Reviewerly/exHarmony huggingface dataset:
Equivalence of the schemes of programs based on the algebraic approach to setting the semantics of programming languages

Objectives . The paper deals with the equivalence of program schemes. According to A.A. Lyapunov and Yu.I. Yanov, the founders of this theory, a program scheme is understood as a program model wherein abstraction from contensive values of operators and expressions is performed. In this case, the program structure containing symbolic notation of operators and expressions remains unchanged while maintaining their execution sequence. The programming language model presented in the paper contains basic constructs of sequential languages and is the core of the existing sequential programming languages. The paper aimed at developing an effective algorithm for studying equivalence (nonequivalence) of program schemes of sequential programming languages. Methods . An algebraic approach to specifying semantics of programming languages was used for studying the equivalence of program schemes. Results . A process semantics being the new algebraic approach to specifying the formal semantics of sequential programming languages was proposed. The process semantics was specified by matching programs (program schemes) with a set of computation sequences. The computation sequence was understood as the execution sequence of actions (commands and tests) of the program. Two types of concatenation operations (test–command and command–command) and the merge operation, which properties are given by axiomatic systems, were defined in the introduced semantic domain. The finiteness of the semantic value representation in the form of systems of recursive equations was proved. The algorithm for proving the equivalence (nonequivalence) of systems of recursive equations characterizing semantic values for a pair of program schemes was proposed, which implies the equivalence (nonequivalence) of programs in the strong sense. Conclusions . The paper demonstrates the efficient use of the proposed algorithm for proving the equivalence of sequential program schemes excluding side effects when calculating expressions, i.e., sequential computation of the expression more than once does not change anything. The example of proving the equivalence of program schemes by two methods—the well-known de Bakker–Scott fixed-point induction method and the method proposed by the author—is given. Comparison of the above methods testifies not only to the new method′s effectiveness but also to its significant simplicity, proved in practice by students who performed corresponding tasks when studying the Semantics of Programming Languages at the Institute of Information and Computing Technologies at the National Research University Moscow Power Engineering Institute (Moscow, Russia).
Learning Canonical $\mathcal{F}$-Correlation Projection for Compact Multiview Representation

Canonical correlation analysis (CCA) matters in multi-view representation learning. But, CCA and its most variants are essentially based on explicit or implicit covariance matrices. It means that they have no ability to model the nonlinear relationship among features due to intrinsic linearity of covariance. In this paper, we address the preceding problem and propose a novel canonical F-correlation framework by exploring and exploiting the nonlinear relationship between different features. The framework projects each feature rather than observation into a certain new space by an arbitrary nonlinear mapping, thus resulting in more flexibility in real applications. With this frame-work as a tool, we propose a correlative covariation projection (CCP) method by using an explicit nonlinear mapping. Moreover, we further propose a multiset version of CCP dubbed MCCP for learning compact representation of more than two views. The proposed MCCP is solved by an iterative method, and we prove the convergence of this iteration. A series of experimental results on six benchmark datasets demonstrate the effectiveness of our proposed CCP and MCCP methods.
Digital Platforms for Cross-Border Settlement of CBDC

Digitalization of global economic processes requires a new means of value to be created instead of traditional fiat money. Thus, the concept of central bank digital currencies has been introduced. However, the creation of new financial instruments would not ensure changes in the world economy unless special platforms for digital currency had been created. While central banks have mostly solved the problem of creating platforms for their digital national currency, the issue of operating international transactions with CBDC has not been properly faced. Currently, there are a few projects led by the World Bank and Bank for International Settlements in cooperation with central banks of different states to create digital platforms for cross-border settlement of CBDC. One of the most intriguing experiments is the Project Jura which was held in November 2021. The attempts to develop a new high-tech market for CBDC created numerous financial and legal issues. Based on the conceptual features of CBDC and following the experience of implementing digital platforms by BISBIS and World Bank, the chapter is dedicated to the analysis of legal and regulatory issues of using such instruments for cross-border operations.
An Optimized and Secure Energy-Efficient Blockchain-Based Framework in IoT

