Reviewerly/exHarmony huggingface dataset:
Object Tacking with Siamese Convolution Network Re-Detection

Traditional tracking often have an online updating process, which often leads to heavy computing burden. We propose an off-line tracking re-detection framework, which is composed of a Siamese convolution network and a correlation filtering tracker. The Siamese convolution network is trained on a large-scale video data to obtain high object discrimination ability, to be a re-detection network. We use this Siamese network to re-detect the tracking results of correlation filtering tracker. In the tracking process, we do not have to update detection network online due to its high object discrimination ability. This mechanism reduces the computational burden of the algorithm and achieves high tracking speed. We also propose a hybrid judgment mechanism to make the re-detection network get better tracking decisions, as well as to re-detect tracked objects after tracking challenges occurring. Experiment results on large-scale data sets demonstrate that the proposed tracking framework outperforms the compared methods and high tracking speed.
Improving Lightweight AdderNet via Distillation from ℓ<sub>2</sub> to ℓ<sub>1</sub>-Norm

To achieve efficient inference with a hardware-friendly design, Adder Neural Networks (ANNs) are proposed to replace expensive multiplication operations in Convolutional Neural Networks (CNNs) with cheap additions through utilizing ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm for similarity measurement instead of cosine distance. However, we observe that there exists an increasing gap between CNNs and ANNs with reducing parameters, which cannot be eliminated by existing algorithms. In this paper, we present a simple yet effective Norm-Guided Distillation (NGD) method for ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm ANNs to learn superior performance from ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm ANNs. Although CNNs achieve similar accuracy with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm ANNs, the clustering performance based on ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -distance can be easily learned by ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm ANNs compared with cross correlation in CNNs. The features in ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm ANNs are encouraged to achieve intra-class centralization and inter-class decentralization to amplify this advantage. Furthermore, the roughly estimated gradients in vanilla ANNs are modified to a progressive approximation from ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm to ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm so that a more accurate optimization can be achieved. Extensive evaluations on several benchmarks demonstrate the effectiveness of NGD on lightweight networks. For example, our method improves ANN by 10.43% with 0.25x GhostNet on CIFAR-100 and 3.1% with 1.0x GhostNet on ImageNet.
MAFUNet: Multi-Attention Fusion Network for Medical Image Segmentation

The purpose of medical image segmentation is to identify target organs, tissues or lesion areas at the pixel level to help doctors evaluate and prevent diseases. Therefore, the model is required to have higher accuracy and robust representation. At present, the proposed models focus on the improvement of performance, and ignore the number of trainable parameters. This paper proposes a lightweight ECA-Residual module to build a model encoder, which can effectively extract features while reducing the number of parameters. The feature fusion of encoder and decoder using simple skip connections will produce semantic differences. Therefore, in this paper a spatial attention gating module is designed to solve this problem. The module suppresses the image irrelevant area and improves the performance of the model while ensuring the computational efficiency. The experiment is carried out on the Synapse dataset, and the results are superior to the current advanced models in terms of accuracy and parameter quantity with 84.21 % dice coefficient and 12.79 HD95. In addition, the accuracy of the model on the ACDC dataset is also better than the current advanced model, which proves the robustness and generalization of the model.
Accelerating Volumetric CT and MRI Imaging by Reference-Free Deep Learning Transformation from Low-Resolution to High-Resolution

High-resolution (HR) images are important in precision radiation oncology. However, acquiring HR volumetric CT and MRI images is often time consuming; also, the resolution in some direction(s) (e.g., z-direction in the case of CT) is often limited by imaging hardware or fundamental imaging principle. Super-resolution (SR) imaging, i.e., the low-resolution (LR) to HR image transformation, is widely used to improve image resolution. Data-driven deep learning (DL) methods have achieved great success in SR imaging, yet they can hardly be applied to medical imaging as they require large amount of LR-HR image pairs to train the model. We therefore propose a reference-free DL method to increase resolutions of volumetric medical images in an efficient way.We propose a maximum likelihood estimation (MLE)-based implicit neural representation (INR) network for SR imaging. The INR network aims to represent an image as a continuous function parameterized by a coordinate-based multi-layer perceptron. The INR network takes image coordinates as input and outputs corresponding pixel intensities. To train the network without using any HR images, we use a MLE framework to model LR observations' statistics and their relation to the latent HR image. The predicted HR image from the INR's output is transformed to LR images based on the MLE, and the network parameters are then optimized by minimizing the distance between the transformed LR images and actual LR observations. We demonstrate the efficacy of the proposed method on CT and MRI images for 2x, 4x, and 8x SR using only one or two LR image(s). The performance is compared with a conventional SR method named plain MLE, in terms of visual quality and numerical qualities of PSNR and SSIM.Our method outperformed the plain MLE method in the experiment. Table 1 reports the numerical improvements of our method over the compared plain MLE method. For 2x SR with a single LR image, our method achieved significant improvements in both PSNR and SSIM. When using two LR images, the better structural restoration capability of our method became more obvious with higher SR magnifications, as indicated by the increased SSIM differences. Better noise suppression capability of our method is observed in all our studies, as indicated by the PSNR values. In visual quality evaluation, we observed sharper image details with less noise in SR images generated by the proposed method, compared with the plain MLE method.The proposed novel reference-free DL method can efficiently provide high-quality HR images with only one or two LR images for CT and MRI imaging. This method can be easily generalized to many other radiation therapy related applications without the requirement for HR reference images.
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling

