Reviewerly/exHarmony huggingface dataset:
Few-Shot Aerial Image Semantic Segmentation Leveraging Pyramid Correlation Fusion

Few-shot semantic segmentation has gained significant attention due to its ability to segment novel objects using only a limited number of labeled samples, thereby addressing the problem of overfitting caused by a lack of training data. Although this technique is widely studied in the field of computer vision, there are few methods for remote sensing images. Prevalent few-shot semantic segmentation methods can achieve remarkable results for natural images, but they are difficult to apply to remote sensing image processing because existing methods rarely take into consideration the large scale and resolution differences in remote sensing images. Consequently, it is hard for them to obtain correct semantic guidance from few annotated remote sensing images. To tackle these problems, this article proposes the pyramid correlation fusion network (PCFNet) to promote the ability to mine helpful information by calculating multi-scale pixel-wise semantic correspondence. Particularly, the dual distance correlation (DDC) module is designed to simultaneously compute the cosine similarity and Euclidean distance between query features and support features, producing adequate guidance information to determine the category of each pixel. Moreover, to improve segmentation accuracy for small objects, the scale-aware cross-entropy loss (SACELoss) is introduced to dynamically assign loss weights according to the actual sizes of objects. This enables smaller objects to be assigned larger weight values and thus receive more attention during training. Comprehensive experiments on both the iSAID-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>i</i></sup> and DLRSD-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>i</i></sup> datasets demonstrate that our method outperforms state-of-the-art few-shot semantic segmentation methods. Our code is available at https://github.com/TinyAway/PCFNet.
Sizing a grid-connected photovoltaic system under economic and environmental uncertainties

Several looming challenges confront the implementation of PV systems. The decision-maker must consider technical, economic, and environmental uncertainties to maximize the PV systems' efficiency under specific operating constraints. In this research, we have developed a two-stage stochastic programming model using a multiobjective optimization technique, extending the approach originally introduced by Attia et al. (2021), to optimize the performance of a grid-coupled PV system considering economic and environmental uncertainties. The capacity of a PV system is determined by deciding the number of PV arrays, the amount of energy imported from the grid, and the amount exported to external utilities. The inflation rate, hourly irradiation, ambient temperature, and energy demand are considered uncertain parameters associated with economic and environmental factors. Uncertain parameters are characterized by possible scenarios associated with the corresponding probability of occurrence. A case study of sizing a grid-connected PV system to provide power to the housing area is presented to demonstrate the usefulness of the proposed technique. A sensitivity analysis is conducted by changing the levels of the primary parameters to grasp the tradeoffs that govern the research framework. It is found that the system can meet the entire demand using 1,566 PV arrays at an annual cost of M$1.47 and a reduction of CO2 emissions by 96.32%.
A Design-Oriented Framework for Interactive Digital Narrative in Virtual Reality - Focus on Branching Narrative and Agency

This research aims to find a design-oriented framework for interactive digital narrative (IDN) in virtual reality (VR), focusing on branching narrative and agency. Incorporating a practice-based research methodology, this study analyzed three virtual reality projects focusing on agency and three products from different media (print, movie, VR experience) focusing on branching narratives. The extracted framework comprises six sections: the interactor, actions, decisions, feedback and acknowledgment, VR features, and accessibility.
Anti-crosstalk absolute phase retrieval method for microscopic fringe projection profilometry using temporal frequency-division multiplexing

In microscopic fringe projection profilometry (MFPP), the traditional absolute phase retrieval method using composite frequency fringe has the shortcomings of low accuracy and poor robustness due to mutual crosstalk of harmonic from the different channels of frequency-division multiplexing. In this study, an absolute phase retrieval method that avoids the inter-channel crosstalk is proposed. By introducing guard bands to accommodate the frequency channels corresponding to the second harmonic that dominate the high order harmonics, the aliasing between the second harmonic and the fundamental is eliminated. Consequently, phase maps without crosstalk can be demodulated using appropriate phase-shifting algorithms. The proposed method is well-suited for high-precision three-dimensional shape measurement scenarios in many fields such as integrated circuit manufacturing process control and micro-electro-mechanical system quality inspection. The experiment results demonstrate that the anti-crosstalk method is effective and can realize three-dimensional reconstruction for discontinuous planar surface and spherical surface.
Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data Fusion

