse sites are employed for DLM training and testing. Three hybrid models, amalgamating KOA, CNN, LSTM, and Bi-LSTM techniques, are introduced and evaluated. Comparative assessment of these models across distinct PV sites yields insightful observations. Performance evaluation, focused on short-term PV power forecasting, underscores the hybrid DLM’s superiority over individual CNN and LSTM models. This hybrid approach achieves remarkable accuracy and resilience in predicting power output under varying weather conditions, showcasing its potential for efficient PV power plant management.
Coreference resolution helps visual dialogs to focus

Visual Dialog is a multi-modal task involving both computer vision and dialog systems. The goal is to answer multiple questions in conversation style, given an image as the context. Neural networks with attention modules are widely used for this task, because of their effectiveness in reasoning the relevance between the texts and images. In this work, we study how to further improve the quality of such reasoning, which is an open challenge. Our baseline is the Recursive Visual Attention (RVA) model, which refines the vision-text attention by iteratively visiting the dialog history. Building on top of that, we propose to improve the attention mechanism with contrastive learning. We train a Matching-Aware Attention Kernel (MAAK) by aligning the deep feature embeddings of an image and its caption, to provide better attention scores. Experiments show consistent improvements from MAAK. In addition, we study the effect of using Multimodal Compact Bilinear (MCB) pooling as a three-way feature fusion for the visual, texutal and dialog history embeddings. We analyze the performance of both methods in the discussion section, and propose further ideas to resolve current limitations.
Determining Empirical Relationship of Rubber Drying Process using Machine Learning

Rubber is considered an important material for humankind and it is one of the most important products in Southeast Asian countries. However, the production of rubber could harm the environment due to the conventional use of acid and salt. We propose a rubber drying process using heat and constructed a rubber heating tunnel. We also propose a strategy to determine the time it takes to dry rubber so that the rubber is sufficiently dried without overheating at different temperature levels. We found that this strategy could not make use of conventional curve fitting methods based on least squares since it cannot handle discrete or categorical input data very well. We propose a non-linear Machine Learning regression technique based on neural network and found that neural network has the ability to predict the output variable quite well despite the input variables contain discrete or categorical values.
A Comparative Study of Estimation of Video Viewer Emotion Using YouTube Video Comments

YouTube is a online video sharing service that is that many people view. If we can know the emotions of video viewers, we can use them to improve usability. In this study, we propose two method for estimating the emotion of YouTube videos from the comments of each video: a BERT-based method and a rule-based method. For the former, we use BERT with fine-turning by 350 video comments to estimate the emotion. In the rule-based method, emotion values are calculated using a Japanese emotional expression dictionary. To evaluate these methods, we obtained emotion values from 100 respondents who were asked to fill out questionnaires as ground truth. The results of the evaluation using cosine similarity showed that BERT-method was able to estimate emotions with higher accuracy than the rule-based method.
Attendance System using Amazon Rekognition

This work proposes a cloud-based attendance system that uses face recognition technology to authorize identity. The system uses the Amazon Web Services (AWS) Rekognition service and a serverless architecture. The proposed system provides a reliable and tamperproof solution to track attendance, eliminating the need for manual record keeping and minimizing human involvement. It also offers potential benefits, such as improved security and transparency in attendance management.
Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%.