Data scarcity is a significant obstacle hindering the learning of powerful machine learning models in critical healthcare applications. Data-sharing mechanisms among multiple entities (e.g., hospitals) can accelerate model training and yield more accurate predictions. Recently, approaches such as Federated Learning (FL) and Split Learning (SL) have facilitated collaboration without the need to exchange private data. In this work, we propose a framework for medical imaging classification tasks called Federated Split learning of Vision transformer with Block Sampling (FeSViBS). The FeSViBS framework builds upon the existing federated split vision transformer and introduces a block sampling module, which leverages intermediate features extracted by the Vision Transformer (ViT) at the server. This is achieved by sampling features (patch tokens) from an intermediate transformer block and distilling their information content into a pseudo class token before passing them back to the client. These pseudo class tokens serve as an effective feature augmentation strategy and enhances the generalizability of the learned model. We demonstrate the utility of our proposed method compared to other SL and FL approaches on three publicly available medical imaging datasets: HAM1000, BloodMNIST, and Fed-ISIC2019, under both IID and non-IID settings. Code: https://github.com/faresmalik/FeSViBS .
Decoupled Consistency for Semi-supervised Medical Image Segmentation

By fully utilizing unlabeled data, the semi-supervised learning (SSL) technique has recently produced promising results in the segmentation of medical images. Pseudo labeling and consistency regularization are two effective strategies for using unlabeled data. Yet, the traditional pseudo labeling method will filter out low-confidence pixels. The advantages of both high- and low-confidence data are not fully exploited by consistency regularization. Therefore, neither of these two methods can make full use of unlabeled data. We proposed a novel decoupled consistency semi-supervised medical image segmentation framework. First, the dynamic threshold is utilized to decouple the prediction data into consistent and inconsistent parts. For the consistent part, we use the method of cross pseudo supervision to optimize it. For the inconsistent part, we further decouple it into unreliable data that is likely to occur close to the decision boundary and guidance data that is more likely to emerge near the high-density area. Unreliable data will be optimized in the direction of guidance data. We refer to this action as directional consistency. Furthermore, in order to fully utilize the data, we incorporate feature maps into the training process and calculate the loss of feature consistency. A significant number of experiments have demonstrated the superiority of our proposed method. The code is available at https://github.com/wxfaaaaa/DCNet .
A2FSeg: Adaptive Multi-modal Fusion Network for Medical Image Segmentation

Magnetic Resonance Imaging (MRI) plays an important role in multi-modal brain tumor segmentation. However, missing modality is very common in clinical diagnosis, which will lead to severe segmentation performance degradation. In this paper, we propose a simple adaptive multi-modal fusion network for brain tumor segmentation, which has two stages of feature fusion, including a simple average fusion and an adaptive fusion based on an attention mechanism. Both fusion techniques are capable to handle the missing modality situation and contribute to the improvement of segmentation results, especially the adaptive one. We evaluate our method on the BraTS2020 dataset, achieving the state-of-the-art performance for the incomplete multi-modal brain tumor segmentation, compared to four recent methods. Our A2FSeg (Average and Adaptive Fusion Segmentation network) is simple yet effective and has the capability of handling any number of image modalities for incomplete multi-modal segmentation. Our source code is online and available at https://github.com/Zirui0623/A2FSeg.git .
Predicting the Instance of Breast Cancer within Patients using a Convolutional Neural Network

