Reviewerly/exHarmony huggingface dataset:
Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule

Hybrid quantum-classical optimization algorithms represent one of the most promising application for near-term quantum computers. In these algorithms the goal is to optimize an observable quantity with respect to some classical parameters, using feedback from measurements performed on the quantum device. Here we study the problem of estimating the gradient of the function to be optimized directly from quantum measurements, generalizing and simplifying some approaches present in the literature, such as the so-called parameter-shift rule. We derive a mathematically exact formula that provides a stochastic algorithm for estimating the gradient of any multi-qubit parametric quantum evolution, without the introduction of ancillary qubits or the use of Hamiltonian simulation techniques. The gradient measurement is possible when the underlying device can realize all Pauli rotations in the expansion of the Hamiltonian whose coefficients depend on the parameter. Our algorithm continues to work, although with some approximations, even when all the available quantum gates are noisy, for instance due to the coupling between the quantum device and an unknown environment.
Pinhole-imaging-based learning butterfly optimization algorithm for global optimization and feature selection

Butterfly optimization algorithm (BOA), as a recently proposed meta-heuristic optimization technique, performs competitively in solving numerical optimization problems as well as real-world applications. However, BOA has low precision, slow convergence and may be prone to local optimum, when solving complex or high-dimensional optimization problems. To overcome these defects, a modified BOA (called PIL-BOA) with adaptive gbest-guided search strategy and pinhole-imaging-based learning is proposed. Firstly, a modified position updated equation by introducing the global best (gbest) solution and the inertia weight is designed to efficiently improve the exploitation capability and the solution precision. Secondly, a novel pinhole-imaging learning strategy based on the principle of optics is presented to effectively search the unknown regions and avoid premature convergence. 23 classical problems and 60 complex optimization tasks from CEC 2014 and CEC 2017 are used to further investigate the effectiveness of PIL-BOA. The comparison results demonstrate that PIL-BOA has better performance than most compared algorithms on benchmark test functions. Finally, PIL-BOA is applied to solve feature selection problems and fault diagnosis in real-world wind turbine. The results show that PIL-BOA is superior to other competitors in term of classification accuracy.
Deep virtual adversarial self-training with consistency regularization for semi-supervised medical image classification

Convolutional neural networks have achieved prominent success on a variety of medical imaging tasks when a large amount of labeled training data is available. However, the acquisition of expert annotations for medical data is usually expensive and time-consuming, which poses a great challenge for supervised learning approaches. In this work, we proposed a novel semi-supervised deep learning method, i.e., deep virtual adversarial self-training with consistency regularization, for large-scale medical image classification. To effectively exploit useful information from unlabeled data, we leverage self-training and consistency regularization to harness the underlying knowledge, which helps improve the discrimination capability of training models. More concretely, the model first uses its prediction for pseudo-labeling on the weakly-augmented input image. A pseudo-label is kept only if the corresponding class probability is of high confidence. Then the model prediction is encouraged to be consistent with the strongly-augmented version of the same input image. To improve the robustness of the network against virtual adversarial perturbed input, we incorporate virtual adversarial training (VAT) on both labeled and unlabeled data into the course of training. Hence, the network is trained by minimizing a combination of three types of losses, including a standard supervised loss on labeled data, a consistency regularization loss on unlabeled data, and a VAT loss on both labeled and labeled data. We extensively evaluate the proposed semi-supervised deep learning methods on two challenging medical image classification tasks: breast cancer screening from ultrasound images and multi-class ophthalmic disease classification from optical coherence tomography B-scan images. Experimental results demonstrate that the proposed method outperforms both supervised baseline and other state-of-the-art methods by a large margin on all tasks.
Representation iterative fusion based on heterogeneous graph neural network for joint entity and relation extraction

Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text. However, few existing works consider possible relations information between entities before extracting them, which may lead to the fact that most of the extracted entities cannot constitute valid triples. In this paper, we propose a representation iterative fusion based on heterogeneous graph neural networks for relation extraction (RIFRE). We model relations and words as nodes on the graph and fuse the two types of semantic nodes by the message passing mechanism iteratively to obtain nodes representation that is more suitable for relation extraction tasks. The model performs relation extraction after nodes representation is updated. We evaluate RIFRE on two public relation extraction datasets: NYT and WebNLG. The results show that RIFRE can effectively extract triples and achieve state-of-the-art performance.1 Moreover, RIFRE is also suitable for the relation classification task, and significantly outperforms the previous methods on SemEval 2010 Task 8 datasets.
Revocable Attribute-Based Encryption With Data Integrity in Clouds

