Reviewerly/exHarmony huggingface dataset:
DOMAS: a data management software framework for advanced light sources

Authors:
raw_author_name: Hao Hu
raw_affiliation_string: China Spallation Neutron Source Science Center, No. 1 Zhongziyuan Road, Dalang, Dongguan, Guangdong Province 523803, People's Republic of China; Institute of High Energy PhysicsChinese Academy of Sciences, Beijing 100049, People's Republic of China; University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China
raw_author_name: Haofan Wang
raw_affiliation_string: Institute of High Energy PhysicsChinese Academy of Sciences, Beijing 100049, People's Republic of China
raw_author_name: Qi Luo
raw_affiliation_string: Institute of High Energy PhysicsChinese Academy of Sciences, Beijing 100049, People's Republic of China

Abstract: In recent years, China's advanced light sources have entered a period of rapid construction and development. As modern X-ray detectors and data acquisition technologies advance, these facilities are expected to generate massive volumes of data annually, presenting significant challenges in data management and utilization. These challenges encompass data storage, metadata handling, data transfer and user data access. In response, the Data Organization Management Access Software (DOMAS) has been designed as a framework to address these issues. DOMAS encapsulates four fundamental modules of data management software, including metadata catalogue, metadata acquisition, data transfer and data service. For light source facilities, building a data management system only requires parameter configuration and minimal code development within DOMAS. This paper firstly discusses the development of advanced light sources in China and the associated demands and challenges in data management, prompting a reconsideration of data management software framework design. It then outlines the architecture of the framework, detailing its components and functions. Lastly, it highlights the application progress and effectiveness of DOMAS when deployed for the High Energy Photon Source (HEPS) and Beijing Synchrotron Radiation Facility (BSRF).
Abstract TP31: Identifying Multivariate Nonlinear Bias in Sammpris Using Graph-Based AI Analysis

Authors:
raw_author_name: Fabien Scalzo
raw_affiliation_string: UCLA, Los Angeles, CA
raw_author_name: David S Liebeskind
raw_affiliation_string: UCLA, Los Aeles, CA

Abstract: Background: Clinical trials are the most rigorous way of testing how novel treatments compare with existing ones regarding outcomes. The randomized controlled clinical trial has become the standard means of providing proof of safety and effectiveness across the board in medicine, including stroke. Yet, the risk factors among patients that are randomized might not always be equally distributed such that a bias may occur. In this study, we focus on the SAMMPRIS clinical trial aimed to assess the safety and efficacy of aggressive medical management versus stenting for preventing recurrent stroke in patients with symptomatic intracranial arterial stenosis. Methods: We developed and evaluated a machine learning model to identify potential multivariate bias present in the dataset that could not have been identified using standard statistical tests. Our approach utilizes a machine learning model based on the spectral regression model (SR-KDA) to identify biases created by combinations of variables. SR-KDA is based on a graph representation of the data that is processed via discriminative analysis to identify groups of patients that share common characteristics. By using both the treatment and outcome variables as constraints, the model is able to identify nonlinear relationships between clinical variables and the outcome. It can also be used to identify bias as part of the randomization that could have inadvertently skewed the statistical results with standard methodologies. Results: Our approach was successfully applied to the SAMMPRIS clinical dataset, which includes 121 clinical variables. The algorithm was able to automatically identify 6 clusters of patients that had a different response to treatment when considering the clinical variables. Each group was then assigned a set of the variables' cut-off values that best describe them. Conclusion: We have developed a sophisticated system that is capable of detecting non-linear correlations in clinical data collected during trials. This solution has the potential to mitigate any biases that may arise during the randomization process, which could ultimately improve recruitment efforts. Moving forward, this innovative tool could be applied proactively to future clinical trials.
Two-photon quantum state tomography of photonic qubits

Authors:
raw_author_name: Guilherme P. Temporão
raw_affiliation_string: Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
raw_author_name: Pedro Ripper
raw_affiliation_string: Department of Electrical Engineering, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro 22451-900, Brazil
raw_author_name: Gustavo C. do Amaral
raw_affiliation_string: Quantum Technology Department, The Netherlands Organization for Applied Scientific Research (TNO), Delft 2628 CK, The Netherlands

Abstract: We provide a tool for measuring the Stokes parameters and the degree of polarization of single photons by employing second-order interference, namely, the Hong-Ou-Mandel (HOM) interferometer. It is shown that the technique is able to distinguish a partially polarized photon where the polarization state is coupled to an internal degree of freedom, such as time of arrival, from partial polarization due to external entanglement with the environment. The method does not directly resort to any kind of polarization-selective components and therefore is not limited by the extinction ratio of polarizers. Moreover, the technique can be generalized to any two-level encoding of quantum information in single photons, such as time-bin or orbital angular momentum qubits.
Shadow detection using a cross-attentional dual-decoder network with self-supervised image reconstruction features

