Reviewerly/exHarmony huggingface dataset:
Identification of approaches to architectural and structural solutions in the design of sports buildings

In the modern world, sports facilities are multifunctional complexes that not only meet the needs of the sports infrastructure but also serve as recreational facilities. Sports buildings and facilities are constructed using large-span structures and require significant urban areas for their erection. The paper discusses the results of research carried out by the Moscow State University of Civil Engineering, which provides for the efficient use of the building area of sports facilities and the reduction of the cost of facilities through the use of space within the large-span structures for the placement of a large number of sports infrastructure facilities. The key issue in solving the problem of compactness of the developed area is the use of large-span structures of intermediate floors. Based on the selected cases, the authors classify approaches in the design of sports buildings, based on the use of three types of large-span floor structures: steel-reinforced concrete floor slab, load-bearing floor, and building-bridge. The authors conclude that these approaches can be used in the design of various types of sports facilities: from the municipal level of sports and recreation complexes to facilities designed for major international competitions.
Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm

Smart storage is widely used for its efficient storage and applications. For making dynamic decisions when robots conflict and eliminating robot conflicts and improving efficiency from a global perspective, path-planning Algorithm will be analyzed and improved by integrating dynamic weighted and heat-map algorithm based on the scalable information of multi-robot in this paper. Firstly, a small storage grid model applicable to a variety of storage modes is established. Second, in order to solve the frontal collision problem of robots, an improved reservation table is established, which greatly reduces the storage space occupied by the reservation table while improving the operation efficiency; the A* algorithm is improved to achieve the purpose of avoiding vertex conflict and edge conflict at the same time; dynamic weighting table is added to solve the multi-robot driving strategy of intersection conflict and ensure that the most urgent goods are out of the warehouse firstly; the heat map algorithm is appended to reasonably allocate tasks, avoiding congested areas and realizing the dynamic assignment of tasks. Finally, the simulation was done by the proposed path planning method, the average transportation time was reduced by 14.97% comparing with the traditional path algorithm.
Experimental Realization of Optimal Time‐Reversal on an Atom Chip for Quantum Undo Operations

Abstract The authors report the use of the dressed chopped random basis optimal control algorithm to realize time‐reversal procedures. The latter are aimed for the implementation of quantum undo operations in quantum technology contexts as quantum computing and quantum communications. The last performed operation can be time‐reversed via the undo command so as to perfectly restore a condition in which any new operation, chosen by the external user, can be applied. By generalizing this concept, the undo command can also allow for the reversing of a quantum operation in a generic time instant of the past. Here, thanks to optimal time‐reversal routines, all these functionalities are experimentally implemented on the fivefold Hilbert space of a Bose–Einstein condensate of non‐interacting 87 Rb atoms in the ground state, realized with an atom chip. Each time‐reversal transformation is attained by designing an optimal modulated radio frequency field, achieving on average an accuracy of around 92% in any performed test. The experimental results are accompanied by a thermodynamic interpretation based on the Loschmidt echo. These findings are expected to promote the implementation of time‐reversal operations in a real scenario of gate‐based quantum computing with a more complex structure than the five‐level system considered here.
Modeling Latent Autocorrelation for Session-based Recommendation

Session-based Recommendation (SBR) aims to predict the next item for the current session, which consists of several clicked items in a short period by an anonymous user. Most of the sequential modeling approaches to SBR are focusing on adopting advanced Deep Neural Networks (DNNs), and these methods require increasingly longer training times. Existing studies have shown that some traditional SBR methods can outperform some DNN-based sequential models, however, few studies have attempted to investigate the effectiveness of traditional methods in recent years. In this paper, we propose a novel and concise SBR model inspired by the basic concept of autocorrelation in the Stochastic Process. Autocorrelation measures the correlation of a process at different moments. Therefore, it is natural to use it to model the correlation of clicked item sequences at different time shifts. Specifically, we use Fast Fourier Transforms (FFT) to compute the autocorrelation and combine it with several linear transformations to enhance the session representation. By this means, our proposed method can learn better session preferences and is more efficient than most DNN-based models. Extensive experiments on two public datasets show that the proposed method outperforms state-of-the-art models in both effectiveness and efficiency.
Tiger: Transferable Interest Graph Embedding for Domain-Level Zero-Shot Recommendation