This paper pioneers the use of the extreme learning machine (ELM) approach for surface roughness prediction in ultra-precision milling, leveraging the excellent fitting ability with small datasets and the fast learning speed of the extreme learning machine method. By providing abundant machining information, the machining parameters and force signal data are fused on the feature level to further improve ELM prediction accuracy. An ultra-precision milling experiment was designed and conducted to verify our proposed data-fusion-based ELM method. The results show that the ELM with data fusion outperforms other state-of-art methods in surface roughness prediction. It achieves an impressively low mean absolute percentage error of 1.6% while requiring a mere 18 s for model training.
Performance Evaluation of CNN-based Object Detectors on Embedded Devices

Computer-vision algorithms have been used to enhance hands-free user interactions with smart systems. Traditional approaches for object and gesture detection and recognition are through the processing of video frames by convolutional neural networks (CNN) on cloud or edge servers. However, this approach limits the frame processing rate due to the latency incurred in the video frame offloading process and communication to distant cloud and congested edge servers. Thus, a promising approach is to perform part of the video frame processing locally at the embedded device. Nevertheless, the performance evaluation of CNN-based models on embedded devices has been overlooked. In this paper, we provide a rigorous and practical framework for evaluating object detection models on embedded devices within a smart transportation system application. We conduct an extensive comparative study of six state-of-the-art object detection models on embedded devices, under different video frame conditions and devices' characteristics. Our evaluation framework can serve as a valuable resource for researchers and practitioners working in the fields of computer vision and embedded systems.
A Genuine Multipartite Entanglement Measure Generated by the Parametrized Entanglement Measure

Abstract A genuine multipartite entanglement measure based on the geometric method is investigated in this paper. This measure has desirable properties for quantifying the genuine multipartite entanglement. A lower bound of the genuine multipartite entanglement measure derived with the fidelity‐based method is then presented. The advantages of the measure proposed here with other measures are also presented. At last, examples are presented to show that the genuine entanglement measure has distinct entanglement ordering from other measures.
Female unemployment, mobile money innovations and doing business by females

Abstract The purpose of this study is to complement extant literature by examining how mobile money innovations can moderate the unfavorable incidence of female unemployment on female doing of business in 44 countries from sub-Saharan Africa for the period 2004 to 2018. The empirical evidence is based on interactive quantile regressions. The employed doing business constraints are the procedures a woman has to go through to start a business and the time for women to set up a business, while the engaged mobile money innovations are: (i) registered mobile money agents (registered mobile money agents per 1000 km 2 and registered mobile money agents per 100,000 adults) and (ii) active mobile money agents (active mobile money agents per 1000 km 2 and active mobile money agents per 100,000 adults). The hypothesis that mobile money innovation moderates the unfavorable incidence of female unemployment on business constraints is overwhelmingly invalid. The invalidity of the tested hypothesis is clarified, and the policy implications are discussed.
Scaffolding the human partner by contrastive guidance in an explanatory human-robot dialogue

Explanation has been identified as an important capability for AI-based systems, but research on systematic strategies for achieving understanding in interaction with such systems is still sparse. Negation is a linguistic strategy that is often used in explanations. It creates a contrast space between the affirmed and the negated item that enriches explaining processes with additional contextual information. While negation in human speech has been shown to lead to higher processing costs and worse task performance in terms of recall or action execution when used in isolation, it can decrease processing costs when used in context. So far, it has not been considered as a guiding strategy for explanations in human-robot interaction. We conducted an empirical study to investigate the use of negation as a guiding strategy in explanatory human-robot dialogue, in which a virtual robot explains tasks and possible actions to a human explainee to solve them in terms of gestures on a touchscreen. Our results show that negation vs. affirmation 1) increases processing costs measured as reaction time and 2) increases several aspects of task performance. While there was no significant effect of negation on the number of initially correctly executed gestures, we found a significantly lower number of attempts-measured as breaks in the finger movement data before the correct gesture was carried out-when being instructed through a negation. We further found that the gestures significantly resembled the presented prototype gesture more following an instruction with a negation as opposed to an affirmation. Also, the participants rated the benefit of contrastive vs. affirmative explanations significantly higher. Repeating the instructions decreased the effects of negation, yielding similar processing costs and task performance measures for negation and affirmation after several iterations. We discuss our results with respect to possible effects of negation on linguistic processing of explanations and limitations of our study.
Automatic identification of Dong Son antique glass artifacts using evolving learning

