Reviewerly/exHarmony huggingface dataset:
AI-GAN: Attack-Inspired Generation of Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, which are crafted by adding imperceptible perturbations to inputs. Recently different attacks and strategies have been proposed, but how to generate adversarial examples perceptually realistic and more efficiently remains unsolved. This paper proposes a novel framework called Attack-Inspired GAN (AI-GAN), where a generator, a discriminator, and an attacker are trained jointly. Once trained, it can generate adversarial perturbations efficiently given input images and target classes. Through extensive experiments on several popular datasets e.g., MNIST and CFAR-10, AI-GAN achieves high attack success rates and reduces generation time significantly in various settings. Moreover, for the first time, AI-GAN successfully scales to complicated datasets e.g., CFAR-100 with around 90% success rates among all classes.
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation

Unsupervised disentanglement learning is a crucial issue for understanding and exploiting deep generative models. Recently, SeFa tries to find latent disentangled directions by performing SVD on the first projection of a pretrained GAN. However, it is only applied to the first layer and works in a post-processing way. Hessian Penalty minimizes the off-diagonal entries of the output’s Hessian matrix to facilitate disentanglement, and can be applied to multi-layers. However, it constrains each entry of output independently, making it not sufficient in disentangling the latent directions (e.g., shape, size, rotation, etc.) of spatially correlated variations. In this paper, we propose a simple Orthogonal Jacobian Regularization (OroJaR) to encourage deep generative model to learn disentangled representations. It simply encourages the variation of output caused by perturbations on different latent dimensions to be orthogonal, and the Jacobian with respect to the input is calculated to represent this variation. We show that our OroJaR also encourages the output’s Hessian matrix to be diagonal in an indirect manner. In contrast to the Hessian Penalty, our OroJaR constrains the output in a holistic way, making it very effective in disentangling latent dimensions corresponding to spatially correlated variations. Quantitative and qualitative experimental results show that our method is effective in disentangled and controllable image generation, and performs favorably against the state-of-the-art methods. Our code is available at https://github.com/csyxwei/OroJaR.
Clustered Routing Method in the Internet of Things Using a Moth‐Flame Optimization Algorithm

Summary Internet of Things (IoT) is a set of interrelated devices on the Internet platform that can receive and send data to make human life more efficient and convenient. Clustering is a useful data collection method in the IoT that selectively cuts energy consumption by forming IoT nodes into some clusters. The cluster head can control all cluster nodes, and all intracluster and intercluster connections are made through it. Due to the NP‐Hard nature of the clustering problem, a moth‐flame optimization algorithm is proposed to select the minimum number of necessary clusters for routing in this article. This scheme is extracted from the life cycle of moths and enables effective communication by creating the optimal number of clusters. The proposed fitness function consists of the sum of the distances, the amount of energy remaining, and the degree of the nodes. The obtained experimental results are compared to different clustering algorithms, such as whale optimization algorithm, novel chemical reaction optimization, and cuckoo search optimization. The simulation using MATLAB clearly showed that the proposed method has a low number of cluster head nodes and the most balanced clusters. It also improves lifetime by at least 14.59% compared to the mentioned techniques.
A One-Class Generative Adversarial Detection Framework for Multifunctional Fault Diagnoses

In this article, fault diagnosis is of great significance for system health maintenance. For real applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data are usually abundant than anomaly ones, leading to tremendous diagnosis obstacles. Therefore, it is challenging to use only normal data for fault diagnosis under this imbalanced condition. In addition, a single fault diagnosis model can only conduct one fault diagnosis task in most of cases. Accordingly, a one-class generative adversarial detection (OCGAD) framework based on semisupervised learning is proposed to learn one-class latent knowledge for dealing with multiple semisupervised fault diagnosis tasks, i.e., fault detection using only normal knowledge learning, novelty detection from unknown conditional data, and fault classification with unlabeled data. A bi-directional generative adversarial network (Bi-GAN) is first trained with only normal data. A one-class support vector machine is then established using features exacted by Bi-GAN from signals acquired from an attitude sensor for multifunctional fault detection. The presented OCGAD model is validated using an industrial robot with experiments of three fault detection tasks. The results demonstrate that the present model has good performance for dealing with multiple semisupervised diagnosis problems.
The Role of Explainability in Assuring Safety of Machine Learning in Healthcare

Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against which to assess validity.This problem is exacerbated by the "opaque" nature of ML where the learnt model is not amenable to human scrutiny.Explainable AI (XAI) methods have been proposed to tackle this issue by producing human-interpretable representations of ML models which can help users to gain confidence and build trust in the ML system.However, little work explicitly investigates the role of explainability for safety assurance in the context of ML development.This paper identifies ways in which XAI methods can contribute to safety assurance of ML-based systems.It then uses a concrete ML-based clinical decision support system, concerning weaning of patients from mechanical ventilation, to demonstrate how XAI methods can be employed to produce evidence to support safety assurance.The results are also represented in a safety argument to show where, and in what way, XAI methods can contribute to a safety case.Overall, we conclude that XAI methods have a valuable role in safety assurance of ML-based systems in healthcare but that they are not sufficient in themselves to assure safety.
The art of balance

GPU-accelerated database systems have been studied for more than 10 years, ranging from prototyping development to industry products serving in multiple domains of data applications. Existing GPU database research solutions are often focused on specific aspects in parallel algorithms and system implementations for specific features, while industry product development generally concentrates on delivering a whole system by considering its holistic performance and cost. Aiming to fill this gap between academic research and industry development, we present a comprehensive industry product study on a complete CPU/GPU HTAP system, called RateupDB. We firmly believe "the art of balance" addresses major issues in the development of RateupDB. Specifically, we consider balancing multiple factors in the software development cycle, such as the trade-off between OLAP and OLTP, the trade-off between system performance and development productivity, and balanced choices of algorithms in the product. We also present RateupDB's complete TPC-H test performance to demonstrate its significant advantages over other existing GPU DBMS products.
Privacy-Preserving Keyword Similarity Search Over Encrypted Spatial Data in Cloud Computing

With the proliferation of cloud computing, data owners can outsource the spatial data from the Internet of Things devices to a cloud server to enjoy the pay-as-you-go storage resources and location-based services. However, the outsourced services may raise privacy concerns, since the cloud server may not be fully trusted for both data owners and search users. If the data owners and search users conventionally encrypt the spatial data and query requests, the efficiency and functionality of query processing are weakened. Most of the existing works only focus on spatial data search or keyword search and do not consider spatial keyword search over encrypted data. In this article, we first design a geometric range query (GRQ) scheme, which can generate an arbitrary geometric range to fit the search user’s desired spatial data while protecting location privacy. Furthermore, based on GRQ, we propose a multidimensional spatial keyword similarity search scheme with access control (MSSAC) by integrating the polynomial function and matrix transformation. Specifically, an access control strategy is defined by a role-based polynomial function, which is embedded in the vectors of indices and trapdoors to achieve efficient and lightweight access control. Moreover, MSSAC enables the cloud server to execute compute-then-compare operations for spatial keyword search in a privacy-preserving manner by leveraging techniques of randomizable permutation and matrix multiplication. The formal security analyses and extensive experiments demonstrate that GRQ and MSSAC preserve the privacy of data owners and search users while achieving efficient spatial keyword search.
Binarized neural network for edge intelligence of sensor-based human activity recognition

A wide diversity of sensors has been applied in human activity recognition. These sensors generate enormous amounts of data during human activity monitoring. The long-distance data traveling between sensors and servers increases the costs of bandwidth and latency. However, human activity recognition has a high demand for real-time processing. Recently, edge computing is surging to solve this problem by moving computation and data storage closer to the sensor devices, rather than relying on a central server/cloud. Edge servers are usually designed for low power, low cost, and low computation. They do not support computation-intensive deep learning algorithms or will result in high latency. Fortunately, the development of binarized neural networks enables edge intelligence which supports AI running at the network edge for real-time applications. In this paper, we implement a binarized neural network (BinaryDilatedDenseNet) to enable low-latency and low-memory human activity recognition at the network edge. We applied the BinaryDilatedDenseNet on three sensor-based human activity recognition datasets and evaluated it with four metrics. In comparison, the BinaryDilatedDenseNet outperforms the related work and other three binarized neural networks in accuracy and saves 10 memory and 4.5--8 inference time compared to the FPDilatedDenseNet(the full-precision version of the BinaryDilatedDenseNet).
Shallow pooling for sparse labels

