Reviewerly/exHarmony huggingface dataset:
Everyday practices of pupils of the vocational and technical education system of Crimea in the 1940s – 1950s

Importance. A lot has been written about the relationship of the Soviet person with the authorities and the space of big politics, his life and way of life. However, the author of the article is sure that it is advisable to continue studying the problems of Soviet everyday life. Nowadays, interest in the topic of the history of everyday life and microhistory, as well as attempts to rethink the experience of the Soviet period, is gaining importance. The research deals with the everyday life of pupils of the state system of labor reserves after the end of the Great Patriotic War on the example of educational institutions of the Crimea. Materials and methods. The principles of historicism, consistency, objectivity, research methodology – a systematic approach, as well as a set of complementary general scientific methods were used. Results and Discussion. Vocational education as an object of research was not chosen by chance. For more than 80 years, it has been an institution for the reproduction of skilled labor and at the same time performs an economic function. It also opens the way to mastery for young people, creates the basis for creative growth. As part of the research, new archival data on this topic were introduced into scientific circulation. Conclusion. The significance of referring to the history of vocational education makes it possible to avoid past mistakes, equalize existing contradictions, and also choose the most productive way for the development of vocational education today. Today, new approaches to the system of education and training of workers are being presented. And the knowledge and skills accumulated in vocational education provide unlimited potential for improving the content, forms and methods of education. Therefore, it is necessary to study the activities of the earlier existing schools, colleges and technical schools.
Formalizing the Semantics of a Classical-Quantum Imperative Language in Coq

In order to verify the functional correctness of quantum circuits or algorithms, a prominent approach is to specify them as quantum programs and semi-automatically deduce them in a theorem prover. It is indispensable to first formalize the semantics of the basic quantum language. We formalize in Coq an imperative language which allows for classical and quantum information interactions. We define small-step operational semantics and state-based denotational semantics. Then we prove a consistency theorem between these two semantics. A distribution-based denotational semantics is also defined and related to the state-based one. Finally, we describe a few typical quantum algorithms and utilize the distribution-based denotational semantics to verify their correctness.
Evaluation of Smart Contract Vulnerability Analysis Tools: A Domain-Specific Perspective

With the widespread adoption of blockchain platforms across various decentralized applications, the smart contract’s vulnerabilities are continuously growing and evolving. Consequently, a failure to optimize conventional vulnerability analysis methods results in unforeseen effects caused by overlooked classes of vulnerabilities. Current methods have difficulty dealing with multifaceted intrusions, which calls for more robust approaches. Therefore, overdependence on environment-defined parameters in the contract execution logic binds the contract to the manipulation of such parameters and is perceived as a security vulnerability. Several vulnerability analysis tools have been identified as insufficient to effectively identify certain types of vulnerability. In this paper, we perform a domain-specific evaluation of state-of-the-art vulnerability detection tools on smart contracts. A domain can be defined as a particular area of knowledge, expertise, or industry. We use a perspective specific to the area of energy contracts to draw logical and language-dependent features to advance the structural and procedural comprehension of these contracts. The goal is to reach a greater degree of abstraction and navigate the complexities of decentralized applications by determining their domains. In particular, we analyze code embedding of energy smart contracts and characterize their vulnerabilities in transactive energy systems. We conclude that energy contracts can be affected by a relatively large number of defects. It also appears that the detection accuracy of the tools varies depending on the domain. This suggests that security flaws may be domain-specific. As a result, in some domains, many vulnerabilities can be overlooked by existing analytical tools. Additionally, the overall impact of a specific vulnerability can differ significantly between domains, making its mitigation a priority subject to business logic. As a result, more effort should be directed towards the reliable and accurate detection of existing and new types of vulnerability from a domain-specific point of view.
Stacked Ensemble Models Evaluation on DL Based SCA

side-channel analysis (SCA) has proved its effectiveness in attacking the cryptographic implementations for high security algorithms like Advanced Encryption Standard (AES). The improvement of machine learning (ML) in general and deep learning (DL) in particular in SCA shows that DL is a big threat in hardware security, in which the secret key of SCA countermeasure AES can be revealed with only 40 traces [17]. Combination of multiple DL can improve the effectiveness of SCA more. However, how to combine those models together is still a question. This paper applied stacked ensemble ML to combine the predictions from a number of inputs and sub-models together to enhance the power of DL in attacking AES implementation with SCA countermeasures. We train not only the output probabilities of sub-models but also their maximum likelihood score (MLS) in a stacked ensemble model to improve the performance of convolutional neural network (CNN)-based models. Further more, output probabilities from multiple trace inputs to the same sub-model are also utilized for our stack ensemble model training. A two step training procedure is required, one is for each sub-model and the other is for the stacked ensemble model, which takes inputs as the output probabilities of the above trained sub-models. This paper evaluate the effectiveness of various stacked ensemble models in terms of the number of input traces and the number of sub-models used for the first training stage. Our best model generates state-of-the art results when attacking the ASCAD variable-key database, which has a restricted number of training traces per key, recovering the key within 20 attack traces [15] in comparison to 40 traces as required by the original CNN with Plaintext feature extension (CNNP)-based model.
Towards a Realistic Decentralized Naive Bayes with Differential Privacy

