Reviewerly/exHarmony huggingface dataset:
Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains

Images taken under low-light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of the downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknow low-light conditions. In this paper, we propose to incorporate the contrastive learning into an illumination correction network to learn abstract representations to distinguish various low-light conditions in the representation space, with the purpose of enhancing the generalizability of the network. Considering that light conditions can change the frequency components of the images, the representations are learned and compared in both spatial and frequency domains to make full advantage of the contrastive learning. The proposed method is evaluated on LOL and LOL-V2 datasets, the results show that the proposed method achieves better qualitative and quantitative results compared with other state-of-the-arts.
Improving short text classification with augmented data using GPT-3

Abstract GPT-3 is a large-scale natural language model developed by OpenAI that can perform many different tasks, including topic classification. Although researchers claim that it requires only a small number of in-context examples to learn a task, in practice GPT-3 requires these training examples to be either of exceptional quality or a higher quantity than easily created by hand. To address this issue, this study teaches GPT-3 to classify whether a question is related to data science by augmenting a small training set with additional examples generated by GPT-3 itself. This study compares two augmented classifiers: the Classification Endpoint with an increased training set size and the Completion Endpoint with an augmented prompt optimized using a genetic algorithm. We find that data augmentation significantly increases the accuracy of both classifiers, and that the embedding-based Classification Endpoint achieves the best accuracy of about 76%, compared to human accuracy of 85%. In this way, giving large language models like GPT-3 the ability to propose their own training examples can improve short text classification performance.
Dihedral lattice gauge theories on a quantum annealer

Abstract We study lattice gauge theory with discrete, non-Abelian gauge groups. We extend the formalism of previous studies on D-Wave’s quantum annealer as a computing platform to finite, simply reducible gauge groups. As an example, we use the dihedral group $D_{n}$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>D</mml:mi> <mml:mi>n</mml:mi> </mml:msub> </mml:math> with $n=3,4$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>n</mml:mi> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> <mml:mo>,</mml:mo> <mml:mn>4</mml:mn> </mml:math> on a two plaquette ladder for which we provide proof-of-principle calculations of the ground-state and employ the known time evolution formalism with Feynman clock states.
Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions

Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks, requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures with respect to medical data available for a given registration task/scenario. This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts to conveniently find off-the-shelf registration algorithms for various registration scenarios. Specifically, we establish a triple-level framework to embrace the searching for both network architectures and objectives with a cooperating optimization. Extensive experiments on multiple volumetric datasets and various registration scenarios demonstrate that AutoReg can automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance. The automatically learned network also improves computational efficiency over the mainstream UNet architecture from 0.558 to 0.270 seconds for a volume pair on the same configuration.
Impact of service agents on customer satisfaction and loyalty: mediating role of Chatbots

Purpose This study aims to determine whether or not fashion retail brands can maintain their essence by providing personalized care through conventional face-to-face interactions or the use of e-services. Design/methodology/approach An exploratory investigation is being conducted to attain this goal. According to the findings of this research, Chatbots have an impact on consumer loyalty. The quality of a Chatbot’s system, service and information are all critical to providing a positive consumer experience. Findings The study concluded that Chatbot e-services might potentially enable dynamic and fascinating interactions between firms and their consumers. To personalize a Chatbot, firms might change the tone of the language used. Customers are more likely to use a Chatbot if it resembles a real person, which increases their pleasure and confidence in the product. Originality/value More precisely, the emphasis of the inquiry was on Chatbot, a relatively new digital tool that offers user-friendly, personalized and one-of-a-kind support to customers. Using information supplied by consumers, the authors examine a five-dimensional model that gauges how customers feel about Chatbots in terms of their ability to communicate with users, offer amusement, be trendy, personalize interactions and solve problems.
A comprehensive survey of intestine histopathological image analysis using machine vision approaches

Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
A novel hybrid Coyote–Particle Swarm Optimization Algorithm for three-dimensional constrained trajectory planning of Unmanned Aerial Vehicle

Unmanned Aerial Vehicles (UAVs) have been considered the future of transportation systems. However, mostly the reported optimizers struggle to estimate the flyable trajectories within acceptable accuracy and time bound under various constraints, particularly in a complex 3D environment. Therefore, this work proposes a novel hybrid optimizer (HCPSOA) by combining the Particle Swarm Optimization (PSO) and Coyote Optimization Algorithm (COA). Further, the chaotic logistic map and dynamic weight adjustments have been incorporated to enhance the exploration–exploitation capabilities. After validating the promising performance of HCPSOA against popular metaheuristics (COA, PSO, Improved COA, Glowworm Swarm Optimization, and Hybrid Fireworks PSO) for several benchmark functions, the proposed HCPSOA has been employed to estimate the flyable path by formulating a novel cost function and smoothened by cubic B-spline curve. The simulated results reveal that the HCPSOA provide a non-colliding path with up to 10.00% lesser average cost for the considered real world scenario (map 3), which confirms its supremacy for the estimation of the safe and flyable relative to other compared algorithms.
Towards the ultimate brain: Exploring scientific discovery with ChatGPT AI

