Reviewerly/exHarmony huggingface dataset:
Underwater Image Enhancement Method via Multi-Interval Subhistogram Perspective Equalization

Due to the selective attenuation of light in water, captured underwater images exhibit poor visibility and cause considerable challenges for vision tasks. The structural and statistical properties of different regions of degraded underwater images are damaged at different levels, resulting in a global nonuniform drift of the feature representation, causing further degradation of visual performance. To handle these issues, we present an underwater image enhancement method via multi-interval subhistogram perspective equalization to address the issues posed by underwater images. We estimate the degree of feature drifts in each area of an image by extracting the statistical characteristics of the image, using this information to guide feature enhancement to achieve adaptive feature enhancement, thereby improving the visual effect of degraded images. We first design a variational model that uses the difference between data items and regular items to improve the color correction performance of the method based on subinterval linear transformation. In addition, a multithreshold selection method, which adaptively selects a threshold array for interval division, is developed. Ultimately, a multi-interval subhistogram equalization method, which performs histogram equalization in each subhistogram to improve the image contrast, is presented. Experiments on underwater images with various scenarios demonstrate that our method significantly outperforms many state-of-the-art methods qualitatively and quantitatively.
Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts

Pre-trained large language models (“LLMs”) like GPT-3 can engage in fluent, multi-turn instruction-taking out-of-the-box, making them attractive materials for designing natural language interactions. Using natural language to steer LLM outputs (“prompting”) has emerged as an important design technique potentially accessible to non-AI-experts. Crafting effective prompts can be challenging, however, and prompt-based interactions are brittle. Here, we explore whether non-AI-experts can successfully engage in “end-user prompt engineering” using a design probe—a prototype LLM-based chatbot design tool supporting development and systematic evaluation of prompting strategies. Ultimately, our probe participants explored prompt designs opportunistically, not systematically, and struggled in ways echoing end-user programming systems and interactive machine learning systems. Expectations stemming from human-to-human instructional experiences, and a tendency to overgeneralize, were barriers to effective prompt design. These findings have implications for non-AI-expert-facing LLM-based tool design and for improving LLM-and-prompt literacy among programmers and the public, and present opportunities for further research.
Experimental Twin-Field Quantum Key Distribution over 1000 km Fiber Distance

Quantum key distribution (QKD) aims to generate secure private keys shared by two remote parties. With its security being protected by principles of quantum mechanics, some technology challenges remain towards practical application of QKD. The major one is the distance limit, which is caused by the fact that a quantum signal cannot be amplified while the channel loss is exponential with the distance for photon transmission in optical fiber. Here using the 3-intensity sending-or-not-sending protocol with the actively-odd-parity-pairing method, we demonstrate a fiber-based twin-field QKD over 1002 km. In our experiment, we developed a dual-band phase estimation and ultra-low noise superconducting nanowire single-photon detectors to suppress the system noise to around 0.02 Hz. The secure key rate is $9.53\times10^{-12}$ per pulse through 1002 km fiber in the asymptotic regime, and $8.75\times10^{-12}$ per pulse at 952 km considering the finite size effect. Our work constitutes a critical step towards the future large-scale quantum network.
Minmax Optimization: Stable Limit Points of Gradient Descent Ascent are Locally Optimal.

Minmax optimization, especially in its general nonconvex-nonconcaveformulation, has found extensive applications in modern machine learningframeworks such as generative adversarial networks (GAN), adversarial trainingand multi-agent reinforcement learning. Gradient-based algorithms, inparticular gradient descent ascent (GDA), are widely used in practice to solvethese problems. Despite the practical popularity of GDA, however, itstheoretical behavior has been considered highly undesirable. Indeed, apart frompossiblity of non-convergence, recent results (Daskalakis and Panageas, 2018;Mazumdar and Ratliff, 2018; Adolphs et al., 2018) show that even when GDAconverges, its stable limit points can be points that are not local Nashequilibria, thus not game-theoretically meaningful. In this paper, we initiate a discussion on the proper optimality measures forminmax optimization, and introduce a new notion of local optimality---localminmax---as a more suitable alternative to the notion of local Nashequilibrium. We establish favorable properties of local minmax points, andshow, most importantly, that as the ratio of the ascent step size to thedescent step size goes to infinity, stable limit points of GDA are exactlylocal minmax points up to degenerate points, demonstrating that all stablelimit points of GDA have a game-theoretic meaning for minmax problems.
Improving Black-box Adversarial Attacks with a Transfer-based Prior

