Reviewerly/exHarmony huggingface dataset:
High-security dual-image encryption based on fingerprint key with strong robustness

A high-security dual-image encryption method based on fingerprint key with strong robustness is proposed in this paper by combining a fingerprint higher-order data acquisition technology based on cylinder-code with a security-enhanced optical image encryption method based on chaotic phase Fresnel diffraction. In the proposed method, a large key space is created by employing a portable fingerprint key and a series of easy-stored digital keys, where the fingerprint key exhibits remarkable robustness thanks to the implementation of fingerprint cylinder-code authentication technology during the decryption process, and these digital keys have ultrahigh sensitivities because they are closely mapped with the chaotic initial values in chaotic phase Fresnel diffraction and chaotic phase inverse Fresnel diffraction, which contributes to an extremely high security of dual-image encryption method. Furthermore, the high feasibility, security, and robustness of the proposed dual-image encryption method are demonstrated by performing computational simulations. Most importantly, the strong robustness of fingerprint key, including occlusion robustness, rotation robustness, and blur robustness, is also verified by comparative simulations, demonstrating the high practicability of the proposed dual-image encryption method based on fingerprint key.
Quantum computing to solve scenario-based stochastic time-dependent shortest path routing

ABSTRACTNetworks are inherently uncertain and require scenario-based approaches to handle variability. In stochastic and time-dependent networks, optimal solutions cannot always be found using deterministic algorithms. Furthermore, Stochastic Time Dependent Shortest Path problems are known to be NP-hard. Emerging Quantum Computing Methods are providing new ways to address these problems. In this paper, the STDSP problem is formulated as a Quadratic Constrained Binary Optimization Problem. We show that in the case of independent link costs, the size of the problem increases exponentially. Finally, we find that using the quantum solver provides a linear computational experience with respect to the size of the problem. The proposed solution has implications for stochastic networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains, and epidemiology.KEYWORDS: Stochastic-time-dependent shortest pathquantum annealingquantum computingconstrained quadratic optimization model AcknowledgmentsThis research was supported under the IAG Chair Position. I would also like to thank Dr. Victoria Goliber and Mr. Irwan Owen from DWAVE for their continued support from a technical and logistics standpoint.Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionsConceptualization, V.D., D.R. and T.W.; methodology, V.D.; software, V.D., C.N., and M.L.; validation, V.D., M.L. and C.N.; resources, V.D.; data curation, C.N.; writing – original draft preparation, V.D.; writing – review and editing, V.D., C.N., D.R., M.L. and T.W.Data availability statementThe data are available from the corresponding author upon reasonable request.Notes1. The hybrid CQM solver is able to tune the parameters such as number of reads and penalty weights automatically. The inner specific methods are not known.
Formal versus casual: How do customers respond to service robots’ uniforms? The roles of service type and language style

Although service robots wearing uniforms are increasingly common in hospitality services, little is known about how robot uniforms affect customers' perceptions and behavioral intentions. Based on social response theory and the stereotype content model, this research investigates the effect of robot uniform style and how it interacts with language style on customers' willingness to use robots (CWTUR) in utilitarian and hedonic services. The results of three scenario-based experiments reveal that customers prefer to use robots in formal uniforms for utilitarian services, while for hedonic services, they are likely to use robots in casual uniforms. These effects are mediated by competence perception and warmth perception, respectively. Furthermore, social-oriented (vs. task-oriented) language is found to enhance the effect of uniform style on CWTUR in hedonic services, but not in utilitarian services. We present the theoretical and practical implications for robot aesthetic management.
New example of strongly regular graph with parameters <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.svg" display="inline" id="d1e22"><mml:mrow><mml:mo>(</mml:mo><mml:mn>81</mml:mn><mml:mo>,</mml:mo><mml:mn>30</mml:mn><mml:mo>,</mml:mo><mml:mn>9</mml:mn><mml:mo>,</mml:mo><mml:mn>12</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:math> and a simple group <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg" display="inline" id="d1e44"><mml:msub><mml:mrow><mml…

A new strongly regular graph with parameters (81,30,9,12) is found as a graph invariant under certain subgroup of the full automorphism group of the previously known strongly regular graph discovered in 1981 by J. H. van Lint and A. Schrijver.
Leaders’ behavioral approach in the digital era: task vs relationship

