Reviewerly/exHarmony huggingface dataset:
Twin-Field Quantum Key Distribution with Partial Phase Postselection

Quantum key distribution (QKD) allows two remote parties to share information-theoretically secure keys. In recent years, a revolutionary breakthrough called twin-field (TF) QKD has been developed to overcome the linear key-rate constraint and greatly increases the achievable distance. Phase-randomization and subsequent postselection play important roles in its security proof. Later, no-phase-postselection TF-QKD was proposed and became a popular variant, since the removal of phase postselection leads to a higher key rate. However, the achievable distance is decreased compared to the original one. Here, we propose a TF-QKD protocol with partial phase postselection. Namely, its code mode is still free from global phase randomization and postselection to make sure the advantage of the high key rate remains. On other hand, phase postselection is introduced in the decoy mode to improve the performance. Applying an operator dominance condition, we prove universal security of the proposed protocol in the finite-key case under coherent attacks, and numerical simulations confirm its potential advantages in terms of key rate and achievable distance.
G-DaM: A Distributed Data Storage with Blockchain Framework for Management of Groundwater Quality Data

Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.
Integrated remote sensing data and field investigations for geological mapping and structural analysis. The case of SW Tichoukt ridge (Middle Atlas, Morocco)

Boulemane area is one of the important key sectors to understand the evolution of the central Middle Atlas of Morocco. It is a junction area of three Cretaceous/Paleogene unconformable sedimentary basins on the Triassic-Jurassic deposits. This work is a combination of remote sensing results (optical and radar data) supported by field observations to accurate a high-precision geological map of the study area. For this purpose, we used two types of images that are the radar image (Sentinel-1 data) and the optical multispectral images: Landsat 8 OLI (Optical Land Imager) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The two multispectral images have been spatially enhanced using the panchromatic band of the HRG-2 (High Geometric Resolution) sensor of SPOT 5, allowing the production of high spatial resolution images (5m). The colored compositions that were chosen using OIF (Optimum Index Factor), and PCA (Principal Component Analysis) allowed good discrimination of the main rock. Moreover, ASTER thermal bands have allowed the delimitation of large outcrops of yellow sandstone, conglomerate, phosphate and gypsum based on their thermal emissivity. Structural lineaments are manually extracted from Sentinel-1 radar images after applying directional filters, analyzing Landsat 8 OLI, ASTER high-resolution multispectral images, and interpreting the spatial juxtaposition of lithological features after digitizing the final lithological map. The extracted structural lineaments are grouped into major systems mainly oriented NE-SW, and NW-SE. The NE-SW system is constituted by faults inherited from the Paleozoic basement. The NW-SE transverse system is constituted by faults newly developed during the Middle Liassic.
Thread With Caution: Proactively Helping Users Assess and Deescalate Tension in Their Online Discussions

Incivility remains a major challenge for online discussion platforms, to such an extent that even conversations between well-intentioned users can often derail into uncivil behavior. Traditionally, platforms have relied on moderators to -- with or without algorithmic assistance -- take corrective actions such as removing comments or banning users. In this work we propose a complementary paradigm that directly empowers users by proactively enhancing their awareness about existing tension in the conversation they are engaging in and actively guides them as they are drafting their replies to avoid further escalation. As a proof of concept for this paradigm, we design an algorithmic tool that provides such proactive information directly to users, and conduct a user study in a popular discussion platform. Through a mixed methods approach combining surveys with a randomized controlled experiment, we uncover qualitative and quantitative insights regarding how the participants utilize and react to this information. Most participants report finding this proactive paradigm valuable, noting that it helps them to identify tension that they may have otherwise missed and prompts them to further reflect on their own replies and to revise them. These effects are corroborated by a comparison of how the participants draft their reply when our tool warns them that their conversation is at risk of derailing into uncivil behavior versus in a control condition where the tool is disabled. These preliminary findings highlight the potential of this user-centered paradigm and point to concrete directions for future implementations.
Quantum asymmetric cyclic teleportation of arbitrary single-particle and two-particle states

In this paper, two three-party cyclic teleportation schemes for real arbitrary quantum states of a single particle and two particles are proposed for the first time. In the first ([Formula: see text]) scheme, Alice sends any single particle state to Bob, Bob sends any two-particle states to Charlie, and meanwhile Charlie sends any single particle state to Alice. In the second ([Formula: see text]) scheme, with the help of the controller David, Alice sends any two-particle states to Bob, Bob sends any two-particle states to Charlie, and Charlie sends any single particle state to Alice simultaneously. Based on the maximum entangled quantum channel shared in advance, the communication participants carry out some Bell state measurement (BSM) measurements and carry out corresponding unitary transformation according to the results published by each other. After that, the cyclic teleportation of predetermined quantum information can be successfully realized. The success probability of teleportation of both schemes can reach 100%. Finally, this paper briefly analyzes the security of the two schemes and compares them with some previously published schemes from the perspective of inherent efficiency. The results show that the two schemes proposed in this paper are safe and have high inherent efficiency. Different from the previous symmetric cyclic quantum teleportation methods, the asymmetric cyclic quantum teleportation schemes proposed in this paper are more universal.
Blockchain technology implementation challenges in supply chains – evidence from the case studies of multi-stakeholders

