Reviewerly/exHarmony huggingface dataset:
Two-factor patterns construction in problems of texts classification

Two-factor patterns of empirical distributions of bigram frequencies for machine classification of texts by authors and subject are constructed. Text attributes are recognized by the nearest neighbor method in relation to reference distributions. The proximity between distributions is understood in the sense of the norm in L1. The "author-topic" pair of an unknown text is defined as a nearest neighbor pattern. The problem of recognizing the author regardless of the topic of the text and the topic regardless of the author is analyzed. The possibilities of enlarging and detailing classification features are also being investigated.
A Comparative Study of Prediction of Gas Hold up Using ANN

The primary aim of this paper is to present a way by which the gas hold-up can be predicted for gas non-Newtonian liquid flow. The type of conduit used here are the helical coils of different diameter. The helical coils are vertically oriented. The data are collected from our previous experiment and its subsequent publications. A principle component analysis (PCA) based network performance is compared with the Levenberg-Marquardt (LM) algorithm based network. The closeness of the error parameters indicate that both training algorithm can predict both the hydrodynamic parameters with acceptable accuracy. The final analysis and the evaluation of the statistical parameters related to the errors prove the successful nature of the modelling using the PCA based network.
Semantic Integration and Interoperability

Abstract A key aspect of establishing data spaces is to develop a common understanding of the data to be shared in the data space. Semantic standards and technologies were developed for this purpose since over two decades. In this article, we will discuss the history and importance of semantic integration for data spaces. We will introduce the base concepts of semantic integration—including the global identifiers for data and the W3C standards RDF, RDF-Schema, and OWL. As a result these standards and technologies can be used to devise versatile Knowledge Graphs capturing domain conceptualizations and concrete data representations. We explain how data interoperability can be achieved by linking and mapping between different data and knowledge representations. Finally we will showcase their use with an example for data integration in supply chains with the ScorVoc vocabulary.
Real-Time Piracy Detection Based on Thermogram Analysis and Machine Learning Techniques

Movie Piracy is increasing these days, and it has a profound impact on the economic growth of film industries all over the world. Hence curbing piracy has become a critical step in avoiding massive losses to the film industry. This paper proposes a thermogram based anti-piracy system using Machine learning models. A local dataset is created by capturing the images in different scenarios by employing a thermal camera. AlexNet is used for extracting the features from captured images and the extracted features are trained with several Machine Learning models in MATLAB for their performance evaluation. The images captured in real-time are processed to extract the features and then directed to the classifier to predict the anomaly class. The alert message is sent to theatre officials regarding the presence of active recording device. An accuracy of 99.7% is achieved with Support Vector Machine (SVM) model on the local dataset.
Federated Data Integration in Data Spaces

Abstract Data Spaces form a network for sovereign data sharing. In this chapter, we explore the implications that the IDS reference architecture will have on typical scenarios of federated data integration and question answering processes. After a classification of data integration scenarios and their special requirements, we first present a workflow-based solution for integrated data materialization that has been used in several IDS use cases. We then discuss some limitations of such approaches and propose an additional approach based on logic formalisms and machine learning methods that promise to reduce data traffic, security, and privacy risks while helping users to select more meaningful data sources.
Tokenomics: Decentralized Incentivization in the Context of Data Spaces

Abstract A significant challenge in bootstrapping a jointly used infrastructure such as Data Spaces is to incentivize the participants to invest in setting up the infrastructure. In this chapter, we investigate this challenge and possible solutions, focusing on an approach called “Tokenomics.” The incentivization scheme should be utilized by governance frameworks, in which the participants of Data Spaces remain capable of action and independent through automated, effective, and fair decision-making processes. Also, potential participants should be motivated to participate in the establishment and further development of the system, while on the other hand, undesirable behavior should be penalized. In combination with distributed ledger technology (DLT) and machine-readable, legally compliant smart contracts, participant behavior can be affected in such a way that both data quality and quantity are improved for the whole Data Space. To derive possible design options for Tokenomics approaches, we examine different token frameworks and their impact on participants. The investigation of the frameworks is carried out taking into account five significant domains: technical, behavior, inherent value, coordination, and pseudo-archetypes. Furthermore, we investigate which token designs provide smaller or larger incentives in order to join or maintain a DLT-based ecosystem.
Silicon Economy: Logistics as the Natural Data Ecosystem

