Reviewerly/exHarmony huggingface dataset:
Shadow detection using a cross-attentional dual-decoder network with self-supervised image reconstruction features

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

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

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

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

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

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

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

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

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.
A preliminary geochemical study of the archaeological site of the Battle of Jenipapo (Piauí state, Brazil)

Abstract This paper presents, for the first time, geochemical data related to the Battle of Jenipapo, a significant event that occurred on March 13, 1823, marking a political activity favorable to Brazil's independence. The present work aimed to analyze the contents of lead and copper as markers of historical armaments in sediments for geochemical prospecting of the archaeological area in which the Battle of Jenipapo probably occurred. Nine sediment samples were collected: seven from the prospect area and two control samples. Sedimentological characteristics were evaluated, such as pH (6.1–6.9), acidity (0.19–3.00 mmol L −1 ), organic matter (0.86–3.37% w/w), and granulometric classification (sandy predominance). The metals (lead and copper) were determined by inductively coupled plasma mass spectrometry (ICP‐MS) after acid digestion of the sediments. Lead content ranged from 7.9 to 14.9 mg kg −1 for samples and 5.4 mg kg −1 for control samples. Copper content ranged from 14.2 to 29.3 mg kg −1 . The results showed that only lead could be used for the geochemical prospecting of the area that presented strong evidence of anthropic intervention allowing the carrying out of excavation steps.
China eyes economic gains with AI standards

Significance The document calls for the publication of standards in over 50 areas by 2026, in an effort to steer the development of this emerging technology and its industrial applications. Beijing’s focus on standardisation is an attempt to jumpstart the broad, real-world applications of AI technology at low cost. Impacts The AI standardisation effort further embeds Beijing’s goal of ensuring that these technologies serve its political and economic aims. Beijing will try to align with and influence ongoing moves to standardise AI technologies at international bodies. Standardisation could help China foster an international exports market for its AI, particularly across developing countries.
Decentralized IoT Data Authentication with Signature Aggregation

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.
“Putting your money where your mouth is”: An empirical study on buyers’ preferences and willingness to pay for blockchain-enabled sustainable supply chain transparency

This paper investigates how buyers assess the importance of various attributes of supply chain sustainability disclosed by suppliers. These include different types of disclosure (i.e., product, process, and sourcing network), self- and third-party verified disclosure, partial and full disclosure, as well as the attributes associated with information disclosure using blockchain technology: immutability and update frequency. Building on concepts in signalling theory and inter-organisational trust, our research uses a choice-based conjoint experimental design to elicit responses from 234 managers with decision-making roles in procurement. Using this design, we calculate the relative importance of attributes, part-worth utility, and marginal willingness to pay, and test hypotheses about buyer preferences and willingness to pay. Our research reveals that buyers prefer suppliers with sustainability signals that span across different types of disclosure and methods of disclosure. It emphasises the importance of how sustainability information is disclosed, highlighting buyer trust in self-disclosure and a preference for comprehensive, regularly updated information. However, we find mixed results for buyers' willingness to pay. For instance, buyers prefer third-party verified supply chain transparency, but we do not find a significantly higher willingness to pay for such information compared to self-disclosure. The implications suggest a competitive advantage for suppliers adopting voluntary disclosure, prioritising disclosure based on buyer preferences, and recognising the limited direct impact of blockchain technology. Our research contributes to advancing our understanding of information disclosure in supply chain transparency and presents new avenues of inquiry into the value of blockchain-enabled platforms in supply chain sustainability reporting.
A Budget Constraint Incentive Mechanism Based on Risk Preferences of Collaborators in Edge Computing

Mobile Edge Computing (MEC) is a new distributed computing method based on the mobile communication network. It can provide cloud services and an IT service environment for application developers and service providers at the edge of the network. Computation offloading is a crucial technology of edge computing. However, computation offloading will consume the resources of the edge devices, and therefore the edge devices will not offload computation unconditionally. In addition, the service quality of edge computing applications is related to the cooperation rate of edge devices. Therefore, it is essential to design an appropriate incentive mechanism to motivate edge devices to execute computation offloading. However, the current existing incentive mechanisms have two problems: Firstly, existing mechanisms do not account for probability distortions under uncertainty in collaborator utility valuation models. Secondly, the platform ignores the risk preferences of collaborators in multiple rounds of decision-making. To address these issues, we propose an incentive mechanism based on risk preference, IMRP. The IMRP considers the collaborator’s probability distortion, introduces an uncertain utility bonus scheme, and builds a probability distortion model to influence the collaborator’s willingness to offload tasks. The IMRP also considers the collaborator’s risk preference and builds the collaborator’s risk preference model to influence the collaborator’s bidding decision. Simulation results show that our mechanism effectively improves the cooperation rate of edge devices and the utility of the requester.
Exploring the Integration of ICT in Promoting Inclusive Education: A Case Study of the College of Business and Economics at the University of Rwanda