Purpose The importance of modeling digital leadership in quickly digitizing countries, like the United Arab Emirates (UAE), is inevitable for building leadership capabilities to lead, engage and motivate remote employees in the digital environment. Using Blake and Mouton Grid, the current study examines the behavioral approach used by the leaders from both public and private sectors while managing their workforce digitally in the period of the pandemic, when 70% of the workforce worked remotely for the first-time in the region. Design/methodology/approach An online survey was conducted by the managerial employees working in different firms using self-administered questionnaires and adopting the snowball sampling technique. In total, 476 respondents participated in the study from both the Emirates of Abu Dhabi and Dubai. Findings The analysis using IBM SPSS and Smart PLS software reported that 9 out of 10 leaders positioned their digital leadership style well above the middle-of-the-road management style (5,5) oriented towards team management (9,9); with 7 out of 9 displaying high team management leadership style, while managing remote workers. However, millennials displayed higher task orientation when compared to generation-x leaders, who concentrated more on their relations with the workers. Practical implications The findings have implications for practitioners in technology driven regions. Also the results highlighting the task-oriented approach of millennials digital leaders have implication for owners and board of directors of the firms that seniority is not the only credible approach for leadership positions. Originality/value The study reveals the behavioral styles beneficial for digital leaders to develop their leadership capabilities and increase their effectiveness while managing the workforce digitally. Black Mountain Grid and its two-dimensional leadership matrix has been found to be a useful conceptual approach for understanding digital leadership behaviors, and based on study findings, recommendations have been provided to effectively improve its utilization for leading teams. The findings have implications for practitioners in technology driven regions as well as digital leadership field scholars.
Preface

Extract Many online applications, especially in the financial industries, are running on blockchain technologies in a decentralized manner, without the use of an authoritative entity or a trusted third party. Such systems run in a permissionless environment, using a decentralized consensus mechanism in order to achieve integrity in purely distributed peer-to-peer systems. The blockchain is a public append-only ledger that forms an eventual consistent system, which relies on cryptographic primitives, such as hash functions and digital signatures. However, these cryptographic primitives are vulnerable in the presence of quantum computers, which makes the whole system potentially unsafe. Considering the current literature, one sees that there are many excellent academic papers, which are usually difficult to understand for people without mathematical background. Also, there are no books that make this subject accessible to investors in an appropriate form, if they lack basic knowledge of number theory, higher algebra and cryptography. The purpose of this book is to provide the mathematical and cryptographic concepts used in blockchain systems, as we finally want to understand how such systems work in order to estimate the effects of possible attacks. This book is most suited for upper level undergraduate and graduate courses, such as for security courses, advanced blockchain courses, and could also be used as reference for graduate research in blockchain related topics. However, as this book is targeted to a broad audience, and not only to mathematicians, computer scientists, engineers, security experts, but also to those interested in learning how blockchain applications work, we introduce the necessary mathematical and cryptographic concepts in a way that is accessible for every reader with a solid background in high school level calculus. Thus, we prove every mathematical statement in great detail. Therefore, this book is also well suited as a textbook for undergraduate classes or as a reference book for anyone who is interested in a deep understanding of how blockchain systems work.
Planning and control of drifting-based collision avoidance strategy under emergency driving conditions

Automated emergency collision avoidance (CA) technology has made tremendous progresses in recent years. However, the existing CA approaches, either braking or steering based, cannot handle well the extremely emergency CA scenarios with limited space and time. This is a research gap to be bridged before the zero-fatality vision of road traffic, namely Vision Zero, can be realized. To further extend the safety limits of vehicles, especially that of automated vehicles in future, we propose a drifting-based CA approach that adopts an aggressive, but controllable, over-steering maneuver. To this end, first the trajectory planning of drifting is formulated and solved as a constrained optimal control problem, which considers vehicle dynamics, actuator limits and collision constraints. Then the knowledge of the three CA approaches’ capability limits is obtained and used for CA decision. Given a planned trajectory, a feedback linearization based controller is further designed to complete the drifting maneuver. Finally, using a real vehicle with by-wire actuators, a series of simulations and road tests are carried out. Results show that our proposed drifting-based algorithm can successfully fulfill agile CA tasks in extreme conditions. With further improvements, this preliminary and conceptual work may contribute as a promising complement to current active safety technologies.
Crisis Communication on Social Media: Informalization in the Hour-by-Hour Struggle for Information

Abstract This chapter concerns the communicative conditions in the relation between informal practice of sakprosa and informal style in social media communication. By focusing on knowledge and interaction in authorities’ crisis communication, the objective is to discuss whether the public discourse undergoes an informalization within the framework of critical discourse analysis. The aim of this study is to shed light on communicative strategies in authorities’ prose on social media. The data consist of two corpora from the Twitter platform of the Swedish crisis authority—the first from the terror attack in Stockholm in 2017 and the second from an official false alarm for a total number of 1,951 tweets. Methodologically, the data are arranged chronologically and rest on a qualitative approach to style. A result of this study is that boundaries within the public discourse in social media are not blending, but rather are parallel. When the authority takes the initiative to post informative tweets, they use formal written style. On the contrary, when they write responses to citizens’ tweets, they often use a personal and informal style. Another result is that when the authority does not communicate, it seems to trigger citizens to post informal and sometimes harsh comments.
DASR: Dual-Attention Transformer for infrared image super-resolution

