Reviewerly/exHarmony huggingface dataset:
Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using $1 \times1$ convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance; ii) Compared to building multi-view representations directly from scratch, extracting them automatically using $1 \times1$ convolutions is more economical, and may be more effective since $1 \times1$ convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability.
Study of defense methods for additional generative adversarial networks

With the wide application of deep learning, the security classification models are increasingly challenged. A defense method based on an additional generative adversarial network is proposed for the security of deep learning applications in the field of the image class. In this paper, a new generative adversarial network model is designed as an additional layer, and a divine frequent differential equation network is used as a classification model, and the input samples are reconstructed using the additional generative adversarial network to remove adversarial perturbations before inputting them to the classification network, to achieve the defense function. It is experimentally demonstrated that the additional layer can reconstruct the input samples in a timely and efficient manner, and the reconstructed new samples have both the correct class probability distribution and remain highly similar to the input images. Feeding it into the classification model will give correct classification results. This defense method has good generality and generalization, and can protect against different attack techniques in a short time.
Cross-Attention Based Multi-Scale Feature Fusion Vision Transformer For Breast Ultrasound Image Classification

Breast cancer has become one of the most common cancers in the world, and it is also the most lethal cancer in women. As a non-invasive imaging modality, ultrasonography can diagnose the degree of breast lesions and be used for large-scale screening. However, since the lesions in breast ultrasound(BUS) images are morphologically diverse, accompanied by relatively low contrast and complex textures, BUS image recognition faces greater challenges than natural images. In this study, We propose a novel network architecture that combines convolutional neural network(CNN) with vision transformer(ViT) to aggregate local feature details and long-range feature dependencies. Moreover, in order to perform multi-scale feature fusion, we introduce cross attention between the deep feature map and the shallow feature map in the network block to carry out the interaction between the deep feature and the shallow feature information. To verify the effectiveness of the model, we constructed a large-scale dataset and conducted extensive experiments. The results show that our method achieves an accuracy of 85.33%, under the comparable parameter complexity, which outperforms most convolutional neural networks(CNNs) and vision transformers (ViTs).
Fuzzy kernel-free support vector machine and applications in medicine

Classification is widely used in healthcare management. Support vector machines (SVMs), as an important classification algorithm, have been used in target disease classification and prediction. Considering that SVMs are highly dependent on kernel function or parameters of a kernel and medical data uncertainty. This paper extends the classical kernel-free quadratic surface SVM to the case where the dataset is fuzzy samples set. A concept of fuzzy quadratically separable is first introduced, and then kernel-free fuzzy quadratic surface support vector machine (FQSSVM) is proposed, and the algorithm of the FQSSVM is given. A two-stage FQSSVM algorithm is provided for the unbalanced fuzzy sample set. Finally, FQSSVM is used in the diagnosis of coronary and classification of Haberman’s Survival Data. The actual applications verify the performance of our proposed model.
ASNet: An Adversarial Sparse Network for Multi-task Biomedical Named Entity Recognition

Biomedical named entity recognition (BioNER) is to extract entities, such as genes and proteins, from biomedical texts, where there is often a lack of high-quality training data. Recent work addresses this issue by multi-task learning with multiple datasets. However, these methods are usually over-parameterized, and some even suffer from negative transfer issues. To address above problems, we propose adversarial sparse sharing mechanism, which trains a sparsely shared encoder on multiple tasks with both task-agnostic and task-specific subnetworks. With adversarial training, we guide the task-agnostic subnetwork to learn shared task-invariant features and the task-specific subnetwork to learn task-dependent features. For a particular task, only the shared and its subnetwork are activated, which greatly reduces the number of parameters and avoids interference among tasks. Experimental results on 15 benchmark BioNER datasets show that our proposed method outperforms or is competitive with baseline methods with fewer parameters. Our code is released at: https://github.com/CSU-NLP-Group/ASNet
Investigating anxiety and acceptance of students and parents toward authentic contextual learning with a mobile app under the COVID-19

