Reviewerly/exHarmony huggingface dataset:
The Research on Traffic Flow Statistics Based on YOLOv5 and Attention Mechanism

Traffic flow statistics technology is a very important technology in Intelligent Transportation Systems (ITS), and plays an important role. In recent years, traffic flow statistics technology based on deep learning has become a new research direction. YOLOv5, as a single stage target detection algorithm, has the advantage of fast speed and high accuracy YOLOv5 can be applied to traffic flow statistics. However, in YOLOv5 a large number of feature concatenations were not weighted based on their importance, resulting in a lack of focus in learning. To address this issue, this paper introduces the CA (coordinate attention) attention mechanism into YOLOv5, which increases the mAP0.5:0.95 on the BIT-Vehicle Dataset by 1.4%. Compared to other attention mechanisms our proposed method is better than them and does not affect inference speed. The experimental results demonstrate that our fusion method is feasible and effective.
Safeguarding Learning-based Control for Smart Energy Systems with Sampling Specifications

We study challenges using reinforcement learning in controlling energy systems, where apart from performance requirements, one has additional safety requirements such as avoiding blackouts. We detail how these safety requirements in real-time temporal logic can be strengthened via discretization into linear temporal logic (LTL), such that the satisfaction of the LTL formulae implies the satisfaction of the original safety requirements. The discretization enables advanced engineering methods such as synthesizing shields for safe reinforcement learning as well as formal verification, where for statistical model checking, the probabilistic guarantee acquired by LTL model checking forms a lower bound for the satisfaction of the original real-time safety requirements.
Beyond Conversational Discourse: A Framework for Collaborative Dialogue Analysis

In the collaboration scenario, video calls can not only improve the understanding of the collaborative conversation content but also assist group members in coordinating tasks reasonably and obtaining richer collaboration information. Although they can share or view the recorded call video repeatedly, with the increase in video duration or the influence of surrounding noise, the audio-visual information contained in the collaborative conversation becomes more complex and difficult to understand. So, it's a great challenge for group members to obtain information about the collaboration topic. This paper starts with the video calls in collaborative scenarios and analyzes its interpretable elements from the obtained audio-visual dialogue information. Based on the TM-CTC model (CTC Transformer) and FaceNet algorithm, we construct a framework for deep audio-visual dialogue analysis in the Collaborative Working Environment (CWE). Subsequently, according to the framework mentioned above, we build a collaborative audio-visual dialogue server that is further applied to CWEs to analyze the video calls, generate audio-visual dialogue elements, and present the results to team members. Finally, an application example is utilized to analyze the audio-visual dialogue produced in collaborative video communications. The results show that our proposed framework can improve communication efficiency in team collaboration to a certain extent.
A 3D Model Information Hiding Algorithm Based on Grid Saliency

Aiming to address the issue of noticeable visual quality changes before and after embedding secret information, this paper proposes a novel 3D model information hiding algorithm based on grid saliency. Firstly, use the half-edge folding algorithm to simplify the 3D model. Second, according to the processed data set, the model was effectively labeled by the multi-scale weighted fusion method, and the local significance region was divided. Next, the significant region is divided into key points, sub key points, and background points using Mean Shift clustering analysis. Project the sub-key points onto a two-dimensional plane and embed the secret information according to the interval expression conversion strategy. The distortion rate of the model is greatly reduced. It improves the capacity of secret information embedding while ensuring invisibility performance.
Service-Oriented Componentized Process Control System

In the domain of conventional process control systems, the achievement of diverse functionalities necessitates a considerable amount of code composition to effectively manage control logic and data, giving rise to challenges such as extended development cycles and the onerous upkeep of control programs. Consequently, the rapid realization of software system reconfiguration and updates, coupled with the reduction of development costs and time in response to the dynamic manufacturing demands, has emerged as imperative research concerns. Addressing these challenges, this paper centres its focus on the "Service-Oriented and Intelligent" paradigm, undertaking the conceptualization and development of a component-based process control system tailored for industrial software. This endeavour involves encapsulating hardware resources within the framework of componentization and establishing uniform component service interfaces and interaction protocols. To ensure the authenticity of their design, we have diligently explored and formulated component models, subjecting them to scrutiny through formal modelling techniques, thus verifying their behavioural properties comprehensively.
Analysis of Performance Expressions Using Sequential Tension Values

