Reviewerly/exHarmony huggingface dataset:
Center extraction method for reflected metallic surface fringes based on line structured light

Authors:
raw_author_name: Yunpeng Li
raw_affiliation_string: Tianjin Key Laboratory of Intelligent Control of Electrical Equipment, Tiangong University, Tianjin 300387, China
raw_author_name: Limei Song
raw_affiliation_string: School of Artificial Intelligence, Tiangong University, Tianjin 300387, China

Abstract: Using line structured light to measure metal surface topography, the extraction error of the stripe center is significant due to the influence of the optical characteristics of the metal surface and the scattering noise. This paper proposes a sub-pixel stripe center extraction method based on adaptive threshold segmentation and a gradient weighting strategy to address this issue. First, we analyze the characteristics of the stripe image of the measured metal’s surface morphology. Relying on the morphological features of the image, the image is segmented to remove the effect of background noise and to obtain the region of interest in the image. Then, we use the gray-gravity method to get the rough center coordinates of the stripes. We extend the stripes in the width direction using the rough center coordinates as a reference to determine the center of the stripes for extraction after segmentation. Next, we adaptively determine the boundary threshold utilizing the region’s grayscale. Finally, we use the gradient weighting strategy to extract the sub-pixel stripe center. The experimental results show that the proposed method effectively eliminates the interference of metal surface scattering on 3D reconstruction. The average height error of the measured standard block is 0.025 mm, and the repeatability of the measurement accuracy is 0.026 mm.
A collection of 157 individual neuromelanin-sensitive images accompanied by non-linear neuromelanin-sensitive atlas and a probabilistic locus coeruleus atlas

Authors:
raw_author_name: Tae-Ho Lee
raw_affiliation_string: Department of Psychology, Virginia Tech, USA
raw_author_name: Sun Hyung Kim
raw_affiliation_string: Department of Psychiatry, University of North Carolina, Chapel Hill, USA
raw_author_name: Il Hwan Kim
raw_affiliation_string: Department of Anatomy and Neurobiology, University of Tennessee Health Science Center

Abstract: The current dataset aims to support and enhance the research reliability of neuromelanin regions in the brainstem, such as locus coeruleus (LC), by offering raw neuromelanin-sensitive images. The dataset includes raw neuromelanin-sensitive images from 157 healthy individuals (8-64 years old). In addition, leveraging individual neuromelanin-sensitive images, a non-linear neuromelanin-sensitive atlas, generated through an iterative warping process, is included to tackle the common challenge of a limited field of view in neuromelanin-sensitive images. Finally, the dataset encompasses a probabilistic LC atlas generated through a majority voting approach with pre-existing multiple atlas-based segmentations. This process entails warping pre-existing atlases onto individual spaces and identifying voxels with a majority consensus of over 50 % across the atlases. This LC probabilistic atlas can minimize uncertainty variance associated with choosing a specific single atlas.
Lexicon dataset for the Hausa language

Authors:
raw_author_name: Idi Mohammed
raw_affiliation_string: Computer Science Department, African University of Science and Technology, Abuja, Nigeria
raw_author_name: Rajesh Prasad
raw_affiliation_string: Computer Science Department, African University of Science and Technology, Abuja, Nigeria

Abstract: This paper presents a comprehensive augmented lexicon sentiment analysis dataset for the Hausa language. The dataset was created by adopting words and phrases from a Hausa Language dictionary and then using the data augmentation method to expand the quantity of the dataset. The researchers manually annotated each phrase/sentence with positive, negative, or neutral polarity. The dataset consists of 14,663 rows, with 4,154 positives, 4,310 negatives, and 6,199 neutrals. The dataset is valuable because it contributes to the available resources for sentiment analysis, especially for Hausa, which is a low-resource language. The dataset will benefit researchers in sentiment analysis who want to develop a model to analyze Hausa posts on social media or product reviews in the Hausa language.
Feature-exponence mapping in language contact

Authors:
raw_author_name: Artemis Alexiadou
raw_affiliation_string: Humboldt Universität zu Berlin; Leibniz-Zentrum Allgemeine Sprachwissenschaft (ZAS)

Abstract: Preview this article: Feature-exponence mapping in language contact, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/lab.23046.ale-1.gif
Model and data-centric machine learning algorithms to address data scarcity for failure identification