We consider the black-box adversarial setting, where the adversary has to generate adversarial perturbations without access to the target models to compute gradients. Previous methods tried to approximate the gradient either by using a transfer gradient of a surrogate white-box model, or based on the query feedback. However, these methods often suffer from low attack success rates or poor query efficiency since it is non-trivial to estimate the gradient in a high-dimensional space with limited information. To address these problems, we propose a prior-guided random gradient-free (P-RGF) method to improve black-box adversarial attacks, which takes the advantage of a transfer-based prior and the query information simultaneously. The transfer-based prior given by the gradient of a surrogate model is appropriately integrated into our algorithm by an optimal coefficient derived by a theoretical analysis. Extensive experiments demonstrate that our method requires much fewer queries to attack black-box models with higher success rates compared with the alternative state-of-the-art methods.
Adversarial Monte Carlo denoising with conditioned auxiliary feature modulation

Denoising Monte Carlo rendering with a very low sample rate remains a major challenge in the photo-realistic rendering research. Many previous works, including regression-based and learning-based methods, have been explored to achieve better rendering quality with less computational cost. However, most of these methods rely on handcrafted optimization objectives, which lead to artifacts such as blurs and unfaithful details. In this paper, we present an adversarial approach for denoising Monte Carlo rendering. Our key insight is that generative adversarial networks can help denoiser networks to produce more realistic high-frequency details and global illumination by learning the distribution from a set of high-quality Monte Carlo path tracing images. We also adapt a novel feature modulation method to utilize auxiliary features better, including normal, albedo and depth. Compared to previous state-of-the-art methods, our approach produces a better reconstruction of the Monte Carlo integral from a few samples, performs more robustly at different sample rates, and takes only a second for megapixel images.
Anomaly detection in mixed telemetry data using a sparse representation and dictionary learning

Spacecraft health monitoring and failure prevention are major issues in space operations. In recent years, machine learning techniques have received an increasing interest in many fields and have been applied to housekeeping telemetry data via semi-supervised learning. The idea is to use past telemetry describing normal spacecraft behaviour in order to learn a reference model to which can be compared most recent data in order to detect potential anomalies. This paper introduces a new machine learning method for anomaly detection in telemetry time series based on a sparse representation and dictionary learning. The main advantage of the proposed method is the possibility to handle multivariate telemetry time series described by mixed continuous and discrete parameters, taking into account the potential correlations between these parameters. The proposed method is evaluated on a representative anomaly dataset obtained from real satellite telemetry with an available ground-truth and compared to state-of-the-art algorithms.
A Systematic Review of Past Decade of Mobile Learning: What we Learned and Where to Go

The increasing growth of mobile technology in our society has become a reality. Outdoor learning is one of the very revolutionary developments in modern ages without huge infrastructure and resources. This study is focusing on the area of mobile learning in the year 2009 to 2018 about the areas and working done by the researchers. Mobile learning is relatively a new platform of learning through the mobile technologies, and penetration of smartphones and digital devices is globally increased in a short interval of time. The Scopus database is used to find the literature with the keyword used in search button mobile learning. The PRISMA diagram 2009 shows that the subjects of Social Sciences area to include the literature articles for the review. The final 54 articles with more than 20 citations for the review are selected to analyze the literature to find the gap and direction of mobile learning. Literature is divided in different classifications to find the results.
Meta Matrix Factorization for Federated Rating Predictions

With distinct privacy protection advantages, federated recommendation is becoming increasingly feasible to store data locally in devices and federally train recommender models. However, previous work on federated recommender systems does not take full account of the limitations of storage, RAM, energy and communication bandwidth in the mobile environment. Their model scales are too big to run easily in mobile devices. Moreover, existing federated recommenders need to fine-tune recommendation models in each device, which makes them hard to effectively exploit collaborative filtering (CF) information among users/devices.
Reducing Quantity Hallucinations in Abstractive Summarization

It is well-known that abstractive summaries are subject to hallucination—including material that is not supported by the original text. While summaries can be made hallucination-free by limiting them to general phrases, such summaries would fail to be very informative. Alternatively, one can try to avoid hallucinations by verifying that any specific entities in the summary appear in the original text in a similar context. This is the approach taken by our system, Herman. The system learns to recognize and verify quantity entities (dates, numbers, sums of money, etc.) in a beam-worth of abstractive summaries produced by state-of-the-art models, in order to up-rank those summaries whose quantity terms are supported by the original text. Experimental results demonstrate that the ROUGE scores of such up-ranked summaries have a higher Precision than summaries that have not been up-ranked, without a comparable loss in Recall, resulting in higher F1. Preliminary human evaluation of up-ranked vs. original summaries shows people’s preference for the former.
Can a Public Blockchain Keep a Secret?

