Reviewerly/exHarmony huggingface dataset:
Multi-angle quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good parameters can be found in polynomial time for a test dataset we consider. This new ansatz gives a 33% increase in the approximation ratio for an infinite family of MaxCut instances over QAOA. The optimal performance is lower bounded by the conventional ansatz, and we present empirical results for graphs on eight vertices that one layer of the multi-angle anstaz is comparable to three layers of the traditional ansatz on MaxCut problems. Similarly, multi-angle QAOA yields a higher approximation ratio than QAOA at the same depth on a collection of MaxCut instances on fifty and one-hundred vertex graphs. Many of the optimized parameters are found to be zero, so their associated gates can be removed from the circuit, further decreasing the circuit depth. These results indicate that multi-angle QAOA requires shallower circuits to solve problems than QAOA, making it more viable for near-term intermediate-scale quantum devices.
Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer

Adversarial attacks and backdoor attacks are two common security threats that hang over deep learning.Both of them harness taskirrelevant features of data in their implementation.Text style is a feature that is naturally irrelevant to most NLP tasks, and thus suitable for adversarial and backdoor attacks.In this paper, we make the first attempt to conduct adversarial and backdoor attacks based on text style transfer, which is aimed at altering the style of a sentence while preserving its meaning.We design an adversarial attack method and a backdoor attack method, and conduct extensive experiments to evaluate them.Experimental results show that popular NLP models are vulnerable to both adversarial and backdoor attacks based on text style transfer-the attack success rates can exceed 90% without much effort.It reflects the limited ability of NLP models to handle the feature of text style that has not been widely realized.In addition, the style transfer-based adversarial and backdoor attack methods show superiority to baselines in many aspects.
Security and privacy-aware Artificial Intrusion Detection System using Federated Machine Learning

Beyond 5G networks integrating 5G technology offer efficient services globally with sustainable higher capacity and much lower latency across various applications. Moving into the beyond 5G networks, edge intelligence solutions with data-driven machine learning algorithms and cybersecurity paradigms have become crucial among various real-time applications, including smart transportation, smart health, etc. Since the data gets transferred continuously from the edge devices to the dedicated computing in any edge computing environment, it experiences a higher risk of vulnerability and complexity measures. In this view, this paper firstly defines a federated machine learning mechanism for privacy-enhanced edge intelligence model Beyond 5G networks with Paillier Homomorphic Encryption and differential privacy. Secondly, an Artificial Immune Intrusion Detection System has been designed to monitor and classify the nodes resulting in an anomaly in the edge network so that the network can experience smooth and secure data transmission as per the requirement. The experiments and comparison results show that the proposed system is more optimal and secure than the existing edge security models.
Dual-Awareness Attention for Few-Shot Object Detection

While recent progress has significantly boosted few-shot classification (FSC) performance, few-shot object detection (FSOD) remains challenging for modern learning systems. Existing FSOD systems follow FSC approaches, ignoring critical issues such as spatial variability and uncertain representations, and consequently result in low performance. Observing this, we propose a novel <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dual-Awareness Attention (DAnA)</b> mechanism that enables networks to adaptively interpret the given support images. DAnA transforms support images into <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">query-position-aware</b> (QPA) features, guiding detection networks precisely by assigning customized support information to each local region of the query. In addition, the proposed DAnA component is flexible and adaptable to multiple existing object detection frameworks. By adopting DAnA, conventional object detection networks, Faster R-CNN and RetinaNet, which are not designed explicitly for few-shot learning, reach state-of-the-art performance in FSOD tasks. In comparison with previous methods, our model significantly increases the performance by 47% (+6.9 AP), showing remarkable ability under various evaluation settings.
Multi UAV Coverage Path Planning in Urban Environments

