Reviewerly/exHarmony huggingface dataset:
Dynamic quantum fully homomorphic encryption scheme based on universal quantum circuit

Quantum fully homomorphic encryption (QFHE) allows arbitrary computations which is performed by each server on encrypted quantum states without decrypting them. However, the existing QFHE schemes are not capable to handle the volatility problem with the servers: once a dynamic server wants to leave or join the protocol, it must be updated. Consequently, a dynamic quantum fully homomorphic encryption (DQFHE) scheme based on universal quantum circuit (UQC) is proposed in this paper. Firstly, our scheme could extend the two-part QFHE scheme to m-part. Secondly, the dynamic property means the completed protocol could be retained when involved servers changed which means we allow new servers to join our scheme and the absence of elder servers. Thirdly, UQC enables arbitrary quantum transformations on the encrypted quantum states and could be proved to be perfectly secure, and the key-updating algorithms are also based on the UQC so that the keys could not be revealed. Finally, due to the property of QFHE, compared with the quantum secret sharing, we could finish the sharing procedure efficiently because we cancel the security check procedure.
Adaptive Adversarial Learning based cross-modal retrieval

There exists a heterogeneity gap between multi-modal data, hence it is difficult to directly measure the similarity between them. A common way to solve the problem is representation learning. Due to the unique adversarial optimization principle and the efficient cross-modal correlation learning ability, the cross-modal retrieval based on Generative Adversarial Network (GAN) has received significant attention recently. However, the potential semantic information is not fully explored in most GAN-based cross-modal learning approaches. In this paper, we propose a novel Adaptive Adversarial Learning (AAL) based cross-modal retrieval method. The generator of a specific modality projects heterogeneous data into the potential common subspace, while the discriminator is against the generator to maintain discriminability. In addition, three task-specific loss functions are designed in the generators to comprehensively exploit the semantic and label information. One problem is that directly optimizing the generator network will lead to ignoring the assessment of contribution to multi-loss functions. To overcome the above challenge, we present an adaptive balance strategy to match the appropriate contribution for each loss according to the degree of dispersion. Comprehensive experimental results on three widely-used databases show that the proposed method is effective and superior to the existing cross-modal retrieval methods.
A reversible data hiding scheme for sharing medical data

In this paper, we propose a data hiding technique that helps the receiver to extract the secret data and recover the original image. The receiver first creates a pair of keys using Paillier cryptosystem and shares public key with sender. The sender enlarges cover image of size M × N to (2M − 1) × (2N − 1) by inserting one row (column) between two rows (columns), and then divides it into 3 × 3 blocks such that two boundary blocks have a row or a column in common, and two interior blocks have one row and one column in common. The block’s corner pixels, called as seed pixels, belong cover image and others, called as non-seed pixels, are initialized as 1 that are updated while embedding secret data in seed pixels. The block’s central pixels hide a secret bit using the parity and seed pixels are processed clockwise to hide two secret bits in each using separate procedures for even and odd pixels. The seed’s next pixel is multiplied by a fixed number that helps receiver to determine how it was processed at sender side. All pixels are encrypted using Paillier public key, giving a stegoimage, that is transmitted to the receiver. The receiver decrypts the stegoimage using Paillier private key, extracts the secret data and cover image by performing the data extraction process. The embedding rate of proposed method is 2.24 bits per pixel, which is 12 %, 16 %, 89 %, 83 %, 114 %, better than the Bhardwaj, Wang et al., Chen-Chang, Chi et al., Liu et al., respectively.
An Evaluation of Link Prediction Approaches in Few-Shot Scenarios

Semantic models are utilized to add context information to datasets and make data accessible and understandable in applications such as dataspaces. Since the creation of such models is a time-consuming task that has to be performed by a human expert, different approaches to automate or support this process exist. A recurring problem is the task of link prediction, i.e., the automatic prediction of links between nodes in a graph, in this case semantic models, usually based on machine learning techniques. While, in general, semantic models are trained and evaluated on large reference datasets, these conditions often do not match the domain-specific real-world applications wherein only a small amount of existing data is available (the cold-start problem). In this study, we evaluated the performance of link prediction algorithms when datasets of a smaller size were used for training (few-shot scenarios). Based on the reported performance evaluation, we first selected algorithms for link prediction and then evaluated the performance of the selected subset using multiple reduced datasets. The results showed that two of the three selected algorithms were suitable for the task of link prediction in few-shot scenarios.
Probabilistic Approach to Ranking Search Results Using Bayesian Belief Networks

