Reviewerly/exHarmony huggingface dataset:
Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives

Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.
Vehicle Detection Based on Adaptive Multimodal Feature Fusion and Cross-Modal Vehicle Index Using RGB-T Images

Target detection is a critical task in interpreting aerial images. Small target detection, such as vehicles, is challenging. Different lighting conditions affect the accuracy of vehicle detection. For example, vehicles are difficult to distinguish from the background in RGB images under low illumination conditions. In contrast, under high illumination conditions, the color and texture of vehicles are not significantly different in thermal infrared (TIR) images. To improve the accuracy of vehicle detection under various illumination conditions, we propose an adaptive multi-modal feature fusion and cross-modal vehicle index (AFFCM) model for vehicle detection. Based on the single-stage object detection model, AFFCM uses red, green, blue, and thermal infrared (RGB-T) images. It comprises three parts: 1) the softpooling channel attention (SCA) mechanism calculates the cross-modal feature weights of the RGB and TIR features using a fully connected layer during global weighted pooling. 2) We design a multi-modal adaptive feature fusion (MAFF) module based on the cross-modal feature weights derived from the SCA mechanism. The MAFF selects features with high weight, compresses redundant features with low weight, and performs adaptive fusion using a multi-scale feature pyramid. 3) A cross-modal vehicle index is established to extract the target area, suppress complex background information, and minimize false alarms in vehicle detection. The mean average precision (mAP) on the Drone Vehicle dataset are 14.44% and 5.02% higher than those obtained using only RGB or TIR images. The mAP is 2.63% higher than that of state-of-the-art (SOTA) methods that utilize RGB and TIR images.
A knowledge framework for blockchain-enabled smart contract adoption in the construction industry

Purpose A novel facet of the construction industry's (CI) digital transformation relates to the rise of smart contracts, and the contribution of blockchain technology in this domain appears to be nascent but rapidly gaining traction. Although the benefits of digitalisation for technologically less enthusiastic CI are irrefutable, the adoption of smart contracts has been found to be low pertaining to industry professionals' behavioural factors stimulated by technological perception. The challenge undertook by this study, therefore, is to develop a knowledge framework for blockchain-enabled smart contract adoption in the CI. Design/methodology/approach From a methodological perspective, this study employed a qualitative approach that involved semi-structured interviews with ten (10) highly experienced CI practitioners involved in digital innovations for data collection. Directed content analysis was performed using NVivo 12 software, which enabled the creation of preliminary open codes. Subsequently, these open codes were grouped into similar categories to develop axial codes. Finally, the study presented final themes along with their corresponding descriptions. Findings Notably, research findings expanded the current body of knowledge on perceived attributes and their measurement items to determine the perception of innovation adoption in CI, where a total of nine (9) perceived attributes were associated with thirty-two (32) measurement items. Originality/value The measurement items were seen as having an extensive impact on the CI professionals' decision to adopt blockchain-enabled smart contracts. With ensuing implications, this study represents one of the first to present a knowledge framework exclusively customised for blockchain-enabled smart contracts, laying the groundwork for effective technological adoption by CI professionals.
Systematic review of adopting blockchain in supply chain management: bibliometric analysis and theme discussion

Traditional supply chain management faces challenges such as low level of information sharing and lack of transparency. Due to the features such as tamper-proof, reliability and traceability, the blockchain technology enhances the trust among stakeholders and enables transparent transactions and open collaboration, which can reduce the risk of supply chain disruptions. Accordingly, blockchain technology-based supply chain management has attracted more attention from academics and practitioners. However, existing review studies mainly focus on different industries or topics and lack a more comprehensive analysis. Therefore, this paper presents a systematic review integrating bibliometric analysis with theme analysis to understand the current research state in BT-SCM and discuss its future research trends. On the one hand, VOSviewer is used to conduct bibliometric analysis to explore the influences of existing studies from six aspects: publication year, author, country, keyword, journal, and article citation. On the other hand, the functional application of BT-SCM is discussed from logistics traceability, supply chain finance, supply chain collaboration, sustainable management and risk management to determine the research status, and the enablers and barriers of blockchain application in SCM are summarised to facilitate practitioners to adopt BT, and finally the potential research directions are proposed for scholars in this field.
Criminological Risks of Personal Data: Main Trends and Forecasts

