Reviewerly/exHarmony huggingface dataset:
Deep learning: systematic review, models, challenges, and research directions

Abstract The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a promising framework for higher performance and lower complexity. This ongoing transition undergoes several rapid changes, resulting in the processing of the data by several studies, while it may lead to time-consuming and costly models. Thus, to address these challenges, several studies have been conducted to investigate deep learning techniques; however, they mostly focused on specific learning approaches, such as supervised deep learning. In addition, these studies did not comprehensively investigate other deep learning techniques, such as deep unsupervised and deep reinforcement learning techniques. Moreover, the majority of these studies neglect to discuss some main methodologies in deep learning, such as transfer learning, federated learning, and online learning. Therefore, motivated by the limitations of the existing studies, this study summarizes the deep learning techniques into supervised, unsupervised, reinforcement, and hybrid learning-based models. In addition to address each category, a brief description of these categories and their models is provided. Some of the critical topics in deep learning, namely, transfer, federated, and online learning models, are explored and discussed in detail. Finally, challenges and future directions are outlined to provide wider outlooks for future researchers.
QoE Fairness Resource Allocation in Digital Twin-Enabled Wireless Virtual Reality Systems

Wireless virtual reality (VR) is expected to be a technology that revolutionizes human interaction and perceived media, where the quality of experience (QoE) is an important indicator to measure user service perception. However, existing schemes only consider general and time-invariant QoE optimization, which may suffer performance degradation. Moreover, it is also necessary to ensure the fairness of the individual user’s performance in wireless VR. To address these challenges, we employ digital twin technology to investigate a max-min QoE-optimal problem for wireless VR systems in this paper. Specifically, we maximize the QoE of the worst-case head-mounted displays (HDMs) client, where the QoE model is the linear weighting combination of video quality, service delay, and energy efficiency. The formulated optimization problem is characterized by multidimensional control, which jointly optimizes model selection, transmit power, computation time, and GPU-cycle frequency. Due to the mixed combinatorial features of the optimization problem, we give a low-complexity algorithm design by decoupling the optimization variables. Notably, we first obtain the allocation of the transmit power by employing the generalized fractional programming theory and the Lagrangian dual decomposition, followed by attaining the optimal allocation of GPU-cycle frequency in VR mode is derived by the proposed adaptive modified harmony search algorithm, and finally achieve the computation time by the barrier method. Meanwhile, we devise a greedy-style heuristic algorithm for mode selection. In the simulation, three baseline schemes are established as comparisons to assess the effectiveness of the proposed scheme. Meanwhile, the simulation results manifest that the proposed algorithms have good convergence performance and better increase the QoE of the DT-enabled wireless VR system compared to benchmark solutions.
Brain-like position measurement method based on improved optical flow algorithm

In this paper, a brain-like navigation scheme based on fuzzy kernel C-means (FKCM) clustering assisted pyramid Lucas Kanade (LK) optical flow algorithm is developed to measure the position of vehicle. The Speed Cell and Place Cell in animals' brain are introduced to construct the brain-like navigation mechanism which involves the optical flow method and image template matching to imitate the cells above-mentioned separately. To eliminate the singular values during optical flow calculation, the output of pyramid LK algorithm is clustered by FKCM algorithm firstly. Then, the velocity is calculated and integrated to get the position of the vehicle, and the brain-like navigation scheme is introduced to correct the position measurement errors by eliminating the accumulated errors resulting from velocity integration. The prominent advantages of the presented method are: (i) a pure visual brain-like position measurement method based on the concept of speed cells and place cells is proposed, making visual navigation more accurate and intelligent; (ii) the FKCM algorithm is used to eliminate the singular value of the pyramid LK algorithm, which improves the calculated velocity accuracy. Also, experimental comparison with classical pyramid LK algorithm is given to illustrate the superiority of the proposed method in position measurement.
Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review

