Reviewerly/exHarmony huggingface dataset:
Signs of the Uniqueness of the Ukrainian Printed Book in the Context of the Global Digitalisation of Art

The aim of the article is to explore the features of the uniqueness of a classic printed book as a valuable source of art studies analysis against the backdrop of dynamic transformations in Ukrainian book design. The article seeks to determine the prospects for its development as an artistic and project form, and ways to preserve the relevant design traditions. Results. The perception of traditional printed books in Ukraine today has changed in the context of the global digitisation of art. In this regard, the following opposing positions of scholars are highlighted: some predict a new phase of life for printed books, which involves the transformation of their socio-cultural role and the acquisition of different artistic content, while others underestimate the potential of traditional paper publications compared to innovative electronic formats. However, it is worth noting that the modern Ukrainian printed book has exclusive visual and aesthetic characteristics, demonstrating a unique synthesis of subject-spatial and artistic-design solutions. These characteristics include the long-term preservation of book design traditions, presentation of the designer's own artistic style (illustrator), tactile features (paper type, frame type, etc.), as well as the consistency of the layout at all levels of its implementation. The article proves this fact by comparing the design features of classic book editions with their alternative forms: electronic (multimedia) and interactive editions, as well as art books. The scientific novelty of the article consists in a comprehensive examination of the coexistence of various book forms on the modern domestic book market, their mutual influence, and the formulation of prospects for the development of the Ukrainian traditional printed books based on theoretical and empirical research of relevant sources, which was carried out for the first time. Conclusions. The article demonstrates that the highest potential of a traditional printed book lies in its highly differentiated design, unique artistic, genre and functional characteristics. The guarantee of the future of such publications is the mandatory presence of artistic and creative components in the book ensemble, as well as a genre-functional variable component.
Arts’s Fight for Life or the Triumphant End of the Avant-Garde

The aim of the article is to outline a logical sequence of events that led to radicalism in the art of the 19th and 20th centuries by studying the connections between art, life (reality), and science and technology. From the mid-nineteenth century through the 1970s, art acquired a unique quality: it rapidly radicalised, creating a number of alternative art practices, such as impressionism, abstractionism, cubism, ready-made, etc. Before that, except for random individual phenomena, art for centuries was just changing styles: Baroque, Rococo, Classicism. Results. The article identifies the cause for the revolutionary avant-garde trends in art and the reason for the end of the revolutionary era. It presents a view based on art’s relations with life in competition with science and technology that had different effects on art, ranging from influentially adaptive to revolutionary rebellious. The scientific significance of this study is its innovative approach to the consideration of factors of the emergence and development of avant-garde trends in the art of the 19th and 20th centuries. The rational study and comparable analyses of events in science/ technology and art in relation to life (reality) offers an inventive and coherent reason for the appearance and disappearance of avant-guard art. Conclusions. This article identifies the cause for the revolutionary avant-garde trends in art and the reason for the end of the revolutionary era. It presents a unique view based on art’s relations with life in competition with science and technology and shows how an inadvertent battle of art for life (reality) with photography, film and television triumphed in the twentieth century and broke the very possibility of further revolutionary changes.
A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning

Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO.
Attractive Visuality Generation Within the 360˚-VR Video Format as a Technological Trend in Modern Film Production