Authors:
raw_author_name: Ruben Fernandez-Beltran
raw_affiliation_string: Department of Computer Science and Systems, University of Murcia, 30100 Murcia, Spain
raw_author_name: Angelica Guzman-Ponce
raw_affiliation_string: Institute of New Imaging Technologies, University Jaume I, 12071 Castellón de la Plana, Spain
raw_author_name: Jian Kang
raw_affiliation_string: School of Electronic and Information Engineering, Soochow University, Suzhou 215006, China
raw_author_name: Ginés García-Mateos
raw_affiliation_string: Department of Computer Science and Systems, University of Murcia, 30100 Murcia, Spain

Abstract: Shadow detection is a challenging problem in computer vision due to the high variability in lighting conditions, object shapes, and scene layouts. Despite the positive results achieved by some existing technologies, the problem becomes particularly challenging with complex and heterogeneous images where shadow-casting objects coexist and shadows can have different depths, scales, and morphologies. As a result, more advanced and accurate solutions are still needed to deal with this type of complexities. To address these challenges, this paper proposes a novel deep learning model, called the Cross-Attentional Dual Decoder Network (CADDN), to improve shadow detection by using fine-grained image reconstruction features. Unlike other existing methods, the CADDN uses an innovative encoder-decoder architecture with two decoder segments that work together to reconstruct the input images and their corresponding shadow masks. In this way, the features used to reconstruct the original input image can be used to support the shadow detection process itself. The proposed model also incorporates a cross-attention mechanism to weight the most relevant features for detecting shadows and skip connections with noise to improve the quality of the transferred features. The experimental results, including several benchmark image datasets and state-of-the-art detection methods, demonstrate the suitability of the presented approach for detecting shadows in computer vision applications.
Enhancing Quantum Temporal Steering via Frequency Modulation

Authors:
raw_author_name: Weiwen Cheng
raw_affiliation_string: Institute of Signal Processing & Transmission, Nanjing University of Posts and Telecommunication, Nanjing, 210003, CHINA

Abstract: Abstract Various strategies have been proposed to harness and protect space-like quantum correlations in different models under decoherence. However, little attention has been given to temporal-like correlations, such as quantum temporal steering (TS), in this context. In this work, we investigate TS in a frequency-modulated two-level system coupled to a zero-temperature reservoir in both the weak and strong coupling regimes. We analyze the impact of various frequency-modulated parameters on the behavior of TS and non-Markovian. The results demonstrate that appropriate frequency-modulated parameters can enhance the TS of the two-level system, regardless of whether the system is experiencing Markovian or non-Markovian dynamics. Furthermore, a suitable ratio between modulation strength and frequency (i.e., all zeroes of the 0th Bessel function J 0 (δ/Ω)) can significantly enhance TS in the strong coupling regime. These findings indicate that efficient and effective manipulation of quantum TS can be achieved through a frequency-modulated approach.
A Novel Model for Recommending Information Systems with Suitable Cloud Computing Provider

Authors:
raw_author_name: Samah Ibrahim Abdel Aal

Abstract: Cloud computing has been adopted widely by information systems due to its scalability and availability of resources and services. Cloud computing can provide different types of services through internet for Information Systems (IS). There are many cloud computing providers such as Google, Microsoft, and IBM are seeking for adopting of cloud services. Selecting the suitable cloud provider can maximize the benefits of cloud services. There is a need to determine the suitable cloud computing provider to achieve organizations' goals and maximize the benefits of cloud services. This work aims to introduce a decision support model based on trapezoidal neutrosophic numbers for determining the suitable cloud computing provider. The proposed model is applied by the Zagazig University (ZU) and the results indicated that the model can provide flexible method that can handle uncertainty in decision making for detecting the suitable provider. Also, the results show that the proposed model can support information systems in the choosing the suitable cloud computing provider.
Path planning method for unmanned aerial vehicles

Authors:
raw_author_name: Wenkai Guan
raw_affiliation_string: College of Mechanical and Electronic Engineering, Shanghai Jian Qiao University, Shanghai, China

Abstract: Unmanned aerial vehicles, commonly known as drones, have seen tremendous growth and wide-spread use in the last ten years, largely because of unheard-of technological improvements. It is currently one of the most significant study subjects since it involves many different areas of robotics and control systems. This study aims to thoroughly assess the most popular approaches for unmanned aerial vehicle path planning, while also outlining their benefits, drawbacks, potential uses, and overall effectiveness. The objective is to present the academic community with a summary of the existing environment and the development direction of future unmanned aerial vehicle path planning algorithms to encourage more research for quickly evolving planning of intelligent systems.
ProPane: Image Warping with Fire

