Reviewerly/exHarmony huggingface dataset:
Blockchain Data Secure Transmission Method Based on Homomorphic Encryption

To ensure the security of data transmission and recording in Internet environment monitoring systems, this paper proposes a study of a secure method of blockchain data transfer based on homomorphic encryption. Blockchain data transmission is realized through homomorphic encryption. Homomorphic encryption can not only encrypt the original data, but also ensure that the data result after decrypting the data is the same as the original data. The asymmetric encrypted public key is collected by Internet of things (IoT) equipment to realize the design of blockchain data secure transmission method based on homomorphic encryption. The experimental results show that the accuracy of the first transmission is as high as 88% when using the transmission method in this paper. After several experiments, the transmission accuracy is high by using the design method in this paper. In the last test, the transmission accuracy is still 88%, and the data transmission effect is relatively stable. At the same time, compared to the management method used in this article, the transfer method used in this paper is more reliable than the original transfer method and is not prone to data distortion. It can be seen that this method has high transmission accuracy and short transmission time, which effectively avoids the data tampering caused by too long time in the transmission process.
Stgat-Mad : Spatial-Temporal Graph Attention Network For Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is challenging due to complex temporal and feature correlations and heterogeneity.This paper proposes a novel unsupervised multi-scale stacked spatial-temporal graph attention network for multivariate time series anomaly detection (STGAT-MAD).The core of our framework is to coherently capture the feature and temporal correlations among multivariate time-series data with stackable STGAT networks.Meanwhile, a multi-scale input network is exploited to capture the temporal correlations in different time-scales.Experiments on a new wind turbine dataset(built and released by us) and three public datasets show that our method detects anomalies more accurately than baseline approaches and provide interpretability through observing the attention score among multiple sensors and different times.
Machine Learning

This chapter provides a comprehensive explanation of machine learning including an introduction, history, theory and types, problems, and how these problems can be solved. Then it shows some of the most used machine learning algorithms that are used in image classification, ending with the evaluation matrices calculations that are used to assess the performance of the learning models. The open source libraries also mentioned in this chapter facilitate the used codes for building any learning model with the use of machine learning.
Learning Localization-Aware Target Confidence for Siamese Visual Tracking

Siamese tracking paradigm has achieved great success, providing effective appearance discrimination and size estimation by classification and regression. While such a paradigm typically optimizes the classification and regression independently, leading to task misalignment (accurate prediction boxes have no high target confidence scores). In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA. Within this paradigm, a series of simple, yet effective localization-aware components are introduced to generate localization-aware target confidence scores. Specifically, with the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localization-aware dynamic label</i> (LADL) loss and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localization-aware label smoothing</i> (LALS) strategy, collaborative optimization between the classification and regression is achieved, enabling classification scores to be aware of location state, not just appearance similarity. Besides, we propose a separate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localization-aware quality prediction</i> (LAQP) branch to produce location quality scores to further modify the classification scores. To guide a more reliable modification, a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">localization-aware feature aggregation</i> (LAFA) module is designed and embedded into this branch. Consequently, the resulting target confidence scores are more discriminative for the location state, allowing accurate prediction boxes tend to be predicted as high scores. Extensive experiments are conducted on six challenging benchmarks, including GOT-10 k, TrackingNet, LaSOT, TNL2K, OTB100 and VOT2018. Our SiamLA achieves competitive performance in terms of both accuracy and efficiency. Furthermore, a stability analysis reveals that our tracking paradigm is relatively stable, implying that the paradigm is potential for real-world applications.
A Two-Step Approach to Leverage Contextual Data: Speech Recognition in Air-Traffic Communications

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead to a lower number of incidents caused by misunderstanding and improve air traffic management (ATM) efficiency. Evidently, high accuracy predictions, especially, of key information, i.e., callsigns and commands, are required to minimize the risk of errors. We prove that combining the benefits of ASR and Natural Language Processing (NLP) methods to make use of surveillance data (i.e. additional modality) helps to considerably improve the recognition of callsigns (named entity). In this paper, we investigate a two-step callsign boosting approach: (1) at the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> step (ASR), weights of probable callsign n-grams are reduced in G.fst and/or in the decoding FST (lattices), (2) at the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> step (NLP), callsigns extracted from the improved recognition outputs with Named Entity Recognition (NER) are correlated with the surveillance data to select the most suitable one. Boosting callsign n-grams with the combination of ASR and NLP methods eventually leads up to 53.7% of an absolute, or 60.4% of a relative, improvement in callsign recognition.
A Transfer Learning-Based Deep CNN Approach for Classification and Diagnosis of Acute Lymphocytic Leukemia Cells

