Reviewerly/exHarmony huggingface dataset:
Leveraging knowledge graph for domain-specific Chinese named entity recognition via lexicon-based relational graph transformer

Leveraging knowledge graphs (KGs) has been an emerging direction to improve the performance of deep learning-based Chinese named entity recognition (CNER). Nevertheless, most existing methods directly inject correlated words into sentences but ignore word boundaries that are crucial for CNER. Conflicts among incorrect word segmentations may misguide models to predict incorrect labels. To solve this problem, this work investigates a novel lexicon-based relational graph transformer (LRGT), which combines relational graph-structured inputs and transformer tailored for lexicon-augmented CNER. In LRGT, characters and self-matched lexicon words are fully interacted through a two-phase relational graph softmax message passing mechanism. The finally enhanced character representation in LRGT dynamically integrates both lexical and relative positional information, which is distinguishable for the identification. Results on four benchmark datasets demonstrate that LRGT significantly outperforms several state-of-the-art methods. We further demonstrate that LRGT with KG achieves higher performance on two public specific-domain CNER datasets. LRGT performs up to 3.35 times faster than several typical baselines while achieving better F1-score by up to 1.92% and 2.24%, respectively.
Two-qubit quantum nonlocality dynamics induced by interacting of two coupled superconducting flux qubits with a resonator under intrinsic decoherence

This paper explores the dynamics of two spatially separated superconducting flux qubits, initially in a maximally correlated state, without mutual interaction. The system is modeled using the master intrinsic decoherence equation with the flux-qubits-resonator interaction Hamiltonian. The effects of initial state, detuning, and decoherence on the generated oscillatory correlation dynamics and the sudden death-birth phenomenon of the two flux-qubits entanglement are examined. The results demonstrate that qubit-resonator-qubit interactions have a high ability to generate quantum interferometric power and local quantum uncertainty correlations beyond that of concurrence entanglement.
Early Detection of Breast Cancer using Machine Learning

Globally, Breast cancer is one of the most common types of cancer in women. Additional research is required to address the challenges and limitations of traditional detection process, as well as to create standardized processes for data collection and analysis of breast cancer by utilizing mammograms and other medical imaging data. Several research has been undertaken in recent years to create and assess ML models for breast cancer diagnosis, recurrence prediction, and therapy planning. Machine learning algorithms may also be trained to recognize small changes in breast tissue that may be symptomatic of cancer, even before a tumor can be seen on a mammogram, as done traditionally. The application of ML approaches in breast cancer diagnosis has resulted in higher accuracy and specificity, making it a suitable tool for assisting clinicians in clinical decision-making. Overall, the findings indicate that more study may be required to address the problems and limitations of ML in breast cancer diagnosis, as well as to create standardized processes for data collection and analysis.
Autonomous Patrol Technology and System on Leapfrog-Type Charging UAV-Automatic Positioning of UAV Based on GPS/RTK

In order to solve the problem of endurance in the process of unmanned aerial vehicle (UAV) line patrol, an autonomous UAV line patrol system based on leapfrog charging is designed and implemented. The key to achieving the goal of autonomous line patrol for UAVs is autonomous positioning technology and automatic charging control technology. This paper focuses on the research of autonomous positioning technology for UAVs based on GPS/RTK. First of all, the system structure and operating principle of leapfrog-type charging UAV is proposed, and the combined GPS/RTK navigation control module and automatic identification module are developed. Then, in order to ensure that the UAVs can precise positioning, accurate calibration of cameras and targets are studied. Finally, this paper conducts experimental simulations in the three main links of attitude solving, acceleration compensation and delay compensation in line patrol technology. The experimental results show that the attitude solving algorithm can accurately estimate the flight attitude of the UAVs in flight, and can compensate for the motion acceleration in a timely and fast manner. The delay compensation algorithm can effectively solve the problem of positioning sensor delay. When the barometer output altitude data is unhealthy, the system can identify the health of the data and automatically fuse the altitude data output by GPS. Therefore, the methods and research conclusions proposed in this paper have important engineering guiding significance for the autonomous line patrol of UAVs.
Based-on Computer Vision Applications for Bus Stop Passenger Detection System

Passenger transport is one of the most common ways of commuting in Taiwan. It plays an important role in the transportation system due to its large number of stations, dense frequency, and cheap transportation. Due to the unfriendly transportation environment and a large number of passengers, a blind spot of passenger transportation exists, which leads to traffic accidents at the station. We research to make the "Bus Stop Passenger Detection System". Taking the object detection of "Wheelchairs" into consideration, it is more convenient to assist the disabled to find the passenger transportation system, which makes Taiwan's transportation system more convenient.
Riesz-Quincunx-UNet Variational Autoencoder for Unsupervised Satellite Image Denoising