With the rapid development of the IoT (Internet-of-Things), additional smart gadgets may be associated with the Internet, significantly enhancing data transfer and communication. Software-Defined Networking (SDN) is known as a new model that separates the control plane and the data plane, and is anticipated as a favorable solution for implementing Blockchain, to offer the scalability and adaptability required for IoT. The scalability of the network rises in direct proportion to the users’ enhanced privacy on the network. Blockchain and SDN are two top innovations utilized to create secure network architectures and provide trustworthy data transmission. They offer a strong and trustworthy platform to deal with dangers and problems, including security, privacy, adaptability, scalability, and secrecy. Unfortunately, the attackers can still inject traffic to disrupt a blockchain node’s regular functions. This study provides an optimized Blockchain-based SD IoT architecture for smart networks that is safe and energy-efficient. In this work, it is concentrated on blockchain-based SDN and creates an SDN-Blockchain Classifier. This IDS-based security tool provides a trust-based classifier by handling and reducing harmful traffic through traffic fusion and aggregation. Finally, it is concluded by evaluating the proposed framework SDN-Blockchain Classifier performance against MAC flooding attack in a simulation setting and demonstrating that it can attain optimized average throughput, response time, packet loss of crossing domain path, energy efficiency, end-to-end delay, file transfer operation, energy consumption, and CPU utilization compared to the baselines taken into consideration, thereby achieving efficacy and also security in the proposed smart network.
An Efficient Trust Management Technique Using ID3 Algorithm With Blockchain in Smart Buildings IoT

Because of the rising population density, relationships are necessary to raise living standards through sending and receiving a wide range of services.Because of this, many means of object communication-regardless of their nature-are necessary to meet our daily needs.IoT is a network of physical things integrated with sensors, and software to communicate with each other.To establish a good connection, every object considered to be an associate of another object should meet certain requirements including scalability, interoperability, and trustworthiness.IoT security is a challenging task to protect the hardware and networks in the IoT system and a significant constraint to the deployment and realization of IoT.IoT security may include data confidentiality, authentication, access control, anonymity, and trust among services and products.Exchanging trust information is critical for assessing an entity's trustworthiness.Therefore, trust information must be shared and stored securely to ensure reliability, honesty, and safety.We propose a secure trust management scheme built on blockchain technologies to secure the entire system in transparency, traceability, and material integrity.We implement a blockchain-based trust management architecture for smart buildings that collect node trust proof.It assigns a trust score to each node, securely stores them in an array, then the threshold value is computed using the ID3 Algorithm.IoT threshold value is broadcasted into the blockchain network and stored in the trusted list.According to the findings, our approach encompasses security measures such as tamper-proofing, attack resistance, reliability, and low functionality for IoT in smart buildings.
Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

The Industrial Internet of Things (IIoT) is the key technology of Industry 4.0.The combination of machine learning and IIoT has spawned a thriving smart industry.Machine learning models are trained and predicted based on raw data that contains sensitive information, and data sharing leads to information leakage.Data security and privacy protection in IIoT face serious challenges.Therefore, we propose a federated learning-based privacy-preserving data aggregation scheme (FLPDA) for IIoT.Data aggregation to protect individual user model changes in federated learning against reverse analysis attacks from industry administration centers.Each round of data aggregation uses the PBFT consensus algorithm to select an IIoT device from the aggregation area as the initialization and aggregation node.Paillier cryptosystem and secret sharing are combined to realize data fault tolerance and secure sharing.Security analysis and performance evaluation show that the scheme can effectively protect data privacy and resist various attacks.It has lower communication, computational, and storage overhead than existing schemes.
Improving Sepsis Prediction Performance Using Conditional Recurrent Adversarial Networks

In this paper, we devise a novel method involving deep neural networks (DNNs) that improves the early prediction of sepsis for patients admitted to the intensive care units (ICUs).It is assumed that the patient data sets are dramatically corrupted by missing information, which negatively impacts the detection of the onset of sepsis.We propose a generative learning framework to estimate the missing information in data.Our model involves Conditional Generative Adversarial Networks (GANs) utilizing Long Short-Term Memory (LSTM) networks as the generator and discriminator when conditioned on class labels.A deep LSTM network is also employed for prediction purposes.The prediction network is trained with an output of the conditional GAN and evaluated on an unseen test set to investigate the performance of the proposed model.Here, we show that the proposed framework not only identifies long-term temporal dependencies but also exploits the missing patterns.We present the performance results and compare them to other wellknown techniques.For the 4-hour, 8-hour, and 12-hour prediction of sepsis, the proposed method attains area under the receiver operating characteristic (AUROC) of 94.49%, 93.74%, and 94.01%, respectively.It is shown here that the improvement in imputation and prediction promises a highly effective method that can offer early detection of sepsis in high-risk patients.
Feasibility of using Gyroscope to Derive Keys for Mobile Phone and Smart Wearable