Breast cancer is a widespread disease that affects millions of people across the world. This makes early detection and diagnosis critical for heightened chance of survival. Although there are many ways for breast cancer to be detected, many patients are diagnosed late in their disease progression. In this paper, we present a robust and automated way of early breast cancer detection within patients using machine learning methods. Our method uses medical images of possible breast cancer and classifies them as either non-cancerous or cancerous using a convolutional neural network (CNN). Our results showed that our CNN model had a mean accuracy of 97.25% when averaged using the final 50 percent of samples. These results show that machine learning can be used as an effective way to clinically diagnose breast cancer due to the high accuracy.
Impact of Kindles4Covid Virtual Reading Buddies Program on reading frequency and social connections

The summer reading gap affects children throughout the United States every single year, and with the COVID pandemic, the issue has heightened. As part of the ByKids4Kids organization, we developed the Kindles4Covid Virtual Reading Buddies Program to combat potential reading regression and social isolation in youth during the COVID-19 pandemic in the fall and winter of 2020. The program consisted of groups of two or three reading buddies, along with a facilitator (a high school or college student) who coordinated meetings for the buddies and assisted in choosing a piece of literature to read during the meetings. The participants completed pre- and post-program surveys consisting of short-answer and multiple-choice questions that we assessed to determine whether the students achieved the program goals of improved reading frequency and connections with peers. We hypothesized that routine, weekly contact with other youth and increased exposure to reading materials would help achieve these goals. Our data analysis suggests that our program was able to achieve its goals of increasing reading frequency (37.5% vs. 100%, pre vs. post intervention) and forming connections (37.5% vs. 87.5%, pre vs. post intervention). This work indicates that a structured and routine reading buddies program successfully addressed some challenges brought on by isolation during the pandemic.
Improving Waste Bank Management in Selat Village to Increase Added Value of Processing Waste into Organic Fertilizer with Android-Based Applications

The waste problem in Selat Village has caused severe environmental pollution. The main cause of this problem is because most of the people in Selat Village have not been able to carry out proper and integrated waste management, resulting in uncontrolled accumulation of waste. The general objective of this research is to initiate the progress of village areas in Indonesia regarding Waste Digitalization. The results of this study indicate that the Wita Bersih Application can shorten the time and make it easier for the Selat Village garbage bank in processing plastic waste.
Federated Learning: An Alternative Approach to Improving Medical Data Privacy and Security

While medical data is integral to building robust predictive machine learning models for medical research, obtaining access to medical data is increasingly difficult. The challenges primarily arise from obtaining consent, concerns around the privacy and security of medical data and the technical challenge of migrating what can be huge datasets to a centralised location. As a result, this chapter analyses the question, “How can we make medical data more accessible for medical research whilst addressing the ethical and technical issues around data privacy and data-sharing?” Moreover, this work expands on federated learning that represents a paradigm shift in machine learning from both a technical and sociological perspective. From a technical perspective, federated learning enables machine learning models to be trained in a decentralised manner. It thus allows researchers to utilise data stored in separate locations. From a sociological perspective, federated learning represents a shift in the power dynamic between those providing and those using medical data for research. Under a federated learning framework, raw data never leaves the client’s device. Instead, the centralised server only receives encrypted parameter updates after a shared model is sent and trained locally on each client device. This ensures that the entities that provide medical data have more control over where their data is stored and what information is shared with other parties. Even though federated algorithms have a slightly lower accuracy when compared to non-federated algorithms, it comes with data privacy benefits that non-federated algorithms cannot provide.
Correlation between Scattering Matrix, Return Loss and Interface Reflection Loss in Nicolson Rose Wear Approximation

The phenomenon behind the absorption of electromagnetic waves by absorbingmaterials is resonance phenomenon. When there is a similarity between the value of theimpedance of electromagnetic waves in the air and the impedance of the material, theabsorption of energy by the material is maximized. The phenomenon is measured using anauxiliary instrument, namely the Vector Network Analyzer. This instrument is very effective forcalculating the absorption value of electromagnetic waves. However, the Vector NetworkAnalyzer instrument which is mostly available in Indonesian research institutions cannot directlydisplay the reflection loss of the electromagnetic wave absorbing material. An effective methodthat is effective for calculating the absorption in electromagnetic wave absorbing material isNicolson Rose Wear method. In this article, we design a computational tool based on NicolsonRose Wear approximation to calculate the reflection loss values from scattering matrix andcomparing it with return loss, which is often mistook as reflection loss.
A blockchain authentication scheme for UAV-aided fog computing