Multiresolution deep learning approaches, such as the U-Net architecture, have achieved high performance in classifying and segmenting images. However, these approaches do not provide a latent image representation and cannot be used to decompose, denoise, and reconstruct image data. The U-Net and other convolutional neural network (CNNs) architectures commonly use pooling to enlarge the receptive field, which usually results in irreversible information loss. This study proposes to include a Riesz-Quincunx (RQ) wavelet transform, which combines 1) higher-order Riesz wavelet transform and 2) orthogonal Quincunx wavelets (which have both been used to reduce blur in medical images) inside the U-net architecture, to reduce noise in satellite images and their time-series. In the transformed feature space, we propose a variational approach to understand how random perturbations of the features affect the image to further reduce noise. Combining both approaches, we introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We present qualitative and quantitative experimental results that demonstrate that our proposed RQUNet-VAE was more effective at reducing noise in satellite imagery compared to other state-of-the-art methods. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.
The Foundational Ontology ThingFO: Architectural Aspects, Concepts, and Applicability

This work discusses the concepts, relationships, and constraints of the foundational ontology called ThingFO. Its concepts represent key aspects of the perceived or conceived world, including particular things (classes and individuals/instances), universal things (classes of classes), and assertions (expression classes) about both kinds of things and relations. Furthermore, ThingFO is placed at the top (foundational) level in the framework of a five-tier ontological architecture. Types of ontologies are placed in this architecture according to the level of generality/specificity. Therefore, depending on the goal and scope of a developed ontology, it can be placed in the architecture at the foundational, core, top-domain, low-domain, or instance layer. ThingFO is the single ontology at the top level, so ontologies at lower levels must reuse and specialize its concepts and relationships to ensure harmonization and interoperability. To show the applicability of ThingFO, this paper analyzes enriched concepts and specialized relationships for two core ontologies, particularly, for situation and process ontologies, where their concepts are in turn reused and extended by domain ontologies.
Usability of curved keyboard design on the large smartphone: An empirical study

The curved design is ubiquitous, with a vast user base due to its similarity with in shape to human physiological structure. The curved QWERTY keyboard layout was proposed for one-handed usage on smartphones with ambiguous effects. This study evaluated whether the curved QWERTY could optimize the user experience and input performance on large smartphones better than the traditional straight QWERTY layout. Eight measurements were used to evaluate the usability of each design, six suggesting curved QWERTY failed to achieve outstanding typing performance or subjective user experience, while the other two indicators showed that curved QWERTY had advantages in touch dispersion and touching offset, indicating the possible higher usability it could reach. The results also investigated the potential application of curved designs and provided insights into the optimization methods.
A Participatory Design Approach to Develop a VR-Based Electrocardiogram Training Simulator

Virtual Reality (VR) allows complete immersion in virtual environments. VR is used in many applications including training healthcare professionals in a time and cost effective manner. Effectively incorporating clinical feedback into simulation design to ensure usability of the software is a challenge. In this paper, a discussion of a pipeline which was developed to allow for efficient participatory design for the creation of a VR-based electrocardiogram training simulator. Further, the results from the usability, content validity, and face validity which was conducted to assess the ECG simulator is presented. The results indicate that the proposed approach can be used to create and assess other VR-based medical training simulators.
AI Unreliable Answers: A Case Study on ChatGPT

ChatGPT is a general domain chatbot which is object of great attention stimulating all the world discussions on the power and the consequences of the Artificial Intelligence diffusion in all the field, ranging from education, research, music to software development, health care, cultural heritage, and entertainment. In this paper, we try to investigate whether and when the answers provided by ChatGPT are unreliable and how this is perceived by expert users, such as Computer Science students. To this aim, we first analyze the reliability of the answers provided by ChatGPT by experimenting its narrative, problem solving, searching, and logic capabilities and report examples of answers. Then, we conducted a user study in which 15 participants that already knew the chatbot proposed a set of predetermined queries generating both correct and incorrect answers and then we collected their satisfaction. Results revealed that even if the present version of ChatGPT sometimes is unreliable, people still plan to use it. Thus, it is recommended to use the present version of ChatGPT always with the support of human verification and interpretation.
An Interactive Voicebot for Visually Challenged: Empowering the Realm Among Deaf-Mute and Normal Community

