Reviewerly/exHarmony huggingface dataset:
Synchronization of quantum communications over an optical classical communications channel

Precise synchronization between a transmitter and receiver is crucial for quantum communications protocols such as quantum key distribution (QKD) to efficiently correlate the transmitted and received signals and increase the signal-to-noise ratio. In this work, we introduce a synchronization technique that exploits a co-propagating classical optical communications link and tests its performance in a free-space QKD system. Previously, existing techniques required additional laser beams or relied on the capability to retrieve the synchronization from the quantum signal itself; this approach, however, is not applicable in high channel loss scenarios. On the contrary, our method exploits classical and quantum signals locked to the same master clock, allowing the receiver to synchronize both the classical and quantum communications links by performing a clock-data-recovery routine on the classical signal. In this way, by exploiting the same classical communications already required for post-processing and key generation, no additional hardware is required, and the synchronization can be reconstructed from a high-power signal. Our approach is suitable for both satellite and fiber infrastructures, where a classical and quantum channel can be transmitted through the same link.
An (almost) fuzzy logic of action and preferences, its quasi-model interpretations, and the problem of its decidability

The logical depiction of the concept of action forms a unique method of grasping its semantic content. It finds its reflection in different proposals of epistemic and deontic-logic systems describing actions as the operational behaviour of an agent as a game played over a given game tree. This perspective establishes some unique connection between actions and the agent's preferences. One of the logical-game-theoretic approaches to their modelling has been elaborated in van Benthem's school in the common area of game-theory, epistemic and fixed-point logic. In reference to some ideas of van Benthem's school – the article aims to propose a new (almost) fuzzy logic system AFLAP for actions and preferences describing a game-theoretic behaviour of an agent over a given game tree. Nevertheless, the backward induction strategy is idealised and exchanged for the Church-Rosser property, and a piece of fuzziness is introduced to the system by considering two unique 'epsilon' relations, Move and BI, with their approximation sequences. An abstract model in the form of the so-called quasi-model is proposed for such a system. It forms a multi-level (potentially infinite) construction built due to the pattern recently elaborated and enhanced by Wolter-Kurucz's school. This construction is exploited in the article as a convenient basis for proving the decidability of AFLAP by showing its 'abstract' finite model property. This task is performed by constructing an additional workable quasi-model of the size dependent on the size of the set of subformulae of a given formula of AFLAP. Finally, some R-programming-oriented applications of the earlier theoretic considerations are presented.
Automated invoice processing: Machine learning-based information extraction for long tail suppliers

Automation of the processing of incoming invoices promises to yield vast efficiency improvements in accounting. Until a universal adoption of fully electronic invoice exchange formats has been achieved, machine learning can help bridge the adoption gaps in electronic invoicing by extracting structured information from unstructured invoice formats. Machine learning especially helps the processing of invoices of suppliers who only send invoices infrequently, as the models are able to capture the semantic and visual cues of invoices and generalize them to previously unknown invoice layouts. Since the population of invoices in many companies is skewed toward a few frequent suppliers and their layouts, this research examines the effects of training data taken from such populations on the predictive quality of different machine-learning approaches for the extraction of information from invoices. Comparing the different approaches, we find that they are affected to varying degrees by skewed layout populations: The accuracy gap between in-sample and out-of-sample layouts is much higher in the Chargrid and random forest models than in the LayoutLM transformer model, which also exhibits the best overall predictive quality. To arrive at this finding, we designed and implemented a research pipeline that pays special attention to the distribution of layouts in the splitting of data and the evaluation of the models.
Building Data Literacy Training in College Education

