Reviewerly/exHarmony huggingface dataset:
International experience of using special knowledge in the investigation of counterfeiting

The article highlights the theoretical and practical aspects of the investigation of counterfeiting as a socially dangerous phenomenon that has a transnational character. The need to implement international experience in the use of special knowledge in the investigation of counterfeiting, as well as the formation and role of Interpol in coordinating the work of law enforcement agencies of different countries in this direction, is considerer.The general processes of European and world interconnection, the globalization of economic, political and social relations, supported by new communication and technological opportunities, are increasingly covering the sphere of economic crime, in particular the most widespread and most dangerous phenomenon for society, such as counterfeiting. Increasingly, this is manifested in the fact that, together with law-abiding citizens, it has become easier for criminals to penetrate across state borders, which has caused the spread of criminal manifestations in the transnational space. The exit of criminal activity beyond the borders of specific states gives rise to international crime, which puts it at a much higher level of danger compared to domestic crime. The problems and tasks associated with this phenomenon have become widely spread, and, as a matter of fact, they have become the subject of practical solutions by law enforcement agencies around the world and scientific consideration by scientists.Along with other serious crimes of a transnational nature, such as terrorism, illegal drug trafficking, human trafficking, crimes against the state, smuggling, domestic and foreign scientists also single out counterfeiting, which poses the greatest threat to the integrity of financial and commercial institutions of individual states and the world community in general.
Light Field Surface Feature and Spherical Descriptor in the Surface Scale Space

Depth information calculated from the spatial-angular consistent light field data of a Lambertian scene without occlusion provides a Monge surface representation of the scene surface. The light field surface gradient and Laplace operators can then be defined on the moving frame. The light field surface scale space can be established by solving the diffusion equation, and the light field surface feature (LFSF) can be detected. Then the local surface gradients of each LFSF are measured and form a 48 dimensional rotation invariant spherical descriptor. Experimental results show that the proposed method can detect more features then LiFF and Lisad on most of the scenes. For light field data matching, the proposed method provides more matching pairs than LiFF with SIFT descriptor and procides more accurate matching result.
Fiber-Coupled Common-Path Shearography for Simultaneous Measurement of Surface Deformation and Slope

A mechanical quantity sensing framework is proposed, which is based on fiber-coupled common-path shearography, for the simultaneous measurement of deformation and slope. An optical fiber directs the reference wave into the system for the generation of holograms without breaking the common-path structure. The present shearography possesses three features for mechanical quantity sensing, namely, the zero-approaching shear amount, the separate control of spatial carriers, and the scrambled reference wave. Single-shot phase measurement scheme is implemented by the spatial carrier method, in which the deformation and slope are retrieved from a single frame of interference pattern. Fiber scrambling is brought into the system to flatten the intensity distribution of the fiber-directed reference field and deliver a quality-enhanced phase map. Physical principle is demonstrated. Preliminary experiments are carried out to verify the feasibility and functionality of the proposed framework.
Evaluation of Backwash Time Effect on Mesh Self-Washing Filter Parameters

Purpose: The study aims to determine the effect of the backwash time on the parameters of the mesh self-washing filter. Theoretical framework: The duration of the backwash process can significantly affect the operation parameters of the mesh self-washing filter Method: During the study, experiments were carried out using a laboratory stand on which a mesh self-cleaning filter was installed. In the experiments, the filter operation parameters were measured at different values of the backwash time. Mathematical modelling methods were also used to determine the filtration efficiency. Results and conclusion: The results of the study showed that the backwash time significantly impacts the operation of the mesh self-washing filter. The optimal backwash time is from 20 to 30 seconds, while the percentage of filter clogging due to removing impurities during backwash is reduced by 20-25% compared to a shorter or longer backwash time. Research implications: These filters work on the principle of direct filtration when water passes through a sieve to remove suspended solids. The filter material is periodically cleaned by backwashing, which involves changing the direction of water flow through a sieve to remove accumulated solid particles. Originality/value: Water purification by suspended solids with a size of more than 100 microns is carried out by an amount of more than 99.5%. The filter provides reproducible cleaning quality, and the cleaning efficiency does not decrease from cycle to cycle.
Entity Alignment Method Based on Joint Learning of Entity and Attribute Representations