Abstract The “Silicon Economy” is synonymous with a coming digital infrastructure (digital ecosystem) based on the automated negotiation, disposition, and control of flows of goods, enabling new, digital business models (not only) for logistics. This infrastructure requires and enables the trading of data without losing sovereignty over the data. It is the digital infrastructure and environment for the highly distributed AI algorithms along value networks. In contrast to oligopolistic developments in the B2C sector ( amazon.com , AirBnB, Alibaba, Uber, etc.), the Silicon Economy is a federated and decentralized platform ecosystem, the basic components of which are made available to the general public as open source for free use. The Silicon Economy ecosystem is becoming an enabler of supply chain ecosystems in which goods, autonomously controlled by Artificial Intelligence (AI), undergo orchestrated processes according to the situation. This article focuses on the origins and potentials but also on the technological foundations and challenges of the transformation toward a Silicon Economy.
Exploring CyBOK with Topic Modeling Techniques

Several frameworks that cover cyber security education and professional development have been introduced as a guidance for learners, educators and professionals to the different knowledge areas of the field. One of the most important frameworks is the Cyber Security Body of Knowledge (CyBOK). In this paper, we apply the BERTopic topic modeling technique to CyBOK. We aim, by using this technique, to identify the most relevant topics related to each CyBOK’s knowledge area in an automated way. Our results indicate that it is possible to find a meaningful topic model describing CyBOK and, thus, suggests the possibility of applying related techniques to texts to identify their main themes.
Camera Quality Assessment in Real-World Conditions

In this paper, we propose a novel approach to the problem of camera-quality assessment. We introduce a new dataset that, contrary to existing ones, is composed of multiple shots of various natural scenes, defined by their location and content, but not necessarily on the same day. In this work, we focus specifically on evaluating the capacity of camera devices to preserve fine texture details. We perform reliable image quality annotations from numerous pairwise human preferences between images depicting the same content on natural scenes. Finally, we adopt a learning-based strategy where a deep neural network is trained to predict quality scores corresponding to these human preferences. We also perform an extensive benchmark of existing image quality measures in several settings corresponding to practical scenarios. This benchmark allows us to assess the quantitative advantages of each scenario and to provide practical recommendations for future camera quality assessment research.
Joint Embedding Multiple Feature and Rule for Paper Recommendation

It is a common way to represent paper properties as a heterogeneous academic network graph, such as authorships, citations, by which the latent features of paper can be learnt. To better integrate both text and structural features, we propose the joint embedding method for paper recommendation. We adopt a pre-trained language model to learn the paper semantic features from titles, and adopt a graph convolution network to extract the structural features from the constructed academic network graph. These two embeddings are combined together through the attention mechanism as a joint one. To clarify the real negative samples on uncited papers, we introduce some expert rules as the selection strategy on samples in model training, which can exclude the far-unrelated negative samples and potential positive samples. User interests are modeled by their historical publications and references and thus papers are recommended according to the relatedness between user interests and paper embeddings. We conduct experiments on the ACM academic paper dataset. The results show that our model outperforms baseline methods on personalized recommendation. We also analyze the influence of model structure and parameter setting. The results show that our sample strategy effectively improves the precision of recommendation, which illustrate that the strategy enhances the quality of training data.
Security Fatigue: A Case Study of Data Specialists

Due to the number of data breaches occurring worldwide there is increasing vigilance regarding information security. Organisations employ a variety of technical, formal, and informal security controls but also rely on employees to safeguard information assets. This relies heavily on compliance and constantly challenges employees with security-related tasks. Security compliance behaviour is a finite resource and when employees engage in cost-benefit analyses that extend tolerance thresholds, security fatigue may set in. Security fatigue has been described as a despondency and weariness to experience any further security tasks. This study used a case study approach to investigate employee security fatigue, focusing on data specialists. Primary data was collected through semi-structured interviews with 12 data specialists in a large financial services company. A thematic analysis of the data revealed several interlinked themes that evidence security fatigue. Awareness and understanding of these themes can help organisations to monitor for this and tailor security activities, such as security education, training, and awareness for increased effectiveness.
Bert-Based Feature Extraction for Long-Lived Bug Prediction in Floss: A Comparative Study