The aim of the article is to study the features of the process of attractive visuality generation within the 360˚-VR video format. Results. A comprehensive consideration of the issue with the use of general scientific and special methods (problem-logical, theoretical-informational, art studies, comparative analysis, the method of analogy, abstraction, induction, and deduction) demonstrates that one of the key trends in modern film production is the creation of a “multinarrative space” (A. G. Iñárritu) VR-immersion of the viewer as a recipient and co-creator of this space, accompanied by the transformation of the film into a visually attractive activity and transformation principles of film aesthetics in the 20th century. Scientific novelty. It is proved that the 360˚-VR video format is a unique audiovisual form that requires a special grammatical, technical and technological base (professional or amateur 360˚ camera, effective tools for creating and editing 360˚video content for the different platforms, Kolor Autopano Video Pro, SIFT algorithm, etc.) thus, brings the use of the immersion principle to a qualitatively new level. Conclusions. Due to this, attractive visuality is becoming possible in the quality of a new aesthetic and perceptual-virtual experience that is being formed under the influence of VR-attractions and modern special effects in the digital cinematograph. It leads to the synthesis between cinematograph and theatre when the transition is occurring from montage sequence to virtual-theatrical play where in consequence of 360˚-VR the action has been developing constantly, and the viewer is in the very centre of the events. Thereby, there are grounds to confirm that, including technological development and measureless creative possibilities in the process of projecting different VR-attractions, it is possible for a viewer’s infinite immersion in the space of a digital film; that, from one side, transforms film language on the modern stage, creating the perspectives of the further branch development, and from another side, it conditions a number of psychological problems and risks connected with the viewers’ reaction on a different level of density of the attractive field of modified film language.
Data storage query and traceability method of electronic certificate based on cloud computing and blockchain

In the age of big data, electronic data has developed rapidly and gradually replaced traditional paper documents. In daily life, all kinds of data are saved in the form of electronic documents. In this regard, people have strengthened the development of electronic depository system. Electronic storage refers to the storage of actual events in the form of electronic data through information technology to prove the time and content of events. Its application scenarios are very extensive such as electronic contracts, online transactions and intellectual property rights. However, due to the vulnerability of electronic data, the existing electronic data depository system has certain security risks, and its content is very easy to be tampered with and destroyed, resulting in the loss of depository information. Due to the complexity of the operation of the existing electronic data depository system, some users are likely to reduce the authenticity of the depository information due to the non-standard operation. In order to solve the problems existing in the current electronic data storage system, this paper designed an electronic data storage system based on cloud computing and blockchain technology. The data storage of cloud computing and blockchain was decentralized, and its content cannot be tampered with. It can effectively ensure the integrity and security of electronic information, which is more suitable for the needs of electronic storage scenarios. This paper first introduced the development of electronic data depository system and cloud computing, and optimized the electronic data depository system through the task scheduling model of cloud computing. Finally, the feasibility of the system was verified through experiments. The data showed that the functional efficiency of the system in the electronic data sampling point storage function, the upload of documents to be stored, the download of stored documents, the view of stored information function and the file storage and certificate comparison verification function has reached 0.843, 0.821, 0.798, 0.862 and 0.812 respectively. The final function indexes of each function of the traditional electronic data depository system were 0.619, 0.594, 0.618, 0.597 and 0.622 respectively. This data shows that the electronic data storage system based on cloud computing and blockchain modeling can effectively manage electronic data and facilitate relevant personnel to verify electronic data.
CMCEE: A joint learning framework for cascade decoding with multi-feature fusion and conditional enhancement for overlapping event extraction

Event extraction (EE) is an important natural language processing task. With the passage of time, many powerful and effective models for event extraction tasks have been developed. However, there has been limited research on complex overlapping event extraction. Therefore, we propose a new cascade decoding model: A Joint Learning Framework for Cascade Decoding with Multi-Feature Fusion and Conditional Enhancement for Overlapping Event Extraction. 1) In this model, we introduce a cascade decoding mechanism with multi-feature fusion to better capture the interaction between decoding layers. 2) Additionally, we introduce an enhanced conditional layer normalization (ECLN) mechanism to enhance the interaction between subtasks. Simultaneously, the use of a cascade decoding model effectively addresses the problem of overlapping events. The model successively performs three subtasks, type detection, trigger word extraction and argument extraction. All three subtasks learned together in a framework, and a new conditional normalization mechanism is used to capture dependencies among these subtasks. The experiments are conducted using the overlapping event benchmark, FewFC dataset. The experimental evaluation demonstrates that our model achieves a higher F1 score on the overlapping event extraction task compared to the original overlapping event extraction model.
Workload prediction for SLA performance in cloud environment: ESANN approach