Authors:
raw_author_name: Lareb Zar Khan
raw_affiliation_string: Scuola Superiore Sant'Annahttps://ror.org/025602r80, via G. Moruzzi 1, 56124, Pisa, Italy
raw_author_name: João Pedro
raw_affiliation_string: Infinera Unipessoal Lda, 2790-078 Carnaxide, Portugal; Instituto de Telecomunicações, Instituto Superior Técnico
raw_author_name: Nelson Costa
raw_affiliation_string: Infinera Unipessoal Lda, 2790-078 Carnaxide, Portugal
raw_author_name: Andrea Sgambelluri
raw_affiliation_string: Scuola Superiore Sant'Annahttps://ror.org/025602r80, via G. Moruzzi 1, 56124, Pisa, Italy
raw_author_name: Antonio Napoli
raw_affiliation_string: Infinera, Strategy, Architecture, and Engineering, Munich, Germany
raw_author_name: Nicola Sambo
raw_affiliation_string: Scuola Superiore Sant'Annahttps://ror.org/025602r80, via G. Moruzzi 1, 56124, Pisa, Italy

Abstract: The uneven occurrence of certain types of failures in optical networks results in a scarcity of data for less frequent failures, leading to imbalanced datasets for training machine learning (ML) models. This poses a significant bottleneck in terms of reliability and practical implementation of ML for failure management. Existing research works often overlook this aspect while demonstrating high accuracies by utilizing sufficiently balanced training datasets collected in controlled laboratory setups and simulations. However, this approach does not reflect a realistic network scenario. To address this issue, different model-centric and data-centric approaches have been investigated in this work to determine their potential for improving the learning of ML models, specifically neural networks (NNs), on less frequent failures with such imbalanced training datasets. For failure identification, the obtained results suggest that data-centric approaches tend to perform better in terms of classification accuracy, with an improvement of up to 5.5% in F1-score observed on less frequent failures compared to a baseline NN (i.e., without any model-centric or data-centric treatment). However, some data-centric approaches may also have significant additional computational complexity associated with them, and, therefore, a suitable approach should be chosen based on the desired classification performance and available computational resources.
CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs

Authors:
raw_author_name: Xinyue Liu
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
raw_author_name: Zhen Huang
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
raw_author_name: Yunjian Jia
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
raw_author_name: Wanli Wen
raw_affiliation_string: School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China

Abstract: Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.
Perfect single-photon sources

Authors:
raw_author_name: Fabrice P. Laussy
raw_affiliation_string: Faculty of Science and Engineering, University of Wolverhampton, Wulfruna St, Wolverhampton, WV1 1LY, UK

Abstract: Abstract We introduce the gapped coherent state in the form of a single-photon source (SPS) that consists of uncorrelated photons as a background, except that we demand that no two photons can be closer in time than a time gap $$t_\text{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mtext>G</mml:mtext> </mml:msub> </mml:math> . While no obvious quantum mechanism is yet identified to produce exactly such a photon stream, a numerical simulation is easily achieved by first generating an uncorrelated (Poissonian) signal and then for each photon in the list, either adding such a time gap or removing all photons that are closer in time from each other than $$t_\text{G}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>t</mml:mi> <mml:mtext>G</mml:mtext> </mml:msub> </mml:math> . We study the statistical properties of such a hypothetical signal, which exhibits counter-intuitive features. This provides a neat and natural connection between continuous-wave (stationary) and pulsed single-photon sources, with also a bearing on what it means for such sources to be perfect in terms of single-photon emission.
Who owns (or controls) health data?

Authors:
raw_author_name: Scott D. Kahn
raw_affiliation_string: LunaPBC, San Diego, California, 92121, USA
raw_author_name: Sharon F. Terry
raw_affiliation_string: Genetic Alliance, Damascus, Maryland, 20762, USA

Abstract: The ongoing debate on secondary use of health data for research has been renewed by the passage of comprehensive data privacy laws that shift control from institutions back to the individuals on whom the data was collected. Rights-based data privacy laws, while lauded by individuals, are viewed as problematic for the researcher due to the distributed nature of data control. Efforts such as the European Health Data Space initiative seek to build a new mechanism for secondary use that erodes individual control in favor of broader secondary use for beneficial health research. Health information sharing platforms do exist that embrace rights-based data privacy while simultaneously providing a rich research environment for secondary data use. The benefits of embracing rights-based data privacy to promote transparency of data use along with control of one's participation builds the trust necessary for more inclusive/diverse/representative clinical research.
An intelligent scoring system based on dynamic optical breast imaging for early detection of breast cancer

