Reviewerly/exHarmony huggingface dataset:
A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model

Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details while upholding the temporal coherence among frames. Nevertheless, their extensive parameter counts and high demand for computational resources challenge the deployment of existing deep convolutional neural networks on mobile platforms. In response to these concerns, our research undertakes an in-depth investigation into deep convolutional neural networks and offers a lightweight model for video super-resolution, capable of reducing computational load. In this study, we bring forward a unique lightweight model for video super-resolution, the Deep Residual Recursive Network (DRRN). The model applies residual learning to stabilize the Recurrent Neural Network (RNN) training, meanwhile adopting depth-wise separable convolution to boost the efficiency of super-resolution operations. Thorough experimental evaluations reveal that our proposed model excels in computational efficiency and in generating refined and temporally consistent results for video super-resolution. Hence, this research presents a crucial stride toward applying video super-resolution strategies on devices with resource limitations.
Improved Lightweight Multi-Target Recognition Model for Live Streaming Scenes

Nowadays, the commercial potential of live e-commerce is being continuously explored, and machine vision algorithms are gradually attracting the attention of marketers and researchers. During live streaming, the visuals can be effectively captured by algorithms, thereby providing additional data support. This paper aims to consider the diversity of live streaming devices and proposes an extremely lightweight and high-precision model to meet different requirements in live streaming scenarios. Building upon yolov5s, we incorporate the MobileNetV3 module and the CA attention mechanism to optimize the model. Furthermore, we construct a multi-object dataset specific to live streaming scenarios, including anchor facial expressions and commodities. A series of experiments have demonstrated that our model realized a 0.4% improvement in accuracy compared to the original model, while reducing its weight to 10.52%.
Geothermal Spatial Potential and Distribution Assessment Using a Hierarchical Structure Model Combining GIS, Remote Sensing, and Geophysical Techniques—A Case Study of Dali’s Eryuan Area

The assessment of geothermal potential has gained prominence among scholars, with a focus on establishing a reliable prediction model to reduce development risks. However, little attention has been given to predicting and evaluating the geothermal potential in Dali’s Eryuan area. This study introduces a novel hierarchical model integrating remote sensing, a Geographic Information System (GIS), and geophysics for the first-ever effective prediction of geothermal potential in Eryuan. The dataset includes lithology, seismic epicenter data, fault distribution, Bouguer gravity anomalies, SRTM-DEM images, and Landsat 8 remote sensing images. These datasets are converted into evidence maps and normalized to generate distinct evidence factor layers. Using the Analytic Hierarchy Process (AHP), a hierarchical model establishes weights for each evidence factor, resulting in a comprehensive prediction map. The results reveal the overall favorable geothermal potential in Eryuan, except the central area. Key hotspots include the Niujie–Sanying–Gromwell Lake and Liantie–Qiaohou, followed by the Youshou, Dengchuan, and Xixiang towns. Validation against known hot springs confirms the model’s accuracy and reliability.
Machine-Learning Forensics: State of the Art in the Use of Machine-Learning Techniques for Digital Forensic Investigations within Smart Environments

Recently, a world-wide trend has been observed that there is widespread adoption across all fields to embrace smart environments and automation. Smart environments include a wide variety of Internet-of-Things (IoT) devices, so many challenges face conventional digital forensic investigation (DFI) in such environments. These challenges include data heterogeneity, data distribution, and massive amounts of data, which exceed digital forensic (DF) investigators’ human capabilities to deal with all of these challenges within a short period of time. Furthermore, they significantly slow down or even incapacitate the conventional DFI process. With the increasing frequency of digital crimes, better and more sophisticated DFI procedures are desperately needed, particularly in such environments. Since machine-learning (ML) techniques might be a viable option in smart environments, this paper presents the integration of ML into DF, through reviewing the most recent papers concerned with the applications of ML in DF, specifically within smart environments. It also explores the potential further use of ML techniques in DF in smart environments to reduce the hard work of human beings, as well what to expect from future ML applications to the conventional DFI process.
Security of Cyber-Physical Systems of Chemical Manufacturing Industries Based on Blockchain

