Reviewerly/exHarmony huggingface dataset:
A post-quantum signcryption scheme using isogeny based cryptography

Signcryption is an important cryptographic scheme which is used for both confidentiality and unforgeability. It has many interesting practical applications. Enormous growth of quantum computers makes a warning to the existing classical signcryption schemes due to Shor’s algorithm. As a result, designing signcryption schemes, which can withstand quantum attack, is an interesting direction of research. Isogeny based cryptography (IBC) is an ideal post-quantum candidate that can be employed to build a quantum computer immune signcryption scheme. Less communication cost and a smaller public key is the main advantage of IBC compared to other post quantum cryptographic branches. In this paper, we design the first signcryption employing IBC. Our scheme is relying on three hard problems: Commutative Supersingular Isogeny Decisional Diffie–Hellman (CSSIDDH), Group Action Inverse Problem (GAIP) and Commutative Supersingular Isogeny Knowledge of Exponent (CSSIKOE). It achieves IND − CCA and EUF − CMA security. Ciphertext size in this scheme turns out to be 16622.05 bytes for p128 and 12757.45 bytes for p256 to achieve NIST-1 level of security.
Implementing Ability-Based Design: A Systematic Approach to Conceptual User Modeling

The notion of Ability-Based Design , put forth by Wobbrock et al. [ 80 , 82 ] as a solution to the challenge of creating accessible technology, has been discussed in human-computer interaction research now for over a decade. However, despite being cited as influential on various projects, the concept still lacks a general characterization of how to implement its required focus on abilities. In particular, it lacks a formulation of how to perceive and model users within an articulated design process. To address this shortcoming, we rely on conceptual user modeling to examine Ability-Based Design and propose a characterization of it that is not dependent upon a specific project or research effort but that enables the ability-based design of new technologies in a systematic manner. Our findings show that Ability-Based Design’s focus on abilities requires important changes in typical user modeling approaches that cannot be met with current techniques. Based on the challenges identified through our analysis, we propose a first modification not only of current user modeling but also of current requirements analysis approaches to address abilities and their intertwined dependencies with tasks and contexts as core elements of conceptual models in Ability-Based Design. We thereby demonstrate not only the complexity of modeling users’ abilities, but also draw out promising ideas and perspectives for future research, emphasizing the need for future evaluative work on our approach.
Next-Generation Optimization Models and Algorithms in Cloud and Fog Computing Virtualization Security: The Present State and Future

Virtualization is becoming popular and is gaining widespread attention in recent years. However, with its popularity comes the challenge of securing the virtualized environment. Security measures for virtualized systems cannot always be applied in the same manner as in physical systems. Virtualized environments may have multiple virtual systems on the same physical machine, so different levels of security one needed. The hypervisor is the controlling program that provides the virtual systems’ effective isolation and security. In this article, we present a comprehensive review of the existing security ideas and architectures for virtualized environment and some of the open issues in virtualization security.
A Preliminary Study on the Dramatic Factors of Ancient Chinese Poetry under the Changing Geographical Environment

The formation of Chinese traditional culture is influenced by many factors, and the natural geographical environment is an important factor. Because a country's status is based on its geographical environment, it affects many aspects, such as economic composition and national psychology. As one of the expression forms of traditional Chinese culture, ancient Chinese poetry has a great influence on the emergence and development of Chinese traditional culture. Therefore, this paper mainly discusses the specific influencing factors of the drama of the ancient Chinese poetry, and the preliminary study of it is of certain significance to the development of Chinese traditional culture.
Specific Emitter Identification Based on Multichannel Depth Feature Fusion

Specific emitter identification involves extracting the fingerprint features that represent the individual differences of the emitter. This is achieved by processing the radio-frequency signals. Feature extraction and classifier selection are key factors that affect SEI performance. This paper proposes a deep convolutional neural network model based on multisignal feature fusion to identify the emitters. As part of the implementation model, the methods of singular spectrum analysis (SSA), variational mode decomposition (VMD), and intrinsic time-scale decomposition (ITD) are used to extract various signal features of emitter signals. Finally, a multichannel deep learning model is adopted to fuse each signal feature automatically and identify different signal emitters. Experimental results show that the proposed method completely considers the complementarity and independence of varying signal features and excavates hidden deep feature information. Hence, the process is considered reliable and effective.
Design of Shared Internet of Things System for English Translation Teaching Using Deep Learning Text Classification