Leukemia is a severe malignant illness affecting both adults and children. It can be chronic or acute. Acute Lymphoblastic Leukemia (ALL) is the most prevalent type of blood disease and one of the leading causes of death. This type of cancer invades the blood and spread through neighboring organs and body systems. To classify cancer cells and non-cancer cells, specialists need to perform manual diagnosis through inspection of cell images under a microscope and provide labels through annotation. However, this manual microscopic analysis is tedious and may lead to a false diagnosis. With this concern, the author proposed a computer-aided diagnosis method using a transfer learning-based deep learning approach. In this study, the YOLOv3 model is utilized to train a deep learning model that will classify ALL and normal cells. The model produced promising results as it has a training loss of 2.8% or a 97.2% training accuracy. Based on the model evaluation, the model has an mAP value of 99.8% as well. YOLOv3 was shown to be a useful tool for distinguishing leukemia cells from non-leukemia cells.
Intrusion Detection Techniques Based Secured Data Sharing System for Cloud Computing Using MSVM

Cloud storage has grown in popularity, attracting a lot of interest from academics and IT professionals. In the digital era, cloud computing has grown increasingly vital for scalable resource sharing, smooth computing, and information interchange. Researchers have a challenging task in securing cloud data, resources, applications, and infrastructure. Cloud security methods are used to protect the cloud in the cloud computing environment. Detective controls, for example, are critical in cloud security because they discover possible breaches and defend the cloud environment from attacks. This detective control mechanism is built using an intrusion detection system (IDS). The network traffic data utilised to build the IDS using a machine-learning algorithm has a major influence on the detection accuracy of the IDS. When the performance of the proposed algorithm is compared to that of existing algorithms, it is observed that the new approach outperforms the others. Intrusion detection systems (IDS) have become an important security tool for monitoring and researching network security in mobile cloud computing (MCC). However, several modern techniques still have flaws, such as high false positive rates, poor classification accuracies, and low true positive rates. This paper provides an intrusion detection strategy for MCC based on support vector machines (SVM) and information gain (IG). The suggested approach employs the SVM classifier to categories network data into normal and attack behaviours, and IG to choose the significant features and reduce the unnecessary features, because to the irrelevant and duplicated characteristics seen in KDD datasets. Because it can be accessible from anywhere on the internet, cloud storage has become the most popular data and information storage option. While it has its own set of advantages and benefits, its storage system protection has certain drawbacks. Cloud encryption ensures that cloud storage is secure, but it does not guarantee that the system will not be hacked. Cloud storage, like any other untrustworthy setting, demands the ability to safeguard shared data. This suggested solution gives access control to data handled in the cloud without contacting the vendor.
FINT: Field-Aware Interaction Neural Network for Click-Through Rate Prediction

As a critical component for online advertising and marketing, click-through rate (CTR) prediction has drawn lots of attention from both industry and academia. Recently, deep learning has become the mainstream methodological choice for CTR. Despite sustainable efforts have been made, existing approaches still pose several challenges. On the one hand, high-order interaction between the features is under-explored. On the other hand, high-order interactions may neglect the semantic information from the low-order fields. In this paper, we proposed a novel prediction method, named FINT, that employs the Field-aware INTeraction layer which explicitly captures high-order feature interactions while retaining the low-order field information. To empirically investigate the effectiveness and robustness of the FINT, we perform extensive experiments on the three realistic databases: KDD2012, Criteo and Avazu. The obtained results demonstrate that the FINT can significantly improve the performance compared to the existing methods, without increasing the amount of computation required. Moreover, the proposed method brought about 2.72% increase to the advertising revenue of iQIYI, a big online video app through A/B testing. To better promote the research in CTR field, we released our code as well as reference implementation at: https://github.com/zhishan01/FINT.
Novel Instance Mining with Pseudo-Margin Evaluation for Few-Shot Object Detection