In the past few years, smart healthcare has become a popular topic. Many users wear smart devices to monitor their health conditions. To provide information for health analysis, many sensors are installed in smart wearable devices to collect personal health data. Due to the limitation of space and computational power, private health data is not processed locally. Instead, it is usually sent to a mobile phone for further analysis, which required wireless data exchange between the mobile phone and smart wearable devices. To protect a user’s privacy, it is important to guarantee the connection security of the devices’ network as well as prevent information leakage. A possible method to secure the data exchange process is symmetric encryption. In this paper, we investigate the feasibility of symmetric key generation for communication between a mobile phone and smart wearable device using angular velocity data collected by gyroscopes as data source. We collected over 1000 samples of gait data, totally more than 20000 seconds of movements, using two industrial products including a smart watch and mobile phone placed on wrist and in pocket respectively. We successfully generated the same random number for mobile phone and smart wearable device in 78% samples.
Kernel Relative-prototype Spectral Filtering for Few-Shot Learning

Few-shot learning performs classification tasks and regression tasks on scarce samples. As one of the most representative few-shot learning models, Prototypical Network represents each class as sample average, or a prototype, and measures the similarity of samples and prototypes by Euclidean distance. In this paper, we propose a framework of spectral filtering (shrinkage) for measuring the difference between query samples and prototypes, or namely the relative prototypes, in a reproducing kernel Hilbert space (RKHS). In this framework, we further propose a method utilizing Tikhonov regularization as the filter function for few-shot classification. We conduct several experiments to verify our method utilizing different kernels based on the miniImageNet dataset, tiered-ImageNet dataset and CIFAR-FS dataset. The experimental results show that the proposed model can perform the state-of-the-art. In addition, the experimental results show that the proposed shrinkage method can boost the performance. Source code is available at https://github.com/zhangtao2022/DSFN .
Structural Causal 3D Reconstruction

This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images. Due to ambiguity and intrinsic ill-posedness, this problem is inherently difficult to solve and therefore requires strong regularization to achieve disentanglement of different latent factors. Unlike existing works that introduce explicit regularizations into objective functions, we look into a different space for implicit regularization – the structure of latent space. Specifically, we restrict the structure of latent space to capture a topological causal ordering of latent factors (i.e., representing causal dependency as a directed acyclic graph). We first show that different causal orderings matter for 3D reconstruction, and then explore several approaches to find a task-dependent causal factor ordering. Our experiments demonstrate that the latent space structure indeed serves as an implicit regularization and introduces an inductive bias beneficial for reconstruction.
Explaining Semantic Reasoning Using Argumentation

Multi-Agent Systems (MAS) are popular because they provide a paradigm that naturally meets the current demand to design and implement distributed intelligent systems. When developing a multi-agent application, it is common to use ontologies to provide the domain-specific knowledge and vocabulary necessary for agents to achieve the system goals. In this paper, we propose an approach in which agents can query semantic reasoners and use the received inferences to build explanations for such reasoning. Also, thanks to an internal representation of inference rules used to build explanations, in the form of argumentation schemes, agents are able to reason and make decisions based on the answers from the semantic reasoner. Furthermore, agents can communicate the built explanation to other agents and humans, using computational or natural language representations of arguments. Our approach paves the way towards multi-agent systems able to provide explanations from the reasoning carried out by semantic reasoners.
Sufficiency of Ensemble Machine Learning Methods for Phishing Websites Detection

Phishing is a kind of worldwide spread cybercrime that uses disguised websites to trick users into downloading malware or providing personally sensitive information to attackers.With the rapid development of artificial intelligence, more and more researchers in the cybersecurity field utilize machine learning and deep learning algorithms to classify phishing websites.In order to compare the performances of various machine learning and deep learning methods, several experiments are conducted in this study.According to the experimental results, ensemble machine learning algorithms stand out among other candidates in both detection accuracy and computational consumption.Furthermore, the ensemble architectures still provide impressive capability when the amount of features decreases sharply in the dataset.Subsequently, the paper discusses the factors why ensemble machine learning methods are more suitable for the binary phishing classification challenge in up-date training and real-time detecting environment, which reflects the sufficiency of ensemble machine learning methods in anti-phishing techniques.
Studying Special Operators for the Application of Evolutionary Algorithms in the Seek of Optimal Boolean Functions for Cryptography

The role of Boolean functions in modern cryptography has triggered the necessity of developing methods to construct them with adequate properties, such as balancedness and high non-linearity—making them more resistant to a variety of cryptanalytic attacks. Research into the construction of weight-wise perfectly balanced Boolean functions using Evolutionary Algorithms is scarce but encouraging (e.g., [1]). In this work, we first investigate the effect on an evolutionary algorithm’s performance when relying solely on the penalty function, as opposed to the solution repairment method. Second, we focus on the effect of problem-specific crossover operators (e.g., those used on [2]), and particularly proposing a novel one free of solution repairs to preserve balancedness. The results obtained suggest that an adequate penalty factor and the use of specifically designed evolutionary operators is sufficient to find Boolean functions with weight-wise perfect balancedness and high non-linearity, as desired.
FRAMH: A Federated Learning Risk-Based Authorization Middleware for Healthcare