Blockchains are gaining traction and acceptance, not just for cryptocurrencies, but increasingly as an architecture for distributed computing. In this work we seek solutions that allow a public blockchain to act as a trusted long-term repository of secret information: Our goal is to deposit a secret with the blockchain, specify how it is to be used (e.g., the conditions under which it is released), and have the blockchain keep the secret and use it only in the specified manner (e.g., release only it once the conditions are met). This simple functionality enables many powerful applications, including signing statements on behalf of the blockchain, using it as the control plane for a storage system, performing decentralized program-obfuscation-as-a-service, and many more. Using proactive secret sharing techniques, we present a scalable solution for implementing this functionality on a public blockchain, in the presence of a mobile adversary controlling a small minority of the participants. The main challenge is that, on the one hand, scalability requires that we use small committees to represent the entire system, but, on the other hand, a mobile adversary may be able to corrupt the entire committee if it is small. For this reason, existing proactive secret sharing solutions are either non-scalable or insecure in our setting. We approach this challenge via “player replaceability”, which ensures the committee is anonymous until after it performs its actions. Our main technical contribution is a system that allows sharing and re-sharing of secrets among the members of small dynamic committees, without knowing who they are until after they perform their actions and erase their secrets. Our solution handles a fully mobile adversary corrupting roughly 1/4 of the participants at any time, and is scalable in terms of both the number of parties and the number of time intervals.
A Graph-Transformer for Whole Slide Image Classification

Deep learning is a powerful tool for whole slide image (WSI) analysis. Typically, when performing supervised deep learning, a WSI is divided into small patches, trained and the outcomes are aggregated to estimate disease grade. However, patch-based methods introduce label noise during training by assuming that each patch is independent with the same label as the WSI and neglect overall WSI-level information that is significant in disease grading. Here we present a Graph-Transformer (GT) that fuses a graph-based representation of an WSI and a vision transformer for processing pathology images, called GTP, to predict disease grade. We selected 4,818 WSIs from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the National Lung Screening Trial (NLST), and The Cancer Genome Atlas (TCGA), and used GTP to distinguish adenocarcinoma (LUAD) and squamous cell carcinoma (LSCC) from adjacent non-cancerous tissue (normal). First, using NLST data, we developed a contrastive learning framework to generate a feature extractor. This allowed us to compute feature vectors of individual WSI patches, which were used to represent the nodes of the graph followed by construction of the GTP framework. Our model trained on the CPTAC data achieved consistently high performance on three-label classification (normal versus LUAD versus LSCC: mean accuracy = 91.2 ± 2.5%) based on five-fold cross-validation, and mean accuracy = 82.3 ± 1.0% on external test data (TCGA). We also introduced a graph-based saliency mapping technique, called GraphCAM, that can identify regions that are highly associated with the class label. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification.
An improved whale optimization algorithm based on multilevel threshold image segmentation using the Otsu method

In this paper, an improved multithreshold image segmentation method based on the whale optimization algorithm (RAV-WOA) is proposed, with the between-class variance (Otsu method) as the objective function. The proposed RAV-WOA is able to select satisfactory optimal thresholds while ensuring high efficiency and quality when performing image segmentation on grayscale and color images In the current work, a reverse learning strategy was introduced into the initialization of RAV-WOA populations to improve the quality of the initial population of whales. An adaptive weighting strategy was introduced into the RAV-WOA algorithm, which is influenced by the fitness value and the number of iterations, to balance the global search capability of the algorithm with the local exploitation capability. The proposed RAV-WOA is then applied to the Otsu method to solve the multilevel thresholding image segmentation problem. To better verify the effectiveness of the proposed method, this paper compares the RAV-WOA with some classical heuristic algorithms and performs image segmentation experiments on a set of benchmark images with low and high thresholds. The experimental results show that the convergence speed and convergence accuracy of RAV-WOA are significantly better than other algorithms, and the segmentation results of RAV-WOA in multithreshold image segmentation have better quality and stability than other algorithms.
How does artificial intelligence create business agility? Evidence from chatbots

Artificial intelligence (AI) is gaining increasing attention from business leaders today. As a primary AI tool, chatbots have seen increasing use by companies to support customer service. An understanding of how chatbots are used is essential for improving customer service. Based on the relevant literature, this study examined the impacts of chatbot-enabled agility (namely, internal and external chatbot agility) on customer service performance and explored the antecedents from the perspective of information technology use (both routine and innovative use). We collected data from 294 U.S. marketing employees from various industries, using a survey for the assessment of our research model. The results showed that both routine and innovative use of chatbots were positively related to internal and external agility. In particular, the innovative use of chatbots plays an important role in creating business agility. Moreover, internal and external agility are positively related to customer service performance. Through a close look at chatbots and their use, our study provides insight into the role of AI in creating business agility. Practically speaking, this study suggests that both the routine and the innovative use of chatbots should be encouraged to create agility and develop business sustainability.
Human activity recognition in IoHT applications using Arithmetic Optimization Algorithm and deep learning