The purpose is to adapt to the current social development and promote the English translation teaching reform. Based on the theories of deep learning (DL), text classification (TC), and the Internet of Things (IoT), this work analyzes the current situation of English translation teaching. Additionally, 100 text categories are selected from the English text corpus of Northwestern Polytechnic University as the research objects. The data are classified by the DL-based TC method and analyzed by introducing the simulated annealing algorithm. Finally, the storage and security performance of the shared IoT system are described. The results show that the proposed TC method can overcome the performance loss caused by the function extraction method, greatly reducing the training time and function space. The storage and security performance of the shared IoT system to encrypt English text will increase with the number of model iterations. Therefore, this work designs the English translation teaching-oriented shared IoT system using a DL-based TC. The finding plays an important role in subsequent English translation and enriching the theory of IoT.
Challenges and Innovative Countermeasures Faced by Public Administration in the Context of Big Data and Internet of Things

With the advent of nonrelational databases, data science has gradually become the focus of attention in academia and engineering. The data generated by human social networking is also gradually increasing, and big data technology appears in this context. Its main purpose is to obtain key information from complex data sets and realize the role of comprehensive analysis. In recent years, the concept of the Internet of Everything has gradually entered all walks of life. Similar to the design of smart cities, smart communities, and smart buildings, IoT technology has also been adopted. The development of the market economy and the deepening of the reform of the economic system require that the government’s public services must also be transformed as soon as possible. The responsibilities undertaken by the government are becoming more and more complex and important. The traditional government public management methods cannot meet the needs of all levels of society. The development of big data and the Internet of Things provides new opportunities for the government to innovate management methods, which is also one of the cores of future government work. The government needs to gradually transform the government’s public management method based on big data and the Internet of Things platform, relying on information technology, so as to improve people’s satisfaction and happiness index. Based on the problems existing in traditional public management, this paper focuses on the analysis of local government management methods and takes Shenyang City as the case analysis object to innovate local government public management methods and at the same time provides some reference for other regions to innovate public management methods.
Progress in Research on Implementing Machine Consciousness

Thus far, we have experienced three artificial intelligence (AI) booms. In the third one, we succeeded in developing AI that partially surpassed human capabilities. However, we are yet to develop AI that, like humans, can perform a series of cognitive processes. Consciousness built into devices is called machine consciousness. Related research has been conducted from two perspectives: studying machine consciousness as a tool to elucidate human consciousness and achieving the technological goal of furthering AI research with conscious AI. Herein, we survey the research conducted on machine consciousness from the second perspective. For AI to attain machine consciousness, its implementation must be evaluated. Therefore, we only surveyed attempts to implement consciousness as systems on devices. We collected research results in chronological order and found no breakthroughs that could deliver machine consciousness soon. Moreover, there is no method to evaluate whether an implemented machine consciousness system possesses consciousness, thus making it difficult to confirm the certainty of the implementation. This field of research is a new frontier. It is an exciting field with many discoveries expected in the future.
Special affine wavelet packets: Theory and applications

In the article "Special affine multiresolution analysis and the construction of orthonormal wavelets in L2(R)" (Shah and Lone, 2022), we have introduced a novel multiresolution analysis (MRA) in the realm of the special affine Fourier domain. In continuation to the study, we present the construction of wavelet packets associated with the special affine MRA and investigate their orthonormal properties. Subsequently, we derive an orthogonal decomposition for the proposed special affine wavelet packet subspaces Uj,kM by employing the orthonormal properties of special affine wavelet packets. Besides, a fast wavelet packet algorithm associated with special affine wavelet packets is also presented. Towards the culmination, we extend the scope of the present study by exploring the applications of the special affine wavelet packets to image compression techniques.
A Continuous Deep Learning System Study of Tennis Player Health Information and Professional Input

The health status of elite tennis players and the results of tennis matches are positively proportional under normal circumstances. The physical and psychological functions of tennis players directly affect the athletic ability of tennis players. With the improvement of people's living standards, people's attention to tennis has also increased. Tennis has received increasing attention in China, and the training of tennis players has become increasingly necessary. However, China is still using the traditional means of obtaining athletes' health information to evaluate athletes' health information. This has led to imperfect research into tennis players' health information and professional input systems. This makes the understanding of the health information of athletes incomplete and profound, and it affects the athletic ability of athletes. In this paper, deep learning and a two-factor model are added to tennis players' health information and professional input, and the feasibility of a deep learning system to comprehensively improve health information input is explored. The experimental results show that the application of the convolutional neural network method in the system improves the response speed to the physical fitness state of tennis players by 5%. This adds technical support for timely understanding of tennis players' physical health information and prevents players from making mistakes on the court due to physical reasons.
New Business Form of Smart Supply Chain Management Based on “Internet of Things + Blockchain”