The infrared image super-resolution (SR) method successfully overcomes the hardware limitations of infrared cameras that reconstruct higher-quality images with improved efficiency and cost-effectiveness. However, existing infrared SR methods do not take into account the specificity of infrared images and are primarily designed for small-scale factors. In this paper, we first investigate the domain differences between infrared and visible images and the impact of these differences on super-resolution tasks. It was found that compared with visible SR, the infrared SR will require more global edge structure information rather than local texture information reconstructed by previous CNN-based methods mainly. To address this disparity, we propose a novel infrared SR model, named DASR, which incorporates a Transformer with spatial and channel dual-attention mechanisms. In DASR, spatial attention captures both local and global information through window-based and cross-window contextual long-range interactions, while channel attention captures channel-wise global information through cross-channel interactions. With this new Transformer architecture, our method effectively extracts spatial and channel global information, which cannot be captured by the local receptive field of convolution, thus making it more suitable for infrared SR. Extensive experiments on benchmark datasets have indicated that our method outperforms the state-of-the-art infrared SR methods with less number of parameters and lower computational complexity while having the best visual results.
Adaptative generalisation over a value hierarchy for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si42.svg" display="inline" id="d1e1778"><mml:mi>k</mml:mi></mml:math>-anonymisation of Origin–Destination matrices

The study of transportation relies on mobility data, containing information on the whereabouts and movements of individuals in a study area. These data are often represented in the simple form of origin–destination (OD)-matrices, which are a valuable indicator for the management of transportation networks but also present risks to the privacy of the individuals. In particular, the significant size (i.e., number of distinct flows) and high number of modalities (produced by a high-resolution zoning) of OD-matrices call for an adapted, fast algorithm that can efficiently anonymise them. In this paper, we develop a lightweight approach for the k-anonymisation of OD-matrices that exploits the low dimension of the data to explore a larger solution space than regular generalisation algorithms, while keeping relevant restrictions of the search space in order to be scalable on matrices with high number of flows. We apply it to a variety of real-world large-scale O-D matrices collected by the New York City Taxi and Limousine Commission and derived from the Data for Development (D4D) challenge organised by Orange in Senegal and Côte d’Ivoire. Compared to an extensive benchmark of regular generalisation algorithms and mobility anonymisation state-of-the-art, we show that our method is 27% more precise and 9 times faster than comparable approaches able to scale on the same datasets.
1000 papers published in JOSS

Today we reached a huge milestone at JOSS – we published our 1000&lt;sup&gt;th&lt;/sup&gt; paper! JOSS is a developer friendly, free-to-publish, open-access journal for research software packages. Publishing 1000 papers (and reviewing the corresponding 1000 software packages) over the past ~4 years has been no small feat.
ARFG: Attach-Free RFID Finger-Tracking with Few Samples Based on GAN

Traditional finger-tracking methods using sensors, cameras, and other devices are limited by high costs, environmental sensitivity, and inadequate user privacy protection. To address these challenges, we propose ARFG, an attach-free RFID finger-tracking system based on Generative Adversarial Network (GAN). ARFG captures time-series reflection signal changes resulting from finger movements in front of RFID tag arrays. These signals are transformed into feature maps that serve as inputs for DS-GAN, a fully supervised classification model using a semi-supervised algorithm. ARFG achieves accurate recognition of finger traces, with soft thresholding used to overcome the challenge of limited dataset training of conventional GANs. Extensive experiments demonstrate ARFG’s high accuracy, with average accuracy of 94.69% and up to 97.50% in conditions with few samples, various traces, users, finger speeds, and surroundings, showcasing its robustness and cross environmental capabilities.
Multimodal Event Detection on Chinese Glyphs

At present, most of the event detection researches focus on the single text modality, and multimodal event extraction is in its infancy, which mainly used the feature information of images to enhance text information. However, obtaining the corresponding high-quality images for texts still is a challenge for those plain texts. Since Chinese characters are pictographs, some radical features of event trigger words in their glyphs have certain and auxiliary effects on detecting trigger words. In this paper, we propose a multimodal event detection method based on Chinese text and the glyph of Chinese character. Specifically, we use different fonts to transform texts into text images, and then extract the features of the text images through residual network. Finally, we fuse them with the text features obtained from the dynamic multi-pooling mechanism to form the feature vectors for multimodal event detection. Experimental results on the ACE 2005 Chinese corpus show that our proposed model outperforms the best baseline by 1.9 in F1-score.
Terminology-Enriched Meta-curriculum Learning for Domain Neural Machine Translation