Relevance of the article is due to the global growth worldwide of crimes committed against or using human personal data. In terms of the number of leaks to the Global Network of Personal Confidential Information, Russia ranks second in the world, and therefore one of the serious problems in our country can be crime associated with the illegal trafficking of personal data. The article presents an original analysis of the main trends and criminological risks of this type of crime as a new segment of Russian crime. Using examples of investigative and judicial practice, statistical data, expert assessments, the main trends of this type of crime are shown and a forecast of its further development is based. The purpose of the study is to identify and formulate the main trends and criminological risks associated with the illegal receipt and/or use of personal confidential information. Objectives: on the basis of materials of criminological research, statistical data, investigative and judicial practice, to identify and subject to subsequent comprehension the main trends and criminological risks of illegal trafficking in personal data. Methodology . In the process of work, methods of theoretical research (analysis, synthesis, induction and deduction), as well as statistical, documentary and formal-logical methods were used. The results . Based on expert assessments, statistical data and investigative and judicial practice, the main trends in the development of crime with personal data have been established, which affect the qualitative and quantitative transformation of all Russian crime as a whole. A certain gap in criminological science has been filled in terms of the formulation of criminological risks, the development of which can lead to an increase in latent criminal violence, fraudulent actions, corruption, extortion, violation of human rights and freedoms, etc. Conclusion . Until now, the state and the legislator have not fully understood the socially dangerous consequences of committing crimes with personal data. Their illegal trafficking has significant criminogenic potential, which is minimized by the prevention measures developed by science and the legislator.
Securing networked image transmission using public-key cryptography and identity authentication

Nowadays, the data transmission in the network is working with the latest technology including 3G, 4G, and 5G. These technologies are replacing old technologies including 2G. Many techniques were proposed and implemented by different researchers in the directions of encryption, decryption, digital signatures, secure routing, WSNs, and cryptographic algorithms. Even though, no systems provide a better result as per the real world requirement. For this purpose, a new model is designed and implemented in this research work. Moreover, it provides data security while performing data transmission in WSN with the incorporation of steganography and digital signatures. The different cryptosystem is the novelty of different algorithms is proposed for providing security services. In this field, there are many combinations with different result were classified under various types. However, in the existing scheme, accommodate their type that forms the algorithms into cryptography oriented methods and other different techniques combined to get a high secured image. Moreover, the various related works in this direction were discussed under all types.
RecruitPro: A Pretrained Language Model with Skill-Aware Prompt Learning for Intelligent Recruitment

Recent years have witnessed the rapid development of machine-learning-based intelligent recruitment services. Along this line, a large number of emerging models have been proposed, achieving remarkable performance in various tasks, such as person-job fit, job classification and salary prediction. However, existing studies are usually domain/task specific, which significantly hinders the adaptation of models for different industries/tasks with limited training data. To this end, in this paper, we propose a novel skill-aware prompt-based pretraining framework, namely RecruitPro, which is capable of learning unified representations on the recruitment data and adapting for various downstream tasks of intelligent recruitment services. To be specific, we first present a contextualized embedding model that is pretrained on a large-scale recruitment dataset. Then, we construct 13 downstream benchmark tasks that are representative in the recruitment process. Along this line, we propose a skill-aware prompt learning module to enhance the adaptability of the pretrained model on downstream tasks. This module includes a skill-related prompt, which is designed to explore key semantic information (i.e., skills) from recruitment text, and a task-related prompt, which is designed to bridge the gap between the pretrained model and different downstream tasks. Moreover, we propose a strategy for extracting potential skills to further improve the performance of our skill-aware prompt learning module. Finally, extensive experiments have clearly demonstrated the effectiveness of RecruitPro. In addition, a case study has been presented to discuss the privacy preserving issue of our RecruitPro.
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning

Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of the PLM weights for every task resulting in increased cost for storing, sharing and serving the models.To address this, parameter-efficient fine-tuning (PEFT) techniques were introduced where small trainable components are injected in the PLM and updated during fine-tuning.We propose AdaMix as a general PEFT method that tunes a mixture of adaptation modules -given the underlying PEFT method of choice -introduced in each Transformer layer while keeping most of the PLM weights frozen.For instance, AdaMix can leverage a mixture of adapters like Houlsby (Houlsby et al., 2019) or a mixture of low rank decomposition matrices like LoRA (Hu et al., 2021) to improve downstream task performance over the corresponding PEFT methods for fully supervised and few-shot NLU and NLG tasks.Further, we design AdaMix such that it matches the same computational cost and the number of tunable parameters as the underlying PEFT method.By only tuning 0.1 -0.2% of PLM parameters, we show that AdaMix outperforms SOTA parameter-efficient fine-tuning and full model fine-tuning for both NLU and NLG tasks.Code and models are made available at https://aka.ms/AdaMix.
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life.Recently, many data-driven approaches are proposed for the development of CGEC research.However, there are two major limitations in the CGEC field: First, the lack of high-quality annotated training corpora prevents the performance of existing CGEC models from being significantly improved.Second, the grammatical errors in widely used test sets are not made by native Chinese speakers, resulting in a significant gap between the CGEC models and the real application.In this paper, we propose a linguistic rulesbased approach to construct large-scale CGEC training corpora with automatically generated grammatical errors.Additionally, we present a challenging CGEC benchmark derived entirely from errors made by native Chinese speakers in real-world scenarios.Extensive experiments 1 and detailed analyses not only demonstrate that the training data constructed by our method effectively improves the performance of CGEC models, but also reflect that our benchmark is an excellent resource for further development of the CGEC field.
A Design Vocabulary for Data Physicalization

Although physical artifacts that represent data have been used for centuries, the research field—known as data physicalization—has only recently gained traction. Compared to data visualization, there is no established vocabulary for analyzing and discussing the properties of physicalizations. Through a grounded analysis of examples and literature, we propose a comprehensive design vocabulary, which consist of three separate, but connected parts: explicit variables , implicit properties , and consequential aspects . Explicit variables build on visual variables known from visualization and extend it to account for physicalization’s multi-modal nature. Implicit properties concern elements which are central to the design intention and user experience of physicalizations, yet are not a result of “explicit” encoding strategies. Finally, consequential aspects refer to unintentional effects of design decisions, that influence how a physicalization is experienced. Our work illustrates how physicalizations incorporate opportunities and challenges that are not afforded in other data representations, such as embodiment and imagined touch . With this, we contribute to generating theory on physicalization. Our design vocabulary can support (1) creators through informing their design processes and highlighting design strategies, (2) educators, and (3) academics and practitioners to analyse existing physicalizations, and reflect on the impact of design decisions on interpretation and experience.
Revocable and Unbounded Attribute-Based Encryption Scheme With Adaptive Security for Integrating Digital Twins in Internet of Things

Internet of Things (IoTs) has been a burgeoning field that transforms the ubiquitous objects to interconnected devices and intelligent system. Today, with the emerging of innovative technologies such as cloud computing, the IoT sector is in a race to leverage these novel technologies to achieve optimal performance. Naturally the Digital Twins (DTs) architecture acts as an indispensable intermediary bridge to couple the IoT domain with these lastest technologies together. However, a tremendous obstacle is that the current Revocable Attribute-Based Encryption (RABE) schemes applied in the DTs paradigm fail to balance the efficiency, security and scalability simultaneously. In this paper, we tackle this challenge by presenting an unbounded and efficient direct RABE scheme with adaptive security. Compared with the previous schemes in this domain, our approach achieves revocable and fine-grained access control efficiently by employing the arithmetic span program (ASP) as the access structure. In this way, the expensive bilinear pairing and exponentiation operations are reduced significantly. Moreover, the unbounded property is satisfied in our scheme since the parameters are not required to be predefined in the setup phase. At last, with the support of the Matrix Decisional Diffie-Hellman (MDDH) assumption, the proposed scheme is proved to achieve adaptive security by adopting dual system encryption methodology. Theoretical comparison and implementation results demonstrate our proposed scheme possesses prominent practicability, scalability and efficiency.
Information flow-based fuzzy cognitive maps with enhanced interpretability