The traditional manufacturing systems are often enterprise-centric systems, whereas the modern chemical industry is oriented towards industrial chain integration. Enterprise entities present a loosely coupled state at the scale of the industrial chain, with decentralized characteristics. This poses greater challenges and requirements for the industrial safety system. Based on the characteristics of the chemical manufacturing industry and blockchain, the application of the information security of blockchain in the chemical manufacturing industry is studied herein and the cyber-physical systems security architecture model of dual blockchains is proposed. The first-layer blockchain is applied at the system’s core function level to solve security issues at the system level and provide security guarantees for communication, transactions, and billing between users and manufacturers. Meanwhile, the second layer involves the system resource layer, which not only solves the security problem of cross-level platform data interaction, but also enables the point-to-point security of the device-level cyber-physical system to ensure internal equipment communication information security. A domestic commercial concrete manufacturing company’s real production and operation data were used to simulate basic functions such as transaction requests, trade success, and blockchain queries. After multiple tests, results show that its basic blockchain, query response, transaction creation, and block creation functions are all finished within milliseconds, meeting the industrial requirements. Its safety verification can meet the requirements of safety, efficiency, and low latency for production control in chemical industry sites, proving the feasibility of applying the dual blockchain model in the chemical manufacturing industry. Based on data security, privacy, and integrity requirements, the blockchain technology proposed in this article provides a more efficient, transparent, and secure operation and management solution for the chemical industry.
Tailored multi-organ segmentation with model adaptation and ensemble

Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in multi-organ segmentation tasks. However, due to expensive labor costs and expertise, the availability of multi-organ annotations is usually limited and hence poses a challenge in obtaining sufficient training data for deep learning-based methods. In this paper, we aim to address this issue by combining off-the-shelf single-organ segmentation models to develop a multi-organ segmentation model on the target dataset, which helps get rid of the dependence on annotated data for multi-organ segmentation. To this end, we propose a novel dual-stage method that consists of a Model Adaptation stage and a Model Ensemble stage. The first stage enhances the generalization of each off-the-shelf segmentation model on the target domain, while the second stage distills and integrates knowledge from multiple adapted single-organ segmentation models. Extensive experiments on four abdomen datasets demonstrate that our proposed method can effectively leverage off-the-shelf single-organ segmentation models to obtain a tailored model for multi-organ segmentation with high accuracy.
Application of the Quality Evaluation Method (Qem) in the Evaluation of City Government Websites in South Sumatra Province

In general, the city government website is pretty good. The need to evaluate existing web pemerinahan city on accessing the website used easily by the public and by various types of web browsers, search engines use the content and the content of the web site if the city government is based reference guide to the implementation of local government web sites, therefore the website Urban Governance in South Sumatra province should be measured based on criteria of accessibility, speed, navigability and site content based on quality evaluation method (QEM). Sampelyang used 100 respondents, measurement Likert scale. In the study conducted was obtained result is the quality of Palembang City Government Web site of 3.1, by 3.6 Prabumulih City, City of Pagar Alam by 3.1 and by 3.3 Linggau Lubuk City. Value above that of the highest value sites and the lowest we Prabumulih city of Palembang city website and city of Pagar Alam, while the assessment of internval score it can be concluded that the quality of government websites Palembang in South Sumatra is pretty good.
Approximate Inverse Model Explanations (AIME): Unveiling Local and Global Insights in Machine Learning Models

Data-driven decision-making has become pervasive in the fields of interpretive machine learning and Explainable AI (XAI). While both fields aim to improve human comprehension of machine learning models, they differ in focus. Interpretive machine learning centers on deciphering outcomes in transparent, or ’glass-box,’ models, whereas XAI focuses on creating tools for explaining complex ’black-box’ models in a human-understandable way. Some existing interpretable machine learning and explainable AI methods have utilized a forward problem to derive how the prediction and estimation output results of a black-box model change with respect to the input. However, methods adopting the forward problem lead to non-intuitive explanations. Therefore, hypothesizing that the inverse problem can yield more intuitive explanations, we propose approximate inverse model explanations (AIME), which offer unified global and local feature importance by deriving approximate inverse operators for black-box models. Additionally, we introduce a representative instance similarity distribution plot, aiding comprehension of the predictive behavior of the model and target dataset. In our experiments with LightGBM, AIME proved effective across diverse data types, from tabular and handwritten digit images to text data. Results demonstrate that AIME’s explanations are not only simpler but more intuitive than those generated by well-established methods like LIME and SHAP. It also visualizes similarity distribution with the target dataset, illustrating the relation between different predictions. Furthermore, AIME estimates local and global feature importance and provides fresh insights by visualizing the similarity distribution between representative estimation instances and the target dataset.
Statistical Analytics for Health Data Science with SAS and R

