Reviewerly/exHarmony huggingface dataset:
Resource analysis and modifications of quantum computing with noisy qubits for elliptic curve discrete logarithms

Authors:
raw_author_name: Jinyoung Ha
raw_affiliation_string: School of Electrical Engineering, Korea University, Seoul, 02841, Republic of Korea
raw_author_name: Jonghyun Lee
raw_affiliation_string: School of Electrical Engineering, Korea University, Seoul, 02841, Republic of Korea
raw_author_name: Jun Heo
raw_affiliation_string: School of Electrical Engineering, Korea University, Seoul, 02841, Republic of Korea

Abstract: We estimate the number of physical qubits and execution time by decomposing an implementation of Shor's algorithm for elliptic curve discrete logarithms into universal gate units at the logical level when surface codes are used. We herein also present modified quantum circuits for elliptic curve discrete logarithms and compare our results with those of the original quantum circuit implementations at the physical level. Through the analysis, we show that the use of more logical qubits in quantum algorithms does not always lead to the use of more physical qubits. We assumed using rotated surface code and logical qubits with all-to-all connectivity. The number of physical qubits and execution time are expressed in terms of bit length, physical gate error rate, and probability of algorithm failure. In addition, we compare our results with the number of physical qubits and execution time of Shor's factoring algorithm to assess the risk of attack by quantum computers in RSA and elliptic curve cryptography.
Analysing environmental opinion using highly customisable visualisation tools to understand citizens’ attitudes and barriers

Authors:
raw_author_name: Luz Calvo
raw_affiliation_string: Barcelona Supercomputing Center, CASE, 08034, Barcelona, Spain
raw_author_name: Marta Terrado
raw_affiliation_string: Barcelona Supercomputing Center, Earth Sciences, 08034, Barcelona, Spain
raw_author_name: Mario Pérez-Montoro
raw_affiliation_string: Faculty of Information and Audiovisual Media at the University of Barcelona, Melcior de Palau, 140, 08014, Barcelona, Spain
raw_author_name: Fernando Cucchietti
raw_affiliation_string: Barcelona Supercomputing Center, CASE, 08034, Barcelona, Spain

Abstract: Abstract Knowledge of public opinion is key to understanding citizens' attitudes towards environmental policies. However, large polls and surveys generate complex datasets from which it is not always easy to draw conclusions. In addition, tailor-made solutions for analysing public opinion face the challenge of handling too many layers of information, which can easily lead to an overwhelming user experience and impair decision-making. Service design methodologies can support the design of ad hoc visualisation tools focused on user needs. We present Op-e-nion , a case study of a visualisation tool for the analysis of public opinion regarding environmental issues, aimed at administrations and public institutions. The involvement of experts from different fields allowed for the identification of the main metrics necessary to target the least engaged socio-demographic groups as well as the barriers that limited their environmental actions. Experts also highlighted useful aspects of the design process and the final prototype to help them define more effective campaigns and policies to address social challenges and promote citizen action. An innovative step was introduced in the methodology by involving non-state actors in the evaluation of the tool, ensuring problem detection and enhancing the sustainability of the final product. Important aspects for the visualisation of multi-categorical data included simplifying the interaction with the tool while prioritising relevant information, and using highly customizable visualisations to answer specific user requirements and changing needs (i.e. analytical vs. managerial tasks). Improved visualisations of public opinion data will, in turn, better support the development of policies shaped by citizens’ concerns.
Category-Aware Saliency Enhance Learning Based on CLIP for Weakly Supervised Salient Object Detection

Authors:
raw_author_name: Zhili Zhang
raw_affiliation_string: School of Computer Science and Technology, Anhui University, Hefei, China
raw_author_name: Tianshan Liu
raw_affiliation_string: Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong, 999077, China
raw_author_name: Jun Kong
raw_affiliation_string: Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, 214122, China