Cloud computing enables enterprises and individuals to outsource and share their data. This way, cloud computing eliminates the heavy workload of local information infrastructure. Attribute-based encryption has become a promising solution for encrypted data access control in clouds due to the ability to achieve one-to-many encrypted data sharing. Revocation is a critical requirement for encrypted data access control systems. After outsourcing the encrypted attribute-based ciphertext to the cloud, the data owner may want to revoke some recipients that were authorized previously, which means that the outsourced attribute-based ciphertext needs to be updated to a new one that is under the revoked policy. The integrity issue arises when the revocation is executed. When a new ciphertext with the revoked access policy is generated by the cloud server, the data recipient cannot be sure that the newly generated ciphertext guarantees to be decrypted to the same plaintext as the originally encrypted data, since the cloud server is provided by a third party, which is not fully trusted. In this article, we consider a new security requirement for the revocable attribute-based encryption schemes: integrity. We introduce a formal definition and security model for the revocable attribute-based encryption with data integrity protection (RABE-DI). Then, we propose a concrete RABE-DI scheme and prove its confidentiality and integrity under the defined security model. Finally, we present an implementation result and provide performance evaluation which shows that our scheme is efficient and practical.
Deep reinforcement learning for blockchain in industrial IoT: A survey

With the ambitious plans of renewal and expansion of industrialization in many countries, the efficiency, agility and cost savings potentially resulting from the application of industrial Internet of Things (IIoT) are drawing attentions. Although blockchain and machine learning technologies (especially deep learning and deep reinforcement learning) may provide the next promising use case for IIoT, they are working in an adversarial way to some extent. While blockchain facilitates the data collection that is critical for machine learning under the premise of data regulation rules like privacy protection, it may suffer from privacy leakage due to big data analytics with the help of machine learning. To make machine learning and blockchain useful and practical for diversified services in industrial sectors, it is of paramount importance to have a comprehensive understanding of the development of both technologies in the context of IIoT. To this end, in this paper we summarize and analyze the applications of blockchain and machine learning in IIoT from three important aspects, i.e., consensus mechanism, storage and communication. This survey provides a deeper understanding on the security and privacy risks of the key components of a blockchain from the perspective of machine learning, which is useful in the design of practical blockchain solutions for IIoT. In addition, we provide useful guidance for future research in the area by identifying interesting open problems that need to be addressed before large-scale deployments of IIoT applications are practicable.
BossNAS: Exploring Hybrid CNN-transformers with Block-wisely Self-supervised Neural Architecture Search

A myriad of recent breakthroughs in hand-crafted neural architectures for visual recognition have highlighted the urgent need to explore hybrid architectures consisting of diversified building blocks. Meanwhile, neural architecture search methods are surging with an expectation to reduce human efforts. However, whether NAS methods can efficiently and effectively handle diversified search spaces with disparate candidates (e.g. CNNs and transformers) is still an open question. In this work, we present Block-wisely Self-supervised Neural Architecture Search (BossNAS), an unsupervised NAS method that addresses the problem of in-accurate architecture rating caused by large weight-sharing space and biased supervision in previous methods. More specifically, we factorize the search space into blocks and utilize a novel self-supervised training scheme, named ensemble bootstrapping, to train each block separately before searching them as a whole towards the population center. Additionally, we present HyTra search space, a fabric-like hybrid CNN-transformer search space with searchable down-sampling positions. On this challenging search space, our searched model, BossNet-T, achieves up to 82.5% accuracy on ImageNet, surpassing EfficientNet by 2.4% with comparable compute time. Moreover, our method achieves superior architecture rating accuracy with 0.78 and 0.76 Spearman correlation on the canonical MBConv search space with ImageNet and on NATS-Bench size search space with CIFAR-100, respectively, surpassing state-of-the-art NAS methods. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems Under Resource Uncertainty