This research aimed to investigate the role of Information and Communication Technology (ICT) in fostering inclusive education within the College of Business and Economics at the University of Rwanda, focusing on the experiences, challenges, and outcomes associated with ICT integration. The study involved 210 undergraduate students in the faculty of business administration, with a targeted sample of 84 students selected through stratified random sampling. Employing a mixed methods design, qualitative data were gathered through in-depth interviews, employing thematic analysis to identify patterns and perspectives. Additionally, structured questionnaires were used for quantitative data collection, focusing on the extent of ICT integration, its influence on inclusive education, and perceived barriers. The findings revealed predominantly positive outcomes, including improved accessibility, enhanced student engagement, and the importance of teacher training. However, challenges such as addressing diverse learning styles, digital literacy, and ongoing adaptation were acknowledged. Recommendations included addressing digital literacy skills, infrastructure gaps, and monitoring to ensure equitable access and positive outcomes for all students, emphasizing the transformative influence of ICT on learning experiences while acknowledging the need for continuous efforts to overcome challenges and maximize inclusivity.
Assessing the Ability of Genetic Programming for Feature Selection in Constructing Dispatching Rules for Unrelated Machine Environments

The automated design of dispatching rules (DRs) with genetic programming (GP) has become an important research direction in recent years. One of the most important decisions in applying GP to generate DRs is determining the features of the scheduling problem to be used during the evolution process. Unfortunately, there are no clear rules or guidelines for the design or selection of such features, and often the features are simply defined without investigating their influence on the performance of the algorithm. However, the performance of GP can depend significantly on the features provided to it, and a poor or inadequate selection of features for a given problem can result in the algorithm performing poorly. In this study, we examine in detail the features that GP should use when developing DRs for unrelated machine scheduling problems. Different types of features are investigated, and the best combination of these features is determined using two selection methods. The obtained results show that the design and selection of appropriate features are crucial for GP, as they improve the results by about 7% when only the simplest terminal nodes are used without selection. In addition, the results show that it is not possible to outperform more sophisticated manually designed DRs when only the simplest problem features are used as terminal nodes. This shows how important it is to design appropriate composite terminal nodes to produce high-quality DRs.
Atom-based Optical Polarization Modulator

In this work, we employ 87Rb atoms as rotation media to manipulate the polarization of optical fields in both magnetic and magnetic-free environments. Employing the nonlinear magneto-optical rotation mechanism, we achieve a state-of-the-art magneto-optical rotation coefficient of 1.74×108 rad⋅T-1⋅m-1 which is four orders of magnitude higher than commonly employed materials. Additionally, in a magnetic-free environment, we achieve all-optical cross-polarization modulation between the pump and probe light via Rb atoms. The nonlinear magneto-optical rotation configuration introduces inventive techniques for a new type of magneto-optical modulator while the all-optical configuration paves the way for exploring photonic integrated circuit (PIC) devices free from disruptions caused by electrical or magnetic crosstalk.
AR Fitting Room and Beyond

&lt;p&gt;Over the years, the AR technology has started to bloom, more and more customers are enjoying using their mobile device to experience this advanced technology. This research project mainly focuses on creating an AR integrated fitting room mobile application or prototype which offers a more detailed and refined virtual try-on experience. It has a five-stage process: Detection, detect user’s body features and setup referencing points. Selection, we will generate a specific body type for users to choose according to the user information we gathered. Interaction, fashion items can be freely try-on on the user's virtual body. Acquiring, users can simply go to the retailer website through our app. Social hub, where users can share their fashion try-on and share it within the community. This application can be utilized in physical retail fashion stores, fashion e-commerce, and basically anywhere with a mobile device. The design of the AR fitting room will be focusing on improving user flow, customer satisfaction, brand engagement and brand acceptance.&lt;/p&gt;