Purpose The aim of the research is to identify and prioritise the implementation challenges of blockchain technology and suggests ways for its implementation in supply chains. Design/methodology/approach Underlined by the technology, organisational, and external environment model, a conceptual framework with four challenge categories and sixteen challenges is proposed. Data collected from three stakeholder groups with experience in the implementation of blockchain technology in India is analysed by employing an analytical hierarchy process method-based case study. Further, a criticality–effort matrix analysis is performed to group challenges and suggest ways for implementation. Findings The analysis revels that all stakeholders perceive complexity challenge associated with the technology, organisational structure, and external environment, and issues of compatibility with existing systems, software, and business practices to be high on the criticality and effort scales, which thus require meticulous planning to manage. Likewise, top-management support issues related to insufficient understanding of how technology fits with the organisation’s policy and benefits offered by the technology requires high effort to address this challenge. Research limitations/implications The results were obtained by focusing on the Indian context and therefore may not apply to other nations’ contexts. Practical implications By investigating the challenges that the developers, consultants, and client organisations need to address, this study assists managers in developing plans to facilitate coordination among these organisations for successful blockchain implementation. Originality/value To the authors’ knowledge this study is the first to identify and prioritise the challenges from the perspectives of multiple stakeholder groups with experience in blockchain technology implementation.
Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods

The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the Bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods.
Software assistants in software engineering: A systematic mapping study

Abstract The increasing essential complexity of software systems makes current software engineering methods and practices fall short in many occasions. Software assistants have the ability to help humans achieve a variety of tasks, including the development of software. Such assistants, which show human‐like competences such as autonomy and intelligence, help software engineers do their job by empowering them with new knowledge. This article investigates the research efforts that have been conducted on the creation of assistants for software design, construction and maintenance paying special attention to the user‐assistant interactions. To this end, we followed the standard systematic mapping study method to identify and classify relevant works in the state of the art. Out of the 7580 articles resulting from the automatic search, we identified 112 primary studies that present works which qualify as software assistants. We provide all the resources needed to reproduce our study. We report on the trends and goals of the assistants, the tasks they perform, how they interact with users, the technologies and mechanisms they exploit to embed intelligence and provide knowledge, and their level of automation. We propose a classification of software assistants based on interactions and present an analysis of the different automation patterns. As outcomes of our study, we provide a classification of software assistants dealing with the design, construction and maintenance phases of software development, we discuss the results, identify open lines of work and challenges and call for new innovative and rigorous research efforts in this field.
The effect of lean and agile operations strategy on improving order-winners: Empirical evidence from the UAE food service industry

This research aims to assess the impact of lean and agile operational strategies on improving order winners in the food service industry in the UAE. Research disclosed a few attributes with a dimensional review of lean and agile strategies that enhance strategic alignment in the food service industry of UAE to achieve the maximum benefits that have never been identified in research before. Data from 85 Sharjah-based food service companies were used for the analysis. A quantitative method with descriptive, causal and exploratory research design was used, along with convenient cluster sampling. A valid sample size of 255 respondents was used to assess the model through regression and ANOVA using SPSS. Research findings show a significant direct impact of lean strategies on order winners, and agile strategies significantly positively impact order winners. In contrast, both variables have a significant direct impact on order winners. This research is limited to assessing the impact of lean and agile strategies to achieve maximum order winners. Future research should consider a manufacturing industry to increase generalizability and a comprehensive focus on the lean and agile dimensional impact on competitive advantage. Customer loyalty and satisfaction lead a business toward order winners. An exemplary implementation of lean and agile strategies can translate into high business performance.
Graph Neural Networks in Computer Vision - Architectures, Datasets and Common Approaches

Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph Convolutional Networks (GCN), and Graph Recurrent Networks (GRN). An increase in their usability in computer vision is also observed. The number of GNN applications in this field continues to expand; it includes video analysis and understanding, action and behavior recognition, computational photography, image and video synthesis from zero or few shots, and many more. This contribution aims to collect papers published about GNN-based approaches towards computer vision. They are described and summarized from three perspectives. Firstly, we investigate the architectures of Graph Neural Networks and their derivatives used in this area to provide accurate and explainable recommendations for the ensuing investigations. As for the other aspect, we also present datasets used in these works. Finally, using graph analysis, we also examine relations between GNN-based studies in computer vision and potential sources of inspiration identified outside of this field.
Critical quantum sensing based on the Jaynes-Cummings model with a squeezing drive