As the digital economy promotes economic growth, the Internet of Things can solve the problem of productivity, and the blockchain can solve the problem of production relations, which realizes the industrial transformation of blockchain smart contract technology and becomes a new driving force for the industry. It is becoming more and more difficult for companies to control their own raw material supply, production, and sales by relying only on their own strength, and this is the key to influencing a company to become bigger and stronger. This problem will be efficiently solved by the implementation of “Internet of Things + Blockchain” technology. As a result, research into a new sort of smart supply chain management based on “Internet of Things + Blockchain” is required. From the perspective of building a smart supply chain, this paper makes full use of literature data methods, theoretical analysis methods, case analysis methods, logic analysis methods, and other methods. To study the effectiveness of IoT and blockchain technology through case study of changes in the order quantity in the procurement link of the supply chain. By understanding the current status of related research at home and abroad, as well as the Internet of Things technology and blockchain technology, this paper analyzes China. The main problems existing in the supply chain management of enterprises are the combination of the Internet of Things technology and the blockchain with the enterprise supply chain to create a smart supply chain platform, and the feasibility and functional efficiency of the smart supply chain platform. The related check was found, its deficiencies were found, and remedial measures were taken. The research results show that this intelligent supply chain management platform based on the Internet of Things (IoT) and blockchain can make the operation of the entire supply chain clearly visible. Information and data sharing can be achieved among the various departments of the supply chain to achieve scientific management and precision of the enterprise prediction. And this model can also be used in other industries to achieve industrial upgrading.
Multi-objective Hybrid Autoscaling of Microservices in Kubernetes Clusters

The cloud community has accepted microservices as the dominant architecture for implementing cloud native applications. To efficiently execute microservice-based applications, application owners need to carefully scale the required resources, considering the dynamic workload of individual microservices. The complexity of resource provisioning for such applications highlights the crucial role of autoscaling mechanisms. Kubernetes, the common orchestration framework for microservice-based applications, mainly proposes a horizontal pod autoscaling (HPA) mechanism, which, however, lacks efficiency. To hinder resource wastage and still achieve the requested average response time of microservices, we propose a multi-objective autoscaling mechanism. Based on machine learning techniques, we introduce a toolchain for hybrid autoscaling of microservices in Kubernetes. Comparing several machine learning techniques and also our in-house performance modeling tool, called Extra-P, we propose the most adequate model for solving the problem. Our extensive evaluation on a real-world benchmark application shows a significant reduction of resource consumption while still meeting the average response time specified by the user, which outperforms the results of common HPA in Kubernetes.
Analysis of the Basic Characteristics and Teaching Environment and Mode of Music Appreciation Course Based on Core Literacy

The main goals of a music appreciation course are to develop college students' emotions, improve their musical abilities, and strengthen their conceptual framework. This essay primarily introduces the fundamental elements and instructional approach of a music appreciation course built on core literacy. In order to clarify the need for and orientation of basic literacy in college music appreciation teaching, as well as to understand the special value of the subject, we conducted research on the relationship between basic literacy and college music appreciation teaching. Based on this, a new detection algorithm that fuses feature fusion and AM (attention mechanism) is proposed, and visual AM is added to the algorithm based on AM features to help the agent learn the best course of action quickly. The findings indicate that this method's cumulative normalized discount gain increases by about 0.07 and that the recommended list's overall quality has significantly improved. The hit ranking is decreased by about 0.05 when compared to the collaborative neural network filtering method without AM.
GK: Implementing Full First Order Default Logic for Commonsense Reasoning (System Description)

Abstract Our goal is to develop a logic-based component for hybrid – machine learning plus logic – commonsense question answering systems. The paper presents an implementation GK of default logic for handling rules with exceptions in unrestricted first order knowledge bases. GK is built on top of our existing automated reasoning system with confidence calculation capabilities. To overcome the problem of undecidability of checking potential exceptions, GK performs delayed recursive checks with diminishing time limits. These are combined with the taxonomy-based priorities for defaults and numerical confidences.
Ground Joinability and Connectedness in the Superposition Calculus