Abstract: Abstract Weakly supervised salient object detection (SOD) using image-level category labels has been proposed to reduce the annotation cost of pixel-level labels. However, existing methods mostly train a classification network to generate a class activation map, which suffers from coarse localization and difficult pseudo-label updating. To address these issues, we propose a novel Category-aware Saliency Enhance Learning (CSEL) method based on contrastive vision-language pre-training (CLIP), which can perform image-text classification and pseudo-label updating simultaneously. Our proposed method transforms image-text classification into pixel-text matching and generates a category-aware saliency map, which is evaluated by the classification accuracy. Moreover, CSEL assesses the quality of the category-aware saliency map and the pseudo saliency map, and uses the quality confidence scores as weights to update the pseudo labels. The two maps mutually enhance each other to guide the pseudo saliency map in the correct direction. Our SOD network can be trained jointly under the supervision of the updated pseudo saliency maps. We test our model on various well-known RGB-D and RGB SOD datasets. Our model achieves an S-measure of 87.6 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> on the RGB-D NLPR dataset and 84.3 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> on the RGB ECSSD dataset. Additionally, we obtain satisfactory performance on the weakly supervised E-measure, F-measure, and mean absolute error metrics for other datasets. These results demonstrate the effectiveness of our model.
A Novel Procedure for the AHP Method for the Site Selection of Solar PV Farms

Authors:
raw_author_name: A. Ersin Dinçer
raw_affiliation_string: Department of Civil Engineering, Hydraulics Laboratory, Abdullah Gül University, Kayseri 38080, Türkiye
raw_author_name: Kutay Yılmaz
raw_affiliation_string: Department of Civil Engineering, Hydraulics Laboratory, Yaşar University, İzmir, Türkiye

Abstract: This study proposes a novel approach to enhance the analytic hierarchy process (AHP) for the selection of suitable sites for solar photovoltaic (PV) farms. This approach is particularly beneficial when it is possible to establish a predefined objective relation in the final weights of the AHP method. The methodology focuses on achieving this predefined relation introducing a systematic revision of the constants of related constraints. In this study, the costs of constructing a unit transmission line and road in the Kayseri Province are objectively related, and the initial constant matrix of the AHP method is iteratively revised until the relation of the final weights converges to the predefined one. The suitability of solar PV farm locations is classified into five classes, revealing approximately 28% (40-100% of suitability) of the province as favorably suitable and designating about 67% as restricted zones. The findings reveal notable distinctions between the revised weights and those derived from the conventional AHP method. The disparity in weights for various constraints varies from 13.5% to 7.2%. Consequently, the alterations in the area of suitability regions range from 3.4% to 50%. The revision of AHP weights results in a reduction in higher-suitability areas, coupled with a significant expansion in the region exhibiting lower suitability. Notably, the extent of change in the suitability map increases when the difference in ratios between two criteria obtained from the AHP and the predefined objective relation is high. The proposed method demonstrates its applicability in regions like Kayseri where an objective relation between criteria can be established. Given the inherent subjectivity of the AHP method, the proposed procedure becomes essential to attain more objective weights. Since the methodology objectively adjusts weights based on known ratios, it increases the accuracy and reliability of site selection studies.
Analysis of Forced Aligner Performance on L2 English Speech

Authors:
raw_author_name: Paul Foulkes
raw_affiliation_string: Department of Language and Linguistic Science, University of York, York, UK
raw_author_name: Vincent Hughes
raw_affiliation_string: Department of Language and Linguistic Science, University of York, York, UK

Abstract: There is growing interest in how speech technologies perform on L2 speech. Largely omitted from this discussion are tools used in the early data processing steps, such as forced aligners, that can introduce errors and biases. This study adds to the conversation and tests how well a model pre-trained for the alignment of L1 American English speech performs on L2 English speech. We test and discuss the impact of language variety, demographic factors, and segment type on the performance of the forced aligner. We also examine systematic errors encountered. Forty-five speakers representing nine L2 varieties were selected from the Speech Accent Archive and force aligned using the Montreal Forced Aligner. The phoneme-level boundary placements were manually corrected in order to assess differences between the automatic and manual alignments. Results show marked variation in the performance across language groups and segment types for the two metrics used to assess accuracy: Onset Boundary Displacement, a distance metric between the automatic and manual boundary placements, and Overlap Rate, which indicates to what extent the automatically aligned segment overlaps with the manually aligned segment. The highest accuracy on both measures was obtained for German and French, and lowest accuracy for Russian. The aligner's performance on all varieties was comparable to that on conversational American English and non-standard varieties of English. Furthermore, the percentage of boundary placements within 10 and 20ms of the corrected boundary was similar to that observed between transcribers. Apart from errors due to variety mismatch, most issues encountered in the alignment were due to issues not exclusive to L2 speech such as inaccurate orthographic transcriptions, hesitations, specific voice qualities, and background noise. The results of this study can inform the use of automatic aligners on L2 English speech and provide a baseline of potential errors and information to help the development of more robust alignment tools for further development of automatic systems using L2 English.
The role of lifestyle factors, biological sex, and racial identity for (Visually Induced) Motion Sickness Susceptibility: Insights from an online survey