Data literacy (DL) is increasingly viewed as one of the most important foundational skills in today’s professional market (Reinitz et al., 2022). Although DL definitions differ for different settings and disciplines, the core idea centers around an individual’s ability to locate, understand and use data (ALA, n.d). Despite the variety of online professional development programs and resources, college students could benefit significantly from DL instructions in school before entering the workforce. Instructions and resources from academic libraries are critical in building up students’ knowledge of data concepts and basic data skills. The study thus aims to explore the current landscape of academic libraries’ DL instruction and resources, and to gain a better understanding of how librarians perceive their service provisions in supporting the DL initiatives. We examine the following research questions: 1). What DL instructions and resources are provided in academic libraries, and how is DL defined and approached in them? 2). What do academic librarians gauge of students’ current DL levels and the effectiveness of existing programs and resources in addressing the needs? We adopted a mixed-method research design to integrate data from a content analysis of academic libraries’ DL LibGuides from a random sample of R1, R2, and D/PU universities across 50 states, and quantitative data from an online survey with academic librarians across the US. Insights gained are helpful in identifying gaps in training contents, and may imply effective instructional approaches, creative and cost-effective approaches/strategies for DL training implementation.
Bridging the Gap Between Designers and Users

In this paper, we describe our approach to bridging the designer-user gap by engaging 20 older adults in codesign workshops to generatively re-imagine health-related voice agent (VA) design for voice user interfaces (VUI) for older adults, by older adults. Drawing on recent research in Library and Information Science (LIS), we describe codesign as a method, as well as how the 20 older adults perceived codesign in our two case studies of health-related VA design for health learning and exercise. Older adult codesigners perceived codesign as transformative, enjoyable, educational, and useful. We also share specific experiential recommendations of strategies for conducting codesign, including strategies for transitioning from expert to facilitator, creating experience-based questions, probing for deeper design ideation and priorities, and ways of approaching codesign through humility and care. We describe the codesign method’s generative benefits in our two case studies and encourage LIS researchers to consider including codesign methods in their research.
A framework for analyzing authentication risks in account networks

Our everyday life depends more and more on online services and, therefore, access to related user accounts. The security of user accounts, again, is tied to the security of the corresponding primary and fallback authentication methods. Accounts can be linked to each other – by fallback authentication, through SSO, or by using the same authentication devices – creating an account network. These account networks enhance login comfort and are needed in case of account recovery, but they also increase each account's attack surface. In addition, misconfigurations might result in account inaccessibility. However, these problems can only be detected by analyzing single accounts first and then the resulting account networks. Despite the importance to understand account security and accessibility, almost no analysis methods exist. To address this need, this article presents the Authentication Analysis Framework (AAF). AAF evaluates account types and primary and fallback authentication methods for each account, before analyzing the overall account network. By detecting transitive risks, weak links can be discovered and subsequently strengthened. We further propose maturity models to rank the primary and fallback authentication methods based on risks and a description language to exchange the required information. AAF is implemented as a plugin for the password manager KeePass to assist end users and as a standalone tool for researchers.
Res-FLNet: human-robot interaction and collaboration for multi-modal sensing robot autonomous driving tasks based on learning control algorithm

Res-FLNet presents a cutting-edge solution for addressing autonomous driving tasks in the context of multimodal sensing robots while ensuring privacy protection through Federated Learning (FL). The rapid advancement of autonomous vehicles and robotics has escalated the need for efficient and safe navigation algorithms that also support Human-Robot Interaction and Collaboration. However, the integration of data from diverse sensors like cameras, LiDARs, and radars raises concerns about privacy and data security.In this paper, we introduce Res-FLNet, which harnesses the power of ResNet-50 and LSTM models to achieve robust and privacy-preserving autonomous driving. The ResNet-50 model effectively extracts features from visual input, while LSTM captures sequential dependencies in the multimodal data, enabling more sophisticated learning control algorithms. To tackle privacy issues, we employ Federated Learning, enabling model training to be conducted locally on individual robots without sharing raw data. By aggregating model updates from different robots, the central server learns from collective knowledge while preserving data privacy. Res-FLNet can also facilitate Human-Robot Interaction and Collaboration as it allows robots to share knowledge while preserving privacy.Our experiments demonstrate the efficacy and privacy preservation of Res-FLNet across four widely-used autonomous driving datasets: KITTI, Waymo Open Dataset, ApolloScape, and BDD100K. Res-FLNet outperforms state-of-the-art methods in terms of accuracy, robustness, and privacy preservation. Moreover, it exhibits promising adaptability and generalization across various autonomous driving scenarios, showcasing its potential for multi-modal sensing robots in complex and dynamic environments.
Examination of student opinions on the use of the my mind notes application created using QR code technology in nursing education in clinical practice

