Reviewerly/exHarmony huggingface dataset:
Research on the development and application of CNN model in mobile payment terminal and blockchain economy

As the most widely used payment method at this stage, mobile payment is more and more closely related to the blockchain economy. Traditional methods lack a certain degree of accuracy. This research proposes a feature-based and sequential-based Bilateral AM (BAM) and Convolutional Neural Network (CNN)-gated recurrent unit for the development and application of mobile payment and blockchain economy (Gated Recurrent Unit, GRU) hybrid model (BAM-CNN-GRU), select 5 feature parameters with high correlation with the blockchain for multivariate prediction. The introduction of BAM can automatically quantify the correlation between the input variables and the blockchain, and strengthen the expression of historical key information on the predicted output; the introduction of CNN can extract high-dimensional features that reflect the non-stationary dynamic changes of the blockchain. The proposed hybrid model achieves good results in both single-step and multi-step long-term series and multivariate input blockchain prediction. Compared with the other six methods, MAE is reduced by 75.45%, 64.74%, 62.84%, respectively. 59.41%, 45.54%, 44.16%. Compared with the BAM-GRU model, the CNN-GRU model, the GRU model, the LSTM model, the support vector machine SVM model and the BP model, the prediction accuracy of the hybrid model has been greatly improved, and it has a broader application prospect.
Calculation and analysis of the impact of Microsoft security-assisted physical education model on college basketball teaching in the Internet information era

With the rapid development of information technology in China in recent years, the network-sharing function in social software such as "WeChat" has begun to serve as a safety aid for educational work and promote the emergence of a new teaching model, so this paper proposes a new idea of basketball teaching based on the WeChat safety-aided sports model. Firstly, under the method of research, the importance of the practical part of the teaching content of the basketball specialization course is analyzed by principal component analysis; then the suitability of basketball teaching theory under WeChat safety-assisted is analyzed according to the correlation reliability test, measurement ANOVA and post hoc test of score rate; based on Kinect sensing technology, a basketball teaching model based on Kinect motion capture and joint construction is constructed; finally Obtain weights according to different weight calculation methods and construct a basketball teaching quality evaluation system based on fuzzy synthesis. The results showed that the experimental class achieved 3.16±1.609 (pcs) and 82.38±8.450 (points) in the 4-meter fixed distance passing into the zone; 35.18±9.167 (sec) and 84.72±10.798 (points) in the half-court dribbling and folding marching layup; 4.8-meter fixed distance one-handed over-the-shoulder basketball in the experimental class. The experimental class achieved the standard score of 3.63±2.282 (one) and the technical evaluation score of 82.28±7.857 (points) for the 4.8m fixed distance one-handed over-the-shoulder shot.
The Optimization Path of Higher Education Resource Allocation in China Based on Fuzzy Set Theory

The use of fuzzy set theory to adjust the educational resource allocation model is the optimal path to achieve the allocation of educational resources. The optimal path realized by fuzzy set theory promotes the healthy development of education and addresses the current important and urgent tasks in the field of higher education in China. This study utilizes the basic theory of fuzzy sets and analyzes dynamic fuzzy set theory, fuzzy relations and fuzzy matrices. The indexes of optimal allocation are selected according to the classification of higher education resources, the fuzzy set multi-objective planning model is designed, and finally, the combination of factors of education production in colleges and universities is optimized. The experiment proves that: the average allocation efficiency of excellent colleges and universities is 0.186, and the average improvement is 35.41% after optimization. The average allocation efficiency of ordinary colleges and universities is 0.174, which is improved by 22.12% on average after optimization. It can be found that the resource allocation efficiency of excellent colleges and universities is generally higher than that of ordinary colleges and universities, and the role of excellent colleges and universities with more abundant resources themselves is greater after the optimization of college resource allocation. It indicates that by adjusting the quantity and structure of educational resources according to the optimization path, the optimization of educational resource allocation can be achieved. This verifies that: the emergence of optimal paths for solving resource allocation realized by fuzzy set theory has greatly expanded the application of fuzzy methods in the field of higher education and provided a new theoretical tool for solving resource allocation.
Application of Bibliometric Analysis in the Study of Climate Change and Sustainable Development Practices

Climate change is a global issue and it needs the attention of everyone for which the United Nations has covered this under Sustainable Development Goals (SDGs13). In this context, this paper attempts to make a bibliometric analysis to get an overview of research works conducted in the field of climate change and sustainable development practices. A total of 889 articles from the Dimension database have been considered for the study and they have been analysed with the help of Biblioshiny and VOS viewer software. Various bibliometric components like thematic evolution, keyword co-occurrence map, top authors etc. have been discussed in the study. We found that the concerned field is an important research area as the number of documents is showing an increasing trend. So, more studies are expected in future. This paper will help future researchers in choosing the right area of work in the concerned field.
Pedestrian Avoidance Method Considering Passenger Comfort for Autonomous Personal Mobility Vehicles