Human performers interpret and perform a piece of music based on their own musical sensibilities and the musical characteristics they read from the information in the score. In doing so, the changes in speed and volume that appear in the performance are called performance expressions. This performance expression changes each time the music piece or performer changes, and we believe that formalizing this expression will make it possible to perform more human-like performances on a computer.
THEORY OF DIGITAL TRACE AS CRIMINALISTICS TRACE

The author tries to shortly characterize the content and scope of actual specific parts of evidence used in modern Criminalistic and Forensic science. The last twenty years used as frequent evidence in connection with crimes committed via information and communication technologies known in English as digital evidence, or exactly in Criminalistic as Digital trace. The study analyses the possibilities of digital trace, as a new part of criminalistics theory, and/or as forensic traces. Those are typical for crimes connected with the abuse of digital information and communication content, especially on the Internet. In the research of crime activities, authors distinguish a number of classifications of digital trace types. The author of this study demonstrates various ground parameters of digital traces as only one type of trace in Criminalistic. This contribution is the result of the project implementation of Centrum excelentnosti bezpecnostneho vyskumu kod ITMS: 26240120034 supported by the Research and Development Operational Programme funded by the ERDF, task 3.3.
Dynamic Recommendation System Based on Event Knowledge Graph in Telecom O&amp;M (Telecom Operations and Maintenance Field)

With the accumulation of time and the increasing volume of telecommunications business, the company has accumulated a large number of network fault work orders. These work orders record the process of various fault resolution through textual information, which is of great reference value to the existing network fault resolution. In order to make full use of the work order data, this paper designs the work order event mapping structure and combines the information extraction algorithm to extract the information of unstructured work order data and its inner connection. To address the difficulty of extracting long text with low entity coverage, a text summarization algorithm is used to remove redundant information and then a pointer network, globalpointer, is applied to extract entities. For event information, the UIE (Unified Structure Generation for Universal Information Extraction) framework is used for event extraction. It is also applied to the dynamic recommendation field of similar work orders in dialogue scenarios. The experimental results verify the advantages of the main design of work order event mapping and the effectiveness of the recommendation algorithm in this paper, which is of great significance for forming a structured knowledge base in the telecom operation and maintenance industry and supporting the intelligent development of telecom operation and maintenance.
A Novel Video Understanding Network Based on Poolformer and Transformer

This paper introduces a new video understanding network referred to as 'ViViP' (Video Vision Poolformer), which leverages poolformer and transformer techniques. It begins by encoding video frames using sequential poolformer layers. The ensuing output token is then processed by a Transformer, and the class token is subsequently used in a Multilayer Perceptron (MLP) for classification. ViViP shows notable results across multiple action recognition benchmarks, achieving the highest reported accuracy on UCF-101 and substantial accuracy on HMDB-51. Additionally, our model exhibits faster training speeds than other popular networks, such as SlowFast, TimeSformer, and TSN, allowing its usage with longer video clips. Our experiment, which compares different token-mixer modules, finds that the poolformer achieves the best classification accuracy among the examined design choices, reaching 98.4% on UCF-101 and 71.6% on HMDB-51.
Few-Shot Transmission Line Foreign Object Detection Based on Transfer Learning

Presence1 of foreign objects on power transmission lines can jeopardize the safe operation of the transmission lines. However, foreign objects on transmission lines are small in size and the available data for collection is limited. When using few-shot object detection algorithms for detection, there is a high risk of misclassifying of few-shot data and network bias, making the task of foreign object detection on transmission lines challenging. To address this, we propose a method for detecting foreign objects on transmission lines based on few-shot object detection. Firstly, we introduce an attention mechanism in transfer learning to enhance the network's focus on few-shot data and mitigate network bias. Secondly, we propose a method for online difficult sample selection to address the issues of few-shot data classification and the occurrence of false positives and false negatives due to small object sizes. Additionally, we used involution to improve the accuracy of foreign object detection. Furthermore, we construct the finely labeled Transmission Line Foreign Object Dataset (TLFOD). Our method achieves an average precision of 98.4% in foreign object detection on the challenging TLFOD dataset, surpassing existing state-of-the-art methods and demonstrating outstanding performance in classification regression and position regression.
Research on Construction of Chinese Technology Literature Question Answering System Based on Knowledge Graph