Everybody deserves to live independently, especially those who are disabled. In the last decades, technology has given attention to the disabled to make them control their life as much as possible. There is an immense need to build a system that will help the visually impaired to let them know what is around them, to whom they are interacting, and lead life without the help of others. So, they are at a disadvantage as they need manual aid to urge information about their surrounding environment. In this paper, an interactive voice agent is designed which will provide information about the persons in the surrounding via voice. The agent identifies the persons with their face and provides output as the name of the person or stranger as a voice message to the blind people, agent also provides a description of the person in the surroundings if queried by the blind people. The information such as age, gender, and emotion of the person will be given as a voice message to blind people. Due to the pandemic, the people are wearing clinical masks which makes the recognition process difficult as half of the face is covered, addressed by the system. Proposed system provides efficient communication between deaf-mutes and visually impaired and a novel feature extraction for face recognition based on the local binary patterns and histogram of orientations. The interactive voice agent is implemented in Python and tested in various lighting conditions.
Towards a Taxonomy for Reversible Computation Approaches

Reversible computation is a paradigm allowing computation to proceed not only in the usual, forward direction, but also backwards. Reversible computation has been studied in a variety of models, including sequential and concurrent programming languages, automata, process calculi, Turing machines, circuits, Petri nets, event structures, term rewriting, quantum computing, and others. Also, it has found applications in areas as different as low-power computing, debugging, simulation, robotics, database design, and biochemical modeling. Thus, while the broad idea of reversible computation is the same in all the areas, it has been interpreted and adapted to fit the various settings. The existing notions of reversible computation however have never been compared and categorized in detail. This work aims at being a first stepping stone towards a taxonomy of the approaches that co-exist under the term reversible computation. We hope that such a work will shed light on the relation among the various approaches.
AI-based sentiment analysis approaches for large-scale data domains of public and security interests

Organizational service learn-leadership design for adapting and predicting machine learning-based sentiments of sociotechnical systems is being addressed in segmenting textual-producing agents in classes. In the past, there have been numerous demonstrations in different language models (LMs) and (naıve) Bayesian Networks (BN) that can classify textual knowledge origin for different classes based on decisive binary trees from the future prediction aspect of how public text collection and processing can be approached, converging the root causes of events. An example is how communication influence and affect the end-user. Within service providers and industry, the progress of processing communication relies on formal clinical and informal non-practices. The LM is based on handcrafted division on machine learning (ML) approaches representing the subset of AI and can be used as an orthogonal policy-as-a-target leadership tool in customer or political discussions. The classifiers which use the numeric representation of textual information are classified in a Neural Network (NN) by characterizing, for instance, the communication using cross-sectional analysis methods. The textual form of reality collected in the databases has significant processable value-adding opportunities in different management and leadership, education, and climate control sectors. The data can be used cautiously for establishing and maintaining new and current business operations and innovations. There is currently a lack of understanding of how to use most NN and DN methods. The operations and innovations management and leadership support the flow of communication for effectiveness and quality.
CALOSYS — A Robust Blockchain-based Marketing Loan Ecosystem for Small Businesses

Entrepreneurship is on the rise due to technological advancements, greater access to funding, and a growing emphasis on innovation and self-employment. However, attracting customers in a highly competitive market remains a significant challenge for many small and medium-sized enterprises (SMEs). While seeking loans for marketing operations may seem like a viable solution, there is no guarantee for success due to various challenges, such as audience reception, market competition, and shifting trends. To address these issues, we propose a groundbreaking system called “Calosys,” a blockchain-based P2P lending platform that employs augmented reality (AR) and smart contracts to create a token-based loan system that caters to entrepreneurs' marketing needs. Our system employs innovative lending processes, utilizing smart contracts that ensure secure transactions while allocating loan money for small business marketing activities. By utilizing augmented reality (AR), Calosys enhances customer engagement, boosting success rates and benefits for all stakeholders involved.
Cryptanalysis of Full-Round Magpie Block Cipher