During the autonomous navigation of personal mobility vehicles (PMVs) in pedestrian spaces, avoiding collisions with pedestrians walking nearby is necessary. The avoidance paths of PMVs are affected by the behaviors of the pedestrians, which may also affect passenger comfort. Herein, a local path-planning method that considers passenger comfort to realize comfortable pedestrian avoidance during the autonomous navigation of PMVs in pedestrian spaces is proposed. First, the avoidance path and pedestrian behavior parameters affecting passenger comfort are investigated by evaluating passenger comfort in scenarios with different avoidance paths and pedestrian behaviors. Next, the requirements of a pedestrian avoidance method that considers passenger comfort are set based on the parameters affecting passenger comfort. Finally, a novel path-planning method that satisfies the requirements is proposed. The method is shown to generate comfortable paths in pedestrian spaces via a numerical simulation and participant experiment.
Mopsa-C: Modular Domains and Relational Abstract Interpretation for C Programs (Competition Contribution)

Abstract Mopsa is a multilanguage static analysis platform relying on abstract interpretation. It is able to analyze C, Python, and programs mixing these two languages; we focus on the C analysis here. It provides a novel way to combine abstract domains, in order to offer extensibility and cooperation between them, which is especially beneficial when relational numerical domains are used. The analyses are currently flow-sensitive and fully context-sensitive. We focus only on proving programs to be correct, as our analyses are designed to be sound and terminating but not complete. We present our first participation to SV-Comp, where Mopsa earned a bronze medal in the SoftwareSystems category.
Extending a High-Performance Prover to Higher-Order Logic

Abstract Most users of proof assistants want more proof automation. Some proof assistants discharge goals by translating them to first-order logic and invoking an efficient prover on them, but much is lost in translation. Instead, we propose to extend first-order provers with native support for higher-order features. Building on our extension of E to $$\lambda $$ -free higher-order logic, we extend E to full higher-order logic. The result is the strongest prover on benchmarks exported from a proof assistant.
Designing Noise-Robust Quantum Networks Coexisting in the Classical Fiber Infrastructure

The scalability of quantum networking will benefit from quantum and classical communications coexisting in shared fibers, the main challenge being spontaneous Raman scattering noise. We investigate the coexistence of multi-channel O-band quantum and C-band classical communications. We characterize multiple narrowband entangled photon pair channels across 1282 nm-1318 nm co-propagating over 48 km installed standard fiber with record C-band power (>18 dBm) and demonstrate that some quantum-classical wavelength combinations significantly outperform others. We analyze the Raman noise spectrum, optimal wavelength engineering, multi-photon pair emission in entangled photon-classical coexistence, and evaluate the implications for future quantum applications.
Systematic Review on Decentralised Artificial Intelligence and Its Applications

Initially, Artificial Intelligence (AI) models were centralized. This resulted in various challenges. To overcome this challenge, the decentralized or distributed frameworks were developed. Recent advancements in blockchain technology and cryptography have accelerated the decentralization process. Decentralized Artificial Intelligence (DAI) is gaining a significant research attention in recent times. This study reviews various DAI techniques such as Decentralized machine learning frameworks, Federated Learning and Distributed AI marketplaces. In particular, this study focuses on reviewing the recent developments in DAI by analyzing its potential advantages and challenges.
Towards Practical Multi-Client Order-Revealing Encryption: Improvement and Application

Order-revealing encryption (ORE) enables the untrusted server to perform greater-than-comparison over ciphertext without compromising data privacy, which allows anyone to evaluate the lexicographic ordering of two arbitrary ciphertexts with a public comparison algorithm. However, most ORE constructions merely support ciphertext comparison for single-user. Recently, a variant of ORE named delegatable ORE has been introduced, which achieves cross-user ciphertext comparison by employing token mutual authorization technique at the cost of weak security, i.e., reveals the most significant differing bit of underlying plaintexts. To tackle this problem, we first present a deterministic property-preserving hash called DPPH with short-size hash value, and then propose a novel multi-client ORE scheme (m-ORE) from DPPH that supports ciphertext comparison among multiple users while hiding the most significant differing bits. Furthermore, we present an enhanced construction dubbed m-H-ORE by introducing a two-phase comparison method, which can achieve supper-efficient comparison in some cases, i.e., two ciphertexts with different bit-length. Finally, we provide formal security proofs of the proposed schemes and run extensive experiments to evaluate their performance on real-world and synthetic datasets. The results demonstrate that both of the proposed schemes can achieve a speedup of 47× and 138× in comparison cost to that of parameter-hiding ORE, respectively.
An Input-weighted, Multi-Objective Evolutionary Fuzzy Classifier, for Alcohol Classification