In this paper, a novel data offloading decision-making framework is proposed, where users have the option to partially offload their data to a complex Multi-access Edge Computing (MEC) environment, consisting of both ground and UAV-mounted MEC servers. The problem is treated under the perspective of risk-aware user behavior as captured via prospect-theoretic utility functions, while accounting for the inherent computing environment uncertainties. The UAV-mounted MEC servers act as a common pool of resources with potentially superior but uncertain payoff for the users, while the local computation and ground server alternatives constitute safe and guaranteed options, respectively. The optimal user task offloading to the available computing choices is formulated as a maximization problem of each user’s satisfaction, and confronted as a non-cooperative game. The existence and uniqueness of a Pure Nash Equilibrium (PNE) are proven, and convergence to the PNE is shown. Detailed numerical results highlight the convergence of the system to the PNE in few only iterations, while the impact of user behavior heterogeneity is evaluated. The introduced framework’s consideration of the user risk-aware characteristics and computing uncertainties, results to a sophisticated exploitation of the system resources, which in turn leads to superior users’ experienced performance compared to alternative approaches.
TXI: Texture and Color Enhancement Imaging for Endoscopic Image Enhancement

Recognition of lesions with subtle morphological and/or color changes during white light imaging (WLI) endoscopy remains a challenge. Often the endoscopic image suffers from nonuniform illumination across the image due to curvature in the lumen and the direction of the illumination light of the endoscope. We propose an image enhancement technology to resolve the drawbacks above called texture and color enhancement imaging (TXI). TXI is designed to enhance three image factors in WLI (texture, brightness, and color) in order to clearly define subtle tissue differences. In our proposed method, retinex-based enhancement is employed in the chain of endoscopic image processing. Retinex-based enhancement is combined with color enhancement to greatly accentuate color tone differences of mucosal surfaces. We apply TXI to animal endoscopic images and evaluate the performance of TXI compared with conventional endoscopic enhancement technologies, conventionally used techniques for real-world image processing, and newly proposed techniques for surgical endoscopic image augmentation. Our experimental results show that TXI can enhance brightness selectively in dark areas of an endoscopic image and can enhance subtle tissue differences such as slight morphological or color changes while simultaneously preventing over-enhancement. These experimental results demonstrate the potential of the proposed TXI algorithm as a future clinical tool for detecting gastrointestinal lesions having difficult-to-recognize tissue differences.
A fuzzy recommendation system for predicting the customers interests using sentiment analysis and ontology in e-commerce

In Electronic commerce, customer reviews play a significant role in purchase making decision. Most of the existing recommendation systems consider the customer reviews, user purchase history and product rating for predicting the recommended product. Since the users interest are varying over time, the existing recommendation systems lack in finding the current relevant items to the customers. To overcome this problem, this article proposes a new fuzzy logic-based product recommendation system which dynamically predicts the most relevant products to the customers in online shopping according to the users’ current interests. A novel algorithm has been proposed in this paper for computing the sentimental score of the product with associated end user target category. Finally, the proposed fuzzy rules and ontology-based recommendation system uses ontology alignment for making decisions that are more accurate and predict dynamically based on the search context. The experimental results of the proposed recommendation system show better performance than the existing product recommendation systems in terms of prediction accuracy of the relevant products for target users and in the time taken to provide such recommendations.
GCNAX: A Flexible and Energy-efficient Accelerator for Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNs) have emerged as an effective approach to extend deep learning for graph data analytics. Given that graphs are usually irregular, as nodes in a graph may have a varying number of neighbors, processing GCNs efficiently pose a significant challenge on the underlying hardware. Although specialized GCN accelerators have been proposed to deliver better performance over generic processors, prior accelerators not only under-utilize the compute engine, but also impose redundant data accesses that reduce throughput and energy efficiency. Therefore, optimizing the overall flow of data between compute engines and memory, i.e., the GCN dataflow, which maximizes utilization and minimizes data movement is crucial for achieving efficient GCN processing.In this paper, we propose a flexible and optimized dataflow for GCNs that simultaneously improves resource utilization and reduces data movement. This is realized by fully exploring the design space of GCN dataflows and evaluating the number of execution cycles and DRAM accesses through an analysis framework. Unlike prior GCN dataflows, which employ rigid loop orders and loop fusion strategies, the proposed dataflow can reconFigure the loop order and loop fusion strategy to adapt to different GCN configurations, which results in much improved efficiency. We then introduce a novel accelerator architecture called GCNAX, which tailors the compute engine, buffer structure and size based on the proposed dataflow. Evaluated on five real-world graph datasets, our simulation results show that GCNAX reduces DRAM accesses by a factor of $8.1 \times$ and $2.4 \times$, while achieving $8.9 \times, 1.6 \times$ speedup and $9.5 \times$, $2.3 \times$ energy savings on average over HyGCN and AWB-GCN, respectively.
New Image Encryption Algorithm Using Hyperchaotic System and Fibonacci Q-Matrix