Regarding the Dong Son culture, given the diverse range of artifacts discovered, we propose the utilization of an artificial intelligence system for the automated and comprehensive identification of Dong Son glass jewelry through SEM gemological analysis. This approach, which has gained prominence in the field of archaeology worldwide over the past five years, aims to integrate advanced technology into Vietnamese archaeology. Our research is motivated by the unique conditions present in archaeology, where we seek to employ evolving learning algorithms to archaeological databases, comparing and selecting the most suitable model that aligns with the archeological dataset's performance. We have developed the Recognition Automatic System for Dong Son Antique Glasses (RAS-DSA), capable of accurately distinguishing between Dong Son and non-Dong Son glass ornaments, and is freely distributed to experts and archaeologists. This collaborative research involves Nam Can Tho University, Hanoi University of Mining and Geology, the Vietnamese Institute of Archaeology, and the UNESCO Center for Research and Conservation of Vietnamese Antiquities.
Anatomy-Aware Masking for Inpainting in Medical Imaging

Inpainting has recently been employed as a successful deep-learning technique for unsupervised model discovery in medical image analysis by taking advantage of the strong priors learned by models to reconstruct the structure and texture of missing parts in images. Even though the learned features depend on the masks as well as the images, the masks used for inpainting are typically random and independent of the dataset, due to the unpredictability of the content of images, i.e., different objects and shapes can appear in different locations in images. However, this is rarely the case for medical imaging data since they are obtained from similar anatomies. Still, random square masks are the most popular technique for inpainting in medical imaging. In this work, we propose a pipeline to generate, position and sample the masks to efficiently learn the shape and structures of the anatomy and generate a myriad of diverse anatomy-aware masks, aiding the model in learning the statistical shape prior to the topology of the organs of interest. We demonstrate the impact of our approach compared to other masking mechanisms in the reconstruction of anatomy. We compare the effectiveness of our proposed masking approach over square-shaped masks, which are traditionally used in medical imaging, and irregular shape masks, which are used in SOTA inpainting literature. Project Page: https://anatomyaware.github.io/ .
Computational Intelligence Conceptions to Automated Diagnosis: Feature Grouping for Performance Improvement

HIGHLIGHTS An integrated framework for feature extraction in medical images is proposed. It is investigated the automatic diagnosis in chest X-ray images using several algorithms. It is show that performance is increased when different feature extraction algorithms are merged. Experiments were performed with fourteen different IA algorithms and the results were compared.
Exact and Efficient Bayesian Inference for Privacy Risk Quantification

Data analysis has high value both for commercial and research purposes. However, disclosing analysis results may pose severe privacy risk to individuals. Privug is a method to quantify privacy risks of data analytics programs by analyzing their source code. The method uses probability distributions to model attacker knowledge and Bayesian inference to update said knowledge based on observable outputs. Currently, Privug uses Markov Chain Monte Carlo (MCMC) to perform inference, which is a flexible but approximate solution. This paper presents an exact Bayesian inference engine based on multivariate Gaussian distributions to accurately and efficiently quantify privacy risks. The inference engine is implemented for a subset of Python programs that can be modeled as multivariate Gaussian models. We evaluate the method by analyzing privacy risks in programs to release public statistics. The evaluation shows that our method accurately and efficiently analyzes privacy risks, and outperforms existing methods. Furthermore, we demonstrate the use of our engine to analyze the effect of differential privacy in public statistics.
Parallel Top-K Algorithms on GPU: A Comprehensive Study and New Methods

The top-K problem is an essential part of many important applications in scientific computing, information retrieval, etc. As data volume grows rapidly, high-performance parallel top-K algorithms become critical. We propose two parallel top-K algorithms, AIR Top-K (Adaptive and Iteration-fused Radix Top-K) and GridSelect, for GPU. AIR Top-K employs an iteration-fused design to minimize CPU-GPU communication and device data access. Its adaptive strategy eliminates unnecessary device memory traffic automatically under various data distributions. GridSelect can process data on-the-fly. It adopts a shared queue and parallel two-step insertion to decrease the frequency of costly operations. We comprehensively compare 8 open-source GPU implementations and our methods for a wide range of problem sizes and data distributions. For batch sizes 1 and 100, respectively, AIR Top-K shows 1.98--21.48× and 8.01--574.78× speedup over previous radix top-K algorithm, and 1.44--7.34× and 1.38--31.91× speedup over state-of-the-art methods. GridSelect shows up to 882.29× speedup over its baseline.
Using Virtual Reality in the Care of Older Adults With Dementia: A Randomized Controlled Trial