Recommender systems play a significant role in online services and have attracted wide attention from both academia and industry. In this paper, we focus on an important, practical, but often overlooked task: domain-level zero-shot recommendation (DZSR). The challenge of DZSR mainly lies in the absence of collaborative behaviors in the target domain, which may be caused by various reasons, such as the domain being newly launched without existing user-item interactions, or users' behaviors being too sensitive to collect for training. To address this challenge, we propose a Transferable Interest Graph Embedding technique for Recommendations (Tiger). The key idea is to connect isolated collaborative filtering datasets with a knowledge graph tailored to recommendations, then propagate collaborative signals from public domains to the zero-shot target domain. The backbone of Tiger is the transferable interest extractor, which is a simple yet effective graph convolutional network (GCN) aggregating multiple hops of neighbors on a shared interest graph. We find that the bottom layers of GCN preserve more domain-specific information while the upper layers represent universal interest better. Thus, in Tiger, we discard the bottom layers of GCN to reconstruct user interest so that collaborative signals can be successfully propagated to other domains, and retain the bottom layers of GCN to include domain-specific information for items. Extensive experiments with four public datasets demonstrate that Tiger can effectively make recommendations for a zero-shot domain and outperform several alternative baselines.
<i>RecipeMind:</i> Guiding Ingredient Choices from Food Pairing to Recipe Completion using Cascaded Set Transformer

We propose a computational approach for recipe ideation, a downstream task that helps users select and gather ingredients for creating dishes. To perform this task, we developed RecipeMind, a food affinity score prediction model that quantifies the suitability of adding an ingredient to set of other ingredients. We constructed a large-scale dataset containing ingredient co-occurrence based scores to train and evaluate RecipeMind on food affinity score prediction. Deployed in recipe ideation, RecipeMind helps the user expand an initial set of ingredients by suggesting additional ingredients. Experiments and qualitative analysis show RecipeMind's potential in fulfilling its assistive role in cuisine domain.
Distributed Supervision Model for Enterprise Data Asset Trading Based on Blockchain Multi-Channel in Industry Alliance

Compared with traditional physical commodities, data are intangible and easy to leak, and the related trading process has problems, such as complex participating roles, lengthy information flow, poor supervisory coverage and difficult information traceability. To handle these problems, we construct a distributed supervision model for data trading based on blockchain, and conduct multi-party hierarchical and multi-dimensional supervision of the whole process of data trading through collaborative supervision before the event, at present and after the event. First, the characteristics of information flow in the data trading process are analyzed, and the main subject and key supervision information in the data trading process are sorted out and refined. Secondly, combined with the actual business process of data trading supervision, a multi-channel structure of distributed supervision is proposed by adopting an access–verification–traceability strategy. Finally, under the logical framework of the supervision model, the on-chain hierarchical structure and the data hybrid storage method of “on-chain + off-chain” are designed, and multi-supervisor-oriented hierarchical supervision and post-event traceability are realized through smart contracts. The results show that the constructed blockchain-based distributed supervision model of data trading can effectively isolate and protect sensitive and private information between data trading, so as to realize the whole process, multi-subject and differentiated supervision of key information of data trading, and provide an effective and feasible method for the controllable and safe supervision of data trading.
Cybercrime as a Threat to the National Security of the Baltic States and Ukraine: The Comparative Analysis

The article is devoted to comparative legal research of cybercrime as a threat to the national security of the Baltic States and Ukraine. The purpose of the scientific article is to study the systems of criminal legislation in the part of cybercrime in the Baltic States and Ukraine, to determine the public danger of cybercrime, as well as to study ways of countering this criminal category. The object of research is cybercrime as a criminal and legal category. The subject of the study is the peculiarities of combating cybercrimes in the Baltic States and Ukraine, and carrying out a comparative analysis of their criminal legislation. The authors analyze the essence of the concept of cybercrime and identify the main types of cybercrime that are committed most often. Thus, in this article, "cybercrime" means any crime committed with the help of information technologies or in the information space. Special attention is focused on cybercrime in the Baltic States, in particular Latvia, Lithuania, and Estonia. The main types of cybercrimes, the responsibility for the commission of which is provided for by Chapter XVII of the Criminal Code of Ukraine, are described. The Convention on Cybercrime was considered, and it was determined which norms should be implemented in the legislation of the Baltic countries and Ukraine. It was determined that the issue of countering cybercrime in the Baltic States. The main threats, prevention, and countermeasures against cybercrimes in the Baltic States and Ukraine and the main features of international and legal cooperation with cybercrimes within the framework of the European Union are outlined. It was concluded that in order to combat crimes committed with the use of modern information technologies in Ukraine, it is necessary to constantly increase the security of information systems, develop modern information technologies, improve legislation in the field of information crimes, develop competitive means of informatization, expand international cooperation in the field of safe use of information resources.
Analyses and optimizing of bidirectional quantum teleportation using a five-qubit cluster state as quantum channel