ContextThe correct prediction of long-lived bugs could help maintenance teams to build their plan and to fix more bugs that often adversely affect software quality and disturb the user experience across versions in FLOSS. Machine Learning (ML) and Text Mining methods have been applied to solve many real-world prediction problems, including those related to bug report handling.ObjectiveThe purpose of our research is to compare the accuracy of ML classifiers on long-lived bug prediction in FLOSS using BERT- or TF-IDF-based feature extraction. Besides that, we aim to investigate fine-tuning on BERT variants on the same task.MethodWe collected bug reports from six popular FLOSS and used the ML classifiers to predict long-lived bugs. Furthermore, we compare different feature extractors, based on BERT and TF-IDF methods, in long-lived bug prediction.ResultsWe found that long-lived bug prediction using BERT-based feature extraction systematically outperformed the TF-IDF. The SVM and Random Forest outperformed other classifiers in almost all datasets using BERT. Furthermore, smaller BERT architectures show themselves competitive.Conclusion: Our results demonstrated a promising avenue to predict long-lived bugs based on BERT contextual embedding features and fine-tunnig procedures.
A Novel Bayesian General Medical Diagnostic Assistant Achieves Superior Accuracy With Sparse History

Online AI symptom checkers and diagnostic assistants (DAs) have tremendous potential to reduce misdiagnosis and cost, while increasing the quality, convenience, and availability of healthcare, but only if they can perform with high accuracy. We introduce a novel Bayesian DA designed to improve diagnostic accuracy by addressing key weaknesses of Bayesian Network implementations for clinical diagnosis. We compare the performance of our prototype DA (MidasMed) to that of physicians and six other publicly accessible DAs (Ada, Babylon, Buoy, Isabel, Symptomate, and WebMD) using a set of 30 publicly available case vignettes, and using only sparse history (no exam findings or tests). Our results demonstrate superior performance of the MidasMed DA, with the correct diagnosis being the top ranked disorder in 93% of cases, and in the top 3 in 96% of cases.
Privacy in Mobile Sensing

In this chapter, we discuss the privacy implications of mobile sensing used in modern psycho-social sciences. We aim to raise awareness of the multifaceted nature of privacy, describing the legal, technical and applied aspects in some detail. Not only since the European GDPR was introduced, these aspects lead to a broad spectrum of challenges of which data processors cannot be absolved by a simple consent form from their users. Instead appropriate technical and organizational measures should be put in place through a proper privacy engineering process. Throughout the chapter, we illustrate the importance of privacy protection through a number of examples and also highlight technical approaches to address these challenges. We conclude this chapter with an outlook on privacy in mobile sensing, digital phenotyping and, psychoinformatics.
An Adaptive Dance Motion Smart Detection Method Using BP Neural Network Model under Dance Health Teaching Scene

As a body movement art, dance has its special form of expression. In terms of dance vocabulary, it can be roughly divided into two parts: external body movement and internal modality. In the process of body movement, it conveys information through silent language and the audience directly feels the information given by the dance image through vision. This is the special way of expressing emotion and meaning in dance art. This paper combines artificial intelligence technology and BP neural network (BPNN) algorithm to intelligently control dance teaching and solve complex nonlinear control problems. This paper studies dance teaching based on artificial intelligence technology. In this paper, BPNN algorithm and PCA-BPNN algorithm are used to test the dance teaching training of dance language, dance music, and stage art. The average accuracy of the BPNN evaluation model is 85.35% when the time reaches 80, while the average accuracy of the PCA-BPNN evaluation model is 65.64%. This shows that the accuracy of the BPNN evaluation model is higher than that of the PCA-BPNN evaluation model. Under the artificial intelligence technology, the dance using BPNN algorithm brings more intense sensory stimulation to the viewer because of the accompaniment of music, so as to achieve the infection and enjoyment of beauty and achieve the harmonious unity of sports and art.
Quantum coherence of a circularly accelerated atom in a spacetime with a reflecting boundary

Abstract We investigate, in the paradigm of open quantum systems, the dynamics of quantum coherence of a circularly accelerated atom coupled to a bath of vacuum fluctuating massless scalar field in a spacetime with a reflecting boundary. The master equation that governs the system evolution is derived. Our results show that in the case without a boundary, the vacuum fluctuations and centripetal acceleration will always cause the quantum coherence to decrease. However, with the presence of a boundary, the quantum fluctuations of the scalar field are modified, which makes that quantum coherence could be enhanced as compared to that in the case without a boundary. Particularly, when the atom is very close to the boundary, although the atom still interacts with the environment, it behaves as if it were a closed system and quantum coherence can be shielded from the effect of the vacuum fluctuating scalar field.
Designing Smart Space Services by Virtual Reality-Interactive Learning Model on College Entrepreneurship Education

