Miracle-dz/UniFAR-FTUs huggingface dataset:
Upgrading Bayesian Network Scores for Multi-Relational Data

A model selection score measures how well a model fits a dataset. We describe a new method for extending, or upgrading, a Bayesian network (BN) score designed for single-table i.i. d. data to multi-relational datasets (databases). A multi-relational BN model provides an integrated statistical analysis of the interdependent data tables in a database. We focus on log-linear model selection scores. Our key theoretical desideratum is preserving consistency: if a model selection score is statistically consistent for single-table data, then the upgraded score should be statistically consistent for relational data as well. It is difficult if not impossible to define an upgrade method for log-linear model scores that preserves consistency, if the upgraded score is a function of a single model only. We therefore develop a novel approach where an upgraded model score is a gain function that compares two models: a current vs. an alternative BN structure. Our main theorem establishes that model search based on our upgraded gain function preserves consistency. Empirical evaluation on six benchmark relational databases shows that our upgraded scores select an informative BN structure that strikes a balance between overly sparse and overly dense graph structures.
An automated procedure for cell-phase imaging identification

Advances in fluorescent probing and microscopic imaging technology provide important tools for biomedical research in studying the structures and functions of cells and molecules. Such studies require the processing and analysis of huge amounts of image data, and manual image analysis is very time consuming, thus costly, and also potentially inaccurate and poorly reproducible. Stages of an automated cellular imaging analysis consist of segmentation, feature extraction, classification, and tracking of individual cells in a dynamic cellular population. Image classification of cell phases in a fully automatic manner presents the most difficult task of such analysis. We are particularly interested in combining several advanced computational, probabilistic, and fuzzy-set methods for the computerized classification of cell nuclei in different mitotic phases. We tested our proposed methods with real image sequences recorded over a period of twenty-four hours at every fifteen minutes with a time-lapse fluoresence microscopy, and the experimental results have shown that the proposed methods are effective for the identification task.
Time Series Clustering For Novelty Detection: An Empirical Approach

This paper presents some results of DANTE project: Detection of Anomalies and Novelties in Time sEries with self-organizing networks, whose goal is to devise and evaluate self-organizing models for detecting novelties or anomalies in univariate time series. The methodology to detect novelty consists in finding non-parametric confidence intervals, who are computed from the quantization errors obtained at the training phase, used at the testing phase as decision thresholds for classifying data samples as novelties/anomalies. We compared the performance achieved among variations of the self-organizing neural architectures using as input patterns a non-stationary data series composed by distinct dynamical regimes. Keywords— Anomaly Detection, Time Series, Local Linear Models, Self-Organizing Maps. Resumo— Este trabalho apresenta resultados do projeto DANTE: Detecção de Anomalias e Novidades em séries TEmporais. O objetivo do projeto é avaliar o desempenho de diversas redes auto-organizadas ao detectar anomalias/novidades em padrões de dados dinâmicos. A metodologia consiste em se determinar intervalos de confiança não-paramétricos a partir de erros de quantização obtidos na fase de treinamento, usados posteriormente na fase de teste como limiares de decisão para classificar amostras como sendo uma anomalia ou novidade. Realizou-se uma comparação do desempenho de variantes da rede SOM, utilizando-se como padrões de entrada uma série não-estacionária composta de regimes dinâmicos distintos. Keywords— Detecção de Novidades, Séries Temporais, Modelos Lineares Locais, Mapas Auto-Organizáveis.
Cyber-Physical Attacks Targeting Communication-Assisted Protection Schemes

The dependence of modern societies on electric energy is ever increasing by the emergence of smart cities and electric vehicles. This is while unprecedented number of cyber-physical hazards are threatening the integrity and availability of the power grid on a daily basis. On one hand, physical integrity of power systems is under threat by more frequent natural disasters and intentional attacks. On the other hand, the cyber vulnerability of power grids is on the rise by the emergence of smart grid technologies. This underlines an imminent need for the modeling and examination of power grid vulnerabilities to cyber-physical attacks. This paper examines the vulnerability of the communication-assisted protection schemes like permissive overreaching transfer trip to cyberattacks using a co-simulation platform. The simulation results show that the transient angle stability of power systems can be jeopardized by cyberattacks on the communication-assisted protection schemes. To address this vulnerability, two physical solutions including the deployment of communication channel redundancy, and a more advanced communicated-assisted protection scheme, i.e., directional comparison unblocking scheme (DCUB), are considered and tested. The proposed solutions address the vulnerability of the communication-assisted protection schemes to distributed denial of service attack to some extent. Yet, the simulation results show the vulnerability of the proposed solutions to sophisticated cyberattacks like false data injection attacks. This highlights the need for the development of cyber-based solutions for communication channel monitoring.
Physical Properties of Dairy Manure Pre- and Post-Anaerobic Digestion

