Reviewerly/exHarmony huggingface dataset:
Secure Health Information System with Blockchain Technology

This paper focuses on highlighting the problems that are associated with the absence of privacy and security of medical records in a healthcare system. It seeks to bridge the gap between the currently used security protocols in the management of health information, and encryption algorithms that should be used. Extant health information systems have always been developed with conventional databases. With all the privileges to read, write and execute assigned to the administrator, who has centralised control over all medical records, there is the likelihood of the misuse, distortion and loss of such records in the event that the administrator becomes compromised or inadvertent system failure. To solve this problem, the use of decentralised and distributed databases becomes paramount. Blockchain technology has recently received much attention due to its ability to permit a peer-to-peer network with distributed databases that can be stored locally on each node in the network. Subsequently, all updates on records in a database are communicated to all participating parties, hence addressing the problem of centralised control. In this paper, we propose a health information system on a blockchain to create a trust-free system for both health personnel and patients. From the results obtained, we achieved the decentralisation of the medical records’ database to enhance the security and privacy of data on the modeled peer-to-peer network.
Scrambling and quantum teleportation

Abstract Scrambling is a concept introduced from information loss problem arising in black hole. In this paper we discuss the effect of scrambling from a perspective of pure quantum information theory regardless of the information loss problem. We introduce 7-qubit quantum circuit for a quantum teleportation. It is shown that the teleportation can be perfect if a maximal scrambling unitary is used. From this fact we conjecture that “the quantity of scrambling is proportional to the fidelity of teleportation”. In order to confirm the conjecture, we introduce $$\theta $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>θ</mml:mi> </mml:math> -dependent partially scrambling unitary, which reduces to no scrambling and maximal scrambling at $$\theta = 0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>θ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>0</mml:mn> </mml:mrow> </mml:math> and $$\theta = \pi / 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>θ</mml:mi> <mml:mo>=</mml:mo> <mml:mi>π</mml:mi> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , respectively. Then, we compute the average fidelity analytically, and numerically by making use of qiskit (version 0.36.2) and 7-qubit real quantum computer ibm $$\_$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>_</mml:mi> </mml:math> oslo. Finally, we show that our conjecture can be true or false depending on the choice of qubits for Bell measurement.
A joint guidance and control framework for autonomous obstacle avoidance in quadrotor formations under model uncertainty

This paper presents a novel joint guidance and control framework for autonomous obstacle avoidance and swift maneuvering of quadrotor formations under model uncertainty. In this framework, the quadrotor formation uses a primary-secondary mode, where only the primary aircraft performs online planning of obstacle avoidance trajectory to form a safe flight corridor; the secondary aircraft follows the primary aircraft for safe flight through control algorithms. Firstly, based on a known and certain 3-DOF dynamics model, a successive convexification online guidance algorithm is proposed to compute a feasible and safe flight corridor through the nonconvex flight space. The planned trajectory contains velocity and attitude angle reference commands with temporal information, which can be used for the design of control algorithms for the 6-DOF dynamics mode. Subsequently, a global controller based on robust adaptive control of the RBF neural network is proposed for compensating external disturbances and model uncertainties. Finally, simulation experiments verify the effectiveness and superiority of this guidance and control framework for quadrotor formation autonomous obstacle avoidance applications under model uncertainty. This paper provides open-source ANSI-C code, which can be deployed directly to the embedded computer to carry out experimental verification. The code link is https://github.com/mazhenwei160/Uav-formation-obstacle-avoidance.git.
Assistive Data Glove for Isolated Static Postures Recognition in American Sign Language Using Neural Network

Sign language recognition is one of the most challenging tasks of today’s era. Most of the researchers working in this domain have focused on different types of implementations for sign recognition. These implementations require the development of smart prototypes for capturing and classifying sign gestures. Keeping in mind the aspects of prototype design, sensor-based, vision-based, and hybrid approach-based prototypes have been designed. The authors in this paper have designed sensor-based assistive gloves to capture signs for the alphabet and digits. These signs are a small but important fraction of the ASL dictionary since they play an essential role in fingerspelling, which is a universal signed linguistic strategy for expressing personal names, technical terms, gaps in the lexicon, and emphasis. A scaled conjugate gradient-based back propagation algorithm is used to train a fully-connected neural network on a self-collected dataset of isolated static postures of digits, alphabetic, and alphanumeric characters. The authors also analyzed the impact of activation functions on the performance of neural networks. Successful implementation of the recognition network produced promising results for this small dataset of static gestures of digits, alphabetic, and alphanumeric characters.
ProMetaS – A Metadata Schema for Process Engineering and Industry