This is an extended version of our work in [16]. In this paper, we introduce two novel algorithms to collaboratively train Naive Bayes models across multiple private data sources: Federated Naive Bayes and Gossip Naive Bayes. Instead of directly providing access to their data, the data owners compute local updates that are then aggregated to build a global model. In order to also prevent indirect privacy leaks from the updates or from the final model, our algorithms protect the exchanged information with differential privacy. We experimentally evaluate our proposed approaches, examining different scenarios and focusing on potential real-world issues, such as different data owner offering different amounts of data or requesting different levels of privacy. Our results show that both Federated and Gossip Naive Bayes achieve similar accuracy to a “vanilla” Naive Bayes while maintaining reasonable privacy guarantees, while being extremely robust to heterogeneous data owners.
SPOT+: Secure and Privacy-Preserving Proximity-Tracing Protocol with Efficient Verification over Multiple Contact Information

At SECRYPT 2022, Masmoudi et al. introduced a group signature scheme that offers an aggregated and batch verification over massive proofs of knowledge, named Sevil. The performance analysis of the proposed scheme demonstrates its efficiency and its applicability to real world applications. In this paper, we introduce Spot+, an extension of Sevil to a concrete use-case referred to as a proximity-tracing protocol. Spot+ is a secure and privacy-preserving proximity-tracing protocol that ensures data consistency and integrity and preserves the privacy of users who share their contact information with people in proximity. Spot+ relies on Sevil to significantly improve the performances of the Spot framework [IEEE Access Journal, 10, 3208697, (2022)] while supporting aggregated and batch verifications over contact information belonging to multiple users. In comparison with Spot, Spot+ construction allows to reduce computation complexity by 50% and 99% for verifying data integrity and consistency, respectively, when considering an asymmetric pairing type and a 128-bit security level.
Language Models as Controlled Natural Language Semantic Parsers for Knowledge Graph Question Answering

We propose the use of controlled natural language as a target for knowledge graph question answering (KGQA) semantic parsing via language models as opposed to using formal query languages directly. Controlled natural languages are close to (human) natural languages, but can be unambiguously translated into a formal language such as SPARQL. Our research hypothesis is that the pre-training of large language models (LLMs) on vast amounts of textual data leads to the ability to parse into controlled natural language for KGQA with limited training data requirements. We devise an LLM-specific approach for semantic parsing to study this hypothesis. To conduct our study, we created a dataset that allows the comparison of one formal and two different controlled natural languages. Our analysis shows that training data requirements are indeed substantially reduced when using controlled natural languages, which is relevant since collecting and maintaining high-quality KGQA semantic parsing training data is very expensive and time-consuming.
ALFR++: A Novel Algorithm for Learning Adversarial Fair Representations

As more decisions in our daily life become automated, the need to have machine learning algorithms that make fair decisions increases. In fair representation learning we are tasked with finding a suitable representation of the data in which a sensitive variable is censored. Recent work aims to learn fair representations through adversarial learning. With this paper, we build upon this work by introducing a novel algorithm, ALFR++, to learn adversarial fair representations independent of the downstream task that may be encountered (supervised or unsupervised). We introduce balanced error rate modulation, to stabilize the interaction between actor and adversary. We couple it with a recently introduced censoring technique called stacking, to learn strong censored representations within a restricted hypothesis space. Results show that our algorithm improves on previous work in both censoring and reconstruction, allowing us to create fairer data without knowing the task beforehand.
A Landmark-Cut Heuristic for Lifted Optimal Planning