In order to improve the knowledge service ability of the library and meet the user's demand for querying Chinese technology literature in natural language. This article proposes a two-stage method to implement the question answering system for Chinese technology literature. In the first stage, the Chinese question classification system based on sentence pattern is designed, and the stacking framework of ensemble learning algorithm is used to classify Chinese question. In the second stage, the pipeline is employed to parse the natural language question sentences and transfer them into Cypher statement. The field of "competitive intelligence" is selected for experiment. The experimental results show that the proposed stacking classification model improves the metric F1 by 10.81% compared with the single model average. Overall, the accuracy of translating Chinese questions into Cypher statements reached 83.96%. The proposed method can effectively convert Chinese questions into Cypher statements and directly return the answer to users. It provides a reference scheme for the application of the question answering based on knowledge map, which can improve the knowledge service ability of the library.
Information Hiding Algorithm of 3D Model Multi-carrier Based on Included Normal Angle

To overcome the limitations of current single-carrier 3D model information hiding algorithms, which are constrained by the number of carriers, a multi-carrier information hiding algorithm based on normal angles of 3D models is proposed in this study. The algorithm follows a four-step approach. Firstly, the 3D model is transformed into an affine invariant space to normalize and preprocess it. Secondly, the carrier set of the 3D model is classified based on the normal angle histogram, and the carrier is encoded according to the type of triangular grid. Thirdly, the local entropy of the vertex normal angle is used to label and classify the 3D model, and vertices with varying energies are obtained. Finally, the secret information is embedded in multiple 3D models based on the matching optimization relationship between secret information and carrier information. In order to improve the invisibility of the algorithm, angles with small changes are preferred for modification during the embedding of secret information. The experimental results demonstrate that compared to single-carrier and fusion-state-based 3D model information hiding algorithms, the proposed algorithm has significantly improved invisibility and robustness and can effectively resist Laplace steganalysis.
Exploring the reliability of inpatient EMR algorithms for diabetes identification

Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms.A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV).The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99.Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery.
Ontology-Based Dialogue System for Domain-Specific Knowledge Acquisition

Building task-oriented dialogue systems for solving specific tasks within companies represents a challenging research objective especially when the specific expertise of the company is not yet machine-readable formalized. Aiming to collect the specific knowledge in a company-scope knowledge graph, we design the architecture of a dialog system integrating the natural language processing modules with the specific concepts described in the domain ontology and the com- pany’s graph of instances. The described system helps growing the knowledge graph with the specific company data, while also providing the NLP building blocks for a future dialog system specifically tuned for the requirements of the target company processes.
Decentralized ID and Self-Sovereign Identity Solutions Using Blockchain: A Systematic Literature Review

Today users do not have control over their digital identities. To access and validate them, they need to authenticate through a third party which they must trust. This is a problem that re- searchers have addressed with the blooming of new paradigms such as Decentralized Identifiers (DIDs) and Self-Sovereign Identity (SSI). In this context, Blockchain appears as a new path that ensures traceability to all transactions. Although there are several primary studies related to this topic, there are not enough contributions that condensate all the information of research groups in a secondary study, which summarizes the techniques and trends of DIDs and SSI. Therefore, this paper presents a systematic literature review to identify research trends, challenges, and so- lutions to DID and SSI using Blockchain. Twenty-three papers published from 2014 to October 2022 were selected following inclusion and exclusion criteria. The investigation points out how most DID and SSI solutions are set in the general domain as academic postulations that could be released in the commercial field.
Self-Identity: Mid-Twentieth Century Philosophical and Literary Interaction on the Experience of “I” and “Non-I”

This article substantiates the autonomy of a philosophical text in relation to a literary text. Max Frisch's novel Stiller 1 is used to illustrate the idea of the dialectical nature of the process of concealing/revealing metaphysical meaning in a work of fiction regarding the problem of self-identification and the construction of one's own “I,” which is pivotal in this novel. The philosophical idea of self-identity as the goal of the process of self-knowledge exists only as a projection; it is not feasible in life. On the contrary, the novel Stiller, considered through the prism of basic ideas of philosophy of the twentieth century, allows the reader not only to better understand the inconsistency of the process of self-knowledge, but that it involves a reconfiguration of meanings in a conversation about being, and therefore requires a philosophical revision of the essence of the problem.
Principal Component Analysis Visualization and State Discovery with Soil Data