Introduction.This paper proposes a probabilistic approach to ranking search results using Bayesian Belief Networks (BBN).The proposed approach utilizes BBN to model the relationships between search queries, web pages, and user feedback, and to calculate the probability of a web page being relevant to a specific query.The approach takes into account various factors, such as keywords, page relevance, domain authority, and user feedback to generate a ranking score for each search result.The purpose of the paper is to conduct an analysis on the feasibility of creating a search engine that uses BBNs and probabilistic ranking methods for improving the accuracy and efficiency of search results.Results.The proposed approach was evaluated on a real-world dataset, and the results showed its effectiveness.Overall, the results suggest that the use of BBNs can provide a promising approach to enhancing search engine performance and user experience.The approach's effectiveness is attributed to its ability to model and reason about uncertainty and dependencies among variables, and its consideration of various factors, such as keywords, page relevance, domain authority, and user feedback. Conclusions.The proposed method has the potential to improve search relevance, reduce user frustration, and increase user satisfaction.However, further research is needed to optimize the proposed approach and to explore its applicability in different contexts.Overall, the study suggests that BBNs can provide a valuable tool for developing more effective and user-friendly search engines.Moreover, the use of Sphinx as a base search system shows promise in enabling the proposed approach to be integrated into practical search systems.Nonetheless, further research is needed to optimize the approach and evaluate its applicability in different contexts.
Revealing Unknown Aspects: Sparking Curiosity and Engagement with a Tourist Destination through a 360-Degree Virtual Tour

Over the past decades, 360-degree virtual tours have been used to provide the public access to accurate representations of cultural heritage sites and museums. The COVID-19 pandemic has contributed to a rise in the popularity of virtual tours as a means of engaging with locations remotely and has raised an interesting question: How could we use such experiences to bring the public closer to locations that are otherwise unreachable in real life or not considered to be tourist destinations? In this study, we examine the effectiveness of promoting engagement with a city through the virtual presentation of unknown and possibly also inaccessible points of interest through a 360-degree panoramic virtual tour. The evaluation of the experience with 31 users through an online questionnaire confirms its potential to spark curiosity, promote engagement, foster reflection, and motivate users to explore the location and its attractions at their leisure, thus enabling them to experience it from their personal point of view. The outcomes highlight the need for further research to explore this potential and identify best practices for virtual experience design.
CONVERSION OF HANDWRITTEN TEXT INTO EDITABLE TEXT

In spite of several advancements in technologies, handwriting have been continuing to persist as means of documenting information for day-to-day life, especially in rural areas.A handwritten recognition system is proposed to analyze and convert into editable text in respective languages.Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) which uses the architecture of Recurrent Neural Network (RNN) and Connectionist Temporal Classification (CTC) are used to perform the conversion task.A detailed ablation study has been done to analyze the contribution of individual modules and present state-of-the-art results for the task of unconstrained line and word recognition on popular datasets such as IAM On-Line Handwriting Database (IAM-OnDB) and Recognition and Indexing of handwritten documents and faxes (RIMES).The neural network is trained and tested using a Hybrid dataset containing handwritten text in local languages, numbers, and symbols.The proposed work is implemented using image segmentation-based handwritten text recognition where OpenCV is used for processing images and Pytorch is used for training and text recognition.This Proposed Framework uses CNN, LSTM and CTC sequentially to covert Handwritten Text to Text.
Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Abstract: Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure deduplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure deduplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data deduplication systems, our system has two advantages. First, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Second, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion. In addition, we put forth a methodology to modify a ciphertext over one access policy into ciphertexts of the same plaintext but under other access policies without revealing the underlying plaintext.
AutoCAD Practicum Teaching Strategies and Systems Using the Team Viewer Application