Authors:
raw_author_name: Narmada Umatheva
raw_affiliation_string: Department of Psychology, Toronto Metropolitan University, Toronto, Canada; KITE Research Institute, Toronto Rehabilitation Institute–University Health Network, Toronto, Canada
raw_author_name: Frank Russo
raw_affiliation_string: Department of Psychology, Toronto Metropolitan University, Toronto, Canada; KITE Research Institute, Toronto Rehabilitation Institute–University Health Network, Toronto, Canada
raw_author_name: Behrang Keshavarz
raw_affiliation_string: Department of Psychology, Toronto Metropolitan University, Toronto, Canada; KITE Research Institute, Toronto Rehabilitation Institute–University Health Network, Toronto, Canada

Abstract: Motion sickness (MS) and visually induced motion sickness (VIMS) are common side-effects when travelling or when using visual devices, respectively. A variety of individual factors may determine one’s susceptibility to MS/VIMS. The role of lifestyle factors including video-game usage, physical activity, diet, and substance use on self-reported susceptibility to MS/VIMS was investigated. Additionally, the role of biological sex and racial identity on the overall prevalence of MS/VIMS were analyzed. A total of 711 responses to an online survey were collected from adults between the ages of 17-49 years. Method of analyses included correlations, comparison tests, and multiple regressions. The results showed that overall, VIMS is a prevalent issue for most participants, with approximately 24% of users reporting nausea at least sometimes when using visual displays. Although some lifestyle factors (video game usage, assorted dietary elements) were identified as potentially relevant for MS/VIMS susceptibility, these factors only explained a minimal amount of the variance in MS/VIMS susceptibility. Significant sex differences were found, with female participants reporting higher susceptibility for MS/VIMS than male participants. Additionally, East Asians participants reported higher MS susceptibility. Our findings do not suggest that lifestyle factors are prominent predictors for the susceptibility of VIMS and MS.
Shape-constrained deformable brain segmentation: Methods and quantitative validation

Authors:
raw_author_name: Chen Li
raw_affiliation_string: ClearPoint Neuro, Clinical Science and Applications, 120 S. Sierra Ave., Suite 100, Solana Beach, 92075, CA, USA
raw_author_name: Fabian Wenzel
raw_affiliation_string: Philips Research Hamburg, Medical Image Processing and Analytics, Röntgenstraße 24-26, Hamburg, 22335, Germany
raw_author_name: Shruti Gupta
raw_affiliation_string: ClearPoint Neuro, Clinical Science and Applications, 120 S. Sierra Ave., Suite 100, Solana Beach, 92075, CA, USA

Abstract: MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.
A framework for efficient cross-chain token transfers in blockchain networks

Authors:
raw_author_name: Hongyu Guo
raw_affiliation_string: School of Cyberspace Security(School of Cryptology),Hainan University, Haikou, 570228, Hainan, China
raw_author_name: Haozhe Liang
raw_affiliation_string: School of Cyberspace Security(School of Cryptology),Hainan University, Haikou, 570228, Hainan, China
raw_author_name: Wei Ou
raw_affiliation_string: School of Cyberspace Security(School of Cryptology),Hainan University, Haikou, 570228, Hainan, China; State Key Laboratory of Information Security, Chinese Academy of Sciences, 100864, Beijing, China
raw_author_name: Wenbao Han
raw_affiliation_string: School of Cyberspace Security(School of Cryptology),Hainan University, Haikou, 570228, Hainan, China
raw_author_name: Qionglu Zhang
raw_affiliation_string: State Key Laboratory of Information Security, Chinese Academy of Sciences, 100864, Beijing, China
raw_author_name: Ruizhi Zhang
raw_affiliation_string: Henan Key Laboratory of Network Cryptography Technology, ZhengZhou, 450001, Henan, China