Data exploration helps to gain understanding of the dataset and the system itself. There are methodologies to handle large number of sensors as well. In this paper operational states are defined to interpret physical behaviour in a soil ecosystem. Dimensionality reduction is achieved with Principal Component Analysis (PCA) method giving another view to the soil dataset from spring term. K-means algorithm groups data densities by clustering the data. This grouping is the basis for defining operational states in the system. Soil data as a part of an ecosystem involves specific features. In the applied approach dynamic visualization including animations constitute an important exploration view. All experiments are realized in Jupyter programming environment with Python 3 programming language. Related literature about data visualization is reviewed. Combining methods and tools with this data as a result soil ecosystem features are recognized.
An Indirect Single-Position Coordinate Determination Method Considering Motion Invariants under Singular Measurement Errors

The problem of indirect single-position coordinate determination based on the smoothed measurements of bearing and the radial velocity of an object is solved considering motion invariants and singular measurement errors. Such errors are represented as an appropriate linear combination with unknown spectral coefficients in a given finite-dimensional functional space. Possible application of the developed method to different models of motion and observation is considered. Analytical relations are derived for estimating accuracy characteristics and methodological errors. A comparative evaluation of computational cost is presented.
A Historical Essay on the Scientific School of V.A. Yakubovich

The milestones of the history of the scientific school on cybernetics (the School), established in 1959 by outstanding scientist V.A. Yakubovich at Leningrad State University (LSU), are presented. The connections of the School with other Russian and foreign scientific schools in related fields are outlined.
Pierre van Houwe and his concept of "Playing with Music" in the history of music education

The object of research of this work is the creative and pedagogical activity of the Dutch conductor, composer and teacher Pierre van Hauwe (1920-2009). The subject of the study is the conceptual foundations of his pedagogical theory of "Playing with Music", which has become widespread not only in European countries, but also in South America, Africa and Asia. The authors of the article consider in detail the features of P. van Hauwe's concept, the foundations of which are based on the elements of the Zoltan Kodai system, which became widespread in Hungary, and the method of musical education of Karl Orff, whom P. van Hauwe met in Salzburg. Special attention is paid to the methodical publication "Playing with Music" ("Spielen mit Musik"), where P. van Hauwe outlined his concept step by step, accompanied by specific recommendations for teachers. The research used theoretical (analysis, synthesis, etc.) and empirical (comparative-historical, structural-typological) research methods. The main conclusions of the study: P. van Hauwe's pedagogical activity had a great impact on the development of music education not only in the Netherlands, but also in other countries of the world. His concept of "Playing with music" ("Spielen mit Musik") combined elements of the educational systems of two outstanding teachers of the twentieth century – Z. Kodai and K. Orff, as a result of which it became a natural continuation of the development of European musical and pedagogical thought. A special contribution of the authors of the article to the study of the topic, reflecting its novelty, was a detailed analysis of the methodological publication "Spielen mit Musik" ("Playing with music"), on the basis of which the essential relationship of the ideas of P. van Hauwe and his predecessors was revealed. Also, for the first time in Russian musicology, the facts of the musician's biography are more fully disclosed.
An Experimental Overview of Neural-Symbolic Systems

Neural-symbolic AI is the field that seeks to integrate deep learning with symbolic, logic-based methods, as they have complementary strengths. Lately, more and more researchers have encountered the limitations of deep learning, which has led to a rise in the popularity of neural-symbolic AI, with a wide variety of systems being developed. However, many of these systems are either evaluated on different benchmarks, or introduce new benchmarks that other systems have not been tested on. As a result, it is unclear which systems are suited to which tasks, and whether the difference between systems is actually significant. In this paper, we give an overview and classification of the tasks used in state-of-the-art neural-symbolic system. We show that most tasks fall in one of five categories, and that very few systems are compared on the same benchmarks. We also provide a methodological experimental comparison of a variety of systems on two popular tasks: learning with distant supervision and structured prediction. Our results show that a systems based on (probabilistic) logic programming achieve superior performance on these tasks, and that the performance amongst these methods does not differ significantly. Finally, we also discuss how the properties of the (probabilistic) logic programming-based systems are desirable for most neural-symbolic tasks.