Abstract The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum of uses like Computational linguistics, image identification, and autonomous systems. With the increasing demand for intelligent systems, it has become crucial to comprehend the different categories of machine acquiring knowledge systems along with their applications in the present world. This paper presents actual use cases of machine learning, including cancer classification, and how machine learning algorithms have been implemented on medical data to categorize diverse forms of cancer and anticipate their outcomes. The paper also discusses supervised, unsupervised, and reinforcement learning, highlighting the benefits and disadvantages of each category of Computational intelligence system. The conclusions of this systematic study on machine learning methods and applications in cancer classification have numerous implications. The main lesson is that through accurate classification of cancer kinds, patient outcome prediction, and identification of possible therapeutic targets, machine learning holds enormous potential for improving cancer diagnosis and therapy. This review offers readers with a broad understanding as of the present advancements in machine learning applied to cancer classification today, empowering them to decide for themselves whether to use these methods in clinical settings. Lastly, the paper wraps up by engaging in a discussion on the future of machine learning, including the potential for new types of systems to be developed as the field advances. Overall, the information included in this survey article is useful for scholars, practitioners, and individuals interested in gaining knowledge about the fundamentals of machine learning and its various applications in different areas of activities.
An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions

The rapidly growing use of cloud computing raises security concerns. This study paper seeks to examine cloud security frameworks, addressing cloud-associated issues and suggesting solutions. This research provides greater knowledge of the various frameworks, assisting in making educated decisions about selecting and implementing suitable security measures for cloud-based systems. The study begins with introducing cloud technology, its issues and frameworks to secure infrastructure, and an examination of the various cloud security frameworks available in the industry. A full comparison is performed to assess the framework’s focus, scope, approach, strength, limitations, implementation steps and tools required in the implementation process. The frameworks focused on in the paper are COBIT5, NIST (National Institute of Standards and Technology), ISO (International Organization for Standardization), CSA (Cloud Security Alliance) STAR and AWS (Amazon Web Services) well-architected framework. Later, the study digs into identifying and analyzing prevalent cloud security issues. This contains attack vectors that are inherent in cloud settings. Plus, this part includes the risk factor of top cloud security threats and their effect on cloud platforms. Also, it presents ideas and countermeasures to reduce the observed difficulties.
EdgeYOLO: An Edge-Real-Time Object Detector

An efficient, low-complexity, and anchor-free object detector based on the state-of-the-art YOLO framework is proposed in this paper, which can be implemented in real time on edge computing platforms. An enhanced data augmentation method is developed to effectively suppress overfitting during training, and a hybrid random loss function is designed to improve the detection accuracy of small objects. Inspired by FCOS, a lighter and more efficient decoupled head is proposed, and its inference speed can be improved with little loss of precision. Our baseline model can reach the accuracy of 50.6% AP50:95 and 69.8% AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> in MS COC02017 dataset, 26.9% AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50:95</inf> and 45.4% AP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> in VisDrone2019-DET dataset, and it meets real-time requirements (FPS230) on edge-computing device Nvidia Jetson AGX Xavier. And as is shown in Fig. 1, lighter models with less parameters designed for edge computing devices with lower computing power also show better performances. Our source code, hyper-parameters and model weights are all available at https://github.com/LSH9832/edgeyolo.
Investigating barriers to blockchain adoption in construction supply chain management: A fuzzy-based MCDM approach

This study aims to investigate the barriers to the adoption of Blockchain Technology (BT) within the field of Construction Supply Chain Management (CSCM) by unravelling their interrelationships. In doing so, a Pythagorean fuzzy-set-based DEMATEL method to assess these barriers in a complex environment is proposed. The results of the study suggest that “market-based risks”, “high costs for maintaining sustainability”, and “usage in the underground economy” are the most influential barriers to the adoption of BT in CSCM. Furthermore, the study identifies that “inadequate knowledge/employee training”, “contractual risk”, and “scalability issues” play a pivotal role within the CSCM system. The reliability and validation of the obtained results were also shown using sensitivity analysis and the focus-group-discussion approach. The findings of this study provide valuable insights for stakeholders in the construction industry to design and implement effective measures to overcome the identified barriers to the adoption of BT in CSCM. By addressing these barriers, stakeholders can facilitate the adoption of BT, leading to more efficient and effective construction supply chain management practices.
Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial

Abstract Autonomous artificial intelligence (AI) promises to increase healthcare productivity, but real-world evidence is lacking. We developed a clinic productivity model to generate testable hypotheses and study design for a preregistered cluster-randomized clinical trial, in which we tested the hypothesis that a previously validated US FDA-authorized AI for diabetic eye exams increases clinic productivity (number of completed care encounters per hour per specialist physician) among patients with diabetes. Here we report that 105 clinic days are cluster randomized to either intervention (using AI diagnosis; 51 days; 494 patients) or control (not using AI diagnosis; 54 days; 499 patients). The prespecified primary endpoint is met: AI leads to 40% higher productivity (1.59 encounters/hour, 95% confidence interval [CI]: 1.37–1.80) than control (1.14 encounters/hour, 95% CI: 1.02–1.25), p &lt; 0.00; the secondary endpoint (productivity in all patients) is also met. Autonomous AI increases healthcare system productivity, which could potentially increase access and reduce health disparities. ClinicalTrials.gov NCT05182580.
State estimation of a physical system with unknown governing equations

State estimation is concerned with reconciling noisy observations of a physical system with the mathematical model believed to predict its behaviour for the purpose of inferring unmeasurable states and denoising measurable ones1,2. Traditional state-estimation techniques rely on strong assumptions about the form of uncertainty in mathematical models, typically that it manifests as an additive stochastic perturbation or is parametric in nature3. Here we present a reparametrization trick for stochastic variational inference with Markov Gaussian processes that enables an approximate Bayesian approach for state estimation in which the equations governing how the system evolves over time are partially or completely unknown. In contrast to classical state-estimation techniques, our method learns the missing terms in the mathematical model and a state estimate simultaneously from an approximate Bayesian perspective. This development enables the application of state-estimation methods to problems that have so far proved to be beyond reach. Finally, although we focus on state estimation, the advancements to stochastic variational inference made here are applicable to a broader class of problems in machine learning.
Solar energy status in the world: A comprehensive review

The utilization of renewable energy as a future energy resource is drawing significant attention worldwide. The contribution of solar energy (including concentrating solar power (CSP) and solar photovoltaic (PV) power) to global electricity production, as one form of renewable energy sources, is generally still low, at 3.6%. However, it has firmly established itself among other renewable energy technologies, comprising nearly 31% of the total installed renewable energy capacity in 2022, making it the second most installed renewable energy resource behind hydropower energy. The present review study, through a detailed and systematic literature survey, summarizes the world solar energy status along with the published solar energy potential assessment articles for 235 countries and territories as the first step toward developing solar energy in these regions. A comparison of the solar power status among countries and territories has been provided, considering their concentrated solar power and PV installed capacities for each continent. Although there has been a significant increase of approximately 22% in global solar energy installed capacity between 2021 and 2022, the literature survey reveals that clear gaps still exist in the field of solar energy. In the next three decades, the solar PV field can advance to become the second prominent generation source by constructing more solar farms, allowing countries to generate approximately 25% of the world's total electricity needs by 2050.
ZeroTrustBlock: Enhancing Security, Privacy, and Interoperability of Sensitive Data through ZeroTrust Permissioned Blockchain

With the digitization of healthcare, an immense amount of sensitive medical data are generated and shared between various healthcare stakeholders—however, traditional health data management mechanisms present interoperability, security, and privacy challenges. The centralized nature of current health information systems leads to single points of failure, making the data vulnerable to cyberattacks. Patients also have little control over their medical records, raising privacy concerns. Blockchain technology presents a promising solution to these challenges through its decentralized, transparent, and immutable properties. This research proposes ZeroTrustBlock, a comprehensive blockchain framework for secure and private health information exchange. The decentralized ledger enhances integrity, while permissioned access and smart contracts enable patient-centric control over medical data sharing. A hybrid on-chain and off-chain storage model balances transparency with confidentiality. Integration gateways bridge ZeroTrustBlock protocols with existing systems like EHRs. Implemented on Hyperledger Fabric, ZeroTrustBlock demonstrates substantial security improvements over mainstream databases via cryptographic mechanisms, formal privacy-preserving protocols, and access policies enacting patient consent. Results validate the architecture’s effectiveness in achieving 14,200 TPS average throughput, 480 ms average latency for 100,000 concurrent transactions, and linear scalability up to 20 nodes. However, enhancements around performance, advanced cryptography, and real-world pilots are future work. Overall, ZeroTrustBlock provides a robust application of blockchain capabilities to transform security, privacy, interoperability, and patient agency in health data management.
Modal Logic, an Introduction

For analytic philosophy, formalization is a fundamental tool for clarifying language, leading to better understanding of thoughts expressed through language. Formalization involves abstraction and idealization. This is true in the sciences as well as in philosophy.
Diffusion Probabilistic Modeling for Video Generation

Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual and probabilistic forecasting metrics. We propose an autoregressive, end-to-end optimized video diffusion model inspired by recent advances in neural video compression. The model successively generates future frames by correcting a deterministic next-frame prediction using a stochastic residual generated by an inverse diffusion process. We compare this approach against six baselines on four datasets involving natural and simulation-based videos. We find significant improvements in terms of perceptual quality and probabilistic frame forecasting ability for all datasets.
All-analog photoelectronic chip for high-speed vision tasks

Abstract Photonic computing enables faster and more energy-efficient processing of vision data 1–5 . However, experimental superiority of deployable systems remains a challenge because of complicated optical nonlinearities, considerable power consumption of analog-to-digital converters (ADCs) for downstream digital processing and vulnerability to noises and system errors 1,6–8 . Here we propose an all-analog chip combining electronic and light computing (ACCEL). It has a systemic energy efficiency of 74.8 peta-operations per second per watt and a computing speed of 4.6 peta-operations per second (more than 99% implemented by optics), corresponding to more than three and one order of magnitude higher than state-of-the-art computing processors, respectively. After applying diffractive optical computing as an optical encoder for feature extraction, the light-induced photocurrents are directly used for further calculation in an integrated analog computing chip without the requirement of analog-to-digital converters, leading to a low computing latency of 72 ns for each frame. With joint optimizations of optoelectronic computing and adaptive training, ACCEL achieves competitive classification accuracies of 85.5%, 82.0% and 92.6%, respectively, for Fashion-MNIST, 3-class ImageNet classification and time-lapse video recognition task experimentally, while showing superior system robustness in low-light conditions (0.14 fJ μm −2 each frame). ACCEL can be used across a broad range of applications such as wearable devices, autonomous driving and industrial inspections.
Educational data augmentation in physics education research using ChatGPT

Generative AI technologies such as large language models show novel potential to enhance educational research. For example, generative large language models were shown to be capable of solving quantitative reasoning tasks in physics and concept tests such as the Force Concept Inventory (FCI). Given the importance of such concept inventories for physics education research, and the challenges in developing them such as field testing with representative populations, this study seeks to examine to what extent a generative large language model could be utilized to generate a synthetic dataset for the FCI that exhibits content-related variability in responses. We use the recently introduced ChatGPT based on the GPT 4 generative large language model and investigate to what extent ChatGPT could solve the FCI accurately (RQ1) and could be prompted to solve the FCI as if it were a student belonging to a different cohort (RQ2). Furthermore, we study, to what extent ChatGPT could be prompted to solve the FCI as if it were a student having a different force- and mechanics-related preconception (RQ3). In alignment with other research, we found that ChatGPT could accurately solve the FCI. We furthermore found that prompting ChatGPT to respond to the inventory as if it belonged to a different cohort yielded no variance in responses, however, responding as if it had a certain preconception introduced much variance in responses that approximate real human responses on the FCI in some regards.Received 28 July 2023Accepted 3 October 2023DOI:https://doi.org/10.1103/PhysRevPhysEducRes.19.020150Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasConcepts & principlesResearch methodologyPhysics Education Research
Orientation Cues-Aware Facial Relationship Representation for Head Pose Estimation via Transformer

Head pose estimation (HPE) is an indispensable upstream task in the fields of human-machine interaction, self-driving, and attention detection. However, practical head pose applications suffer from several challenges, such as severe occlusion, low illumination, and extreme orientations. To address these challenges, we identify three cues from head images, namely, critical minority relationships, neighborhood orientation relationships, and significant facial changes. On the basis of the three cues, two key insights on head poses are revealed: 1) intra-orientation relationship and 2) cross-orientation relationship. To leverage two key insights above, a novel relationship-driven method is proposed based on the Transformer architecture, in which facial and orientation relationships can be learned. Specifically, we design several orientation tokens to explicitly encode basic orientation regions. Besides, a novel token guide multi-loss function is accordingly designed to guide the orientation tokens as they learn the desired regional similarities and relationships. Experimental results on three challenging benchmark HPE datasets show that our proposed TokenHPE achieves state-of-the-art performance. Moreover, qualitative visualizations are provided to verify the effectiveness of the token-learning methodology.