Abstract Fuzzy Cognitive Maps (FCMs) are a graph-based methodology successfully applied for knowledge representation of complex systems modelled through an interactive structure of nodes connected with causal relationships. Due to their flexibility and inherent interpretability, FCMs have been used in various modelling and prediction tasks to support human decisions. However, a notable limitation of FCMs is their susceptibility to inadvertently capturing spurious correlations from data, undermining their prediction accuracy and interpretability. In addressing this challenge, our primary contribution is the introduction of a novel framework for constructing FCMs using the Liang-Kleeman Information Flow (L-K IF) analysis, a quantitative causality analysis rigorously derived from first principles. The novelty of the proposed approach lies in the identification of actual causal relationships from the data using an automatic causal search algorithm. These relationships are subsequently imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the aggregate predictive and explanatory power of the model. Numerical simulations validate the superiority of our method against state-of-the-art FCM-based models, thereby bolstering the reliability, accuracy, and interpretability of FCMs.
Proof-of-Principle Demonstration of Fully Passive Quantum Key Distribution

Quantum key distribution (QKD) offers information-theoretic security based on the fundamental laws of physics. However, device imperfections, such as those in active modulators, may introduce side-channel leakage, thus compromising practical security. Attempts to remove active modulation, including passive decoy intensity preparation and polarization encoding, have faced theoretical constraints and inadequate security verification, thus hindering the achievement of a fully passive QKD scheme. Recent research [W. Wang et al., Phys. Rev. Lett. 130, 220801 (2023).; V. Zapatero et al., Quantum Sci. Technol. 8, 025014 (2023).] has systematically analyzed the security of a fully passive modulation protocol. Based on this, we utilize the gain-switching technique in combination with the postselection scheme and perform a proof-of-principle demonstration of a fully passive quantum key distribution with polarization encoding at channel losses of 7.2 dB, 11.6 dB, and 16.7 dB. Our work demonstrates the feasibility of active-modulation-free QKD in polarization-encoded systems.
Closed-form analytic expressions for shadow estimation with brickwork circuits

Properties of quantum systems can be estimated using classical shadows, which implement measurements based on random ensembles of unitaries. Originally derived for global Clifford unitaries and products of single-qubit Clifford gates, practical implementations are limited to the latter scheme for moderate numbers of qubits. Beyond local gates, the accurate implementation of very short random circuits with two-local gates is still experimentally feasible and, therefore, interesting for implementing measurements in near-term applications. In this work, we derive closed-form analytical expressions for shadow estimation using brickwork circuits with two layers of parallel two-local Haar-random (or Clifford) unitaries. Besides the construction of the classical shadow, our results give rise to sample-complexity guarantees for estimating Pauli observables.We then compare the performance of shadow estimation with brickwork circuits to the established approach using local Clifford unitaries and find improved sample complexity in the estimation of observables supported on sufficiently many qubits.
Is Metaverse in Education Blessing in Disguise?

The advancements in technology have led to the emergence of the metaverse, as a landscape where people interact virtually as avatars, especially in the realm of academic achievement in areas such as entertainment, academic practice, instructional design, and more. According to tech experts, this year could distinguish intelligentsia from laborers, and the advanced methods developed in the last few years will make the first moves last year in creating the virtual world a reality. The properties of the metaverse are explained in-depth, along with a clear definition of the metaverse in education and a comprehensive framework for it. Further potential uses of the metaverse in education are also discussed, along with their justifications and use cases, such as online and blended learning, language learning, competency-based education, and inclusive practice. The challenges of using the metaverse for academic purposes are also discussed. According to academics, one of the most significant applications of the metaverse is education, which has a promising future. We believe that the existence of the metaverse can serve as a new learning environment, and the use of the metaverse in training can be viewed as an educational setting enhanced by technologies related to the metaverse that combine elements of virtual and actual learning environments. It allows students to access the educational environment through wearable technology without being restricted by time or location, and it can lead to improved outcomes, create employment opportunities, and enhance global competitiveness.