Coverage path planning (CPP) is a field of study which objective is to find a path that covers every point of a certain area of interest. Recently, the use of Unmanned Aerial Vehicles (UAVs) has become more proficient in various applications such as surveillance, terrain coverage, mapping, natural disaster tracking, transport, and others. The aim of this paper is to design efficient coverage path planning collision-avoidance capable algorithms for single or multi UAV systems in cluttered urban environments. Two algorithms are developed and explored: one of them plans paths to cover a target zone delimited by a given perimeter with predefined coverage height and bandwidth, using a boustrophedon flight pattern, while the other proposed algorithm follows a set of predefined viewpoints, calculating a smooth path that ensures that the UAVs pass over the objectives. Both algorithms have been developed for a scalable number of UAVs, which fly in a triangular deformable leader-follower formation with the leader at its front. In the case of an even number of UAVs, there is no leader at the front of the formation and a virtual leader is used to plan the paths of the followers. The presented algorithms also have collision avoidance capabilities, powered by the Fast Marching Square algorithm. These algorithms are tested in various simulated urban and cluttered environments, and they prove capable of providing safe and smooth paths for the UAV formation in urban environments.
Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review

Abstract Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.
Generating Images with Physics-Based Rendering for an Industrial Object Detection Task: Realism versus Domain Randomization

Limited training data is one of the biggest challenges in the industrial application of deep learning. Generating synthetic training images is a promising solution in computer vision; however, minimizing the domain gap between synthetic and real-world images remains a problem. Therefore, based on a real-world application, we explored the generation of images with physics-based rendering for an industrial object detection task. Setting up the render engine's environment requires a lot of choices and parameters. One fundamental question is whether to apply the concept of domain randomization or use domain knowledge to try and achieve photorealism. To answer this question, we compared different strategies for setting up lighting, background, object texture, additional foreground objects and bounding box computation in a data-centric approach. We compared the resulting average precision from generated images with different levels of realism and variability. In conclusion, we found that domain randomization is a viable strategy for the detection of industrial objects. However, domain knowledge can be used for object-related aspects to improve detection performance. Based on our results, we provide guidelines and an open-source tool for the generation of synthetic images for new industrial applications.
An optimized deep neural network-based financial statement fraud detection in text mining

Identifying Financial Statement Fraud (FSF) events is very crucial in text mining.The researcher's community is mostly utilized the data mining method for detecting FSF.In this direction, mostly the quantitative data has utilized by research i.e. the financial ratio is presented for detecting fraud in financial statements.On the text investigation there is no researches like auditor's remarks present in published reports.For this reason, this paper develops the optimized deep neural network-based FSF detection in the qualitative data present in financial reports.The pre-processing of text is performed initially using filtering, lemmatization, and tokenization.Then, the feature selection is done by the Harris Hawks Optimization (HHO) algorithm.Finally, a Deep Neural Network-Based Deer Hunting Optimization (DNN-DHO) is utilized to identify the fraud or no-fraud report in the financial statements.The developed FSF detection methodology executed in Python environment using financial statement datasets.The output of the developed approach gives high classification accuracy (96%) in comparison to the standard classifiers like DNN, CART, LR, SVM, Bayes, BP-NN, and KNN.Also, it provides better outcomes in all performance metrics.
Active Learning for Domain Adaptation: An Energy-Based Approach

Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains. However, there is still enormous potential to be tapped to reach the fully supervised performance. In this paper, we present a novel active learning strategy to assist knowledge transfer in the target domain, dubbed active domain adaptation. We start from an observation that energy-based models exhibit free energy biases when training (source) and test (target) data come from different distributions. Inspired by this inherent mechanism, we empirically reveal that a simple yet efficient energy-based sampling strategy sheds light on selecting the most valuable target samples than existing approaches requiring particular architectures or computation of the distances. Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of target data that incorporate both domain characteristic and instance uncertainty into every selection round. Meanwhile, by aligning the free energy of target data compact around the source domain via a regularization term, domain gap can be implicitly diminished. Through extensive experiments, we show that EADA surpasses state-of-the-art methods on well-known challenging benchmarks with substantial improvements, making it a useful option in the open world. Code is available at https://github.com/BIT-DA/EADA.
DCNN based human activity recognition framework with depth vision guiding