Few-shot object detection (FSOD) enables the detector to recognize novel objects only using limited training samples, which could greatly alleviate model’s dependency on data. Most existing methods include two training stages, namely base training and fine-tuning. However, the unlabeled novel instances in the base set were untouched in previous works, which can be re-used to enhance the FSOD performance. Thus, a new instance mining model is proposed in this paper to excavate the novel samples from the base set. The detector is thus fine-tuned again by these additional free novel instances. Meanwhile, a novel pseudo-margin evaluation algorithm is designed to address the quality problem of pseudo-labels brought by those new novel instances. The experimental results on MS-COCO dataset show the effectiveness of the proposed model, which does not require any additional training samples or parameters. Our code is available at: https://github.com/liuweijie19980216/NimPme.
Trainable segmentation for transmission electron microscope images of inorganic nanoparticles

We present a trainable segmentation method implemented within the python package ParticleSpy. The method takes user labelled pixels, which are used to train a classifier and segment images of inorganic nanoparticles from transmission electron microscope images. This implementation is based on the trainable Waikato Environment for Knowledge Analysis (WEKA) segmentation, but is written in python, allowing a large degree of flexibility and meaning it can be easily expanded using other python packages. We find that trainable segmentation offers better accuracy than global or local thresholding methods and requires as few as 100 user-labelled pixels to produce an accurate segmentation. Trainable segmentation presents a balance of accuracy and training time between global/local thresholding and neural networks, when used on transmission electron microscope images of nanoparticles. We also quantitatively investigate the effectiveness of the components of trainable segmentation, its filter kernels and classifiers, in order to demonstrate the use cases for the different filter kernels in ParticleSpy and the most accurate classifiers for different data types. A set of filter kernels is identified that are effective in distinguishing particles from background but that retain dissimilar features. In terms of classifiers, we find that different classifiers perform optimally for different image contrast; specifically, a random forest classifier performs best for high-contrast ADF images, but that QDA and Gaussian Naïve Bayes classifiers perform better for low-contrast TEM images.Measurement of the size, shape and composition of nanoparticles is routinely performed using transmission electron microscopy and related techniques. Typically, distinguishing particles from the background in an image is performed using the intensity of each pixel, creating two sets of pixels to separate particles from background. However, this separation of intensity can be difficult if the contrast in an image is low, or if the intensity of the background varies significantly. In this study, an approach that takes into account additional image features (such as boundaries and texture) was investigated to study electron microscope images of metallic nanoparticles. In this ‘trainable segmentation’ approach, the user labels examples of particle and background pixels in order to train a machine learning algorithm to distinguish between particles and background. The performance of different machine learning algorithms was investigated, in addition to the effect of using different features to aid the segmentation. Overall, a trainable segmentation approach was found to perform better than use of an intensity threshold to distinguish between particles and background in electron microscope images.
Improved cost-sensitive representation of data for solving the imbalanced big data classification problem

Abstract Dimension reduction is a preprocessing step in machine learning for eliminating undesirable features and increasing learning accuracy. In order to reduce the redundant features, there are data representation methods, each of which has its own advantages. On the other hand, big data with imbalanced classes is one of the most important issues in pattern recognition and machine learning. In this paper, a method is proposed in the form of a cost-sensitive optimization problem which implements the process of selecting and extracting the features simultaneously. The feature extraction phase is based on reducing error and maintaining geometric relationships between data by solving a manifold learning optimization problem. In the feature selection phase, the cost-sensitive optimization problem is adopted based on minimizing the upper limit of the generalization error. Finally, the optimization problem which is constituted from the above two problems is solved by adding a cost-sensitive term to create a balance between classes without manipulating the data. To evaluate the results of the feature reduction, the multi-class linear SVM classifier is used on the reduced data. The proposed method is compared with some other approaches on 21 datasets from the UCI learning repository, microarrays and high-dimensional datasets, as well as imbalanced datasets from the KEEL repository. The results indicate the significant efficiency of the proposed method compared to some similar approaches.
An extreme learning machine based virtual sample generation method with feature engineering for credit risk assessment with data scarcity