Magpie is a lightweight block cipher proposed by Li et al. in Acta Electronica Sinica volumn 45, issue 10. It adopts an substitution-permutation network (SPN) structure with a block size of 64 bits and the key size of 96 bits, respectively. To achieve the consistency of the encryption and decryption, which is both hardware and software friendly, 16 bits of the key are used as control signals to select S-boxes and another 16 bits of the key are used to determine the order of the operations. As the designers claimed, the security might be improved as different keys generate different ciphers. This paper analyzes the security of Magpie, studies the difference propagation of Magpie, and finally finds that the cipher has a set of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">80</sup> weak keys which makes the full-round encryption weak, and corrects the lower bound of the number of active S-boxes to 10 instead of 25 proposed by the designers. In the weak key model, the security of the cipher is reduced by the claimed 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">80</sup> to only <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\times 2^{16}$</tex> .
Understanding the Time to First Response in GitHub Pull Requests

The pull-based development is widely adopted in modern open-source software (OSS) projects, where developers propose changes to the codebase by submitting a pull request (PR). However, due to many reasons, PRs in OSS projects frequently experience delays across their lifespan, including prolonged waiting times for the first response. Such delays may significantly impact the efficiency and productivity of the development process, as well as the retention of new contributors as long-term contributors.In this paper, we conduct an exploratory study on the time-to-first-response for PRs by analyzing 111,094 closed PRs from ten popular OSS projects on GitHub. We find that bots frequently generate the first response in a PR, and significant differences exist in the timing of bot-generated versus human-generated first responses. We then perform an empirical study to examine the characteristics of bot- and human-generated first responses, including their relationship with the PR’s lifetime. Our results suggest that the presence of bots is an important factor contributing to the time-to-first-response in the pull-based development paradigm, and hence should be separately analyzed from human responses. We also report the characteristics of PRs that are more likely to experience long waiting for the first human-generated response. Our findings have practical implications for newcomers to understand the factors contributing to delays in their PRs.
Feel the Music!—Audience Experiences of Audio–Tactile Feedback in a Novel Virtual Reality Volumetric Music Video

The creation of imaginary worlds has been the focus of philosophical discourse and artistic practice for millennia. Humans have long evolved to use media and imagination to express their inner worlds outwardly via artistic practice. As a fundamental factor of fantasy world-building, the imagination can produce novel objects, virtual sensations, and unique stories related to previously unlived experiences. The expression of the imagination often takes a narrative form that applies some medium to facilitate communication, for example, books, statues, music, or paintings. These virtual realities are expressed and communicated via multiple multimedia immersive technologies, stimulating modern audiences via their combined Aristotelian senses. Incorporating interactive graphic, auditory, and haptic narrative elements in extended reality (XR) permits artists to express their imaginative intentions with visceral accuracy. However, these technologies are constantly in flux, and the precise role of multimodality has yet to be fully explored. Thus, this contribution to Feeling the Future—Haptic Audio explores the potential of novel multimodal technology to communicate artistic expression via an immersive virtual reality (VR) volumetric music video. We compare user experiences of our affordable volumetric video (VV) production to more expensive commercial VR music videos. Our research also inspects audio–tactile interactions in the auditory experience of immersive music videos, where both auditory and haptic channels receive vibrations during the imaginative virtual performance. This multimodal interaction is then analyzed from the audience’s perspective to capture the user’s experiences and examine the impact of this form of haptic feedback in practice via applied human–computer interaction (HCI) evaluation practices. Our results demonstrate the application of haptics in contemporary music consumption practices, discussing how they affect audience experiences regarding functionality, usability, and the perceived quality of a musical performance.
Towards Efficient and Trustworthy Pandemic Diagnosis in Smart Cities: A Blockchain-Based Federated Learning Approach

In the aftermath of the COVID-19 pandemic, the need for efficient and reliable disease diagnosis in smart cities has become increasingly serious. In this study, we introduce a novel blockchain-based federated learning framework tailored specifically for the diagnosis of pandemic diseases in smart cities, called BFLPD, with a focus on COVID-19 as a case study. The proposed BFLPD takes advantage of the decentralized nature of blockchain technology to design collaborative intelligence for automated diagnosis without violating trustworthiness metrics, such as privacy, security, and data sharing, which are encountered in healthcare systems of smart cities. Cheon–Kim–Kim–Song (CKKS) encryption is intelligently redesigned in BFLPD to ensure the secure sharing of learning updates during the training process. The proposed BFLPD presents a decentralized secure aggregation method that safeguards the integrity of the global model against adversarial attacks, thereby improving the overall efficiency and trustworthiness of our system. Extensive experiments and evaluations using a case study of COVID-19 ultrasound data demonstrate that BFLPD can reliably improve diagnostic accuracy while preserving data privacy, making it a promising tool with which smart cities can enhance their pandemic disease diagnosis capabilities.