The smartphone-based human activity recognition (HAR) provides abundant healthcare guidance for telemedicine and clinic treatment. The continually increasing daily activities cause many difficulties for recognition and labeling. Although multimodal data fusion and artificial intelligence (AI) techniques can solve these problems, big data collection and labeling are still heavy. This paper proposes a remarkable depth data-guided framework based on smartphones for complex HAR and automatic labeling. The hardware platform is utilized to collect information of depth vision from the Microsoft Kinect camera and Inertial Measurement Unit (IMU) signals from the smartphone simultaneously. This framework consists of five clustering layers and deep learning (DL) based classification model to identify 12 complex daily activities. The results show that the hierarchical k-medoids (Hk-medoids) algorithm obtains the labels with high accuracy (93.89%). Furthermore, the performance evaluation of the DCNN model is evaluated better by comparing it with other machine learning (ML) and DL methods.
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Dense retrieval approaches can overcome the lexical gap and lead to significantly improved search results.However, they require large amounts of training data which is not available for most domains.As shown in previous work (Thakur et al., 2021b), the performance of dense retrievers severely degrades under a domain shift.This limits the usage of dense retrieval approaches to only a few domains with large training datasets.In this paper, we propose the novel unsupervised domain adaptation method Generative Pseudo Labeling (GPL), which combines a query generator with pseudo labeling from a cross-encoder.On six representative domainspecialized datasets, we find the proposed GPL can outperform an out-of-the-box state-of-theart dense retrieval approach by up to 9.3 points nDCG@10.GPL requires less (unlabeled) data from the target domain and is more robust in its training than previous methods.We further investigate the role of six recent pre-training methods in the scenario of domain adaptation for retrieval tasks, where only three could yield improved results.The best approach, TSDAE (Wang et al., 2021) can be combined with GPL, yielding another average improvement of 1.4 points nDCG@10 across the six tasks.The code and the models are available 1 .
EvoDCNN: An evolutionary deep convolutional neural network for image classification

Developing Deep Convolutional Neural Networks (DCNNs) for image classification is a complicated task that needs considerable effort and knowledge. By employing an evolutionary computation approach, one can automatically generate the network models. However, the Neuroevolution is computationally expensive, and in some cases it needs hundreds of GPU days for training. Therefore, there is a need to find optimum Neuroevolutionary models with minimum computation to deal with this problem. In this paper, by utilising a Genetic Algorithm (GA), we introduce EvoDCNN, as a block-based evolutionary model for developing an evolutionary deep convolutional network for image classification. Such that by using the proposed fixed-length encoding model, we can generate variable-length networks with high accuracy while using less computation. The proposed model by utilising a straightforward evolutionary framework is able to establish small networks with high classification accuracy. Eight datasets: CIFAR10, MNIST, and six versions of EMNIST, that include balanced and unbalanced datasets, are used for evaluation of the proposed model. We did a comprehensive evaluation where we compared the results with many previous works, and outperformed the previous state-of-the-art accuracy for classification of five of the datasets.
Teachers’ perceptions of using virtual reality technology in classrooms: A large-scale survey

High-immersion virtual reality (VR) technology is often associated with gaming. Yet, it is increasingly popular in educational contexts due to its potential to engage and motivate learners. Prior to VR technology integration in the classroom, the acceptance or resistance toward VR needs to be explored. This paper reports the results obtained from a large-scale (N = 20,876) survey on teachers’ attitudes toward the use of VR for education. The survey explored the relationships between the teachers’ VR integration level and their instructional approaches, as well as the frequency of VR use. Furthermore, the survey yielded answers on the relationship between the availability of information technology (IT) personnel and the frequency of VR use. Overall, teachers had moderately positive perceptions of the use of VR in education. There was no strong correlation between instructional approaches and the level of VR integration, but lower levels of VR integration were associated with more traditional teaching approaches. The results revealed a positive correlation between the level of VR integration and the frequency of VR use. However, the VR frequency use had a weak correlation with the availability of IT personnel.
Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text