Recent years have seen enormous gains in core information retrieval tasks, including document and passage ranking. Datasets and leaderboards, and in particular the MS MARCO datasets, illustrate the dramatic improvements achieved by modern neural rankers. When compared with traditional information retrieval test collections, such as those developed by TREC, the MS MARCO datasets employ substantially more queries—thousands vs. dozens – with substantially fewer known relevant items per query—often just one. For example, 94% of the nearly seven thousand queries in the MS MARCO passage ranking development set have only a single known relevant passage, and no query has more than four. Given the sparsity of these relevance labels, the MS MARCO leaderboards track improvements with mean reciprocal rank (MRR). In essence, the known relevant item is treated as the "right answer" or "best answer", with rankers scored on their ability to place this item as high in the ranking as possible. In working with these sparse labels, we have observed that the top items returned by a ranker often appear superior to judged relevant items. Others have reported the same observation. To test this observation, we employed crowdsourced workers to make preference judgments between the top item returned by a modern neural ranking stack and a judged relevant item for the nearly seven thousand queries in the passage ranking development set. The results support our observation. If we imagine a hypothetical perfect ranker under MRR, with a score of 1 on all queries, our preference judgments indicate that a searcher would prefer the top result from a modern neural ranking stack more frequently than the top result from the hypothetical perfect ranker, making our neural ranker "better than perfect". To understand the implications for the leaderboard, we pooled the top document from available runs near the top of the passage ranking leaderboard for over 500 queries. We employed crowdsourced workers to make preference judgments over these pools and re-evaluated the runs. Our results support our concerns that current MS MARCO datasets may no longer be able to recognize genuine improvements in rankers. In future, if rankers are measured against a single answer, this answer should be the best answer or most preferred answer, and maintained with ongoing judgments. Since only the best known answer is required, this ongoing maintenance might be performed with shallow pooling. When a previously unjudged document is surfaced as the top item in a ranking, it can be directly compared with the previous best known answer.
Deep learning based offloading for mobile augmented reality application in 6G

Mobile Augmented Reality (MAR) applications are fast becoming popular with the growth in use of smartphones and smart wearable devices. Apart from gaming, MAR finds useful application in any field for attractive visualization of the environment. The computer vision algorithms used in MAR applications are both data and computation extensive which renders them difficult to use in delay sensitive applications, given the present network scenario. But the network standard 6G expected to be deployed around 2030 is supposed to operate at a GHz to THz frequency. This will increase the bandwidth of the network in manifolds and can support the seamless real time transfer of the multimedia data. The article proposes to divide the various phases of an MAR application into sequential and parallel tasks and attempts to offload the task to the nearby devices with the help of Deep Reinforcement Algorithm (DRL) depending on transmission, task and energy constraints.
A Novel SVM-Based Decoder for Remote Sensing Image Captioning

Most of the remote sensing image captioning (IC) models are based on encoder–decoder frameworks where a convolutional neural network (CNN) encodes the image information and a recurrent neural network (RNN) decodes the image information into a sentence description. In order to achieve good accuracies, encoder–decoder frameworks relying on RNNs typically require a huge amount of annotated samples. Furthermore, they demand high and expensive computational power in order to have reasonable training and testing time. In this article, we aim to address these issues by introducing a novel decoder that is based on support vector machines (SVMs). In particular, instead of RNNs, we propose a novel network of SVMs to decode the image information into a sentence description. The proposed IC system is particularly interesting when just a limited amount of training samples is available. Experiments conducted on four different IC datasets confirm the promising capability of the proposed IC system to generate descriptions that are highly correlated with the image content. The proposed IC system is characterized by short training and inference times compared to other state-of-the-art models.
Analysis of public opinion evolution of COVID-19 based on LDA-ARMA hybrid model

The aim of this study was to explore a method for developing an emotional evolution classification model for large-scale online public opinion of events such as Coronavirus Disease 2019 (COVID-19), in order to guide government departments to adopt differentiated forms of emergency management and to correctly guide online public opinion for severely afflicted areas such as Wuhan and those afflicted elsewhere in China. We propose the LDA-ARMA deep neural network for dynamic presentation and fine-grained categorization of a public opinion events. This was applied to a huge quantity of online public opinion texts in a complicated setting and integrated the proposed sentiment measurement algorithm. To begin, the Latent Dirichlet Allocation (LDA) was employed to extract information about the topic of comments. The autoregressive moving average model (ARMA) was then utilized to perform multidimensional sentiment analysis and evolution prediction on large-scale textual data related to COVID-19 published by netizens from Wuhan and other countries on Sina Weibo. The results show that Wuhan netizens paid more attention to the development of the situation, treatment measures, and policies related to COVID-19 than other issues, and were under greater emotional pressure, whereas netizens in the rest of the country paid more attention to the overall COVID-19 prevention and control, and were more positive and optimistic with the assistance of the government and NGOs. The average error in predicting public opinion sentiment was less than 5.64%, demonstrating that this approach may be effectively applied to the analysis of large-scale online public sentiment evolution.
Design of English Interactive Teaching System Based on Association Rules Algorithm