The success of the evolutionary computational methods in scanning at problem's solution space and the ability to produce robust solutions, are important advantages for fuzzy systems, especially in terms of "interpretability" and "accuracy".Many techniques have been introduced for multi-objective evolutionary fuzzy classifiers by considering this advantage.However, these techniques are mostly fuzzy rule-based methods.In this study, instead of designing an optimal rule table or determining optimal rule weights, the inputs are weighted, and no rules are used.The average of the degrees of membership obtained with their Membership Function (MF) is calculated as the "input membership degree (μInp)" for each input.The μInps are then weighted, and a single coefficient is generated to be used for the output.With the output, results are obtained for different objective functions.The weights of the inputs and the MFs parameters of all variables (inputs and outputs) are optimized with NSGA-II.The performance of the method has been tested for alcohol classification.As a result, it has been proven that the method can generate designs that can classify at shallow error levels with different sensors at different gas concentrations.In addition, it has been observed that the proposed method produces more successful solutions for alcohol classification problems when compared to other MOEFC techniques.
Penerapan Algoritma K-Means dan K-Medoid untuk Pengelompokkan Data Pasien Covid-19

At the end of 2019 in Wuhan, China, a new virus was discovered, that is Corona Virus Disease 2019. This virus causes serious health problems and has been declared as pandemic since March 11, 2020. It has caused death and claimed thousands of lives. This virus spreads very quickly and in various ways such as direct contact with patients, travelers, owners of congenital diseases and many other transmissions. To suppress the spread of this virus, the government has carried out various ways such as social distancing and screening or impromptu swabs in crowded centers. Due to the many types of transmission from this virus, this research was conducted to classify Covid-19 cases in Dumai City. It is hoped that the results of this study can be used as an illustration of the grouping of Covid-19 patient data by applying K-Means and K-Medoid as a grouping algorithm based on the type of transmission, age, gender, health services and district. Based on this research, the K-Means algorithm is more optimal than K-Medoid in classifying Covid-19 patient data, especially in Dumai City. It is proven that the best DBI K-Means value is 0.139 with k = 4
Crossing the AI Chasm in Neurocritical Care

Despite the growing interest in possible applications of computer science and artificial intelligence (AI) in the field of neurocritical care (neuro-ICU), widespread clinical applications are still missing. In neuro-ICU, the collection and analysis in real time of large datasets can play a crucial role in advancing this medical field and improving personalized patient care. For example, AI algorithms can detect subtle changes in brain activity or vital signs, alerting clinicians to potentially life-threatening conditions and facilitating rapid intervention. Consequently, data-driven AI and predictive analytics can greatly enhance medical decision making, diagnosis, and treatment, ultimately leading to better outcomes for patients. Nevertheless, there is a significant disparity between the current capabilities of AI systems and the potential benefits and applications that could be achieved with more advanced AI technologies. This gap is usually indicated as the AI chasm. In this paper, the underlying causes of the AI chasm in neuro-ICU are analyzed, along with proposed recommendations for utilizing AI to attain a competitive edge, foster innovation, and enhance patient outcomes. To bridge the AI divide in neurocritical care, it is crucial to foster collaboration among researchers, clinicians, and policymakers, with a focus on specific use cases. Additionally, strategic investments in AI technology, education and training, and infrastructure are needed to unlock the potential of AI technology. Before implementing a technology in patient care, it is essential to conduct thorough studies and establish clinical validation in real-world environments to ensure its effectiveness and safety. Finally, the development of ethical and regulatory frameworks is mandatory to ensure the secure and efficient deployment of AI technology throughout the process.
Parallel Longest Increasing Subsequence and van Emde Boas Trees

This paper studies parallel algorithms for the longest increasing subsequence (LIS) problem. Let n be the input size and k be the LIS length of the input. Sequentially, LIS is a simple problem that can be solved using dynamic programming (DP) in O(n log n) work. However, parallelizing LIS is a long-standing challenge. We are unaware of any parallel LIS algorithm that has optimal O(n log n) work and non-trivial parallelism (i.e., Õ(k) or o(n) span).
Use of Information Technologies in Higher Education From The Aspect of Management