Authors:
raw_author_name: Yaoyao Li
raw_author_name: Yipei Zhang
raw_author_name: Qiang Yu
raw_author_name: Chenglong He

Abstract: Early detection of breast cancer can significantly improve patient outcomes and five-year survival in clinical screening. Dynamic optical breast imaging (DOBI) technology reflects the blood oxygen metabolism level of tumors based on the theory of tumor neovascularization, which offers a technical possibility for early detection of breast cancer. In this paper, we propose an intelligent scoring system integrating DOBI features assessment and a malignancy score grading reporting system for early detection of breast cancer. Specifically, we build six intelligent feature definition models to depict characteristics of regions of interest (ROIs) from location, space, time and context separately. Similar to the breast imaging-reporting and data system (BI-RADS), we conclude the malignancy score grading reporting system to score and evaluate ROIs as follows: Malignant (≥ 80 score), Likely Malignant (60-80 score), Intermediate (35-60 score), Likely Benign (10-35 score), and Benign (<10 score). This system eliminates the influence of subjective physician judgments on the assessment of the malignant probability of ROIs. Extensive experiments on 352 Chinese patients demonstrate the effectiveness of the proposed system compared to state-of-the-art methods.
Achieving widely distributed feature matches using flattened-affine-SIFT algorithm for fisheye images

Authors:
raw_author_name: Yakun Zhang
raw_author_name: Yunpeng Ma
raw_author_name: Yang Wu
raw_author_name: Lingling Liu

Abstract: Performing correction first is the most common methods to address feature matching issues for fisheye images, but corrections often result in significant loss of scene details or stretching of images, leaving peripheral regions without matches. In this paper, we propose a novel approach, named flattened-affine-SIFT, to find widely distributed feature matches between stereo fisheye images. Firstly, we establish a new imaging model that integrates a scalable model and a hemisphere model. Utilizing the extensibility of the imaging model, we design a flattened array model to reduce the distortion of fisheye images. Additionally, the affine transformation is performed on the flattened simulation images, which are computed using the differential expansion and the optimal rigidity transformation. Then feature matches are extracted and matched from the simulated images. Experiments on indoor and outdoor fisheye images show that the proposed algorithm can find a large number of reliable feature matches. Moreover, these matches tend to be dispersed over the entire effective image, including peripheral regions with dramatic distortion.
Challenging the Limits of Binarization: A New Scheme Selection Policy Using Reinforcement Learning Techniques for Binary Combinatorial Problem Solving

Authors:
raw_author_name: Marcelo Becerra-Rozas
raw_affiliation_string: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
raw_author_name: Broderick Crawford
raw_affiliation_string: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
raw_author_name: Ricardo Soto
raw_affiliation_string: Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile
raw_author_name: El-Ghazali Talbi
raw_affiliation_string: CNRS UMR 9189, Centre de Recherche en Informatique Signal et Automatique de Lille (CRIStAL), University of Lille, F-59000 Lille, France
raw_author_name: Jose M. Gómez-Pulido
raw_affiliation_string: Health Computing and Intelligent Systems Research Group (HCIS), Department of Computer Science, University of Alcalá, 28805 Alcalá de Henares, Spain

Abstract: In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields.
On the compatibility of models with experiments

Authors:
raw_author_name: Gregory Scontras
raw_affiliation_string: University of California, Irvine

Abstract: Comment This is a commentary article in response to the following content: The importance of features and exponents
Multi-scale fusion for image enhancement in shield tunneling: a combined MSRCR and CLAHE approach

Authors:
raw_author_name: Zhendong He
raw_affiliation_string: Collaborative Innovation Center of Intelligent Tunnel Boring Machine, Zhengzhou, People's Republic of China; College of Electrical Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, People's Republic of China
raw_author_name: Yanqiu Xiao
raw_affiliation_string: Collaborative Innovation Center of Intelligent Tunnel Boring Machine, Zhengzhou, People's Republic of China
raw_author_name: Lianhui Jia
raw_affiliation_string: Collaborative Innovation Center of Intelligent Tunnel Boring Machine, Zhengzhou, People's Republic of China
raw_author_name: Pengpeng Wang
raw_affiliation_string: Collaborative Innovation Center of Intelligent Tunnel Boring Machine, Zhengzhou, People's Republic of China
raw_author_name: Guangzhen Cui
raw_affiliation_string: China Railway Engineering Equipment Group Co. Ltd, Zhengzhou, People's Republic of China; Collaborative Innovation Center of Intelligent Tunnel Boring Machine, Zhengzhou, People's Republic of China