Lifted planning – finding plans directly on the PDDL input model – has attracted renewed attention during the last years. This avoids the process of grounding, which can become computationally prohibitive very easily. However, the main focus of recent research in this area has been on satisficing, i.e., (potentially) suboptimal planning. We present a novel heuristic for optimal lifted planning. Our basic idea is inspired by the LM-cut heuristic, which has been very successful in grounded optimal planning. Like LM-cut, we generate cut-based landmarks via back-chaining from the goal, generating cuts of partially grounded actions. However, exactly mimicking the ground formulation is not feasible, this includes computing the hmax heuristic several times for one computation of the LM-cut heuristic (which is already NP-hard to compute). We show that our heuristic is admissible and evaluate it in a cost optimal setting.
Task-Sensitive Discriminative Mutual Attention Network for Few-Shot Learning

Many few-shot image classification methods focus on learning a fixed feature space from sufficient samples of seen classes that can be readily transferred to unseen classes. For different tasks, the feature space is either kept the same or only adjusted by generating attentions to query samples. However, the discriminative channels and spatial parts for comparing different query and support images in different tasks are usually different. In this paper, we propose a task-sensitive discriminative mutual attention (TDMA) network to produce task-and-sample-specific features. For each task, TDMA first generates a discriminative task embedding that encodes the inter-class separability and within-class scatter, and then employs the task embedding to enhance discriminative channels respective to this task. Given a specific query and different support images, TDMA further incorporates the task embedding and long-range dependencies to locate the discriminative parts in the spatial dimension. Experimental results on miniImageNet, tieredImageNet and FC100 datasets show the effectiveness of the proposed model.
Decidable fragments of the Quantified argument calculus

Abstract This paper extends the investigations into logical properties of the quantified argument calculus (Quarc) by suggesting a series of proper subsystems which, although retaining the entire vocabulary of Quarc, restrict quantification in such a way as to make the result decidable. The proof of decidability is via a procedure that prunes the infinite branches of a derivation tree in what is a syntactic counterpart of semantic filtration. We demonstrate an application of one of these systems by showing that Aristotle’s assertoric syllogistic is embeddable within, thus also providing another method of showing its decidability.
Causal Graphs

Causal graphs help us understand issues in causal inference. Although anchored in mathematics, they provide visual cues that aid intuition and understanding (Elwert 2013).
RStudio and R

R is an open-access software that has impressive data analysis capabilities. RStudio makes working with R easier. In this chapter, we provide an introduction to RStudio and working with R.
Visual design of green information in urban environment based on global similarity calculation and multi-dimensional visualization technology

In recent years, the escalating prevalence of elevated consumption and carbon emissions within urban operations has reached a disconcerting extent. This surge in resource depletion and environmental pollution exerts an adverse influence on the well-being of individuals, while impeding societal progress and hindering the enhancement of overall quality of life. Within the domain of urban environmental design, the integration of visual displays emerges as a superior approach to facilitate the assimilation and analysis of green and low-carbon information. However, urban environmental data usually contains multiple dimensions, so it is a problem to realize the data representation of multiple dimensions while maintaining the correlation and interactivity between data. To surmount the challenge of visualizing such intricate information, this investigation initially employs a sophisticated memory-based clustering algorithm for information extraction, accompanied by a global similarity algorithm that meticulously computes attribute component quantities within specific dimensions of the vector. Furthermore, leveraging the inherent power of Vue's bidirectional data binding capabilities, the study adopts the esteemed MVVM (Model-View-View-Model) pattern, fostering seamless two-way interaction through the established logical relationship. As a result, the amalgamation of multidimensional visualization technology empowers comprehensive data mining through a captivating visual augmentation. Concurrently, the application of data visualization dimension control delivers tailored displays tailored to green and low-carbon scenarios within urban environmental design. Experimental results impeccably validate the effectiveness of the proposed algorithm, substantiated by a mere 1.77% false alarm rate for data stream difference detection and a clustering difference of 1.34%. The aforementioned algorithm accentuates the efficacy of visual displays, thus engendering a profound synergy between the industrial and supply chains. Moreover, it facilitates the design, production, and utilization of environmentally friendly products and energy sources. This, in turn, serves as a catalyst, propelling the widescale adoption of green and low-carbon practices throughout the entire industrial chain, fueled by the seamless integration of multimedia data.
BIoTS-Path: Certification Transmission of Supply Chains Based on Blockchain–Internet of Things Architectures by Validating the Information Path