Manure characteristics change through processing, including anaerobic digestion (AD). These changes can alter handling of manure during downstream operations. This study analyzed the density, total solids (TS) content, and volatile solids (VS) content of pre-digested and anaerobically digested dairy manure from seven dairy farms in Wisconsin. The density of pre-digested manure increased from 990 to 1065 kg m−3 as the TS level increased from 1.5% to 13.0%. Density and TS for pre-digested manure from facilities using separated solids as bedding were related with a linear model for TS ranging from 1.5% to 13.0% and with a polynomial model for TS ranging from 1.5% to 50%. The model shows that density decreases with an increasing TS content when TS is greater than 8.0%. Manure from dairy facilities that used sand bedding had a VS/TS ratio of 0.87. This ratio was higher than the ratio when manure solids were used as bedding (0.81) and when food waste was incorporated into the digester (0.77). This study also provides a simple methodology to estimate biogas yield by using the density of pre- and post-digested manure.
Boresight Calibration Based Fast Registration Between Airborne LiDAR Points Clouds and Synchronous Images

There is a different imaging mechanism between LiDAR data and aerial imagery,so it is difficult to adopt traditional automated registration method.On the basis of comprehensive analysis of boresight misalignment,high-precision LiDAR data integrated with high-resolution aerial imagery are used to precisely position 3-dimentional object space points.Then a combined adjustment model for LiDAR data and aerial images can be built to achieve boresight registration without any control fields,which can guarantee high-precision registration of LiDAR data and aerial image.
Hydroelectric Generating Unit Fault Diagnosis Using 1-D Convolutional Neural Network and Gated Recurrent Unit in Small Hydro

Machine learning algorithm based on hand-crafted features from the raw vibration signal has shown promising results in the hydroelectric generating unit (HGU) fault diagnosis in recent years. Such methodologies, nevertheless, can lead to important information loss in representing the vibration signal, which intrinsically relies on engineering experience of diagnostic experts and prior knowledge about feature extraction techniques. Therefore, in this paper, an effective and stable HGU fault diagnosis system using one-dimensional convolutional neural network (1-D CNN) and gated recurrent unit (GRU) based on the sequence data structure is proposed. First, the raw vibration data is reconstructed by data segmentation, which can improve training efficiency. Second, the reconstruction data under the influence of different running conditions and various fault factors can be effectively and adaptively learned by 1-D CNN-GRU and then determine information fault categories via network inference. Finally, four machine learning methods are applied to diagnosis the reconstruction data based on the experimental dataset. The performance of the proposed method is verified by comparing with the results of other machine learning techniques. Furthermore, the fault diagnostic model, which is trained by the practical vibration signal, has successfully applied in engineering practice.
Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.
Analysis of a Sorter Cascade Applied to Control a Wheelchair

The precise eye state detection is a fundamental stage for various activities that require human-machine interaction (HMI). This chapter presents an analysis of the implementation of a system for navigating a wheelchair with automation (CRA), based on facial expressions, especially eyes closed using a Haar cascade classifier (HCC). Aimed at people with locomotor disability of the upper and lower limbs, the state detection was based on two steps: the capture of the image, which concentrates on the detection actions and image optimization; actions of the chair, which interprets the data capture and sends the action to the chair. The results showed that the model has excellent accuracy in identification with robust performance in recognizing eyes closed, bypassing well occlusion issues and lighting with about 98% accuracy. The application of the model in the simulations opens the implementation and marriage opportunity with the chair sensor universe aiming a safe and efficient navigation to the user.
A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data