In the age of Information Technology, the day-life required transmitting millions of images between users. Securing these images is essential. Digital image encryption is a well-known technique used in securing image content. In image encryption techniques, digital images are converted into noise images using secret keys, where restoring them to their originals required the same keys. Most image encryption techniques depend on two steps: confusion and diffusion. In this work, a new algorithm presented for image encryption using a hyperchaotic system and Fibonacci Q-matrix. The original image is confused in this algorithm, utilizing randomly generated numbers by the six-dimension hyperchaotic system. Then, the permutated image diffused using the Fibonacci Q-matrix. The proposed image encryption algorithm tested using noise and data cut attacks, histograms, keyspace, and sensitivity. Moreover, the proposed algorithm’s performance compared with several existing algorithms using entropy, correlation coefficients, and robustness against attack. The proposed algorithm achieved an excellent security level and outperformed the existing image encryption algorithms.
Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning

Nowadays online reviews play a significant role in influencing the decision of consumers. Consumers show their experience and information about product quality in their reviews. Product Reviews from Amazon to Restaurant Reviews from Yelp are facing problems with fake reviews and fake numeric ratings. Online reviews typically consist of qualitative (text format) and quantitative (rating) formats. In the case of Google Play store fake numeric ratings can play a big role in the success of apps. People tend to believe that a high-star rating may be significantly attached with a good review. However, user star level rating information does not usually match with text format of review. Despite many efforts to resolve this issue, Apple App Store and Google Play Store are still facing this problem. This study proposes a novel Google App numeric reviews & ratings contradiction prediction framework using Deep Learning approaches. The framework consists of two phases. In the first phase, the polarity of reviews are predicted using sentiment analysis tool to build ground truth. In the second phase, star ratings are predicted from text format of reviews after training deep learning models on ground truth obtained in the first phase. Experimental results demonstrate that based on actual user reviews the proposed framework significantly predicts unbiased star rating of app.
Using deep neural networks for kinematic analysis: Challenges and opportunities

Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such analyses without markers, making outdoor applications feasible. In this paper I summarise 2D markerless approaches for estimating joint angles, highlighting their strengths and limitations. In computer science, so-called “pose estimation” algorithms have existed for many years. These methods involve training a neural network to detect features (e.g. anatomical landmarks) using a process called supervised learning, which requires “training” images to be manually annotated. Manual labelling has several limitations, including labeller subjectivity, the requirement for anatomical knowledge, and issues related to training data quality and quantity. Neural networks typically require thousands of training examples before they can make accurate predictions, so training datasets are usually labelled by multiple people, each of whom has their own biases, which ultimately affects neural network performance. A recent approach, called transfer learning, involves modifying a model trained to perform a certain task so that it retains some learned features and is then re-trained to perform a new task. This can drastically reduce the required number of training images. Although development is ongoing, existing markerless systems may already be accurate enough for some applications, e.g. coaching or rehabilitation. Accuracy may be further improved by leveraging novel approaches and incorporating realistic physiological constraints, ultimately resulting in low-cost markerless systems that could be deployed both in and outside of the lab.
Line Graph Neural Networks for Link Prediction

We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications. With the advances of deep learning, current link prediction methods commonly compute features from subgraphs centered at two neighboring nodes and use the features to predict the label of the link between these two nodes. In this formalism, a link prediction problem is converted to a graph classification task. In order to extract fixed-size features for classification, graph pooling layers are necessary in the deep learning model, thereby incurring information loss. To overcome this key limitation, we propose to seek a radically different and novel path by making use of the line graphs in graph theory. In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task. Experimental results on fourteen datasets from different applications demonstrate that our proposed method consistently outperforms the state-of-the-art methods, while it has fewer parameters and high training efficiency.