Abstract Using a five-qubit cluster state as quantum channel, a novel bidirectional quantum teleportation protocol has been proposed by Wang and Li (2022 Quantum Inf. Process. 21 44) recently in which there are two legitimate parties, Alice and Bob. Alice can teleport a two-qubit pure entangled state to Bob, simultaneously Bob can teleport a single-qubit state to Alice. In this paper, based on the same five-qubit cluster state as quantum resource, we put forward an optimized protocol. A comparison with Wang and Li’s protocol is made which reveals distinct advantages of the optimized protocol in reducing the classical resources and degrading the complexity of necessary quantum operations, as well as increasing the inherent efficiency. Additionally, some mistakes in Wang and Li’s protocol are pointed out and amended.
Structured Light Field by Two Projectors Placed in Parallel for High-Speed and Precise 3D Feedback

In recent years, it is required to acquire three-dimensional information at high speed in various fields. Previously, a structured light field (SLF) method for high-speed three dimensional measurement in 1 ms was proposed by our group. However, the SLF method has a drawback of worse depth estimation error by several tens millimeters. In this paper, a novel method to generate SLF with two projectors placed in parallel is proposed. This arrangement could produce bigger pattern change depending on the depth and made more precise estimation possible. The depth estimation experiments for precision evaluation and dynamic projection mapping experiment successfully demonstrated precise depth estimation with the error of several millimeters and high-speed estimation within 1 ms, though the measurement range was limited to approximately 100 mm.
Multi-sensor Fault Detection, Exclusion and Readmission

In this work, we present a fault detection, identification, exclusion, and readmission method in a multi-sensor navigation architecture. The example multi-sensor navigation architecture used in this work comprises of different sensors coupled with an inertial measurement unit (IMU) in individual Kalman filters (KF), where each filter separately provides its navigation solution. These decentralized individual KF solutions are later combined to provide the resultant navigation solution using a weighted fusion. In contrast to a centralized KF where faulty sensor measurements corrupt the navigation solution due to KF memory, the decentralized structure allows fault(s) to be removed from the resultant navigation solution instantaneously after detection and exclusion of the faulty filter(s). Solution separation among subsets of filters is used to detect faults and then identify the faulty sensors or filters, after which they are excluded from the resultant navigation solution. We also provide protection levels against faulty filters, which are identified and excluded. In order to readmit the declared faulty filters, we utilize solution separation techniques again to design a readmission test. This readmission test also allows us to reset a faulty filter in case the fault caused the filter navigation solution to be corrupted. We provide integrity risk and protection level formulas that account for missed detection, and wrong readmission. We validate the developed algorithm for detection, identification, exclusion, and readmission of faulty sensor using real sensor data set and simulated fault scenarios.
Professional Development of Teachers and Future Teachers: Factors and Challenges

The article discusses the features of professional development and self-development of teachers and students of pedagogical educational institutions in Russia, India, Brazil. Russian researchers analyze the influence of activity of future teachers in digital environment on their professional development and self-development, as well as the relationship of professional development with students' self-determination, namely, goal setting. Empirically, the relationship between the digital activity of students of the pedagogical magistracy and the desire for professional self-development was revealed, which, in turn, depends on the severity of the goals and their orientation on obtaining a teaching profession. The study of Indian scientists is aimed at studying the quality of life of teachers on the effectiveness of professional development. It was found that the majority of Indian teachers has a low level of professional development. Factors that negatively affect the level of professional development were identified, namely: low financial status, unfavorable family environment, physical and psychological well-being, job security. Researchers also consider reasons for professional development, including lack of time, lack of accountability for professional development activities etc. The Brazilian study examines the features of social perceptions of teachers about teacher education and their professional development. The authors depicted social perceptions of teachers about teacher education based on four categories: planning, didactic procedures; assessment and teacher-student relationship. The article also provides recommendations for the successful application of pedagogical practice and the creation of an environment for the teachers’ professional development, taking into account the results of all three blocks by countries participating in this study.
Real-world data mining meets clinical practice: Research challenges and perspective