Authors:
raw_author_name: A S G Robotham
raw_affiliation_string: ICRAR , M468, University of Western Australia, Crawley, WA 6009, Australia
raw_author_name: R Tobar
raw_affiliation_string: ICRAR , M468, University of Western Australia, Crawley, WA 6009, Australia
raw_author_name: S Casura
raw_affiliation_string: Hamburger Sternwarte , Universität Hamburg, Gojenbergsweg 112, D-21029 Hamburg, Germany
raw_author_name: R H W Cook
raw_affiliation_string: ICRAR , M468, University of Western Australia, Crawley, WA 6009, Australia
raw_author_name: L J Davies
raw_affiliation_string: ICRAR , M468, University of Western Australia, Crawley, WA 6009, Australia
raw_author_name: J Li
raw_affiliation_string: ICRAR , M468, University of Western Australia, Crawley, WA 6009, Australia
raw_author_name: L P Garate-Nuñez
raw_affiliation_string: ICRAR , M468, University of Western Australia, Crawley, WA 6009, Australia

Abstract: Abstract In this paper we introduce the software package ProPane, written for the r data analysis language. ProPane combines the full range of wcslib projections with the C++ image manipulation routines provided by the CImg library. ProPane offers routines for image warping and combining (including stacking), and various related tasks such as image alignment tweaking and pixel masking. It can stack an effectively unlimited number of target frames using multiple parallel cores, and offers threading for many lower level routines. It has been used for a number of current and upcoming large surveys, and we present a range of its capabilities and features. ProPane is already available under a permissive open-source LGPL-3 license at github.com/asgr/ProPane (DOI: 10.5281/zenodo.10057053).
Post-Earthquake Serviceability Assessment Using the Convolutional Neural Networks and Deep Neural Networks

Authors:
raw_author_name: Saiful Hasan
raw_author_name: Dr. SM Abbas

Abstract: There have been studies using image processing techniques to assess damage of structure especially columns without sending individual to onsite. Convolutional neural networks (CNNs) and the entire Deep neural network (DNN) have shown state-of-art results in object detection and image classification tasks. This study proposed cascaded deep learning network for post-earthquake structural serviceability assessment. Major target deficiency components (crack, spalled area, transverse bar, and longitudinal bar) were used to determine the proposed damage states to assess serviceability of structure. Concrete surface cracks are major defect in civil structures. Structural Inspection which is done for the evaluation of rigidity and tensile strength of the building. Crack detection plays a major role in the building inspection, finding the cracks and determining the Structural health in the case of post-earthquake. The research paper is all about finding the serviceability results of the structure.
Optical voice encryption based on speckle-illuminated fourier ptychography and plaintext-related chaotic random phase mask

Authors:
raw_author_name: Jiaxin Li
raw_affiliation_string: College of Electronic and Information Engineering, Hebei University, Baoding 071000, People's Republic of China
raw_author_name: Wenjun Xu
raw_affiliation_string: College of Science, Hebei Agricultural University, Baoding 071001, People's Republic of China
raw_author_name: Yonghui Wang
raw_affiliation_string: College of Electronic and Information Engineering, Hebei University, Baoding 071000, People's Republic of China
raw_author_name: Fei Li
raw_affiliation_string: College of Electronic and Information Engineering, Hebei University, Baoding 071000, People's Republic of China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071000, People's Republic of China
raw_author_name: Shuaiqi Liu
raw_affiliation_string: College of Electronic and Information Engineering, Hebei University, Baoding 071000, People's Republic of China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071000, People's Republic of China
raw_author_name: Yonggang Su
raw_affiliation_string: College of Electronic and Information Engineering, Hebei University, Baoding 071000, People's Republic of China; Machine Vision Technology Innovation Center of Hebei Province, Baoding 071000, People's Republic of China

Abstract: Abstract In this paper, we propose an optical voice encryption scheme based on speckle-illuminated Fourier ptychography (FP) and plaintext-related chaotic random phase mask (CRPM). In this proposed encryption scheme, the plaintext-related CRPMs are generated by chaotic Lozi map and secure hash algorithm (SHA-256). During the encryption process, the voice signal to be encrypted is first converted into a two-dimensional (2D) voice map. Then, with the help of CRPMs and speckle-illuminated FP, the voice map is encrypted into a series of noise-like low-resolution images. During the decryption process, the original voice signal can be recovered from the series of noise-like low-resolution images via Fourier ptychographic phase retrieval algorithm and the CRPMs. To the best of our knowledge, it is the first time to use the Fourier ptychography and chaotic random phase mask to implement the optical encryption of the voice signal. In addition, in this proposed encryption scheme, the chaotic parameters can replace the whole random phase masks as the secret keys, which makes the management and transmission of the secret keys become more convenient. Moreover, since the plaintext-related chaotic parameter keys can be updated dynamically, the security of the proposed encryption scheme can be further improved. The feasibility, security and robustness of the proposed encryption scheme are further analyzed by numerical simulations.
Interaction graph engineering in trapped-ion quantum simulators with global drives