Abstract This paper presents a novel approach to scientific discovery using an artificial intelligence (AI) environment known as ChatGPT, developed by OpenAI. This is the first paper entirely generated with outputs from ChatGPT. We demonstrate how ChatGPT can be instructed through a gamification environment to define and benchmark hypothetical physical theories. Through this environment, ChatGPT successfully simulates the creation of a new improved model, called GPT 4 , which combines the concepts of GPT in AI (generative pretrained transformer) and GPT in physics (generalized probabilistic theory). We show that GPT 4 can use its built‐in mathematical and statistical capabilities to simulate and analyze physical laws and phenomena. As a demonstration of its language capabilities, GPT 4 also generates a limerick about itself. Overall, our results demonstrate the promising potential for human‐AI collaboration in scientific discovery, as well as the importance of designing systems that effectively integrate AI's capabilities with human intelligence.
An ensemble learning framework for rooftop photovoltaic project site selection

The selection of suitable locations for rooftop photovoltaic projects (RPVP) is critical for optimizing power generation efficiency and return on investment. However, traditional methods of site selection that rely on subjective assessments of index weights can compromise accuracy, while complex calculations may limit adaptability to changing real-world data. In this study, we proposed a data-driven ensemble learning framework that integrates socio-economic, environmental, climate, and geography factors to optimize RPVP site selection. Using data from 1589 counties in China, we mapped eight criteria to feature variables to facilitate machine learning classification. Furthermore, the K-means algorithm was employed to enhance the model's robustness against outliers. The findings indicate that the proposed stacking model exhibits superior performance in comparison to other classifiers, as evidenced by the higher scores of performance metrics. Specifically, for positive instance prediction, the stacking model achieves the highest Precision scores. According to the rankings of Precision scores derived from the four ensembled models, we categorized counties suitable for RPVP development into five priority tiers. The ensemble learning framework provides a valuable and reusable tool for advancing county-level RPVP site selection and serves as a motivation for selecting other renewable power plant sites.
Smart Contract Synthesis Modulo Hyperproperties

Smart contracts are small but highly security-critical programs that implement wallets, token systems, auctions, crowd funding systems, elections, and other multi-party transactions on the blockchain. A broad range of methods has been developed to ensure that a smart contract is functionally correct. However, smart contracts often additionally need to satisfy certain hyperproperties, such as symmetry, determinism, or an information flow policy. In this paper, we show how a synthesis method for smart contracts can ensure that the contract satisfies its desired hyperproperties. We build on top of a recently developed synthesis approach from specifications in the temporal logic TSL. We present HyperTSL, an extension of TSL for the specification of hyperproperties of infinite-state software. As a preprocessing step, we show how to detect if a hyperproperty has an equivalent formulation as a (simpler) trace property. Finally, we describe how to refine a synthesized contract to adhere to its HyperTSL specification.
Investigating Membership Inference Attacks under Data Dependencies

Training machine learning models on privacy-sensitive data has become a popular practice, driving innovation in ever-expanding fields. This has opened the door to new attacks that can have serious privacy implications. One such attack, the Membership Inference Attack (MIA), exposes whether or not a particular data point was used to train a model. A growing body of literature uses Differentially Private (DP) training algorithms as a defence against such attacks. However, these works evaluate the defence under the restrictive assumption that all members of the training set, as well as non-members, are independent and identically distributed. This assumption does not hold for many real-world use cases in the literature. Motivated by this, we evaluate membership inference with statistical dependencies among samples and explain why DP does not provide meaningful protection (the privacy parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> scales with the training set size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> ) in this more general case. We conduct a series of empirical evaluations with off-the-shelf MIAs using training sets built from real-world data showing different types of dependencies among samples. Our results reveal that training set dependencies can severely increase the performance of MIAs, and therefore assuming that data samples are statistically independent can significantly underestimate the performance of MIAs.
Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning

Data generated at the network edge can be processed locally by leveraging the paradigm of edge computing (EC). Aided by EC, decentralized federated learning (DFL), which overcomes the single-point-of-failure problem in the parameter server (PS) based federated learning, is becoming a practical and popular approach for machine learning over distributed data. However, DFL faces two critical challenges, i.e., system heterogeneity and statistical heterogeneity introduced by edge devices. To ensure fast convergence with the existence of slow edge devices, we present an efficient DFL method, termed FedHP, which integrates adaptive control of both local updating frequency and network topology to better support the heterogeneous participants. We establish a theoretical relationship between local updating frequency and network topology regarding model training performance and obtain a convergence upper bound. Upon this, we propose an optimization algorithm, that adaptively determines local updating frequencies and constructs the network topology, so as to speed up convergence and improve the model accuracy. Evaluation results show that the proposed FedHP can reduce the completion time by about 51% and improve model accuracy by at least 5% in heterogeneous scenarios, compared with the baselines.
Healthcare predictive analytics using machine learning and deep learning techniques: a survey