Abstract Unmanned Aerial Vehicle (UAV) swarms not only expanded the application areas of UAVs but also brought more complex challenges to security. Both the data collected by UAVs and UAVs themselves are easy targets for hackers. Once a hacker has intercepted communication information or hijacked a UAV, it can cause the disclosure of information and even affect the normal flight of the UAV. In terms of authentication, existing research focuses on building peer-to-peer networks through devices that support blockchain deployments at the edge nodes to form blockchain networks. UAVs cannot run blockchains directly, so fog nodes need to be introduced to assist UAVs in achieving authentication. This paper proposes a UAV authentication solution based on the fog node-assisted blockchain. The solution incorporates smart contracts to ensure trust, along with on-chain registration and authentication for UAVs. In the design of this paper, the main use is made of fog nodes at the edge of each block. The fog nodes serve as agents to register and authenticate the UAVs in their area on the blockchain, thus enabling the authentication of UAVs in different fog node areas. In this paper, the analysis of gas consumption yields a total economic benefit cost of about $$\yen $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>¥</mml:mo> </mml:math> 12.77, which is negligible for the safety of the UAVs. Meanwhile, the paper compares the computational overheads, and within 10 UAVs, the computational overhead of this scheme is less than 50 ms, which is significantly lower than the other two schemes.
ColonNet: A novel polyp segmentation framework based on LK-RFB and GPPD

Colorectal cancer (CRC) holds the distinction of being the most prevalent malignant tumor affecting the digestive system. It is a formidable global health challenge, as it ranks as the fourth leading cause of cancer-related fatalities around the world. Despite considerable advancements in comprehending and addressing colorectal cancer (CRC), the likelihood of recurring tumors and metastasis remains a major cause of high morbidity and mortality rates during treatment. Currently, colonoscopy is the predominant method for CRC screening. Artificial intelligence has emerged as a promising tool in aiding the diagnosis of polyps, which have demonstrated significant potential. Unfortunately, most segmentation methods face challenges in terms of limited accuracy and generalization to different datasets, especially the slow processing and analysis speed has become a major obstacle. In this study, we propose a fast and efficient polyp segmentation framework based on the Large-Kernel Receptive Field Block (LK-RFB) and Global Parallel Partial Decoder(GPPD). Our proposed ColonNet has been extensively tested and proven effective, achieving a DICE coefficient of over 0.910 and an FPS of over 102 on the CVC-300 dataset. In comparison to the state-of-the-art (SOTA) methods, ColonNet outperforms or achieves comparable performance on five publicly available datasets, establishing a new SOTA. Compared to state-of-the-art methods, ColonNet achieves the highest FPS (over 102 FPS) while maintaining excellent segmentation results, achieving the best or comparable performance on the five public datasets. The code will be released at: https://github.com/SPECTRELWF/ColonNet.
Design and Application of Data Management System for the Coronavirus Pandemic

COVID-19 has quickly become a global health emergency ever since it was first discovered in 2019. The increasingly large amount of data emerging from the pandemic has created a request for effective database management systems for tracking and managing COVID-19 related information. This paper introduces a SQL-based database management system that tracks the important information related to COVID-19, with a focus on managing data related to people and their activities. The system is designed by first translating a composed list of mission objectives and requirements into an Entity-Relationship Diagram (ERD), which establishes and visualizes the structure of the system. From the finalized ERD, data tables are created for each of the entities in the ERD, which are then created inside an Oracle SQL database, and actual data entries are also populated into the database using SQL statements. After the creation of tables and insertion of data entries, the database is set up and ready for queries. In order to make the system operable for non-SQL experts, a graphical interface is developed using Python and PyQT5. The graphical interface includes a login window, a main window for selecting entities, and operation windows for each of the entities in the database. Inside each operation window, users can view/add to/remove from/modify data entries in the database. Ultimately, the goal of this work is to provide a robust and efficient database management system that authorities can utilize to track, analyze and manage the large amount of data that emerged from the COVID-19 pandemic.
AATiENDe: Automatic ATtention Evaluation on a Non-invasive Device

The study of student attention is an important topic in education because this type of analysis provides important information to teachers to potentially improve the quality of their classes. In this paper, we present AATiENDe, a system that uses emotion recognition, gaze direction approximation and body posture analysis as features to classify whether students are paying attention to their computer screens. To do this, we use a mixture of deep learning-based techniques and novel machine learning techniques applied to tabular classifiers to produce the final predictions. We also capture and label a customized dataset to train the models. Our approach provides over 90% accuracy using two cameras and over 80% accuracy using only the foreground camera.