Cloud computing offers internet-based services to customers. Infrastructure as a service offers consumers virtual computer resources including networking, hardware, and storage. Cloud-hosting startup delays hardware resource allocation by several minutes. Predicting computer demand will address this problem. The performance comparison showed that combining these algorithms was the best way to create a dynamic cloud data centre that efficiently used its resources. One of these challenges is the need of practicing effective SLA management in order to prevent the possibility of SLA breaches and the repercussions of such violations. Exponential Smoothing and Artificial Neural Network (ANN) models in terms of managing SLAs from the point of view of cloud customers as well as cloud providers. We proposed an Exponential Smoothing and Artificial Neural Network model (ESANN) for SLA violation and predict the CPU utilization from time series data. This model includes SLA monitoring, energy consumption, CPU utilization, and accuracy prediction. Experiments show that the suggested approach helps cloud providers reduce service breaches and penalties. ESANN outperforms Exponential Smoothing, LSTM, RACC-MDT, and ARIMA by attaining 6.28%, 16.2%, 27.33%, and 31.2% on the combined performance indicator of Energy SLA Violation, which measures both energy consumption and SLA compliance.
Generate custom travel magazine layouts

Among the problems of specifying the style and number of elements of a travel magazine, the problem of generating magazine layout by constraining text, and constraining graph layout remains a complex and unsolved problem. In this paper, we generate layouts of text satisfying constraints via GAN. Due to the complexity and variety of graph designs, we enhance the performance of the discriminator and the generator so that the layouts generated by the generator are more constrained. Add non-corresponding constraint text and real layout pairs to the discriminator to enhance the performance of the discriminator; then add a spatial attention mechanism to the layout encoder to extract the features of the layout and generate high-quality layouts. We demonstrate that the proposed method can generate high-quality layouts of text satisfying the constraints, and we validate the effectiveness of this method through user ratings.
Enhancing HMM-based POS tagger for Mizo language

The process of associating words with their relevant parts of speech is known as part-of-speech (POS) tagging. It takes a substantial amount of well-organized data or corpora and significant target language research to obtain good performance for a tagger. Mizo is a language that needs more research attention in computational linguistics due to its under-resourced nature. The limited availability of corpora and relevant literature adds complexity to the task of assigning POS labels to Mizo text. This paper explores two methods to potentially improve the Hidden Markov Model (HMM)-based POS tagger for the Mizo language. The proposed taggers are compared with the baseline HMM tagger and the N-gram taggers on the designed Mizo corpus, which consists of 72,077 manually tagged tokens. The experimental results proved that the two proposed taggers enhanced the HMM-based Mizo POS tagger, achieving 81.52% and 84.29% accuracy, respectively. Moreover, a comprehensive analysis of the performance of the suggested hybrid tagger was conducted, yielding a weighted average precision, recall, and F1-score of 83.09%, 77.88%, and 79.64% respectively.
Learning bayesian multinets from labeled and unlabeled data for knowledge representation

The Bayesian network classifiers (BNCs) learned from labeled training data are expected to generalize to fit unlabeled testing data based on the independent and identically distributed (i.i.d.) assumption, whereas the asymmetric independence assertion demonstrates the uncertainty of significance of dependency or independency relationships mined from data. A highly scalable BNC should form a distinct decision boundary that can be especially tailored to specific testing instance for knowledge representation. To address the issue of asymmetric independence assertion, in this paper we propose to learn k-dependence Bayesian multinet classifiers in the framework of multistage classification. By partitioning training set and pseudo training set according to high-confidence class labels, the dependency or independency relationships can be fully mined and represented in the topologies of the committee members. Extensive experimental results indicate that the proposed algorithm achieves competitive classification performance compared to single-topology BNCs (e.g., CFWNB, AIWNB and SKDB) and ensemble BNCs (e.g., WATAN, SA2DE, ATODE and SLB) in terms of zero-one loss, root mean square error (RMSE), Friedman test and Nemenyi test.
A multi-instance multi-label learning algorithm based on radial basis functions and multi-objective particle swarm optimization