Modern neural language models can produce remarkably fluent and grammatical text.So much, in fact, that recent work by Clark et al. (2021) has reported that conventional crowdsourcing can no longer reliably distinguish between machine-authored (GPT-3) and humanauthored writing.As errors in machine generations become ever subtler and harder to spot, it poses a new challenge to the research community for robust machine text evaluation.We propose a new framework called SCARE-CROW for scrutinizing machine text via crowd annotation.To support the broad range of real machine errors that can be identified by laypeople, the ten error categories of SCARECROWsuch as redundancy , commonsense errors , and incoherence -are identified through several rounds of crowd annotation experiments without a predefined ontology.We then use SCARECROW to collect over 41k error spans in human-written and machinegenerated paragraphs of English language news text.We isolate factors for detailed analysis, including parameter count, training data, and various decoding-time configurations.Our approach successfully quantifies measurable gaps between human authored text and generations from models of several sizes, including fourteen configurations of GPT-3.In addition, our analysis unveils new insights, with detailed rationales provided by laypeople, e.g., that the commonsense capabilities have been improving with larger models while math capabilities have not, and that the choices of simple decoding hyperparameters can make remarkable differences on the perceived quality of machine text.We release our training material, annotation toolkit and dataset at https: //yao-dou.github.io/scarecrow/.
A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images

The tumor grading of laryngeal cancer pathological images needs to be accurate and interpretable. The deep learning model based on the attention mechanism-integrated convolution (AMC) block has good inductive bias capability but poor interpretability, whereas the deep learning model based on the vision transformer (ViT) block has good interpretability but weak inductive bias ability. Therefore, we propose an end-to-end ViT-AMC network (ViT-AMCNet) with adaptive model fusion and multiobjective optimization that integrates and fuses the ViT and AMC blocks. However, existing model fusion methods often have negative fusion: 1). There is no guarantee that the ViT and AMC blocks will simultaneously have good feature representation capability. 2). The difference in feature representations learning between the ViT and AMC blocks is not obvious, so there is much redundant information in the two feature representations. Accordingly, we first prove the feasibility of fusing the ViT and AMC blocks based on Hoeffding’s inequality. Then, we propose a multiobjective optimization method to solve the problem that ViT and AMC blocks cannot simultaneously have good feature representation. Finally, an adaptive model fusion method integrating the metrics block and the fusion block is proposed to increase the differences between feature representations and improve the deredundancy capability. Our methods improve the fusion ability of ViT-AMCNet, and experimental results demonstrate that ViT-AMCNet significantly outperforms state-of-the-art methods. Importantly, the visualized interpretive maps are closer to the region of interest of concern by pathologists, and the generalization ability is also excellent. Our code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Baron-Huang/ViT-AMCNet</uri> .
On the use of deep learning in software defect prediction

Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome, time consuming and hardly capture the semantic information reported in bug reporting tools. Deep learning (DL) techniques provide practitioners with the opportunities to automatically extract and learn from more complex and high-dimensional data. The purpose of this study is to systematically identify, analyze, summarize, and synthesize the current state of the utilization of DL algorithms for SDP in the literature. We systematically selected a pool of 102 peer-reviewed studies and then conducted a quantitative and qualitative analysis using the data extracted from these studies. Main highlights include: (1) most studies applied supervised DL; (2) two third of the studies used metrics as an input to DL algorithms; (3) Convolutional Neural Network is the most frequently used DL algorithm. Based on our findings, we propose to (1) develop more comprehensive DL approaches that automatically capture the needed features; (2) use diverse software artifacts other than source code; (3) adopt data augmentation techniques to tackle the class imbalance problem; (4) publish replication packages.