Entity alignment helps discover and link entities from different knowledge graphs (KGs) that refer to the same real-world entity, making it a critical technique for KG fusion. Most entity alignment methods are based on knowledge representation learning, which uses a mapping function to project entities from different KGs into a unified vector space and align them based on calculated similarities. However, this process requires sufficient pre-aligned entity pairs. To address this problem, this study proposes an entity alignment method based on joint learning of entity and attribute representations. Structural embeddings are learned using the triples modeling method based on TransE and PTransE and extracted from the embedding vector space utilizing semantic information from direct and multi-step relation paths. Simultaneously, attribute character embeddings are learned using the N-gram-based compositional function to encode a character sequence for the attribute values, followed by TransE to model attribute triples in the embedding vector space to obtain attribute character embedding vectors. By learning the structural and attribute character embeddings simultaneously, the structural embeddings of entities from different KGs can be transferred into a unified vector space. Lastly, the similarities in the structural embedding of different entities were calculated to perform entity alignment. The experimental results showed that the proposed method performed well on the DBP15K and DWK100K datasets, and it outperformed currently available entity alignment methods by 16.8, 27.5, and 24.0% in precision, recall, and F1 measure, respectively.
Cooperative Localization and Target Tracking in Mobile Sensor Networks With Uncertainties in Measurement Models

This paper studies the problem of cooperative localization and target tracking for mobile sensor networks in the presence of both Gaussian additive and multiplicative measurement noises. A group of more complex relative measurement models involving uncertainties, that is, the means and variances of the multiplicative noises being unknown, are considered. By introducing the Normal-inverse-Gamma distribution to model the unknown means and variances, a distributed algorithm is designed based on the variational Bayesian technique. Within the distributed framework, the proposed algorithm ensures that each robot can jointly infer its state and all targets' states, as well as the unknown noise statistics, by using only the local relative measurements and intermittent absolute measurements. Moreover, the covariance intersection fusion strategy is employed, which guarantees consensus among all robots on their inferred posterior distributions of each target's state. As a result, the proposed algorithm is independent on the true statistical characteristics of the multiplicative noises, which thereby gets rid of the limitation of the existing results requiring the means and variances to be precisely known. The effectiveness of the proposed algorithm is validated by simulation results.
Why Is the Value of Bitcoin Dropping?

In November 2021, Bitcoin reached an all-time high value of $69,000, captivating the attention of investors, news outlets, and social media influencers. However, the subsequent months saw a staggering decline in value, with Bitcoin today trading below $30,000. Today itâ€™s just under $30,000. What happened to the price of Bitcoin? What are some things that contributed to the crash of Bitcoin?
Current situation and path of foreign minority language protection based on Internet of Things from the perspective of ethnic identity

Ethnic identity is an important reason for national unity. The formation of ethnic groups is mostly due to external and internal reasons, with a complex mechanism structure. When people’s environment is destroyed, this value identity will be stronger, national cohesion and solidarity will grow sharply, an unprecedented high. Minority languages are characterized by a wide variety, cultural generation, and dynamic evolution. The languages are complex and diverse, and are deeply influenced by their cultural heritage. This has led to the fact that for a long time, minority languages have been learned almost exclusively by people of their own ethnic group. This has led to a steady decline in the number of minority language speakers. However, the birth of the Internet of Things (IoT) has created an opportunity for the development of minority languages. Foreign minority languages have the following status (1) rapid decline (2) many endangered languages (3) low willingness to learn. In order to further investigate the current situation of minority language protection, the team conducted a survey in the minority cluster A. The results of the survey are as follows: most of the villagers are in love with the ethnic language and must inherit and promote the ethnic language, which is the wealth of a nation, but there are also a few residents who think that Mandarin should be used to replace the ethnic language. This reflects that most of the residents love the national language, but their young people as the inherited generation lose their love for the national language and are not willing to inherit and promote it. To solve this problem, an Internet of Things (IoT)-based ethnic language data system has been constructed so that the data system for ethnic minority language protection built through IoT technology can become the last line of defense for protecting ethnic languages, while allowing ethnic languages to be known and understood by more people through IoT technology, fostering the love and reverence of ethnic minority people for ethnic languages, and enhancing other ethnic groups’ sense of identity and support for ethnic languages. We propose to protect minority languages. Ultimately, we propose recommendations for the preservation of minority languages. (1) Increase publicity for minority language preservation. (2) Construct laws and regulations related to minority language protection. (3) Conduct in-depth research on minority language work and build an information base. (4) Promote minority language education.
Global path planning for airport energy station inspection robots based on improved grey wolf optimization algorithm