Authors:
raw_author_name: Antonis Kyprianidis
raw_affiliation_string: Physics, Indiana University, Bloomington, Indiana, UNITED STATES
raw_author_name: A. J. Rasmusson
raw_affiliation_string: Physics, Indiana University, Bloomington, Indiana, UNITED STATES
raw_author_name: Philip Richerme
raw_affiliation_string: Physics, Indiana University, Bloomington, Indiana, UNITED STATES

Abstract: Abstract Trapped-ion quantum simulators have demonstrated a long history of studying the physics of interacting spin-lattice systems using globally addressed entangling operations. Yet despite the multitude of studies so far, most have been limited to studying variants of the same spin interaction model, namely an Ising model with power-law decay in the couplings. Here, we demonstrate that much broader classes of effective spin-spin interactions are achievable using exclusively global driving fields. Specifically, we find that these new categories of interaction graphs become achievable with perfect or near-perfect theoretical fidelity by tailoring the coupling of the driving fields to each vibrational mode of the ion crystal. Given the relation between the ion crystal vibrational modes and the accessible interaction graphs, we show how the accessible interaction graph set can be further expanded by shaping the trapping potential to include specific anharmonic terms. Finally, we derive a rigorous test to determine whether a desired interaction graph is accessible using only globally driven fields. These tools broaden the reach of trapped-ion quantum simulators so that they may more easily address open questions in materials science and quantum chemistry.
Eye-tracking on virtual reality: a survey

Authors:
raw_author_name: J. Roberto Jiménez-Pérez
raw_affiliation_string: Computer Science Department, University of Jaén, 23071, Jaén, Spain
raw_author_name: Juan M. Jurado
raw_affiliation_string: Software Engineering Department, University of Granada, 18071, Granada, Spain

Abstract: Abstract Virtual reality (VR) has evolved substantially beyond its initial remit of gaming and entertainment, catalyzed by advancements such as improved screen resolutions and more accessible devices. Among various interaction techniques introduced to VR, eye-tracking stands out as a pivotal development. It not only augments immersion but offers a nuanced insight into user behavior and attention. This precision in capturing gaze direction has made eye-tracking instrumental for applications far beyond mere interaction, influencing areas like medical diagnostics, neuroscientific research, educational interventions, and architectural design, to name a few. Though eye-tracking’s integration into VR has been acknowledged in prior reviews, its true depth, spanning the intricacies of its deployment to its broader ramifications across diverse sectors, has been sparsely explored. This survey undertakes that endeavor, offering a comprehensive overview of eye-tracking’s state of the art within the VR landscape. We delve into its technological nuances, its pivotal role in modern VR applications, and its transformative impact on domains ranging from medicine and neuroscience to marketing and education. Through this exploration, we aim to present a cohesive understanding of the current capabilities, challenges, and future potential of eye-tracking in VR, underscoring its significance and the novelty of our contribution.
Decentralized IoT Data Authentication with Signature Aggregation

Authors:
raw_author_name: Jay Bojič Burgos
raw_affiliation_string: Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
raw_author_name: Matevž Pustišek
raw_affiliation_string: Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia

Abstract: The rapid expansion of the Internet of Things (IoT) has introduced significant challenges in data authentication, necessitating a balance between scalability and security. Traditional approaches often rely on third parties, while blockchain-based solutions face computational and storage bottlenecks. Our novel framework employs edge aggregating servers and Ethereum Layer 2 rollups, offering a scalable and secure IoT data authentication solution that reduces the need for continuous, direct interaction between IoT devices and the blockchain. We utilize and compare the Nova and Risc0 proving systems for authenticating batches of IoT data by verifying signatures, ensuring data integrity and privacy. Notably, the Nova prover significantly outperforms Risc0 in proving and verification times; for instance, with 10 signatures, Nova takes 3.62 s compared to Risc0’s 369 s, with this performance gap widening as the number of signatures in a batch increases. Our framework further enhances data verifiability and trust by recording essential information on L2 rollups, creating an immutable and transparent record of authentication. The use of Layer 2 rollups atop a permissionless blockchain like Ethereum effectively reduces on-chain storage costs by approximately 48 to 57 times compared to direct Ethereum use, addressing cost bottlenecks efficiently.