The aim of this study is to assess the effectiveness of using the “My Mind Notes” Application, which was developed by utilising QR code technology, in the clinical field of nursing education. This descriptive study was conducted with medical-surgical nursing students from a university located in Istanbul (n=78) between February and July 2021. The information that the student would need in clinical practice was included in the educational material of the QR code. QR codes were utilised throughout the 10-day clinical practice and students’ views were obtained at the end of the practice. The form for “Student Views on the Effectiveness of My Mind Notes Application” was used to collect the data. The data were analysed using descriptive statistical methods such as number, percentage, and mean. In their education lives, 78.2% of the students had never used a QR code application. The students’ mean scores of psycho-motor skills, cognitive/theoretical information, and anxiety reduction in clinical practice concerning my mind notes application were 7.76±1.74, 8.34±1.71, and 6.46±2.36, respectively. The “My Mind Notes” application, prepared by using QR code technology, will enhance nursing students’ learning experiences irrespective of time or space and foster them to become lifelong learners.
Privacy-preserving DeepFake face image detection

All the existing models for DeepFake detection focus on plaintext faces. However, outsourced computing is usually considered in practical applications for DeepFake detection and the input data may contain private and sensitive information. Thus, a privacy-preserving model named Secure DeepFake Detection Network (SecDFDNet) is proposed for the first time in this paper. The SecDFDNet uses the additive secret sharing method for secure DeepFake face detection. Specifically, firstly, some multi-party secure interaction protocols are designed for non-linear activation functions, i.e., SecReLU for ReLU function, SecSigm for sigmoid function, SecSpatial for spatial attention, and SecChannel for channel attention. Their security is proved in theory. Our protocols have low communication and space complexity. Then, the SecDFDNet model is proposed by using the designed secure protocols and trained plaintext DeepFake detection network (DFDNet). The experimental results show that the proposed SecDFDNet can detect DeepFake faces without revealing anything of private input, achieve the same accuracies as the plaintext DFDNet and outperform some existing models. The source code is available at https://github.com/imagecbj/Privacy-Preserving-DeepFake-Face-Image-Detection.
Modified maximum likelihood space registration method for shipborne multi-radar signal processing

Aiming at the spatial registration problem for networking of multi-platform shipborne radars, a modified maximum likelihood (MML) absolute registration algorithm based on singular value decomposition (SVD) is proposed. Firstly, the four major biases (radar detection biases, platform positioning biases, attitude biases, and baseline conversion biases) which affect shipborne radar detection are analyzed, and an equivalent model of shipborne radar detection biases is established to make the bias parameters more efficiently. Secondly, the maximum likelihood registration method is improved. According to the singular value decomposition, the conditional index is used to evaluate the observability in the maximum likelihood registration method in realtime, and the abnormal conditional index is corrected to effectively reduce the fluctuation of the bias estimation. Finally, the simulation results of two different scenarios are conducted, and the results show that the new method has significant improvements in estimation accuracy and algorithm stability compared with the existing algorithms.
Competence-based approach to development personnel potential of the higher educational institution

The article characterizes the influence of human resources on the efficiency of the functioning of a higher educational institution in the conditions of competition in the market of educational services. The content of the personnel potential of the university is substantiated as a set of personal qualities and professional competencies that determine the effectiveness of the professional activity of a university teacher; the structure of the professional competence of a university teacher is considered from the positions of various authors and the author’s model of the competence approach to the development of the personnel potential of the university is presented.
Impact of AI-Based Special Education on Educators and Students

Artificial intelligence has the ability to radically change how teachers interact with pupils who have special needs. Although the effects of AI and other technologies have been far-reaching in the private and public sectors, they have also had a major impact on the academic world, making learning easier for students with sensory, motor, cognitive, and other forms of impairments. The application of AI in the classroom has encouraged the development of more accessible and inclusive teaching practices for all pupils. This chapter analyzed how AI has influenced special education in the classroom. The study analyzed (a) AI's impact on SEN education and (b) AI's impact on SEN education through its positive effects on SEN educators. This chapter explores how AI has the potential to enhance the lives, careers, and working environments of educators. It also explores how special education supported by AI could personalize lessons for each student and encourage greater involvement on their behalf.
AI-Assisted Models for Dyslexia and Dysgraphia