Abstract Data collection in process industry yields a variety of data types. To maintain the knowledge about the data and to enable finding and reusing them (FAIR data), a common description with metadata is necessary. In the project KEEN, ProMetaS – the Process Engineering/Industry Metadata Schema – was developed. It defines various metadata categories adhering to available metadata standards. In this article, ProMetaS, its implementation into the KEEN data platform, and its application in two use cases is described.
A compositional analysis of systemic risk in European financial institutions

Abstract Systemic risk is a complex and multifaceted phenomenon that needs to be addressed from different perspectives. In this work we propose a Compositional Data (CoDa) approach to analyze the distribution of relative contributions to systemic risk associated with major European countries during the period 2008–2021. We represent systemic risk measures corresponding to those countries as percentage shares, or parts, of a compositional dataset and we perform a multivariate statistical analysis using specific CoDa procedures. The proposed approach sheds new light on some variability patterns and cross-country relationships that appear to be linked to the composition of systemic risk parts in the system.
Pointwise norm-based clustering of data in arbitrary dimensional space

In this paper, a novel nonparametric norm-based clustering algorithm is proposed to classify real-valued continuous data sets given in arbitrary dimensional space. For data univariate, multivariate or high-dimensional, with the number of study variables close to or larger than the data size, our straightforward algorithm is implemented throughout under an univariate set-up, where we make use of the observation-wise (or pointwise) norms which quantify the distances of the observations from the origin zero or the null vector. The method begins with determination of the sample quantile using nonparamteric bootstrapping on the computed norms and always converges independently. By its design, the suggested algorithm is fast enough to detect the number of existing clusters itself and to form well-defined groups. Data study demonstrates its competitiveness in comparison to 2 popular clustering algorithms K-means and K-medoids.
A Novel End-to-End Turkish Text-to-Speech (TTS) System via Deep Learning

Text-to-Speech (TTS) systems have made strides but creating natural-sounding human voices remains challenging. Existing methods rely on noncomprehensive models with only one-layer nonlinear transformations, which are less effective for processing complex data such as speech, images, and video. To overcome this, deep learning (DL)-based solutions have been proposed for TTS but require a large amount of training data. Unfortunately, there is no available corpus for Turkish TTS, unlike English, which has ample resources. To address this, our study focused on developing a Turkish speech synthesis system using a DL approach. We obtained a large corpus from a male speaker and proposed a Tacotron 2 + HiFi-GAN structure for the TTS system. Real users rated the quality of synthesized speech as 4.49 using Mean Opinion Score (MOS). Additionally, MOS-Listening Quality Objective evaluated the speech quality objectively, obtaining a score of 4.32. The speech waveform inference time was determined by a real-time factor, with 1 s of speech data synthesized in 0.92 s. To the best of our knowledge, these findings represent the first documented deep learning and HiFi-GAN-based TTS system for Turkish TTS.
Cross-Domain Access Control Model in Industrial IoT Environment

The Industrial Internet of Things (IIoT) accelerates smart manufacturing and boosts production efficiency through heterogeneous industrial equipment, intelligent sensors, and actuators. The Industrial Internet of Things is transforming from a traditional factory model to a new manufacturing mode, which allows cross-domain data-sharing among multiple system departments to enable smart manufacturing. A complete industrial product comes from the combined efforts of many different departments. Therefore, secure and reliable cross-domain access control has become the key to ensuring the security of cross-domain communication and resource-sharing. Traditional centralized access control schemes are prone to single-point failure problems. Recently, many researchers have integrated blockchain technology into access control models. However, most blockchain-based approaches use a single-chain structure, which has weak data management capability and scalability, while ensuring system security, and low access control efficiency, making it difficult to meet the needs of multi-domain cooperation in IIoT scenarios. Therefore, this paper proposes a decentralized cross-domain access model based on a master–slave chain with high scalability. Moreover, the model ensures the security and reliability of the master chain through a reputation-based node selection mechanism. Access control efficiency is improved by a grouping strategy retrieval method in the access control process. The experimental benchmarks of the proposed scheme use various performance metrics to highlight its applicability in the IIoT environment. The results show an 82% improvement in the throughput for the master–slave chain structure over the single-chain structure. There is also an improvement in the throughput and latency compared to the results of other studies.
Hand gesture recognition with focus on leap motion: An overview, real world challenges and future directions