Nowadays, people use smart devices everywhere and for different applications such as healthcare. The Internet of Healthcare Things (IoHT) generates enormous amounts of data daily, which need exploitation and analysis to help healthcare professionals make decisions and provide a fast diagnosis. Human Activity Recognition (HAR) has received more attention due to its importance in elderly care, lifestyle improvement, and IoT systems. This paper presents a novel HAR system based on optimizing two algorithms: convolutional neural network (CNN) and the recently proposed optimization algorithm, Arithmetic Optimization Algorithm (AOA), to boost the HAR performance with fewer resources. The proposed CNN is applied to learn and extract features from input data where a modified AOA algorithm, called Binary AOA (BAOA), is used to select the most optimal features. Finally, the support vector machine (SVM) is adopted to classify the selected feature based on different activities. We evaluate the proposed HAR model with three different public datasets, UCI-HAR, WISDM-HAR, and KU-HAR datasets. Moreover, we compare the feature selection method, BAOA, to various optimization algorithms using several evaluation measures, and we found that BAOA recorded the best performance. Furthermore, we compare the proposed model to several existing HAR methods. The outcomes confirmed the competitive performance of the proposed model, which achieved 95.23%, 99.5%, and 96.8% for UCI-HAR, WISDM-HAR, and KU-HAR datasets, respectively.
A comprehensive review of augmented reality-based instruction in manual assembly, training and repair

In recent years, the multi-dimensional, multi-faceted, and multi-level applications of augmented reality/mixed reality have attracted more and more attention from researchers in the field of smart manufacturing. However, the disadvantage is that AR interaction cues supporting user cognition have not been fully analyzed and summarized in manual assembly, training and repair. This article reviews the current research status, projects and technical characteristics of manual operation instructions in the past 30 years, and extensively discusses these latest works. It is worth mentioning that this article provides comprehensive academic information for the development of AR-based assembly instructions, thereby providing unique insights for researchers in related fields. In short, it will help researchers designing AR instructions.
Extending nn-UNet for Brain Tumor Segmentation

Brain tumor segmentation is essential for the diagnosis and prognosis of patients with gliomas. The brain tumor segmentation challenge has provided an abundant and high-quality data source to develop automatic algorithms for the task. This paper describes our contribution to the 2021 competition. We developed our methods based on nn-UNet, the winning entry of last year’s competition. We experimented with several modifications, including using a larger network, replacing batch normalization with group normalization and utilizing axial attention in the decoder. Internal 5-fold cross-validation and online evaluation from the organizers showed a minor improvement in quantitative metrics compared to the baseline. The proposed models won first place in the final ranking on unseen test data, achieving a dice score of 88.35%, 88.78%, 93.19% for the enhancing tumor, the tumor core, and the whole tumor, respectively. The codes, pretrained weights, and docker image for the winning submission are publicly available. ( https://github.com/rixez/Brats21_KAIST_MRI_Lab https://hub.docker.com/r/rixez/brats21nnunet )
Weighted Random k Satisfiability for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e4005" altimg="si2.svg"><mml:mrow><mml:mi>k</mml:mi><mml:mo linebreak="goodbreak" linebreakstyle="after">=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> (r2SAT) in Discrete Hopfield Neural Network

Current studies on non-systematic satisfiability in Discrete Hopfield Neural Network are able to avoid production of repetitive final neuron states which improves the quality of global solutions retrieved. This finding has made it possible to obtain diverse final neuron states without additional modifications in Discrete Hopfield Neural Network. However, non-systematic satisfiability exhibited a drawback for neglecting the distribution of positive and negative literals in the logical structure as a neuron representation. Thus, this paper considers a new class of non-systematic satisfiability logic named Weighted Random k Satisfiability for k = 1 , 2 with an inclusive of weighted ratio of negative literals. A logic phase is proposed as an organized layer to assign the distribution of negative literals unbiasedly by using Genetic Algorithm. The performance of Genetic Algorithm in producing the right structure of Weighted Random k Satisfiability will be compared with exhaustive search. Additionally, the training capability of Discrete Hopfield Neural Network to minimize the cost function will be investigated by embedding state of the art metaheuristics. Overall analysis demonstrated the effect of different ratios of negative literals and advanced training algorithm positively impacted the synaptic weight management and production of global minima solutions. • Weighted Random k Satisfiability ( r 2SAT) is proposed to represent the neurons in DHNN model. • The genetic algorithm is proposed to distribute the negative literals of the r 2SAT during logic phase. • Experimental evaluation shows that the proposed method able to outperform the state-of-the-art propositional logic.