Abstract: The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.
Cross-validated tree-based models for multi-target learning

Authors:
raw_author_name: Amichai Painsky
raw_affiliation_string: Department of Industrial Engineering, Tel Aviv University, Israel

Abstract: Multi-target learning (MTL) is a popular machine learning technique which considers simultaneous prediction of multiple targets. MTL schemes utilize a variety of methods, from traditional linear models to more contemporary deep neural networks. In this work we introduce a novel, highly interpretable, tree-based MTL scheme which exploits the correlation between the targets to obtain improved prediction accuracy. Our suggested scheme applies cross-validated splitting criterion to identify correlated targets at every node of the tree. This allows us to benefit from the correlation among the targets while avoiding overfitting. We demonstrate the performance of our proposed scheme in a variety of synthetic and real-world experiments, showing a significant improvement over alternative methods. An implementation of the proposed method is publicly available at the first author's webpage.
Graph Neural Network contextual embedding for Deep Learning on tabular data

Authors:
raw_author_name: Belén Carro Martinez
raw_affiliation_string: Universidad de Valladolid, Valladolid, 47011, Spain

Abstract: All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on seven public datasets, also achieving competitive results when compared to boosted-tree solutions.
Data-driven model selections of second-order particle dynamics via integrating Gaussian processes with low-dimensional interacting structures

Authors:
raw_author_name: Jinchao Feng
raw_affiliation_string: School of Sciences, Great Bay University, Dongguan, Guangdong, China
raw_author_name: Sui Tang
raw_affiliation_string: Departments of Mathematics, University of California, Santa Barbara, Isla Vista, CA, United States of America

Abstract: In this paper, we focus on the data-driven discovery of a general second-order particle-based model that contains many state-of-the-art models for modeling the aggregation and collective behavior of interacting agents of similar size and body type. This model takes the form of a high-dimensional system of ordinary differential equations parameterized by two interaction kernels that appraise the alignment of positions and velocities. We propose a Gaussian Process-based approach to this problem, where the unknown model parameters are marginalized by using two independent Gaussian Process (GP) priors on latent interaction kernels constrained to dynamics and observational data. This results in a nonparametric model for interacting dynamical systems that accounts for uncertainty quantification. We also show that our estimators can be efficiently derived through the use of preconditioned techniques, ensuring scalability. Moreover, we perform a theoretical analysis to interpret the methodology and investigate the conditions under which the kernels can be recovered. We demonstrate the effectiveness of the proposed approach on various prototype systems, including the selection of the order of the systems and the types of interactions. In particular, we present applications to modeling two real-world fish motion datasets that display flocking and milling patterns up to 248 dimensions. Despite the use of small data sets, the GP-based approach learns an effective representation of the nonlinear dynamics in these spaces and outperforms competitor methods.
Pedagogy and technology: the transformative impact of digital tools in education

Authors:
raw_author_name: Jacson King Valério Oliveira

Abstract: This bibliographic review addressed the transformative impact of digital tools on education, focusing on the interplay between pedagogy and technology. The central problem was to understand how technology reshapes education, both in pedagogical practices and in the development of 21st-century skills. The main objective was to explore the nature and extent of this impact, considering opportunities and challenges for students and teachers. The methodology adopted was a literature review, allowing a critical and comprehensive analysis of relevant publications. The results showed that digital technologies offer significant opportunities to innovate in education, but also present challenges such as unequal access, the need for teacher training, and the adaptation of methodologies. Successful experiences highlighted how effective integration of technology can promote critical skills and educational inclusion. It was concluded that technology is a transformative element in education, capable of improving the quality and effectiveness of teaching. However, overcoming existing barriers is essential to maximize its potential. The research underlined the continuous need for innovation and investigation in educational technology.