Global path planning is one of the key technologies for airport energy station inspection robots to achieve autonomous navigation. Due to the complexity of airport energy station buildings with numerous mechanical and electrical equipment and narrow areas, planning an optimal global path remains a challenge. This paper aimed to study global path planning for airport energy station inspection robots using an improved version of the Grey Wolf Optimizer (IGWO) algorithm. Firstly, the initialization process of the Grey Wolf Optimizer algorithm selects several grey wolf individuals closer to the optimal solution as the initial population through the lens imaging reverse learning strategy. The algorithm introduces nonlinear convergence factors in the control parameters, and adds an adaptive adjustment strategy and an elite individual reselection strategy to the location update to improve the search capability and to avoid falling into local optima. Benchmark function and global path planning simulation experiments were carried out in MATLAB to test the proposed algorithm’s effectiveness. The results showed that compared to other swarm intelligent optimization algorithms, the proposed algorithm outperforms them in terms of higher convergence speed and optimization accuracy. Friedman’s test ranked this algorithm first overall. The algorithm outperforms others in terms of average path length, standard deviation of path length, and running time.
REPEAT ORDER RECOMMENDATION MODEL TO DIGITALIZE THE CANTEEN

The canteen is a campus facility that the academic community needs. However, because it is considered a complementary facility, business processes in the canteen are carried out conventionally, relying on paper-based recording. In addition, at certain hours, especially at lunchtime, the canteen is overloaded due to too many orders co-occurring. This buildup of charges is due to the need for canteen preparation in preparing orders from its buyers. Therefore, this study developed an ordering system to recommend the menus that customers likely purchase. The case study used in this research is the Keluwih canteen on the Tenggilis campus, owned by the University of Surabaya. This research used the waterfall method and focused on three main canteen processes: online ordering with a repeat order recommendation model for sellers, delivery services, and payments using the e-wallet. The study's results were assessed qualitatively, and the respondents felt they got better and faster service. Furthermore, with this system's e-canteen application, the time needed to complete a transaction at the campus canteen can be done efficiently to increase customer satisfaction and employee productivity from tenants.
Multipartite entanglement measures via Bell-basis measurements

We show how to estimate a broad class of multipartite entanglement measures from Bell basis measurement data. In addition to lowering the experimental requirements relative to previously known methods of estimating these measures, our proposed scheme also enables a simpler analysis of the number of measurement repetitions required to achieve an $\epsilon$-close approximation of the measures, which we provide for each. We focus our analysis on the recently introduced Concentratable Entanglements [Beckey et al. Phys. Rev. Lett. 127, 140501 (2021)] because many other well-known multipartite entanglement measures are recovered as special cases of this family of measures. We extend the definition of the Concentratable Entanglements to mixed states and show how to construct lower bounds on the mixed state Concentratable Entanglements that can also be estimated using only Bell basis measurement data. Finally, we demonstrate the feasibility of our methods by realistically simulating their implementation on a Rydberg atom quantum computer.
Development of the database of the collections of the Geological museum named after A. A. Chernov

In modern museums, information and analytical systems complete many tasks related to museum activities. The application of generally accepted software systems, developed for subordinate museums of the Ministry of Culture of the Russian Federation, is limited in natural science museums. Often, natural science museums develop their own databases and information systems.&#x0D; This article describes the development of a database of collections of the Geological Museum named after A. A. Chernov. We present main results of study — the design of a datalogical model.
Automatic microseismic signal classification for mining safety monitoring using the WaveNet classifier

Abstract Microseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system records various signals. Recently, deep learning‐based classification techniques have achieved high performance in such data classification problems. In this context, we develop an automatic signal classification technique using the modified WaveNet classifier. The main characteristic of the WaveNet structure is its ability to extract features at various frequencies from very long time‐series data, and such an advantage makes the WaveNet suitable for seismic data processing. The data imbalance problem coming from the safe condition of the monitoring target is solved by augmenting the training data with those acquired from another mine and employing class weighting. After training, an optimal classifier is chosen considering the loss function, accuracy and F β score. The optimal classifier shows very high accuracy and excellent performance for the test data prediction. Compared to the random forest model and another one‐dimensional convolutional neural network–based network, the suggested classifier has higher reliability in predicting microseismic signals. Even though the proposed WaveNet model has a much more complex structure than the random forest model, the actual application examples demonstrate that the proposed model achieves high efficiency without any preprocessing. The automatic signal classifier developed in this study can be directly applied to various safety monitoring problems, not only mines, to improve the efficiency and reliability of monitoring systems.
“The Synaptic Board Game” Improves Health Sciences Students’ Learning on Synaptic Transmission