Abstract: Abstract Aiming at addressing the complex lighting conditions in shield construction environments and enhancing the contrast of low-light images while preserving image details, this paper presents an image enhancement technique based on multi-scale fusion. The proposed method first duplicates the input image into two copies. Subsequently, the first copy undergoes color and contrast adjustment via the adaptive weighted Retinex algorithm for color restoration (A-MSRCR) algorithm. Meanwhile, the second copy undergoes contrast enhancement and detail preservation by combining Gamma correction and Contrast Limited Adaptive Histogram Equalization (CLAHE). Finally, a normalized weight map is introduced to facilitate multi-scale fusion of the two enhanced images, ensuring an overall enhancement in both contrast and detail. The experimental results show that the proposed method provides better visual quality and objective data than the compared methods. Moreover, it offers valuable support for subsequent recognition and localization tasks.
Block mapping and dual-matrix-based watermarking for image authentication with self-recovery capability

Authors:
raw_author_name: Wei Wang
raw_affiliation_string: Computer and soft Engineer Department, Anhui Institute of Information Technology, Wuhu, Anhui, China

Abstract: Numerous image authentication techniques have been devised to address the potential security issue of malicious tampering with image content since digital images can be easily duplicated, modified, transformed and diffused via the Internet transmission. However, the existing works still remain many shortcomings in terms of the recovery incapability and detection accuracy with extensive tampering. To improve the performance of tamper detection and image recovery, we present a block mapping and dual-matrix-based watermarking scheme for image authentication with self-recovery capability in this paper. The to-be-embedded watermark information is composed of the authentication data and recovery data. The Authentication Feature Composition Calculation algorithm is proposed to generate the authentication data for image tamper detection and localization. Furthermore, the recovery data for tampered region recovery is comprised of self-recovery bits and mapped-recovery bits. The Set Partition in Hierarchical Trees encoding algorithm is applied to obtain the self-recovery bits, whereas the Rehashing Model-based Block Mapping algorithm is proposed to obtain the mapped-recovery bits for retrieving the damaged codes caused by tampering. Subsequently, the watermark information is embedded into the original image as digital watermarking with the guidance of a dual-matrix. The experimental results demonstrate that comparing with other state-of-the-art works, our proposed scheme not only improves the performance in recovery, but also extends the limitation of tampering rate up to 90%. Furthermore, it obtains a desirable image quality above 40 dB, large watermark payload up to 3.169 bpp, and the effective resistance to malicious attack, such as copy-move and collage attacks.
The Pine Cone Optimization Algorithm (PCOA)

Authors:
raw_author_name: Mahdi Valikhan Anaraki
raw_affiliation_string: Department of Water Engineering and Hydraulics Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran
raw_author_name: Saeed Farzin
raw_affiliation_string: Department of Water Engineering and Hydraulics Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran

Abstract: The present study introduces a novel nature-inspired optimizer called the Pine Cone Optimization algorithm (PCOA) for solving science and engineering problems. PCOA is designed based on the different mechanisms of pine tree reproduction, including pollination and pine cone dispersal by gravity and animals. It employs new and powerful operators to simulate the mentioned mechanisms. The performance of PCOA is analyzed using classic benchmark functions, CEC017 and CEC2019 as mathematical problems and CEC2006 and CEC2011 as engineering design problems. In terms of accuracy, the results show the superiority of PCOA to well-known algorithms (PSO, DE, and WOA) and new algorithms (AVOA, RW_GWO, HHO, and GBO). The results of PCOA are competitive with state-of-the-art algorithms (LSHADE and EBOwithCMAR). In terms of convergence speed and time complexity, the results of PCOA are reasonable. According to the Friedman test, PCOA’s rank is 1.68 and 9.42 percent better than EBOwithCMAR (second-best algorithm) and LSHADE (third-best algorithm), respectively. The authors recommend PCOA for science, engineering, and industrial societies for solving complex optimization problems.