Radial basis function (RBF) neural networks for Multi-Instance Multi-Label (MIML) directly can exploit the connections between instances and labels so that they can preserve useful prior information, but they only adopt Gaussian radial basis function as their RBF whose parameters are difficult to determine. In this paper, parameters can be obtained by multi-objective optimization methods with multi performance measures treated as objectives, specifically, parameter estimation of different RBFs by an improved multi-objective particle swarm optimization (MOPSO) is proposed where Recall rate and Precision rate are chosen to obtain the most desirable Pareto optimal solution set. Furthermore, share-learning factor is proposed to modify the particle velocity in standard MOPSO to improve the global search ability and group cooperative ability. It is experimentally demonstrated that the proposed method can estimate the reliable parameters of different RBFs, and it is also very competitive with the state of art MIML methods.
Exploiting scatter matrix on one-class support vector machine based on low variance direction

When building a performing one-class classifier, the low variance direction of the training data set might provide important information. The low variance direction of the training data set improves the Covariance-guided One-Class Support Vector Machine (COSVM), resulting in better accuracy. However, this classifier does not use data dispersion in the one class. It explicitly does not make use of target class subclass information. As a solution, we propose Scatter Covariance-guided One-Class Support Vector Machine, a novel variation of the COSVM classifier (SC-OSVM). In the kernel space, our approach makes use of subclass information to jointly decrease dispersion. Our algorithm technique is even based on a convex optimization problem that can be efficiently solved using standard numerical methods. A comparison of artificial and real-world data sets shows that SC-OSVM provides more efficient and robust solutions than normal COSVM and other contemporary one-class classifiers.
XOR Chain and Perfect Secrecy at the Dawn of the Quantum Era

In this article, we present a new method that achieves Shannon’s perfect secrecy. To achieve this property, we will introduce the triple XOR cancellation rule. The approach has two execution modes: digital signature and data encryption. We provide perfect secrecy proof of the encryption method. Furthermore, based on our fundamental algorithm, we developed a new strategy for the blockchain system that does not require proof of work (PoW). However, it is a practical mechanism for connecting blocks to the chain. Due to the risk that quantum computers present for current cryptosystems based on prime factorization or discrete logarithm, we postulate that our method represents a promising alternative in the quantum era. We expect our work to have profound implications for the security of communications between mobile devices, the Internet of Things (IoT), and the blockchain.
Incremental density clustering framework based on dynamic microlocal clusters

With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors’ streaming data analysis.
Task scheduling and data replication in cloud with improved correlation strategy

AbstractCloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement (SLA) compliance, as well as protecting their own financial gain. An Improved Correlation strategy-based Task Scheduling and Data Replication in Cloud (ICTSDC) is what this work aims to give. The suggested system's primary phases are as follows: Management of replication and task scheduling. Initial job scheduling will be optimization-based and take into account goals such bottleneck value, migration cost, VM load, enhanced correlation, and replication, respectively. For this, a brand-new extended DMO algorithm called Self-adaptive Dwarf Mongoose Optimization (SADMO) is presented. In the replication management stage, the potential copies must first be identified based on the prior objective. The suggested SADMO model implements the optimization technique for replica placement throughout the replication management process. The outcomes of the ICTSDC technique are evaluated to other methods using a variety of metrics, like bottleneck value, migration cost, Virtual Machine (VM) load, improved correlation, as well as replication efficiency. A lower mean value of 0.324 is gained with the ICTSDC scheme for fitness.KEYWORDS: Task schedulingdata replicationcloudimproved correlationoptimization Disclosure statementNo potential conflict of interest was reported by the author(s).