The development of modern information-communication technologies has enabled the development of electronic knowledge distribution channels that expand the effective level of general and specialist education. This trend is also present in higher education because higher education institutions and institutes increasingly rely on the use of modern information-communication technologies in their operations. The effects of the use of personal computers, the Internet and global social networks are multiple in the higher education system as well. In the process of acquiring new knowledge, developing creativity, as well as ensuring greater activity in the process of education, new learning technologies, with the use of multimedia systems, created the prerequisites for the engagement of all individual and collective resources. The most important source of the state’s competitive advantage is the ability to educate and retain highly educated personnel in the field of information technologies. From that aspect, the paper discusses information technologies as a scientific discipline and general conditions for education in the field of information technologies. The impact of technical and pedagogical changes on education in this area is analyzed with particular attention. It is known that information technologies are suitable for vocational education, and the modern environment sets specific requirements regarding the applicability of knowledge acquired in higher education institutions. The dynamic and turbulent market emphasizes the applicability of knowledge as a key requirement in the educational process. These short researches propose new educational modules that should structure teaching in the field of information technologies in accordance with the global demand in this sector. Every society strives for a more efficient and economical system of education, and therefore the issue of the use of new information technologies in education comes down to the problem of creating a theoretically ideal model of the use of these technologies at all levels of education and work.
Are you Surprised? The Role of Contextual Surprise in Designing Autonomous Systems

It is widely believed that progress towards advanced artificial intelligence (AI) systems will only occur when brain-inspired information processing systems are available to guide autonomous behaviour. The development of such autonomous systems (AS) requires cognitive entities capable of acquiring information, learning, planning, and reasoning that eventually adapt to environmental uncertainties. Cognitive dynamic systems (CDS) provide an engineering tool to design AS. Although the literature addresses various aspects of cognitive dynamic systems, it needs a holistic methodology to analyze alternative techniques concerning the driving module that internally guides the system toward a goal. Since uncertain events far from expectation play a significant role in triggering information-seeking behaviours, contextual surprise can be considered an intrinsic motivator in CDS. This presentation highlights connections and similarities among several definitions of contextual surprise. For demonstration purposes, it reviews the design of a linear Gaussian CDS as a motivating example. It uses a contextual surprise minimization scheme to express information utility and guide CDS’ state estimation and control.
SiamHAS: Siamese Tracker with Hierarchical Attention Strategy for Aerial Tracking

For the Siamese network-based trackers utilizing modern deep feature extraction networks without taking full advantage of the different levels of features, tracking drift is prone to occur in aerial scenarios, such as target occlusion, scale variation, and low-resolution target tracking. Additionally, the accuracy is low in challenging scenarios of visual tracking, which is due to the imperfect utilization of features. To improve the performance of the existing Siamese tracker in the above-mentioned challenging scenes, we propose a Siamese tracker based on Transformer multi-level feature enhancement with a hierarchical attention strategy. The saliency of the extracted features is enhanced by the process of Transformer Multi-level Enhancement; the application of the hierarchical attention strategy makes the tracker adaptively notice the target region information and improve the tracking performance in challenging aerial scenarios. Meanwhile, we conducted extensive experiments and qualitative or quantitative discussions on UVA123, UAV20L, and OTB100 datasets. Finally, the experimental results show that our SiamHAS performs favorably against several state-of-the-art trackers in these challenging scenarios.
Investigating Disfluencies in E-C Sight Translation

Spoken language is marked by para-verbal and non-verbal dimensions, such as silent pause, hesitation, and intonation.Fluency is considered as an essential parameter for evaluating the quality of oral output.Sight translation is a hybrid form of written translation and oral interpreting; however, fluency in sight translation is an underresearched topic.This paper examines the disfluencies in the sight translation of professional and novice translators working from English to Chinese.It adopted a statistical approach to compare the silent pauses and hesitation fillers in the delivery of professional and student participants.According to the independent samples t-test results, the differences were significant in the occurrence and ratio of hesitation between professional and student participants.It can be inferred that professional translators are more apt at coping tactics, such as pausing for several seconds, rather than inserting hesitation fillers unconsciously.Furthermore, the corpus-assisted analysis suggested a higher lexical density among professional participants, followed by the think-aloud approach, which revealed the causes for disfluencies.By drawing upon the Speech Production Theory, the paper found six most influential factors: vocabulary, emotion, syntactic category, speaking habit, lexical ambiguity, and topical difficulty.It is hoped that translation and interpreting studies will not be confined to linguistic dimensions.Paralinguistic signs should have their places in the domain of translatology.Semiotics of translation, which views translation as a pure semiotic act, thereby provides a valued perspective for translation and interpreting studies.