Abstract Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain.
Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results

As a convenient and natural way of human-computer interaction, gesture recognition technology has broad research and application prospects in many fields, such as intelligent perception and virtual reality. This paper summarized the relevant literature on gesture recognition using Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar from January 2015 to June 2023. In the manuscript, the widely used methods involved in data acquisition, data processing, and classification in gesture recognition were systematically investigated. This paper counts the information related to FMCW millimeter wave radar, gestures, data sets, and the methods and results in feature extraction and classification. Based on the statistical data, we provided analysis and recommendations for other researchers. Key issues in the studies of current gesture recognition, including feature fusion, classification algorithms, and generalization, were summarized and discussed. Finally, this paper discussed the incapability of the current gesture recognition technologies in complex practical scenes and their real-time performance for future development.
Dual-Hand Motion Capture by Using Biological Inspiration for Bionic Bimanual Robot Teleoperation

Bionic bimanual robot teleoperation can transfer the grasping and manipulation skills of human dual hands to the bionic bimanual robots to realize natural and flexible manipulation. The motion capture of dual hands plays an important role in the teleoperation. The motion information of dual hands can be captured through the hand detection, localization, and pose estimation and mapped to the bionic bimanual robots to realize the teleoperation. However, although the motion capture technology has achieved great achievements in recent years, visual dual-hand motion capture is still a great challenge. So, this work proposed a dual-hand detection method and a 3-dimensional (3D) hand pose estimation method based on body and hand biological inspiration to achieve convenient and accurate monocular dual-hand motion capture and bionic bimanual robot teleoperation. First, a dual-hand detection method based on body structure constraints is proposed, which uses a parallel structure to combine hand and body relationship features. Second, a 3D hand pose estimation method with bone-constraint loss from single RGB images is proposed. Then, a bionic bimanual robot teleoperation method is designed by using the proposed hand detection and pose estimation methods. Experiment results on public hand datasets show that the performances of the proposed hand detection and 3D hand pose estimation outperform state-of-the-art methods. Experiment results on a bionic bimanual robot teleoperation platform shows the effectiveness of the proposed teleoperation method.
Circular economy is key! Designing a digital artifact to foster smarter household biowaste sorting

Waste volumes are rising. This is a problem, as they worsen environmental challenges such as climate change. At the same time, however, waste bears valuable resources – yet untapped. The concept of a circular economy bears the potential to unleash these resources. In the short run, only changing the waste sorting behavior of citizens can effectively contribute to circular resource flows, as waste management companies cannot ensure the purity of waste by purely technical means. Aligned with the digital realm, this work focuses on designing a digital artifact that contributes to improved household waste sorting. Using a design science research approach, we develop the artifact's design with input from theory, 23 interviews with potential users and practitioners, and evaluate a prototypical instantiation in a large German city. We contribute to literature and theory by generating design knowledge on information systems for improved biowaste and residual waste sorting by individuals. We derive as key design principles the importance of awareness and transparency, social influence, education and confirmation, and incentives. Our solution embracing those principles enhances the awareness of circular economy processes and the relevance of correct waste sorting. At the same time, our solution is relevant to solving waste management companies' current challenges such as the incineration of valuable biowaste as part of residual waste.
Consumers’ Ethical Perceptions of Autonomous Service Robots in Hotels

This study empirically and comprehensively explores consumers’ ethical perceptions of autonomous service robots (ASRs) in hotels. Under the triangulation approach, this study has identified eight themes of consumer perceived ethical issues (privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility). Each theme can be explained from two dimensions: ethical issues arise during the interaction (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues can be raised by the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). This study is the first to propose ethical issues of ASRs from two dimensions with different intelligence levels, and to highlight ethical issues during hotel service interactions. The findings contribute to ethics studies of service robots from consumers’ perspectives and offer managerial insights to reduce ethical concerns and enhance ASRs usage in hotels.
Extending Numeric Automation for Number Theory Formalizations in Mizar

In this paper we present an experimental version of the Mizar proof checker equipped with new built-in routines for automating common numeric computations. This implementation has been directly motivated by recent formalizations of selected number theory topics that required extensive numeric calculations and proving numerical properties of specific numbers. The potential of automating parts of such proofs has been evaluated and, consequently, the Mizar checker has been extended with code that enabled refactoring the current contents of the Mizar Mathematical Library.