After COVID-19 pandemic era, the online teaching process has been adapted and used for various learning purposes. Some of the shortcomings of the online system are the limited interaction in practical learning, such as application-based practice. In addition, the process of practicum is not optimal because some students cannot carry out the instructions given due to the limited equipment of the students such as personal computers or laptops. Therefore, this research was conducted to make modifications to the application-based practicum system (AutoCAD practicum) with a remote system using the Team Viewer application. This study uses mixed method qualitative and quantitative where the questionnaire was given to participants after the training. The population that will be used in this study are all students who take the Introduction to Design engineering course with AutoCAD Practicum material. The results of this study show that the accessibility, interface and operation of the AutoCAD application can be done properly using the Team Viewer application with the students' smartphone media so that the practicum can be done properly. This strategy can overcome the problems of limited equipment and passive interaction during the practicum process. In addition, the strategies and methods can be widely applied to learning in general and other application-based practicums.
Dialogue as Text Containing Emoticons: Jurislinguistic Approach

The article considers dialogues containing emoticons, which have become case materials of various lawsuits. The key problems for legal linguistics related to the functioning of texts containing emoticons in modern electronic communication are considered: verbalization of emoticons, including online verbalization with the help of specialized emoji-translators, and their interpretation and lexicography. Basing on the considered court cases, the problem of similarity between the text, which includes emoticons, and the verbalized text, which does not contain pictograms, is also formulated. Basing on the analysis of pictogram texts, as well as messages containing emoticons, it has been concluded that the similarity between text with pictographic elements and text that does not contain emoticons can be represented to various degree. Three options are possible: 1) the presence of complete similarity (identity) between the text containing emoticons and the text, which is its exact verbalization; 2) the presence of partial similarity between text with emoticons and text without pictographic elements; 3) lack of similarity between text with emoticons and text without pictographic elements. The degree of similarity between texts arises from the phenomena of polysemy, homonymy and synonymy. The appearance of new meanings in the emoticon leads to the development of synonymy between pictograms, and ultimately to the synonymy of message texts. The homonymy of emoticons can be culture determined, and can also be associated with the broadening and narrowing of the communicative context of the situation from the position of the addresser and addressee of the message. Between the text containing emoticons and the text in which there are no emoticons, there may be a dictum similarity (identity), but at the same time accompanied by mode differences. Thus, the degree of similarity between a text containing emoticons and a verbalized text is determined both ontologically, i.e. by the very use of signs, which are characterized by the presence of multifaceted relationships, and epistemologically, i.e. depends on the social, psychological traits, characteristics and attitudes of persons entering into communication.
Synthetic Data Generation for Morphological Analyses of Histopathology Images with Deep Learning Models

In this study, we introduce a new synthetic data generation procedure for augmentation of histopathology image data. This is an extension to our previous research in which we proved the possibility to apply deep learning models for morphological analysis of tumor cells, trained on synthetic data only. The medical problem considered is related to the Ki-67 protein proliferation index calculation. We focused on the problem of cell counting in cell conglomerates, which are considered as structures composed of overlapping tumor cells. The lack of large and standardized data sets is a critical problem in medical image classification. Classical augmentation procedures are not sufficient. Therefore, in this research, we expanded our previous augmentation approach for histopathology images and we proved the possibility to apply it for a cell-counting problem.
Immersive practices in the modern cultural space(world and domestic experience)

The subject of this research is the immersive artistic environments that exist in the modern cultural space. Immersive technologies are used today in educational processes, historical reconstruction, various forms of socialization, in visual arts; immersion is implemented as a special scientific approach in anthropological, ethnographic, art history studies. Interactivity and immersiveness are considered here as complex predictable processes whose algorithms are embedded in the very basis of an artistic work or cultural environment. They make it possible to intensify the entire spectrum of sensory-emotional perception, creating not only a new type of artwork, but also a new type of recipient immersed in the process of unfolding an artistic plot. The classification of functions of immersive spaces is proposed in the study. On the example of Russian and foreign art projects, the essential signs and features of immersive art are analyzed and identified. Immersive projects implemented in recent years are considered on the example of the cultural field of the north-west of Russia, as well as a comparative analysis of foreign and domestic events is carried out. The study concluded that immersive environments have both positive and negative potential: a complex effect on human feelings using special technological techniques that affect perception can lead to both the effect of complete immersion and catharsis, and to destructive hyperstimulation of the psyche. And the artistic immersions themselves are more a form of media communication than finished works.
Education as a Catalyst for Economic Development: A Comparative Study