Modern healthcare systems operate in highly dynamic environments requiring adaptable access control mechanisms. Access to sensitive data and medical equipment should be granted or denied according to the current health situation of the patient. To handle the need for adaptable access control of healthcare scenarios, we propose a novel model that allows dynamic access control decisions based on the context characterizing the source, type of access request, patient, and estimated risk corresponding to the conditions of the patient. Estimating patient status risk requires analyzing vital physiological data whose availability is growing, thanks to the widespread diffusion of the Internet of Medical Things (IoMT) devices. Inferring the patient health status risk through machine learning (ML) techniques is possible, but to achieve better accuracy, the training phase requires the aggregation of vast amounts of data from different sources. This aggregation could be difficult or even impossible due to organization regulations and privacy laws. To address these issues, this article proposes a novel federated learning risk-based authorization middleware for healthcare (FRAMH) that supports risk-based access control to deal with changing and unforeseen medical situations. Our solution infers the risk of health status through a federated learning (FL) approach enriched with blockchain to avoid the weaknesses of centralized servers. The implemented prototype and a large set of experimental results demonstrate the advantages of FL in estimating the risk in healthcare scenarios. Through this approach, even a medical institution with a limited dataset can achieve a satisfying risk estimation and efficient access control enforcement.
Beyond Supervised vs. Unsupervised: Representative Benchmarking and Analysis of Image Representation Learning

By leveraging contrastive learning, clustering, and other pretext tasks, unsupervised methods for learning image representations have reached impressive results on standard benchmarks. The result has been a crowded field - many methods with substantially different implementations yield results that seem nearly identical on popular benchmarks, such as linear evaluation on ImageNet. However, a single result does not tell the whole story. In this paper, we compare methods using performance-based benchmarks such as linear evaluation, nearest neighbor classification, and clustering for several different datasets, demonstrating the lack of a clear front-runner within the current state-of-the-art. In contrast to prior work that performs only supervised vs. unsupervised comparison, we compare several different unsupervised methods against each other. To enrich this comparison, we analyze embeddings with measurements such as uniformity, tolerance, and centered kernel alignment (CKA), and propose two new metrics of our own: nearest neighbor graph similarity and linear prediction overlap. We reveal through our analysis that in isolation, single popular methods should not be treated as though they represent the field as a whole, and that future work ought to consider how to leverage the complimentary nature of these methods. We also leverage CKA to provide a framework to robustly quantify augmentation invariance, and provide a reminder that certain types of invariance will be undesirable for downstream tasks.
Real Time Fake Note Detection using Deep Convolutional Neural Network

Nowadays printing fake notes has become very easy due to the availability of high-tech printing machines and advancements in color printing technology. Notes can be printed with maximum accuracy without any slip mistake so detecting fake notes in today’s world is almost unattainable. Hence the fake notes in the market have reduced the value of the original currency. This counterfeit negatively affects nations’ wealth. So, today it is necessary to detect fake currency. All the currently available methods are based on image processing techniques. Finding fake currencies with these methods is less efficient, unreliable, and time-consuming. The proposed work is based on the deep convolutional neural network used to detect counterfeit currency through the mobile application. The self-generated dataset is used to train the model. Further, it was tested using real-time images captured through the smartphone camera. The training and validation accuracy is 96.66%, while the testing accuracy is 86.65%.
System Dynamics-Based Analysis on Factors Influencing Artificial Intelligence Talents Training

There are gaps between education supply and industrial demand of talents in intelligent era. Thus, the shortage of artificial intelligence(AI) talents becomes the top challenges facing Chinafs companies. Hence it is essential to implement educational reform to improve quality of education supply talents. However, educational reform is affected by multiple and intertwined factors. This research, therefore, developed a system dynamics-based model of education reform to sort out relationships among all factors and predict the influence of several factors on AI talents output. Then, based on this model, series of experiments are carried out to find main factors affecting cultivation of AI talents. Results show that: 1) this model could accurately simulate trends of output ratio AI talents under different educational reform measures; 2) Measures including teaching mode reform, improving the application rate of new teaching model, speeding up the construction of new disciplines in higher education, developing AI, improving ethics of AI, and strengthening the integration of AI and education could play significant roles in promoting cultivation of AI talents.