Background Synaptic Transmission (ST) is a content of Physiology courses in which students frequently experience learning difficulties. Intervention Here, we evaluated the impact of using The Synaptic Board Game (SBG), an educational game created to assist in ST teaching-learning, on students’ knowledge. Methods Sixty-eight students were divided into control (CT) and synaptic board game groups (GG); CT attended one theoretical class (TC) about ST, and GG experienced 1 h of class using the SBG in addition to the same TC. Before and immediately after the intervention, students completed tests about ST. Overall scores and performance in specific questions (general synaptic transmission, electrical, and chemical synapse) were assessed. Results GG scored higher in the immediate post-test compared to the pre-test. In addition, GG showed higher overall scores than CT in the immediate post-test. Both groups scored lower in the post-test in general synaptic transmission questions, but GG performed better than CT. Discussion SBG improves knowledge after practice and seems to help students understand better the differences between electrical and chemical synapses. Limitations and Suggestions (i) the immediate post-test could be applied at the same time for both groups; (ii) future quiz apps developed to be applied in class could collect the data regarding the answers to each question from students while they play the game; (iii) a late post-test could be applied simultaneously with students from both groups without previous communication. Conclusion The results indicate that SBG is an effective teaching material to reinforce ST learning and avoid misunderstandings between electrical and chemical synapses.
Minerals of the Subpolar Ural Crystal-Bearing Province in the collections of the A. A. Chernov Geological Museum

The article provides information about a wide variety of crystal-bearing deposits in the Subpolar Urals. Unique collections of minerals from crystal-bearing nests are collected in the Geological Museum named after A. A. Chernov. The cadastre contains 84 minerals and makes up 65 % of the total cadastre of deposits. Crystal-bearing minerals of the locality and occurrences of the Subpolar Urals of all classes are represented in the Museum funds to varying degrees. The minerals of silica, fluorite, layered silicates and carbonates are most fully represented. A high proportion of fund representativeness is established for the Kozhim, Skalistoye, Pelingichey-3, Puiva, Khasavarka, Vodorozdelnoye, Omega-Shor and Severnaya Lapcha fields. The subsequent study of minerals to replenish the museum funds and form a reference mineralogical collection of unique crystal-bearing objects of the Subpolar Urals is prognosed.
Decoding Harmony Through Algebra. Viktor Petrenko and Psychosemantics in Cinematography

In March 2023, Viktor Fedorovich Petrenko, a renowned Russian psychologist, celebrated his 75th birthday. Holding a doctoral degree in Psychological Sciences and being a Corresponding Member of the Russian Academy of Sciences, Petrenko’s name may not be familiar to everyone, particularly those outside the realm of psychology. Nevertheless, it is highly probable that his groundbreaking methods, which he spent over five decades of his life creating, researching, and applying, have influenced your scientific or practical endeavors. The sheer indispensability of psychosemantics, a field at the core of Professor Petrenko’s work, cannot be overstated when contemplating the landscape of modern science and research practices. Regarded as a trailblazer in this field, he was among the first in the country to apply factor analysis techniques to processing psychological data, paving the way for the evolution of this discipline.
Helmet-wearing detection with intelligent learning approach

In the construction process, wearing a safety helmet is an important guarantee for personnel safety. However, manual detection is time-consuming, labor-intensive, and unable to provide real-time monitoring. To address this issue, a helmet-wearing detection algorithm has been proposed based on YOLOv5s. The algorithm uses the YOLOv5s network and introduces the CoordAtt coordinate attention mechanism module into its backbone to consider global information and improve the network’s ability to detect small targets. To improve feature fusion, the residual block in the backbone network has been replaced by a Res2NetBlock structure. The experimental results show that compared to the original YOLOv5 algorithm, the accuracy and speed of the self-made helmet data set have improved by 2.3 percentage points and 18 FPS, respectively. Compared to the YOLOv3 algorithm, accuracy and speed have improved by 13.8 percentage points and 95 FPS, respectively, resulting in a more accurate, lightweight, efficient, and real-time helmet-wearing detection.