Abstract Problems in many theories axiomatised by unit equalities (UEQ), such as groups, loops, lattices, and other algebraic structures, are notoriously difficult for automated theorem provers to solve. Consequently, there has been considerable effort over decades in developing techniques to handle these theories, notably in the context of Knuth-Bendix completion and derivatives. The superposition calculus is a generalisation of completion to full first-order logic; however it does not carry over all the refinements that were developed for it, and is therefore not a strict generalisation. This means that (i) as of today, even state of the art provers for first-order logic based on the superposition calculus, while more general, are outperformed in UEQ by provers based on completion, and (ii) the sophisticated techniques developed for completion are not available in any problem which is not in UEQ. In particular, this includes key simplifications such as ground joinability, which have been known for more than 30 years. In fact, all previous completeness proofs for ground joinability rely on proof orderings and proof reductions, which are not easily extensible to general clauses together with redundancy elimination. In this paper we address this limitation and extend superposition with ground joinability, and show that under an adapted notion of redundancy, simplifications based on ground joinability preserve completeness. Another recently explored simplification in completion is connectedness. We extend this notion to “ground connectedness” and show superposition is complete with both connectedness and ground connectedness. We implemented ground joinability and connectedness in a theorem prover, iProver, the former using a novel algorithm which we also present in this paper, and evaluated over the TPTP library with encouraging results.
Rensets and Renaming-Based Recursion for Syntax with Bindings

Abstract I introduce renaming-enriched sets ( rensets for short), which are algebraic structures axiomatizing fundamental properties of renaming (also known as variable-for-variable substitution) on syntax with bindings. Rensets compare favorably in some respects with the well-known foundation based on nominal sets. In particular, renaming is a more fundamental operator than the nominal swapping operator and enjoys a simpler, equationally expressed relationship with the variable-freshness predicate. Together with some natural axioms matching properties of the syntactic constructors, rensets yield a truly minimalistic characterization of $$\lambda $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>λ</mml:mi> </mml:math> -calculus terms as an abstract datatype – one involving an infinite set of unconditional equations , referring only to the most fundamental term operators: the constructors and renaming. This characterization yields a recursion principle, which (similarly to the case of nominal sets) can be improved by incorporating Barendregt’s variable convention. When interpreting syntax in semantic domains, my renaming-based recursor is easier to deploy than the nominal recursor. My results have been validated with the proof assistant Isabelle/HOL.
Non-associative, Non-commutative Multi-modal Linear Logic

Abstract Adding multi-modalities (called subexponentials ) to linear logic enhances its power as a logical framework, which has been extensively used in the specification of e.g. proof systems, programming languages and bigraphs. Initially, subexponentials allowed for classical, linear, affine or relevant behaviors. Recently, this framework was enhanced so to allow for commutativity as well. In this work, we close the cycle by considering associativity. We show that the resulting system ( $$\mathsf {acLL}_\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>acLL</mml:mi> <mml:mi>Σ</mml:mi> </mml:msub> </mml:math> ) admits the (multi)cut rule, and we prove two undecidability results for fragments/variations of $$\mathsf {acLL}_\varSigma $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>acLL</mml:mi> <mml:mi>Σ</mml:mi> </mml:msub> </mml:math> .
Quantifying Opinion Strength: A Neutrosophic Inference System for Smart Sentiment Analysis of Social Media Network

The contemporary speed at which opinions move on social media makes them an undeniable force in the field of opinion mining (OM). This may cause the OM challenge to become more social than technical. This is when the process can determinately represent everyone to the degree they are worth. Nevertheless, considering perspectivism can result in opinion dynamicity. Pondering the existence of opinion dynamicity and uncertainty can provide smart OM on social media. This study proposes a neutrosophic-based OM approach for Twitter that handles perspectivism, its consequences, and indeterminacy. For perspectivism, a social network analysis (SNA) was conducted using popular SNA tools (e.g., Graphistry). An influence weighting of users was performed using an artificial neural network (ANN) based on the SNA provided output and people’s reactions to the OM analyzed texts. The initiative adoption of neutrosophic logic (NL) to integrate users’ influence with their OM scores is to deal with both the opinion dynamicity and indeterminacy. Thus, it provides new uncertainty OM scores that can reflect everyone. The OM scores needed for integration were generated using TextBlob. The results show the ability of NL to improve the OM process and accurately consider the innumerable degrees. This will eventually aid in a better understanding of people’s opinions, helping OM in social media to become a real pillar of many applications, especially business marketing.