The Internet has become a new medium, and educational activities have also undergone changes due to the development of new technologies. In the past, teaching activities that required students to teach in a specific time and space have changed to less time and space constraints, which have also made the educational process pay more attention to the university teaching mode under the Internet situation, which makes the teaching process more comfortable and intelligent. In this paper, we propose an English interactive teaching system based on the association rules algorithm. This method can make full use of the relevance between data to improve the efficiency of English teaching. According to the division of UNESCO, education informatization is divided into four stages: start, application, integration, and innovation. With the initial development of network hardware technology construction in recent years, it has gradually entered the integration and innovation stage of education and information technology. The important task at this stage is to carry out innovations that are different from traditional teaching concepts, innovations that are different from traditional talent training methods, and innovations that are different from traditional education methods and evaluation methods. From the perspective of dialectical materialism, different angles of a certain issue can be expressed differently. Educational activities are based on Huluo’s technology to a certain extent, and the closedness of traditional education methods has been changed. In order to verify the performance of the method proposed in this paper, we test the proposed algorithm as well as compare it with the other two strategies. The experimental results show that the proposed has a better performance.
Knowledge Preserving and Distribution Alignment for Heterogeneous Domain Adaptation

Domain adaptation aims at improving the performance of learning tasks in a target domain by leveraging the knowledge extracted from a source domain. To this end, one can perform knowledge transfer between these two domains. However, this problem becomes extremely challenging when the data of these two domains are characterized by different types of features, i.e., the feature spaces of the source and target domains are different, which is referred to as heterogeneous domain adaptation (HDA). To solve this problem, we propose a novel model called Knowledge Preserving and Distribution Alignment (KPDA), which learns an augmented target space by jointly minimizing information loss and maximizing domain distribution alignment. Specifically, we seek to discover a latent space, where the knowledge is preserved by exploiting the Laplacian graph terms and reconstruction regularizations. Moreover, we adopt the Maximum Mean Discrepancy to align the distributions of the source and target domains in the latent space. Mathematically, KPDA is formulated as a minimization problem with orthogonal constraints, which involves two projection variables. Then, we develop an algorithm based on the Gauss–Seidel iteration scheme and split the problem into two subproblems, which are solved by searching algorithms based on the Barzilai–Borwein (BB) stepsize. Promising results demonstrate the effectiveness of the proposed method.
Out-of-Distribution Detection for Deep Neural Networks With Isolation Forest and Local Outlier Factor

Deep Neural Networks (DNNs) are extensively deployed in today’s safety-critical autonomous systems thanks to their high performance. However, they are known to make mistakes unpredictably, e.g., a DNN may misclassify an object if it is used for perception, or issue unsafe control commands if it is used for planning and control. One common cause for such unpredictable mistakes is Out-of-Distribution (OOD) inputs, i.e., test inputs that fall outside of the distribution of the training dataset. In this paper, we present a framework for OOD detection based on outlier detection in the hidden layers of a DNN by applying Isolation Forest (IF) and Local Outlier Factor (LOF) techniques. Extensive experimental evaluation indicates that LOF is a promising method in terms of both the Machine Learning metrics of precision, recall, F1 score and accuracy, and computational efficiency during testing.
Automated Classification of Societal Sentiments on Twitter With Machine Learning

The rapid growth in information sharing on social media has defined a new information age in our society. Microblogging sites, such as Twitter, gained immense popularity during the COVID-19 pandemic. We developed an automated framework to extract the positive, negative, and neutral sentiments from tweets, and to further classify the tweets through machine-learning (ML) techniques. The developed framework can help to understand the sentiments in our society during profound events, such as the COVID-19 pandemic. Our framework is novel in that it is a hybrid framework that combines a lexicon-based technique for tweet sentiment analysis and labeling with supervised ML techniques for tweet classification. We have evaluated the hybrid framework with a range of measures, such as precision, accuracy, recall, and F1 score. Our results indicate that the majority of the sentiments are positive (38.5%) or neutral (34.7%). Furthermore, with an accuracy of 83%, the long short-term memory (LSTM) neural network has been selected as the preferred ML technique in the framework. The evaluation results indicate that our hybrid framework has the potential to automatically classify large tweet volumes, such as the tweets on COVID-19, according to the sentiments in the society.