"Statistical Analytics for Health Data Science with SAS and R." Journal of Quality Technology, ahead-of-print(ahead-of-print), p. 1
A Lean-Congruence Format for EP-Bisimilarity

Enabling preserving bisimilarity is a refinement of strong bisimilarity that preserves safety as well as liveness properties. To define it properly, labelled transition systems needed to be upgraded with a successor relation, capturing concurrency between transitions enabled in the same state. We enrich the well-known De Simone format to handle inductive definitions of this successor relation. We then establish that ep-bisimilarity is a congruence for the operators, as well as lean congruence for recursion, for all (enriched) De Simone languages.
Accelerating Neural Style-Transfer Using Contrastive Learning for Unsupervised Satellite Image Super-Resolution

Contrastive learning is a self-supervised comparison of two samples to identify characteristics and traits that distinguish one data class from another, improving performance on visual tasks. The performance of existing super-resolution-based approaches degrades with increasing scaling factors, hence practically not useful for high-resolution (HR) imaging applications. We proposed a novel framework that uses contrastive training followed by a decoder to generate an “Artificial style image,” which is utilized as a style image for neural style transfer (NST) learning for image super-resolution in an unsupervised manner. The idea is to benefit from HR textures and features as a style and transfer on an original low-resolution (LR) content image as base elements. The proposed framework has three benefits: 1) the framework is capable of super-resolving different modalities of data like single-band remote sensing images, multispectral band images, RGB remote sensing images, and real-world natural images; 2) proposed method outperforms existing unsupervised and also supervised learning-based methods for both visual and qualitative performance; and 3) leveraging NST learning for remote sensing image super-resolution is performed without sacrificing speed and resources. The framework is novel since the work on NST learning to super-resolve remote sensing images in an unsupervised manner has yet to be acknowledged.
RDAOT: Robust Unsupervised Deep Sub-Domain Adaptation Through Optimal Transport for Image Classification

In traditional machine learning, the training and testing data are assumed to come from the same independent and identical distributions. This assumption, however, does not hold up in real-world applications, as differences between the training and testing data may have different distributions. Domain adaptation has emerged as a solution that enables the transfer of knowledge between domains with distinct distributions. In this paper, we primarily utilize domain adaptation in the context of visual recognition tasks despite its growing application in diverse domains. Earlier studies have mainly aimed at minimizing the differences in global distributions between the domains and failed to capture the local, pertinent features crucial for domain alignment. Furthermore, models struggle to perform well and generalize to target data when outliers or noise exist in the datasets. This work addresses these problems and provides unique strategies for unsupervised domain adaptation using RDAOT (Robust Deep Adaptation via Optimal Transport). To capture local information by utilizing LMMD (Local Maximum Mean Discrepancy) to minimize the divergence of the feature distributions between the domains. We examine label noise robustness in the source domain and ROT (Robust Optimal Transport) loss to preserve robustness in domain adaptation, which lessens the cost of transporting source distributions to the target distributions. The significance of our presented technique was assessed through extensive experiments on six different visual recognition domain adaptation datasets. The results demonstrate that our method outperforms the current state-of-the-art techniques, indicating superior performance. Our approach was evaluated against several baselines, and the results significantly improved average accuracy across various datasets. Specifically, the average accuracy improved from on the OfficeCaltech10 (91.8% to 96.85%), OfficeHome (67.7% to 68.10%), Office31 (88.17% to 88.92%), IMAGECLEF-DA (87.9% to 90.24%), PACS (69.08% to 85.72%), and VisDA-2017 (80.2 % to 89.43%) datasets, respectively.
Subtransfer Learning in Human Activity Recognition: Boosting the Outlier User Accuracy

Human activity recognition (HAR) has become one of the most active areas of sensory healthcare research due to its potential to improve the quality of life both for patients in healthcare settings who are susceptible to falls or undergo physical therapy and the public to promote a more active lifestyle. The state-of-the-art machine learning and artificial intelligence algorithms analyze sensory data from multiple users to achieve unprecedented performance in recognizing daily activities. Most research focuses on the average performance across the population of users however, it is evident that the performance distribution across different users is anything but uniform. In fact, the outlier users demonstrate performance degradations of up to 30% compared to median accuracy on any given dataset. This study investigates the impact of outlier users on activity recognition and proposes a novel approach called subtransfer learning to demonstrate that transfer learning principles can be applied within the same dataset when coupled with augmentation techniques. Our results show that subtransfer learning outperforms the source model (SM) on four datasets with different age groups and sensor placements, even when using a few additional samples. Specifically, in the most challenging dataset, the proposed approach outperforms the SM accuracy on low-accuracy subjects (80% compared to 60%) and median-accuracy subjects (98% compared to 91%) while maintaining the same levels for high-accuracy subjects. A statistical significance test further demonstrates performance improvements compared to both source and subject-specific models with p-values 0.009 and 0.0005, respectively, across a range of dataset with demographically diverse users and sensor locations.
Obstacle Avoidance Path Planning in Unstructured Environment With Narrow Passages