Due to the increasing popularity of cloud computing, more and more data owners are motivated to outsource their data to cloud servers for great convenience and reduced cost in data management. However, sensitive data should be encrypted before outsourcing for privacy requirements, which obsoletes data utilization like keyword-based document retrieval. In this paper, we present a secure multi-keyword ranked search scheme over encrypted cloud data, which simultaneously supports dynamic update operations like deletion and insertion of documents. Specifically, the vector space model and the widely-used TF x IDF model are combined in the index construction and query generation. We construct a special tree-based index structure and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and query vectors, and meanwhile ensure accurate relevance score calculation between encrypted index and query vectors. In order to resist statistical attacks, phantom terms are added to the index vector for blinding search results. Due to the use of our special tree-based index structure, the proposed scheme can achieve sub-linear search time and deal with the deletion and insertion of documents flexibly. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.
Wavelength Defragmentation for Seamless Migration

Dynamic traffic in optical networks leads to spectrum fragmentation, which significantly reduces network performance, i.e., increases blocking rate and reduces spectrum usage. Telecom operators face the operational challenge of operating non-disruptive defragmentation, i.e., within the make-before-break (MBB) paradigm when dealing with lightpath rerouting in wavelength division multiplexed fixed-grid optical networks. In this paper, we propose an MBB routing and wavelength assignment defragmentation process, which provides the best possible lightpath network provisioning, i.e., with minimum bandwidth requirement. We tested extensively the models and algorithms we propose on four network topologies with different grade of service defragmentation triggering events. We observe that, for a given throughput, the spectrum usage of the best MBB lightpath rerouting is always less than 2.5% away from that of an optimal lightpath provisioning.
Wideband circularly‐polarised high‐gain diversity antenna loaded with metasurface reflector for small satellite applications

This Letter demonstrates the design of a two-element polarisation diversity antenna, applicable for modern small satellite application systems. The intended antenna mainly consists of two monopole radiators loaded with metasurface reflector as a ground plane. The dimensions of the radiator antenna are 40 × 22 × 1.6 mm 3 . The antenna measured results offers a wider S 11 bandwidth of (2.23-2.91 GHz) and wider axial ratio bandwidth (ARBW) of (2.16-2.92 GHz) with a common S 11 and ARBW of 26.45% is obtained. A maximum gain of 7.02 dBic and isolation better than 14.5 dB is observed along with the operating frequency range. The antenna provides an ARBW( 2.38 dBic) for theta = ± 45°, for all phi values along with the complete operating frequency range. Also, the antenna port-1 provides left-handed circular polarisation and port-2 provides right-handed circular polarisation radiation with directional radiation characteristics.
The Impact of Port Transport-logistics Infrastructure and LPI for Economic Growth

For this study, carried out as an empirical study, 37 landlocked countries have been selected. And a broader economic impact on the national economy, on the quality of the port infrastructure and logistics efficiency has been considered as well as investments on creating a high quality port infrastructure and its contribution to the economy, often questioned by politicians, investors and general public. The Structural Equation Model (SEM) has been used for providing empirical data about a significant economic impact on the port infrastructure quality and logistics efficiency. However, some countries that do not have access to the sea are not, according to international agreements, inferior to other countries in terms of economic growth.
Inferencing design styles using Bayesian Networks

Reasoning with uncertain knowledge and belief has long been recognized as an important research issue in Artificial Intelligence (AI). Several methodologies have been proposed in the past, including knowledge-based systems, fuzzy sets, and probability theory. The probabilistic approach became popular mainly due to a knowledge representation framework called Bayesian networks. Bayesian networks have earned reputation of being powerful tools for modeling complex problem involving uncertain knowledge. Uncertain knowledge exists in domains such as medicine, law, geographical information systems and design as it is difficult to retrieve all knowledge and experience from experts. In design domain, experts believe that design style is an intangible concept and that its knowledge is difficult to be presented in a formal way. The aim of the research is to find ways to represent design style knowledge in Bayesian net works. We showed that these networks can be used for diagnosis (inferences) and classification of design style. The furniture design style is selected as an example domain, however the method can be used for any other domain.
Dissimilarity Analysis of Signal Processing Methods for Texture Classification