Dyslexia and dysgraphia are two common learning disabilities that affect children's ability to read, write, and spell accurately. These disabilities can significantly impede a child's academic performance, leading to lack of self-confidence, anxiety, and frustration. Traditional approaches to address these disabilities often involve one-on-one sessions with a tutor or special education teacher, which can be time-consuming and expensive. Artificial intelligence (AI) language learning models have shown tremendous potential in assisting children with dyslexia and dysgraphia. These models can provide real-time feedback and personalized instruction to help children overcome learning difficulties. This chapter highlights the importance of addressing these challenges and proposes a solution that leverages AI language learning models to assist children with dyslexia and dysgraphia. By embracing AI language learning models, educators and parents can empower children with dyslexia and dysgraphia, providing them with the necessary tools and support to overcome their learning challenges.
Towards Data Storage, Scalability, and Availability in Blockchain Systems: A Bibliometric Analysis

In recent years, blockchain research has drawn attention from all across the world. It is a decentralized competence that is spread out and uncertain. Several nations and scholars have already successfully applied blockchain in numerous arenas. Blockchain is essential in delicate situations because it secures data and keeps it from being altered or forged. In addition, the market’s increased demand for data is driving demand for data scaling across all industries. Researchers from many nations have used blockchain in various sectors over time, thus bringing extreme focus to this newly escalating blockchain domain. Every research project begins with in-depth knowledge about the working domain, and new interest information about blockchain is quite scattered. This study analyzes academic literature on blockchain technology, emphasizing three key aspects: blockchain storage, scalability, and availability. These are critical areas within the broader field of blockchain technology. This study employs CiteSpace and VOSviewer to understand the current state of research in these areas comprehensively. These are bibliometric analysis tools commonly used in academic research to examine patterns and relationships within scientific literature. Thus, to visualize a way to store data with scalability and availability while keeping the security of the blockchain in sync, the required research has been performed on the storage, scalability, and availability of data in the blockchain environment. The ultimate goal is to contribute to developing secure and efficient data storage solutions within blockchain technology.
Activity Recognition Using Different Sensor Modalities and Deep Learning

In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories.
Blockchain Technology and Corporate Default Risk

Blockchain technology is a prominent technological innovation and firms increasingly engage in the initiatives. But blockchain technology is still in its early stage and a thorough understanding of its financial values remains elusive. By employing a large sample of public firms listed in China market, this study aims to estimate the impact of blockchain initiatives on firm default risk. Using an instrumental variable and Heckman two-stage, these results reveal a strong and negative association between blockchain initiatives and firm default risk by model. The authors further empirically demonstrate three underlying economic mechanisms. Specifically, they find that blockchain technology has the potential to enhance trust between firms and stakeholders in the supply chain, optimize business processes, and improve the information disclosure quality, which are beneficial to lower corporate financing cost or improve operation performance.
Fast Radius Outlier Filter Variant for Large Point Clouds

Currently, several devices (such as laser scanners, Kinect, time of flight cameras, medical imaging equipment (CT, MRI, intraoral scanners)), and technologies (e.g., photogrammetry) are capable of generating 3D point clouds. Each point cloud type has its unique structure or characteristics, but they have a common point: they may be loaded with errors. Before further data processing, these unwanted portions of the data must be removed with filtering and outlier detection. There are several algorithms for detecting outliers, but their performances decrease when the size of the point cloud increases. The industry has a high demand for efficient algorithms to deal with large point clouds. The most commonly used algorithm is the radius outlier filter (ROL or ROR), which has several improvements (e.g., statistical outlier removal, SOR). Unfortunately, this algorithm is also limited since it is slow on a large number of points. This paper introduces a novel algorithm, based on the idea of the ROL filter, that finds outliers in huge point clouds while its time complexity is not exponential. As a result of the linear complexity, the algorithm can handle extra large point clouds, and the effectiveness of this is demonstrated in several tests.