A food traceability system (FTS) can record information about processes along a production chain to determine their safety and quality. Under the Internet of Things (IoT) concept, the communication technologies that support FTSs act as platforms for mass access to information with limited security. However, the integrity of the collected data is not immune to security attacks. This paper proposes a point-to-point information transmission path with no edges or access boundaries (no intermediaries) to transmit data with integrity. This route is possible thanks to the architectural articulation of a hardware device (sensor BIoTS) at the perception layer, with the Blockchain architecture at the application layer. This pairing makes an ecosystem with the ability to trace and certify in parallel the products, the supply chain processes, and the data recorded in it possible. The design of the security testing ecosystem is based on the theoretical and technical principles of cybersecurity. It is executed through mathematical models that define the probability of attacks’ success against the transmitted data’s integrity. The security tests performed allow for establishing that this BIoTS information transmission route is unlikely to suffer from transmission vulnerabilities and that it is not prone to security attacks against integrity. This work paves the way toward fully integrating Blockchain technology in dedicated IoT architectures.
Software System for Automatic Grading of Paper Tests

The advent of digital technology has revolutionized numerous aspects of modern life, including the field of assessment and testing. However, paper tests, despite their seemingly archaic nature, continue to hold a prominent position in various assessment domains. The accessibility, familiarity, security, cost-effectiveness, and versatility of paper tests collectively contribute to their continued prominence. Hence, numerous educational institutions responsible for conducting examinations involving a substantial number of candidates continue to rely on paper tests. Consequently, there arises a demand for the possibility of automated assessment of these tests, aiming to alleviate the burden on teaching staff, enhance objectivity in evaluation, and expedite the delivery of test results. Therefore, diverse software systems have been developed, showcasing the capability to automatically score specific question types. Thus, it becomes imperative to categorize related question types systematically, thereby facilitating a preliminary classification based on the content and format of the questions. This classification serves the purpose of enabling effective comparison among existing software solutions. In this research paper, we present the implementation of such a software system using artificial intelligence techniques, progressively expanding its capabilities to evaluate increasingly complex question types, with the ultimate objective of achieving a comprehensive evaluation of all question types encountered in paper-based tests. The system detailed above demonstrated a recognition success rate of 99.89% on a curated dataset consisting of 734,825 multiple-choice answers. For the matching type, it achieved a recognition success rate of 99.91% on 86,450 answers. In the case of short answer type, the system achieved a recognition success rate of 95.40% on 129,675 answers.
ADQE: Obtain Better Deep Learning Models by Evaluating the Augmented Data Quality Using Information Entropy

Data augmentation, as a common technique in deep learning training, is primarily used to mitigate overfitting problems, especially with small-scale datasets. However, it is difficult for us to evaluate whether the augmented dataset truly benefits the performance of the model. If the training model is relied upon in each case to validate the quality of the data augmentation and the dataset, it will take a lot of time and resources. This article proposes a simple and practical approach to evaluate the quality of data augmentation for image classification tasks, enriching the theoretical research on data augmentation quality evaluation. Based on the information entropy, multiple dimensional metrics for data quality augmentation are established, including diversity, class balance, and task relevance. Additionally, a comprehensive data augmentation quality fusion metric is proposed. Experimental results on the CIFAR-10 and CUB-200 datasets show that our method maintains optimal performance in a variety of scenarios. The cosine similarity between the score of our method and the precision of model is up to 99.9%. A rigorous evaluation of data augmentation quality is necessary to guide the improvement of DL model performance. The quality standards and evaluation defined in this article can be utilized by researchers to train high-performance DL models in situations where data are limited.
Path Planning of Deep-Sea Landing Vehicle Based on the Safety Energy-Dynamic Window Approach Algorithm

To ensure the safety and energy efficiency of autonomous sampling operations for a deep-sea landing vehicle (DSLV), the Safety Energy-Dynamic Window Approach (SE-DWA) algorithm was proposed. The safety assessment sub-function formed from the warning obstacle zone and safety factor addresses the safety issue arising from the excessive range measurement error of forward-looking sonar. The trajectory comparison evaluation sub-function with the effect of reducing energy consumption achieves a reduction in path length by causing the predicted trajectory to deviate from the historical trajectory when encountering “U”-shaped obstacles. The pseudo-power evaluation sub-function with further energy consumption reduction ensures optimal linear and angular velocities by minimizing variables when encountering unknown obstacles. The simulation results demonstrate that compared with the Minimum Energy Consumption-DWA algorithm, the SE-DWA algorithm improves the minimum distance to an actual obstacle zone by 68% while reducing energy consumption by 11%. Both the SE-DWA algorithm and the Maximum Safety-DWA (MS-DWA) algorithm ensure operational safety with minimal distance to the actual obstacle zone, yet the SE-DWA algorithm achieves a 24% decrease in energy consumption. In conclusion, the path planned by the SE-DWA algorithm ensures not only safety but also energy consumption reduction during autonomous sampling operations by a DSLV in the deep sea.