This extensive research offers a comparative exploration of the intricate dynamics between education and economic development, presenting an unassailable argument for the role of education as a potent catalyst for economic growth. The study employs a sophisticated blend of quantitative and qualitative methodologies, utilizing cross-country panel data regression models and in-depth case studies. The findings underscore those countries which priorities both the quantity and quality of education witness significant enhancement in their economic development trajectories. However, the magnitude and nature of education's impact on economic development are intricately tied to the unique socio-economic and cultural context of each country. The paper concludes by urging policymakers to acknowledge the instrumental role of education in fostering economic growth and tailor their educational strategies accordingly.
Smart heat tariffs in transition to free market

Innovative pricing mechanisms should motivate heat suppliers and consumers to move toward more sustainable energy systems and introduce low-temperature district heating systems and sector coupling in smart energy systems. Therefore, district heating regulation regimes should also be changed to stimulate transformations in the energy sector. The district heating tariffs depend on many factors, including fuel prices, operational parameters, taxes, investments, and other criteria. Therefore, an analysis of the DH tariffs has been implemented to find solutions to motivate DH enterprises towards energy efficiency and climate neutrality. The analysis results are based on the decision-making assessment approach by selecting various criteria and evaluating them from five significant aspects: engineering, environmental, climate, economic and socioeconomic. The central elements within the developed fuzzy cognitive mapping model are investment costs, heat production costs, and primary energy consumption. Considering the set boundary conditions, the most beneficial method for smart heat tariff definition could be heat tariff benchmarking with integrated energy efficiency standards for DH operators.
Experimental Verification of the Self-Driven Algorithms for Solving Max-Sum Labeling Problems

Introduction.Max-sum labeling problems play an essential role in modern pattern recognition and can be used with other methods and a stand-alone approach.An essential step in building a pattern recognition system is the choice of an algorithm to solve the problem, which may require experimentation with different algorithms.This fires a need for software that allows solving different problems with the help of different algorithms for further analysis of the results of experiments and the final selection of the algorithm.The purpose of the paper is to demonstrate the capabilities of the developed software for solving max-sum labeling problems.Results.The software containing various algorithms for solving max-sum labeling problems was developed and experimentally tested.The program operation is shown on the example of image processing problems based on labeling: color image restoration, binary image denoising, posterization and binocular stereo vision.Conclusions.The software described in the article verifies in practice the correctness of the self-driven algorithm for solving max-sum labeling problems.The application allows the operator to choose an algorithm for the labeling task and configure its parameters.This program will be helpful for developers of computer vision systems based on labeling problems and undergraduates, graduate students, and researchers studying structural pattern recognition methods.
Book review

The debate over the semantics of demonstratives is in a stalemate between those positions attributing some referential significance to a speaker's referential intentions and those not doing so. The latter approach is supported by cases driving the non-intentional intuition in which the speakers mistakenly point at objects other than the ones they intend to refer to. The intentionalists, such as Martin Montminy, reply that once we think of potential extensions of such cases in which the speaker explains to the hearer what her referential intention was, it is the intentionalist intuition that prevails. In this paper, I develop a semantics for demonstratives whose task is to accommodate both of these seemingly contradictory intuitions within the general non-intentionalist framework. The proposed idea is that the reference of a use of a demonstrative can change over time, as the discourse develops. This idea is handled formally by the addition of a parameter of the index of evaluation that represents the referentially relevant aspects of the state of the discourse. Also, I provide reasons for preferring my view over two rival positions: one by Palle Leth, and one that I adjust to the demands of the semantics of demonstratives from the theory that Kai von Fintel and Anthony Gillies offer for epistemic modals, and from Laura Delgado's polyreferentialism for proper names.
Data pricing with privacy loss compensation for <scp>cyber‐physical</scp> systems: A Stackelberg game based approach

Abstract The integration of sensors in cyber‐physical systems has given rise to data markets, where data owners can offer their sensing data for sale to potential buyer. However, determining the optimal data price in such markets is a complex issue, which demands a careful consideration of the interests of all parties involved, as well as the potential privacy loss for data sellers. By taking privacy loss into account, this paper proposes a fair compensation model for data sellers and formulates the pricing problem as a Stackelberg game. An automatic data pricing algorithm is developed to calculate the optimal price maximizing the joint benefits of the data sellers and the buyer where the privacy loss of the data sellers are compensated reasonably. Numerical simulations validate the effectiveness of the proposed pricing model in balancing benefits and privacy loss.