As a typical category of data scarcity, small sample often makes it difficult to build a reliable machine learning model in credit risk assessment, and thus many virtual sample generation (VSG) methods have been proposed for sample augmentation based on sample distribution. In particular, when small sample with low dimensionality exists in credit datasets simultaneously, it becomes more difficult to predict the customer credit status for credit institutions. In order to solve these issues, an extreme learning machine (ELM) based VSG methodology with feature engineering is proposed for credit risk assessment with data scarcity. In this methodology, ELM-based VSG methodology is first used to generate virtual samples and solve data instance scarcity (i.e., small sample) issue. Second, feature engineering is used to solve data attribute scarcity (i.e., low dimensionality) issue. Finally, various classifiers are used to predict the performance for generated virtual samples. For verification purpose, two public credit datasets are used to conduct credit classification with data scarcity. The experimental results show that the proposed methodology can effectively improve the classification performance for credit risk assessment with data scarcity.
Auxiliary classification of cervical cells based on multi-domain hybrid deep learning framework

Computer-aided cervical cell classification using Pap smears or Thinprep cytologic test (TCT) have been widely applied as a high effective screening tool, by which the cells are classified into different subclasses. However, existing classification approaches mainly rely on single detection structure, like deep learning or hand-crafted methods, which have huge computation complexity and lower accuracy. So far, no cell spectrum is applied for classification. This paper addresses the limitations by making the first attempt to use the multi-domain hybrid deep learning framework (MDHDN) for the classification of cervical cells. Cell deep features from multi-domain (time and frequency) are extracted by the pretrained VGG-19 (Visual Geometry Group-19), which is the deep Convolutional Neural Network (CNN) with a hashing layer after the last fully connected layer. Hand-crafted features for the original images are processed with the feature selection, clustering and dimensionality reduction. Then the three subchannels of the proposed framework output the category results using the SVM classifier, the final cell diagnosis is generated by the correlation analysis. Results show that the proposed approach obtains the similar performance with the state-of-the-art models using the novel structure, whose accuracy, sensitivity, and specificity are 98.7%, 98.2%, 98.9% in Herlev dataset when applying five-fold cross-validation, respectively. Similar superior classification performance is achieved on the BJTU dataset, validation on the SIPaKMeD dataset also proves its generalization ability. The proposed novel screening framework is promising for the early diagnosis of cervical cancer, multi-domain and hybrid features are proved feasible in clinical practice.
Virtual reality (VR) therapy for patients with psychosis: satisfaction and side effects

Automated virtual reality therapies are being developed to increase access to psychological interventions. We assessed the experience with one such therapy of patients diagnosed with psychosis, including satisfaction, side effects, and positive experiences of access to the technology. We tested whether side effects affected therapy.In a clinical trial 122 patients diagnosed with psychosis completed baseline measures of psychiatric symptoms, received gameChange VR therapy, and then completed a satisfaction questionnaire, the Oxford-VR Side Effects Checklist, and outcome measures.79 (65.8%) patients were very satisfied with VR therapy, 37 (30.8%) were mostly satisfied, 3 (2.5%) were indifferent/mildly dissatisfied, and 1 (0.8%) person was quite dissatisfied. The most common side effects were: difficulties concentrating because of thinking about what might be happening in the room (n = 17, 14.2%); lasting headache (n = 10, 8.3%); and the headset causing feelings of panic (n = 9, 7.4%). Side effects formed three factors: difficulties concentrating when wearing a headset, feelings of panic using VR, and worries following VR. The occurrence of side effects was not associated with number of VR sessions, therapy outcomes, or psychiatric symptoms. Difficulties concentrating in VR were associated with slightly lower satisfaction. VR therapy provision and engagement made patients feel: proud (n = 99, 81.8%); valued (n = 97, 80.2%); and optimistic (n = 96, 79.3%).Patients with psychosis were generally very positive towards the VR therapy, valued having the opportunity to try the technology, and experienced few adverse effects. Side effects did not significantly impact VR therapy. Patient experience of VR is likely to facilitate widespread adoption.