In the recent years, a steady growth of Hand Gesture Recognition (HGR) based applications has been observed. Thus, significant progress has been made in the field of hand detection and tracking. However, hand poses remain particularly challenging and can deeply affect the results due to several factors especially congenital hand problems. Besides, HGR applications performance is very dependent on the sensor used for gesture acquisition. Owing to its high performance, the Leap Motion Controller (LMC), has recently become a by default sensor for HGR systems and provide high performance in terms of recognition accuracy. This work proposes a comprehensive literature review of HGR based on different modalities with a focus on the LMC based systems. This survey retrieves the main challenges faced in literature with an investigation of the state of the art solutions to address them. Moreover, we discuss numerous existing methods for static and dynamic HGR from different modalities. We reveal remaining challenges in the literature and highlight promising future research directions.
A Study on the Interoperability Technology of Digital Identification Based on WACI Protocol with Multiparty Distributed Signature

In digital identity authentication, credentials are typically stored in a digital wallet and authenticated through a single key-based signature and public key verification. However, ensuring compatibility between systems and credentials can be challenging and the existing architecture can create a single point of failure, which can hinder system stability and prevent data interchange. To address this problem, we propose a multiparty distributed signature structure using FROST, a Schnorr signature-based threshold signature algorithm, applied to the WACI protocol framework for credential interaction. This approach eliminates a single point of failure and secures the signer's anonymity. Additionally, by following standard interoperability protocol procedures, we can ensure interoperability during the exchange of digital wallets and credentials. This paper presents a method that combines a multiparty distributed signature algorithm and an interoperability protocol, and discusses the implementation results.
Multistrategy-Boosted Carnivorous Plant Algorithm: Performance Analysis and Application in Engineering Designs

Many pivotal and knotty engineering problems in practical applications boil down to optimization problems, which are difficult to resolve using traditional mathematical optimization methods. Metaheuristics are efficient algorithms for solving complex optimization problems while keeping computational costs reasonable. The carnivorous plant algorithm (CPA) is a newly proposed metaheuristic algorithm, inspired by its foraging strategies of attraction, capture, digestion, and reproduction. However, the CPA is not without its shortcomings. In this paper, an enhanced multistrategy carnivorous plant algorithm called the UCDCPA is developed. In the proposed framework, a good point set, Cauchy mutation, and differential evolution are introduced to increase the algorithm's calculation precision and convergence speed as well as heighten the diversity of the population and avoid becoming trapped in local optima. The superiority and practicability of the UCDCPA are illustrated by comparing its experimental results with several algorithms against the CEC2014 and CEC2017 benchmark functions, and five engineering designs. Additionally, the results of the experiment are analyzed again from a statistical point of view using the Friedman and Wilcoxon rank-sum tests. The findings show that these introduced strategies provide some improvements in the performance of the CPA, and the accuracy and stability of the optimization results provided by the proposed UCDCPA are competitive against all algorithms. To conclude, the proposed UCDCPA offers a good alternative to solving optimization issues.
Multi-Hop Knowledge Graph Question Answer Method Based on Relation Knowledge Enhancement

Multi-hop knowledge graph question answer (KGQA) is a challenging task because it requires reasoning over multiple edges of the knowledge graph (KG) to arrive at the right answer. However, KGs are often incomplete with many missing links, posing additional challenges for multi-hop KGQA. Recent research on multi-hop KGQA attempted to deal with KG sparsity with relevant external texts. In our work, we propose a multi-hop KGQA model based on relation knowledge enhancement (RKE-KGQA), which fuses both label and text relations through global attention for relation knowledge augmentation. It is well known that the relation between entities can be represented by labels in the knowledge graph or texts in the text corpus, and multi-hop KGQA needs to jump across different entities through relations. First, we assign an activation probability to each entity, then calculate a score for the enhancement relation, and then transfer the score through the activated relations and, finally, obtain the answer. We carry out extensive experiments on three datasets and demonstrate that RKE-KGQA achieves the outperformance result.
Research on a personalized learning path recommendation system based on cognitive graph with a cognitive graph

The necessity for students to clarify their own cognitive structure and the amount of their knowledge mastery for self-reflection is often ignored in building the student model in the adaptive model, which makes the construction of the cognitive structure pointless. Simultaneously, knowledge forgetting causes students’ knowledge level to fall short of what is required to enable the subsequent learning stage. The students’ cognitive map was displayed using cutting-edge cognitive graph technology to address the aforementioned problems, and the reasoning rules were created using the Dual Process Theory and Meaningful Learning Theory. The degree of memory retention in students was calculated using the universal forgetting curve proposed by Ebbinghaus. To improve semantic information, multimodal data were coupled with knowledge graph. Finally, a cognitive graph integrated into the learning path recommendation system was developed. Comparative tests were conducted to verify the system’s efficacy. The experimental group outperformed the control group with an average score of 77.286. The results of the experiment show that the experimental group using the CGLPRS has significantly higher levels of knowledge use and mastery than the control group, showing that the use of cognitive graph and accounting for forgetting can significantly improve conceptual understanding and overall learning satisfaction.
Blockchain for the supply chain of the Italian craft beer sector: tracking and discount coupons