As observed from the literature survey, there is no commonly accepted quantitative definition of visual texture. As a consequence, researchers seeking a quantitative texture measure have been forced to search intuitively for texture features, and then attempt to evaluate their performance by different techniques. Dissimilarity analysis is one of the main requirements from the classifier design point of view and provides information of significant importance regarding feature extraction and selection strategies. This paper explores several texture features of historical and practical significance and presents their comprehensive dissimilarity analysis. An improved post processing scheme has also been proposed for Law's filter based feature extraction technique. Results, validated through dissimilarity measures, show a considerable improvement over existing scheme
Model of DC/DC Dual-Bridge Series Resonant Converter in Buck and Boost Modes for Output Current and Commutation Timing Control

In the paper, a control-affine model for the active dual-bridge series resonant converter is presented. The model has square form and produces cycle averaged output current and two parameters of waveform alignment for the controllable achievement of ZVS and synchronous rectification. The output characteristics of the converter are obtained by using the first harmonic approximation of input voltage applied to the resonant tank. The maximum prediction error of this model is about 10% in comparison to the measured data. This model is suitable for both voltage buck and boost operations, and also demonstrates fundamental control properties of the converter namely high nonlinearity respect to the control inputs, a singularity of output current surface between buck and boost modes, and holonomic constraints for control variables.
Online Pulse Deinterleaving With Finite Automata

Most of the existing deinterleaving methods work offline; they deinterleave pulse streams in a recursive way via multipass searching. Such methods do not fit online processing applications, and they have very low deinterleaving efficiency. In this paper, I address the deinterleaving problem of streams with repetitive periods, such as streams with constant or stagger pulse repetition intervals. Transitions between the pulses of different states are illustrated with regular grammars, and finite automata are established accordingly to realize online pulse deinterleaving. The states of pulse streams being deinterleaved are stored and continuously updated in the automata, and newly received pulses are judged by the associated finite state controls to determine whether they come from a certain emitter, so as to realize online deinterleaving. In the automaton-based method, multiple automata can be started to work parallel on the same stream, and they deinterleave intersected streams via one-pass (instead of recursive) searching. Simulation results also demonstrate the superiority of the proposed method in pulse-deinterleaving performances.
Semantic Measures for the Comparison of Units of Language, Concepts or Instances from Text and Knowledge Representation Analysis A Comprehensive Survey and a Technical Introduction to Knowledge-based Measures Using Semantic Graph Analysis

Semantic measures are widely used today to estimate the strength of the semantic relationship between elements of various types: units of language (e.g., words, sentences, documents), concepts or even instances semantically characterized (e.g., diseases, genes, geographical locations). Semantic measures play an important role to compare such elements according to semantic proxies: texts and knowledge representations, which support their meaning or describe their nature. Semantic measures are therefore essential for designing intelligent agents which will for example take advantage of semantic analysis to mimic human ability to compare abstract or concrete objects. This paper proposes a comprehensive survey of the broad notion of semantic measure for the comparison of units of language, concepts or instances based on semantic proxy analyses. Semantic measures generalize the well-known notions of semantic similarity, semantic relatedness and semantic distance, which have been extensively studied by various communities over the last decades (e.g., Cognitive Sciences, Linguistics, and Artificial Intelligence to mention a few). Definitions, related contributions in cognitive sciences, practical applications, and the several approaches used for the definitions of semantic measures are presented. In addition, protocols and benchmarks for their evaluations, as well as, software solutions dedicated to them are introduced. The general presentation of the large diversity of existing semantic measures we propose is further completed by a detailed survey of a specific type of measures relying on knowledge representation analysis. This technical presentation mainly focuses on measures based on graph analyses. These measures are of particular interest for numerous communities and have recently gained a lot of attention in research and application, by taking advantage of several types of graph-based knowledge representations to compare words, concepts, or instances. We conclude this work by highlighting some of the challenges offered to the communities involved in the study of semantic measures.
Research on Structure Learning Theory and Algorithm Based on Probabilistic Graphical Model

Probabilistic graphical model can effectively deal with uncertainty reasoning, and learning the probabilistic graphical model from the sample data is an important problem in practical application. The representation of the probabilistic graphical model consists of two parts, parameters and structure. The learning algorithm is also divided into parameter learning and structure learning. In this paper, the structure learning algorithms based on probabilistic graphical model network are introduced in detail. The structural learning algorithms are also summarized based on the differences in the characteristics of structural learning algorithms, and the algorithm of Markov network structure learning is summarized. Finally, this paper points out the open problems of the probabilistic graphical model learning and its further research directions.