As a data-driven approach, neural machine translation (NMT) heavily depends on large parallel corpora. Nevertheless, such corpora are frequently unavailable in domains of user interest, consequently diminishing the performance of NMT models in terms of domain robustness and adaptability. To tackle this challenge, this paper presents an innovative training method for multi-domain translation that leverages meta-curriculum learning and terminology information. To utilize domain-specific terminology, the proposed method first need to extract, align, and filter terms, thereby integrating bilingual terminology into the training dataset. Following this, the aligned sentences are sorted according to the domain similarity scores with the general domain in a curriculum learning manner. Then, the training data is divided into sub-datasets in ascending order of difficulty. A meta-learning technique is then employed to train the model utilizing these partitioned training datasets as tasks, ultimately yielding a translation model with exceptional domain robustness and remarkable domain adaptability. Experimental results on test data from both seen and unseen domains demonstrate that the proposed method yields an average improvement of 2.44 in BLEU scores on the test sets of multiple domains compared to the pre-training and fine-tuning method, and a 1.54 BLEU scores increase over the meta-curriculum learning approach without terminology information. Upon fine-tuning with a small amount of target domain data, the proposed method outperforms these two baselines by 2.62 and 1.5 in BLEU scores, respectively. These outcomes underscore the efficacy of the proposed method in improving NMT performance in scenarios with limited domain-specific data.
UCM: Personalized Document-Level Sentiment Analysis Based on User Correlation Mining

Personalized document-level sentiment analysis (PDSA) is important in various fields. Although various deep learning models for PDSA have been proposed, they failed to consider the correlations of rating behaviors between different users. It can be observed that in the real-world users may give different rating scores for the same product, but their rating behaviors tend to be correlated over a range of products. However, mining user correlation is very challenging due to real-world data sparsity, and a model is lacking to utilize user correlation for PDSA so far. To address these issues, we propose an architecture named User Correlation Mining (UCM). Specifically, UCM contains two components, namely Similar User Cluster Module (SUCM) and Triple Attributes BERT Model (TABM). SUCM is responsible for user clustering. It consists of two modules, namely Latent Factor Model based on Neural Network (LFM-NN) and Spectral Clustering based on Pearson Correlation Coefficient (SC-PCC). LFM-NN predicts the missing values of the sparse user-product rating matrix. SC-PCC clusters users with high correlations to get the user cluster IDs. TABM is designed to classify the users’ sentiment based on user cluster IDs, user IDs, product IDs, and user reviews. To evaluate the performance of UCM, extensive experiments are conducted on the three real-world datasets, i.e., IMDB, Yelp13, and Yelp14. The experiment results show that our proposed architecture UCM outperforms other baselines.
Power Grid Knowledge Graph Completion with Complex Structure Learning

In recent years, the knowledge graph has become a commonly used storage way for large-scale knowledge in the power grid. It has proved to have an excellent performance which helps people get specialized knowledge easier. However, generating new knowledge automatically in the incomplete knowledge graph is still an urgent problem to be resolved, which name is knowledge graph completion. Previous works do not pay enough attention to the structural information in the power grid knowledge graph, resulting in poor performance. In this paper, we propose a novel framework called Complex Structure Entropy Network (CSEN) to conduct multi-hop reasoning over a power grid knowledge graph with novel two-stage cognitive theory and von Neumann graph entropy. The paper evaluates the model on the power grid defects dataset in the link prediction task and shows the effectiveness of the proposed method compared to a variety of baselines.
Simple but Effective: Keyword-Based Metric Learning for Event Sentence Coreference Identification

Event sentence coreference identification (ESCI) is a fundamental task of news event detection and tracking which aims to group sentences according to events they refer to. Most recent efforts address this task by means of identifying coreferential event sentence pairs. Currently, frameworks based on pre-trained language models like Sentence-BERT (SBERT) are widely used for sentence pair tasks. However, SBERT lacks keyword awareness, while the local features of sentences can demonstrate a strong correlation with the event topic. In addition, the strategy of encoding the whole sentence is less flexible and more time-consuming. After reconsidering the significance of keywords in ESCI task, we propose KeyML, a simple keyword-based metric learning approach which leverages both lexical and semantic features of keywords to capture subject patterns of events. Specifically, a Siamese network is adapted to optimize distance metrics of keyword embeddings, resulting in more separable similarity of event sentence pairs. Then, KeyML considers keywords of data with different granularity and exploits three training strategies, along with their corresponding sampling methods, to investigate co-occurrence relationships. Experimental results show that KeyML outperforms SBERT and SimCSE on three datasets and demonstrate the effectiveness and rationality of our method.