Quantum sensing improves the accuracy of measurements of relevant parameters by exploiting the unique properties of quantum systems. The divergent susceptibility of physical systems near a critical point for quantum phase transition enables criticality-enhanced quantum sensing. The quantum Rabi model (QRM), composed of a single qubit coupled to a single bosonic field, represents a good candidate for realizing such critical enhancement for its simplicity, but it is experimentally challenging to achieve the ultrastrong qubit-field coupling required to realize the critical phenomena. In this work, we explore an alternative to construct the analog of the QRM for the sensing, exploiting the criticality appearing in the Jaynes-Cummings (JC) model whose bosonic field is parametrically driven, not necessitating the ultrastrong coupling condition thus to some extent relaxing the requirement for the practical implementation.
A Model-Agnostic Approach to Mitigate Gradient Interference for Multi-Task Learning

Multitask learning (MTL) is a powerful technique for jointly learning multiple tasks. However, it is difficult to achieve a tradeoff between tasks during iterative training, as some tasks may compete with each other. Existing methods manually design specific network models to mitigate task conflicts, but they require considerable manual effort and prior knowledge about task relationships to tune the model so as to obtain the best performance for each task. Moreover, few works have offered formal descriptions of task conflicts and theoretical explanations for the cause of task conflict problems. In this article, we provide a formal description of task conflicts that are caused by the gradient interference problem of tasks. To alleviate this issue, we propose a novel model-agnostic approach to mitigate gradient interference (MAMG) by designing a gradient clipping rule that directly modifies the interfering components on the gradient interfering direction. Specifically, MAMG is model-agnostic and thus it can be applied to a large number of multitask models. We also theoretically prove the convergence of MAMG and its superiority to existing MTL methods. We evaluate our method on a variety of real-world large datasets, and extensive experimental results confirm that MAMG can outperform some state-of-the-art algorithms on different types of tasks and can be easily applied to various methods.
syntactic Graph Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment classification (ASC) is designed to identify the sentiment orientation of given aspect terms in a sentence. Previous neural networks have used attention mechanisms to align context words with the appropriate aspect terms. Without considering syntactic dependencies, these models may erroneously focus on context words that are not related to the aspect terms. To address this issue, the graph convolution network (GCN) and the graph attention network (GAT) are proposed to build a graph based on the dependency parse tree, allowing the representations of context words to be propagated to the aspect terms according to their syntactic dependencies. However, these models consider all syntactic dependencies to be of the same type, and thus may result in inappropriate propagation of word representations in the graph. To further distinguish between the syntactic dependencies, this study proposes a syntactic graph attention network (SGAN) to incorporate the knowledge of dependency types into the graph attention network. The dependency types are modeled as edge embeddings to learn the attention weight of each edge according to its dependency type. By considering different dependency types and their weights, the proposed method can block inappropriate propagation to better associate the context words to aspect terms. To increase training process stability and enrich the diversity of graph representations, a weighted multihead attention is applied to compose the graph representations generated by different heads. The experimental results on five benchmark datasets show that the SGAN yields more accurate results than existing methods.
Low-Frequency Data Embedding for DFT-Based Image Steganography

As sharing digital media is getting more prominent, there has been a rise in the development of techniques for protecting digital media against misuse. However, the amount of such techniques has been less prominent for printed goods. The data hiding method presented in this paper is suited for the print domain. The method uses the Discrete Fourier Transform to embed data into images and Gray Component Replacement to maintain high image quality by masking embedding artifacts. This research examines whether the method's performance can be optimized using low-frequency ranges for embedding. The aim is to evaluate the usage of low-frequency ranges in order to achieve higher robustness. Different frequency ranges are tested to determine how they affect image quality and detection rates. Tests are conducted in the digital domain. The results show that the method can maintain image quality regardless of the frequency range, and that low-frequency ranges lead to more consistent detection rates. Future research will be done on images in the print domain.
Bie Vote: A Biometric Identification Enabled Blockchain-Based Secure and Transparent Voting Framework

In this research, we investigate the current trends of voting technology, online voting, and possible opportunities to adopt both blockchain technology and biometrics technology in the voting system. According to the literature review, we determine that the existing electronic voting system raises concerns in terms of transparency, security, and trust across the globe. Thus, we aim to propose a biometric-enabled and hyperledger fabric-based architectural framework for e-Voting applications to automate identity verification that shall address the existing concern. In our extension work, we are aiming to provide a high-level architectural framework and implement the system in a real-world scenario.
Rethinking Deep Face Restoration

A model that can authentically restore a low-quality face image to a high-quality one can benefit many applications. While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features that compromise the authenticity of reconstructed faces. Because the human visual system is very sensitive to faces, even minor changes may significantly degrade the perceptual quality. In this work, we argue that the problems of existing models can be traced down to the two sub-tasks of the face restoration problem, i.e. face generation and face reconstruction, and the fragile balance between them. Based on the observation, we propose a new face restoration model that improves both generation and reconstruction. Besides the model improvement, we also introduce a new evaluation metric for measuring models' ability to preserve the identity in the restored faces. Extensive experiments demonstrate that our model achieves state-of-the-art performance on multiple face restoration benchmarks, and the proposed metric has a higher correlation with user preference. The user study shows that our model produces higher quality faces while better preserving the identity 86.4% of the time compared with state-of-the-art methods.