There is a shortage of personnel to provide care for older adults with dementia, and traditional teaching methods could be improved. The teaching method used in the Care for Older Adults With Dementia course is mainly theoretical, lacking real-life care scenarios and practical procedural training. In the current study, we developed a virtual reality (VR) teaching system and designed a randomized controlled trial aimed at testing the availability of the VR-assisted teaching system, filling the gap in teaching through care scenarios, enabling students majoring in intelligent health and oldage care service management to have a more positive attitude toward learning, and improving students' knowledge and course satisfaction. This study showed that the developed VR system can meet the initial needs of daily teaching, help students have a more positive attitude toward learning, and improve their academic performance and course satisfaction. [Journal of Gerontological Nursing, 49(11), 25-32.].
Development and performance evaluation of enhanced image dehazing method using deep learning networks

In this work, a deep learning-based high-performance image dehazing technique is proposed for image processing applications. The end-to-end network model is constructed and implemented using a dehazing network, discriminator network, and fine-tuning network models. These three methods are well-trained individually using appropriate datasets. The individual network models are integrated as an end-to-end network model to enhance the dehazing process. The applied input hazy image is processed by the dehazing network model using the estimation of transmission map and atmospheric light along with parallel convolution layers. A discriminated dehazing image was extracted from the discrimination network. Finally, fine-tuning is carried out based on the results of the discriminator network model. Various hazy images from different datasets are collected and applied to the proposed model, and performance metrics such as PSNR, SSIM, and MSE are evaluated. Qualitative, and quantitative comparison and analysis are carried out between the proposed learning-based image dehazing and existing dehazing methods. The average PSNR value of the proposed dehazing model is obtained by a maximum of 40.7 %, and a minimum of 1.34 %, when compared to the existing works. The average SSIM of the proposed work is increased by a maximum of 22.12 %, and a minimum of 3.38 % with respect to the existing works. The maximum average value of MSE for the proposed model is decreased by 72.6 % and the minimum decrease of MSE is 4.08 % when compared to the state-of-art-works.
Source-free domain adaptive segmentation with class-balanced complementary self-training

Unsupervised domain adaptation (UDA) plays a crucial role in transferring knowledge gained from a labeled source domain to effectively apply it in an unlabeled and diverse target domain. While UDA commonly involves training on data from both domains, accessing labeled data from the source domain is frequently constrained, citing concerns related to patient data privacy or intellectual property. The source-free UDA (SFUDA) can be promising to sidestep this difficulty. However, without the source domain supervision, the SFUDA methods can easily fall into the dilemma of “winner takes all”, in which the majority category can dominate the deep segmentor, and the minority categories are largely ignored. In addition, the over-confident pseudo-label noise in self-training-based UDA is a long-lasting problem. To sidestep these difficulties, we propose a novel class-balanced complementary self-training (CBCOST) framework for SFUDA segmentation. Specifically, we jointly optimize the pseudo-label-based self-training with two mutually reinforced components. The first class-wise balanced pseudo-label training (CBT) explicitly exploits the fine-grained class-wise confidence to select the class-wise balanced pseudo-labeled pixels with the adaptive within-class thresholds. Second, to alleviate the pseudo-labeled noise, we propose a complementary self-training (COST) to exclude the classes that do not belong to, with a heuristic complementary label selection scheme. We evaluated our CBCOST framework on both 2D and 3D cross-modality cardiac anatomical segmentation tasks and brain tumor segmentation tasks. Our experimental results showed that our CBCOST performs better than existing SFUDA methods and yields similar performance, compared with UDA methods with the source data.
Covert Channel Detection and Generation Techniques: A Survey

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. System security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment.
Integration of Federated Learning and Blockchain for the Provision of Secure Big data Analytics: Systematic Literature Review

Big data allows the optimization of complex supply chains while analyzing data based on machine learning. Despite its usefulness, data analysis is followed by some problems and challenges, such as privacy violations. Federated learning is a convenient solution to the privacy problems facing big data that relies on distributed training. The desire of researchers about the security of federal learning has increased, especially with the spread of recent attacks on federated learning algorithms. The current paper reviews the most important studies that combine blockchain technology and federal learning to protect and secure data privacy.