As Big Data Analysis meets healthcare applications, domain-specific challenges and opportunities materialize in all aspects of data science. Advanced statistical methods and Artificial Intelligence (AI) on Electronic Health Records (EHRs) are used both for knowledge discovery purposes and clinical decision support. Such techniques enable the emerging Predictive, Preventative, Personalized, and Participatory Medicine (P4M) paradigm. Working with the Infectious Disease Clinic of the University Hospital of Modena, Italy, we have developed a range of Data–Driven (DD) approaches to solve critical clinical applications using statistics, Machine Learning (ML) and Big Data Analytics on real-world EHR. Here, we describe our perspective on the challenges we encountered. Some are connected to medical data and their sparse, scarce, and unbalanced nature. Others are bound to the application environment, as medical AI tools can affect people's health and life. For each of these problems, we report some available techniques to tackle them, present examples drawn from our experience, and propose which approaches, in our opinion, could lead to successful real-world, end-to-end implementations. DESY report number DESY-22-153.
High-fidelity decryption technology of Visual Cryptography based on optical coherence operation

This paper proposes a high-fidelity decryption technology for secret images based on Visual Cryptography (VC) and optical coherence operation. The secret image is converted into two ciphertext images using the VC encryption algorithm, and loaded into two secret vectors respectively. A coherent optical illumination system is built to align the spatial information of the two secret vectors, and decryption is achieved by generating coherent subtraction at the diffraction center of the image plane, the effect is the same as the “XOR” logic operation between two secret images. Both simulation and optical experiments are compared between the optical coherence “XOR” and “OR” operation, and the decryption results demonstrate that the decryption method based on optical coherence “XOR” logic operation outperforms the method based on optical “OR” logic operation in various evaluation indicators such as Structural Similarity (SSIM), Corresponding Coefficient (CC), and Signal-to-noise ratio (SNR). The decryption method provides a broad prospect for the simple, convenient, and high-fidelity information recovery application of VC.
Directional user similarity model for personalized recommendation in online social networks

With the huge amount of information available in online social networks and the increasing spread of user generated data in different forms, personal recommendation systems represent ideal solutions for optimising users’ efforts and advocating the right information that suits the user’s context. This research proposes a user-based collaborative filtering recommendation technique for social networks that exploits the context and characteristics of social networks in order to recommend personalised content to users. The originality of the approach is to streamline contextual features and social network data to reduce data dimensionality and use learning techniques to uncover contextual information that is not normally included as a typical interest to rank tweets for users. The proposed approach uses a hybrid directional relation user similarity model (DRUSM) that exploits subtle users’ actions and directional interactions’ diversity to match users’ contexts. DRUSM includes two similarity algorithms – author directional similarity (AUDS) and action directional similarity (ACDS) – to account for the abovementioned actions and interactions. It employs a learning algorithm to rank tweets according to a user’s interests. DRUSM was tested and compared with AUDS, ACDS and other algorithms, such as the new heuristic similarity model, content relevance and publisher authority. Experiments show that DRUSM outperformed all the other algorithms. It achieves higher accuracy in recommending tweets due to its suitability to appropriately handle fine details of the user’s context.
Environmental Variables Including Heavy Metals Significantly Shape the Soil Bacterial Community Structure in the Tatun Volcano Group, Northern Taiwan

Recent studies suggested the presence of magma chambers from the Tatun volcano group under northern Taiwan's surface, the result of episodic volcanism for 0.2-2.8 million years. However, the microbial community in volcanic soil has not yet been characterized. Therefore, the present study investigated the spatial distribution of microbial communities and their relationships with environmental variables, including heavy metals. Next-generation sequencing was used to analyze the microbial community structures in three areas with different land uses: Lengshuikeng (recreational area), Zhuzihu (agricultural area), and Huangzuishan (conservation area). High contents of environmental factors, such as nitrogen (0.46-1.14%) and phosphorus (2.01-13.88 ppm), were detected. Large concentrations of heavy metals, such as copper (55.90-127.60 ppm) and zinc (36.13-147.73 ppm), were found among the three sites, whereas those of lead (83.13 ppm) and chromium (48.33 ppm) were higher in the Zhuzihu area. The most prevalent phylum across all sites was Proteobacteria, followed by Actinobacteria, Acidobacteria, and Chloroflexi, while the most abundant bacterial species was Koribacteraceae: NA_01, followed by Cyanobacteria: NA. A network ana-lysis showed that Koribacteracea: NA_01 positively correlated with bacterial groups, including Flavisolibacter sp., Oxalobacteraceae: NA, and Actinomycetales: NA_01. Based on Shannon and Simpson's diversity indices, the diversity of bacteria was significantly less in the Huangzuishan area than in the Lengshuikeng and Zhuzihu areas. Bacterial assemblages also significantly differed (P<0.05) among the three sites. The present results provide clear evidence to show that environmental variables, including heavy metals, are key factors affecting the bacterial community structure in volcanic soil.