In the Italian craft beer market, many breweries and pubs propose a large number of products to beerlovers. In this highly fragmented market, it is hard for the beerlovers to assess the quality of a beer and it is hard for breweries and pubs to inform the beerlover of the quality of their offer. Supply chain tracking is an interesting tool to improve transparency of food markets. However, standard tracking approaches are challenging in highly fragmented contexts for several reasons. In this paper, we show how it is possible to realize a blockchain-based supply chain tracking tailored for the highly fragmented sector of the Italian craft beer. We collaborated with one of the players of that market to analyse the specific problems of that context and we report the results. We show a design that addresses those problems and might be generalized to support tracking in other highly fragmented sectors. We estimate costs and that turn out to be affordable for that sector. Finally, we discuss the adoption of the same blockchain infrastructure to provide a related blockchain-based discount coupon systems, we analyse the advantages of this approach, and we discuss some design aspects to deal with privacy regulation.
Cloud Computing Based Workload Optimization using Long Short Term Memory Algorithm

Great guide control may be extremely important within the cloud, and responsibility expectation is critical for achieving the right guide control. While it is far more plausible to anticipate the jobs of long-running liabilities based entirely on the irregularity of their notable jobs, it is far more difficult to accomplish this for obligations that do not have such ongoing responsibility designs. We examine an excellent response for undertaking responsibility forecasting in this paper. Rather than using the notable responsibility of one Endeavor to predict the predetermination responsibility of another, we can use information to predict the latest job. A reasonable responsibility for calculating supplier serves as the heart of this hybrid distributed computing model. In this cloud platforms work forecasting is the key to successful resource management. Finding work in initial tasks for work in focus on earlier workload is difficult; however, identifying work for activities observed that, without a workload pattern is much more difficult. This paper proposes a novel hybrid forecasting methodology using Long Short Term Memory (LSTM) along with CNN algorithm to predict the workload evaluation. This hybrid cloud estimation model is built on an intelligent workload factoring service that is calculated for proactive workload management.
Evaluating Performance Metrics for Credit Card Fraud Classification

Practitioners and researchers of machine learning should have a deep understanding about the selection of the right performance metrics for classifier evaluation. Using a credit card fraud dataset, we demonstrate that the Area Under the Precision-Recall Curve (AUPRC) metric is a more reliable measurement, for the classification of highly imbalanced data, than the Area Under the Receiver Operating Characteristic Curve (AUC) metric. Furthermore, we establish that AUC is minimally impacted by the use of Random Undersampling (RUS). The classifiers used in this study are ensemble learners: LightGBM, CatBoost, Extremely Randomized Trees (ET), XGBoost, and Random Forest. Our results are governed by the fact that in a highly imbalanced dataset, the comparatively large number of true negative instances has an influence on AUC but not on AUPRC. Hence, AUPRC is able to accurately detect changes in the number of false positives because it ignores the true negatives.
BiGNN: A Bilateral-Branch Graph Neural Network to Solve Popularity Bias in Recommendation

Traditional recommendation methods aim to recom-mend personalized items by analyzing user's history interaction data. They ignore the fact that the data follows a long-tail distribution, which means that a small number of popular items account for most of the interaction records. This phenomenon causes the model to recommend more popular items, resulting in a severe popularity bias. In order to pay more attention to the long-tail items and debias the popular bias, we propose a Bilateral-Branch Graph Neural Network(BiGNN). In the long- tail branch, we construct a separate long-tail sub graph by eliminating the popular items with high degree. When the Graph Neural Network(GNN) aggregates information layer by layer in the subgraph, the receptive field of the single hop becomes larger, which increases the exposure of the long-tail items. Besides, another branch takes the original interaction graph as input to learn the general data distribution and generate the global embeddings of users and items. The two branches use the same GNN structure and share parameters. We employ the point-wise mutual information (PMI) strategy to indicate interaction between users and reconstruct the long-tail sub graph. The two branches are aggregated through an accumulated learning module, which makes the model first learn the conventional patterns and then pay attention to the long-tail data gradually. Extensive experiments on three real-world datasets show that BiGNN evidently outperforms the state-of- the-art methods consistently.