The purpose was to improve the limitations of traditional entrepreneurship education, realize the virtual interactive learning between college students and teachers, and stimulate students' exploration of entrepreneurship. This work first discusses the working principle of Virtual Reality (VR) and builds an Interactive Learning Model (ILM) using VR. Then, the VR-ILM is used to design the Smart Space services. Harris Corner Detector (HCD) is used to detect the pixel grayscale change in the Smart Space image window. Further, the VR-ILM-based Smart Space is proposed according to the Smart Space design requirements and principles. Finally, the proposed VR-ILM-based Smart Space is applied to College Entrepreneurship Education (CEE). Its impact on the CEE market, employment in different industries, and students' satisfaction with CEE are studied. The results show that the proposed VR-ILM-based Smart Space has increased the entrepreneurship teaching courses, entrepreneurship coaching activities, and entrepreneurship practice activities by 4, 6, and 24%, respectively. It has reduced entrepreneurship competitions and other forms of entrepreneurship education by 4 and 16%. The proposed VR-ILM-based Smart Space has dramatically improved the practical teaching of CEE. Meanwhile, real estate services have felt the most significant impact of the proposed VR-ILM-based Smart Space, with an employment increase of 43%. Lastly, students' satisfaction with entrepreneurship education practice and teaching methods has increased by nearly 50%. The satisfaction with the internal environment has increased to 78%. The satisfaction with the curriculum system, teachers, and industry financing has increased from 30 to 45%, 24 to 36%, and 45 to 63%, respectively. The satisfaction with the teaching goal has increased to 62%. Thus, the proposed VR-ILM-based Smart Space has dramatically improved students' satisfaction with CEE and has a different impact on the market, industry, and satisfaction with CE. The finding has a certain reference for the VR interactive model.
Effect of Optimized Deep Belief Network to Patch-Based Image Inpainting Forensics

This paper intends to propose a new model for detecting the patch based inpainting operation using Enhanced Deep Belief Network (E-DBN). The proposing model makes strong supervising of DBN that will capture the manipulated information. In fact, the enhancement is done under optimization concept, where the activation function and weight of DBN is optimally tuned by a new hybrid algorithm termed as Swarm Mutated Lion Algorithm (SM-LA). The hybridization model combines two conventional models: Group Search Optimizer (GSO) and Lion Algorithm (LA). Finally, the performance of proposed model is compared over other conventional models with respect to certain performance measures.
Object re-identification with distribution corrected ranking list

Object re-identification is a retrieval task to identify the same identity, usually utilizing the ranking list to sort the features. However, most existing ranking lists only calculate the distance from the probe sample to its k-nearest neighbor samples in the gallery set but ignore the distribution of the gallery set. Meanwhile, we find that the gallery set tends to converge into different groups. When the probe lies near the boundary, its k-nearest neighbors often contain samples of different identities from the probe sample. This paper presents a Distribution Corrected Ranking List (DCRL) to correct the ranking by considering the distance between the probe sample and its nearest distribution (i.e., the cluster center). Specifically, given a probe sample, first, we cluster the gallery samples with the agglomerative hierarchical clustering method to obtain the cluster centers. Second, we compute the distance between the probe and the cluster centers. Third, the final ranking list is considered as the weighted combination of the original ranking list (i.e., the distance between the probe sample and its k-nearest neighbor in the gallery) and the distribution ranking list (i.e., the distance between the probe sample and its nearest cluster center). The proposed distribution corrected ranking list is suitable for large datasets because it does not require human intervention or labeled data. Experimental results on several benchmark re-identification datasets, such as Market-1501, DukeMTMC-reID, MSMT17, VeRi-776, and VeRi-Wild, show that the proposed method achieves state-of-the-art performance.
A Generative Cramér-Rao Bound on Frequency Estimation with Learned Measurement Distribution

The estimation of the frequency of a single tone signal is a classical problem. The Cramér-Rao lower bound (CRB) on the frequency estimates has been well studied for the case of additive Gaussian noise. In practical applications, however, the probability density function of the noise is rarely Gaussian, or known. Moreover, non-linear effects, as quantization, are often present, making the Gaussian CRB unreachable. In this paper we propose a data-driven approach for evaluating the CRB on frequency estimation with unknown noise and other degradation. Using a learned normalizing flow model, we model the distribution of the measurements by a neural network and obtain an approximate CRB, referred to as a Generative CRB (GCRB). We demonstrate the GCRB on frequency estimation both in cases where the CRB has been previously evaluated, showing the accuracy of the GCRB empirically, and on complex cases where the CRB cannot be evaluated analytically or numerically.