Authentic contextual learning (ACL) with mobile apps near home became prevalent and promising due to the COVID-19 pandemic since students were asked to learn near home without going to school. However, learning with mobile apps may cause problematic issues, such as anxiety and acceptance of either students or parents. This study aims to investigate students’ learning achievements and anxiety and acceptance of students and parents toward ACL with a mobile app, called UG, in two different places, Taoyuan-Taiwan (TG) and Yogyakarta-Indonesia (YG), during the COVID-19 pandemic. The results indicate that students’ achievements in both places were significantly improved. Regarding anxiety, significant differences were found between students’ and parents’ anxiety in both places; a high positive acceptance of students and parents in both places implies no significant difference. In addition, we discuss the different correlations of students’ mobile phone entertainment daily usage, parents’ educational levels, anxiety, acceptance, and achievements between TG and YG.
An Enhanced Load Balancing Technique for Big-data Cloud Computing Environments

The need for cloud computing load balancing is a peculiar area of interest for researchers because it affects both the quality of service provided to users and resource utilisation on the part of cloud service providers. Due to the requirement to minimise processing costs, enhance throughput, improve resource efficiency, and optimise cloud node arrangement, existing cloud computing load balancing methods have been found to be restricted in their capacity to manage big-data cloud system load distribution. This research developed a novel Central-Regional Architecture Based Load Balancing Technique (CRLBT) different from the known central, distributive, and hierarchical cloud architectures. The proposed technique was developed by combining a formulated throughput maximisation algorithm with the algorithms; Throughput Maximised-Particle Swarm Optimisation (TM-PSO) and Throughput Maximised-Firefly optimisation (TM-Firefly). The developed technique was implemented using the MATLAB R2018 software package. The performance of the CRLBT in comparison to the already-in-use PSO and Firefly algorithms was evaluated using response time, throughput, job rejection ratio, and CPU utilisation rate. The significance of the improvement in load balancing brought about by the new approach was further assessed using a statistical T-Test. The results showed that the proposed CRLBT significantly outperformed the PSO and Firefly techniques regarding response time, throughput CPU utilisation rate, and task rejection ratio. Finally, significant improvements in response time, tax rejection ratio, CPU utilisation rate, and network throughput proved the ability of the proposed technique to handle task-resource distribution of big-data cloud centres superiorly.
Investigation of Traffic Classification Applied to an Astronomical Data Transmission Network of the XAO Using Deep Learning

Abstract A telecommunication network used for the transmission of astronomical observation data, telescope remote control and other astronomical research purposes is a critical infrastructure. The monitoring and analysis of network traffic, which help improve the network performance and the utilization of network resources, are a challenging task. The accurate identification of the astronomical data traffic will effectively improve transmission efficiency. In this paper, a classification method applied to types of traffic containing astronomical data using deep learning is proposed. The advantages of a convolutional neural network model in image classification are exploited to classify types of traffic containing astronomical data. The objective is to identify the mixed traffic in the network and accurately identify types of traffic containing astronomical data. The effectiveness of the model in improving classification accuracy is also discussed. Actual traffic data captured by Tcpdump and Wireshark are tested, and the experimental results indicate that the proposed method can accurately classify types of traffic containing astronomical data.
Miniaturized interferometric confocal distance sensor for surface profiling with data rates at ultrasonic frequencies

Abstract Point-wise measuring profilometers like the tactile stylus instruments or optical sensors are established measurement tools in science and industry to measure and characterize surface textures. We present a fiber-coupled laser interferometric confocal distance sensor for surface profiling. The sensor is characterized by small geometric dimensions and by its high lateral scanning speed. The ability to measure specularly reflecting and rough surface textures are demonstrated by performing several measurements on a sinusoidal standard as well as two roughness standards specified by different degree of roughness using various lateral scan velocities up to 75 mm s −1 . These measurement results are compared to those obtained by a tactile stylus instrument used in the identical measurement environment as the introduced optical sensor. Finally, the ability for full-field measurements is presented.