Compared with structured environments, unstructured environments are often more complex, and narrow passages, as a smaller subset of the complex environment, have weak connectivity. In this paper, a grid bridge narrow passage detection algorithm is proposed to solve the problems of collision detection, time consuming and low sampling efficiency of the current widely used bridge detection method, and on this basis, a free-space roadmap construction method and a path adjustment strategy are designed. In the experimental tests of two different scenarios, the path planning time is in milliseconds and 10 milliseconds respectively, demonstrating the real-time implementation value of the proposed method. Meanwhile, the algorithm proposed in this article also shows some superiority in path construction success rate and path optimization.
Rhythm Modeling for Voice Conversion

Voice conversion aims to transform source speech into a different target voice. However, typical voice conversion systems do not account for rhythm, which is an important factor in the perception of speaker identity. To bridge this gap, we introduce Urhythmic—an unsupervised method for rhythm conversion that does not require parallel data or text transcriptions. Using self-supervised representations, we first divide source audio into segments approximating sonorants, obstruents, and silences. Then we model rhythm by estimating speaking rate or the duration distribution of each segment type. Finally, we match the target speaking rate or rhythm by time-stretching the speech segments. Experiments show that Urhythmic outperforms existing unsupervised methods in terms of quality and prosody.
Underestimating the uncertainty of aggregated results: the case of W-Boson mass

Abstract Estimates of uncertainty or variance in experimental means are central to physics. This is especially the case for ‘world averages’ of fundamental parameters in particle physics, which aggregate results from a number of experiments to express current knowledge about these parameters and where variances in these world averages reflect uncertainty in that knowledge. The standard aggregation method used in Particle Data Group reports to estimate such parameters is a form of fixed-effect meta-analysis. One problem with the fixed-effect approach is that it assumes no random variation between experiments (that is, no variation in experimental accuracy, which becomes increasingly important as experimental precision rises). This problem is well-known in the statistical literature, where the typical recommendation is to use random- rather than fixed-effect techniques. We illustrate this problem by applying random-effect meta-analysis to estimates of the W -Boson mass.
Web Application Design And Development As A Support For Reports At The Muhammadiyah Palembang Laboratory

Background: Application is a subclass of computer software that directly utilizes computer capabilities to perform a task that the user wants. system for borrowing goods to manage reports and improve data on borrowing tools and labs that are not optimal online which can be accessed via desktops, smartphones or tablets so that they can facilitate the borrowing process. Research: This study uses data collection techniques using qualitative methods. Namely by interviews and observations as well as with the waterfall design. Result: The results of the research are making the development of the Muhammadiyah Palembang IKesT Lab lending application Conclusion: The conclusion of this research is the Development of an Application for Adding Features for lending special tools and labs for research and Adding Features for the Muhammadiyah Palembang IkesT Lab Report for researchers and students to the Muhammadiyah Palembang IkesT lab
RDD-net: Robust duplicated-diffusion watermarking based on deep network

This paper proposes a novel robust duplicated-diffusion watermarking model based on deep network (RDD-net). RDD-net employs the encoder-noise-decoder structure for end-to-end training to obtain high image watermarking invisibility and robustness. Firstly, a duplicated-diffusion strategy is employed in the encoder to replicate the original watermark iteratively until all copies are diffused to the whole image so that the generalized ability in resisting various noises is obtained. Then, a channel connection technique is designed to extract inherent image features for fusing watermark for robustness by mining correlations of RGB three channels of the color image. Meanwhile, each channel is fused with watermark to increase robustness. Another attempt to improve the watermarking performance is the optimizer, which optimizes the watermark distribution by evaluating the similarities between the encoded image and the original image, as well as between the encoded image and the noised image. Our extensive experimental results demonstrate that the proposed RDD-net not only resists different noises, but also obtains better image quality and higher robustness than the existing watermarking models.