Reviewerly/exHarmony huggingface dataset:
A Cost-Driven Top-K Queries Optimization Approach on Federated RDF Systems

RDF (Resource Description Framework) is a model widely used to construct knowledge bases, while SPARQL (SPARQL Protocol and RDF Query Language) is the standardized structured query language to manipulate RDF data. Recently, many data providers have published their RDF datasets in their own autonomous sites and provided SPARQL query interfaces, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RDF sources</i> . In order to integrate multiple RDF sources, researchers put forward the federated RDF system to support the federated SPARQL queries. However, existing studies can only support efficient basic queries but not top-k queries. Toward this end, we propose a cost-driven top-k queries optimization approach in federated RDF systems, which can support both top-k queries for single variable ordering and expression ordering. Firstly, we propose an optimized query decomposition method to decompose the federated query into multiple subqueries. Secondly, while considering the top-k operator, we propose a cost model to evaluate the query cost and join cost of subqueries. The optimal query plan can be obtained by the costed-based query plan generation algorithm. Finally, combined with the characteristics of top-k queries, an incremental query plan execution strategy is developed to minimize the total query cost. Experimental results show that the proposed method is effective, efficient and scalable.
Research and Application of Knowledge Graph Technology for Intelligent Question Answering

With the development and application of artificial intelligence technology, knowledge graph gradually becomes one of the key technologies. To solve the data sparsity problem of knowledge graph and improve the efficiency of knowledge graph completion, a knowledge representation model (ISTransE) was proposed, which combined the importance information of entities and relationship strength characteristics. We combine the BERT model and knowledge graph to study the key technology of Q&A, complete the relevant design of an intelligent Q&A system. And realize the intelligent Q&A system of automobiles based on the BERT entity recognition module, question relationship extraction module, answer retrieval module and construct the domain knowledge graph module. And the system is tested by corresponding experiments for Q&A. The test results show that this system can accurately answer the user's car-related knowledge.
Automatic Recognition of Native Advertisements for the Slovak Language

In recent years the native advertisement is becoming more and more prevalent in online spaces. Differentiating between genuine content and native advertisement using Natural Language Processing is therefore also becoming a very interesting research topic. In this paper, we examine the possibilities of using deep textual representation for the Slovak language to recognize the “PR (Public relations) articles” (that serve as a native advertisement in this context) from authentic news articles on popular Slovak news websites. We show that the BERT (Bidirectional Encoder Representations from Transformers) embeddings as a text representation are sufficient for this task (achieving accuracy over 80% even with a statistical model - Logistic Regression) and that the models generally perform better without prior lemmatization. We have scraped three Slovak news websites (for a total of 5455 news articles containing both paid-for content and a wide variety of genuine categories), and we have evaluated multiple binary classification methods (Logistic Regression, Random forest classifier and Support Vector Machines) trained on top of generated RoBERTa sentence embeddings. On our testing set, we were able to achieve an accuracy of 85.13%.
Research on Recognition of Human Motion State Based on Force and Motion Sensor Fusion

The exoskeletons, as the wearable mechanical devices with powerful features of physical enhancement on human performance, has gained more and more attention from scientific researchers in the world. It has become a new research hot spot, and has also begun to be gradually applied to the military industry. An exoskeleton can be a coupling system based on human-computer interaction. In order to achieve human-machine motion coordination and adaptation enhancement, the system must have cognitive intelligence. When the exoskeleton is in use, the system needs to operate fast and accurate enough to predict the human intention and movement (such as: walking, standing, sitting or going up and going down stairs) and determine the gait cycle stage. In addition, it must be robust against small errors within the period simultaneously. Therefore, the recognition of human motion status is one of the core technologies in system design for the exoskeleton. In our research, signals from foot force sensors and Inertial Measurement Unit (IMU) sensors were collected, and Support Vector Machine (SVM) was used to identify the human body motion state through adaptive time window segmentation. After that, experimental tests were conducted to verify the algorithm and recognition accuracy.
Robot end effector positioning approach based on single-image 2D reconstruction

Measurement of a pose for a robotic arm is fu-damentental for its calibration and performance measurements along several tasks. The most known methods usually involve using sensors like Laser Trackers, Coordinate Measuring Machines, etc. To simplify these measurements there are another options using novel computer vision techniques which use 2D images and their implementation to recreate environments or objects. In this research article, the implementation of a method that is suitable alternative for calibrating camera using a single image and we can estimate robot end efector position. Robot repeatability was also measured using the proposed camera calibration algorithm.
A federated binarized neural network model for constrained devices in IoT healthcare services

In IoT healthcare environment, the devices are not sufficiently powerful for operating recent deep learning models, and data collected by the devices are usually decentralized. Moreover, data are unavailable to share between devices because of information security issues. Therefore, a concept of federated learning has emerged to overcome data sharing issues, and a concept of binarized neural network has emerged to generate lightweight deep learning models. This paper proposes a federated binarized neural network model to derive a reliable healthcare system in this circumstance. This paper shows an overview of considered system model with constrained IoT healthcare devices. In addition, this paper shows illustrations of implementing the proposed federated learning model with the proposed binarized MLP networks by utilizing an open-source library. The experiment results show that the binarized MLP network shows comparable performances compared to the full-precision MLP network while the binarized MLP requires about 10-times less model size for training.
Deep Neural Networks for Radar Waveform Classification

We consider the problem of classifying radar pulses given raw I/Q waveforms in the presence of noise and absence of synchronization. We also consider the problem of classifying multiple superimposed radar pulses. For both, we design deep neural networks (DNNs) that are robust to synchronization, pulse width, and SNR. Our designs yield more than 100x reduction in error-rate over the current state-of-the-art.
Physically adversarial thermal hydraulics evaluation of deep learning models for pressurized water reactors

LatticeNet is a hierarchical deep learning architecture which has been developed for predicting pin powers and other parameters within single or multiple 2D pressurized water reactor assemblies. It has been shown to be effective at the task of predicting distributions of reactor parameters such as normalized pin powers under changing thermal hydraulics conditions. However, deep learning models are prone to overfitting and to learning rules in the dataset which are either not applicable to the real world or not what the researchers intended. When developing deep learning architectures for reactor modeling and simulation applications it is important to investigate how well these models perform on unseen data which lies outside of their training distribution. In this work, we develop multiple thermal hydraulic datasets which are specifically tailored to provide challenging inference examples, and evaluate these datasets using existing trained LatticeNet models in order to determine how well these models perform on classes of examples which are either statistically unlikely or impossible to be in the training data. We show that these models exhibit surprising generalization capabilities for data outside of their training distribution, and moreover that the error of these examples is not entirely random but semi-continuous. We also show that at least some variants of LatticeNet are particularly vulnerable to adversarial inputs which cause them to produce non-physical answers, and demonstrate a simple method to detect these non-physical regions which requires no generation of new data.
Emotion Detection Based on Bangladeshi People’s Social Media Response on COVID-19

The COVID-19 pandemic is still active on a global scale while the virus was first identified in December 2019 in Wuhan, China. As the pandemic continues to affect millions of lives, several countries including Bangladesh have gone into complete lockdown for a second time. During the lockdown periods, people have expressed their experiences, curiosities, and ideas regarding the problems caused by the pandemic in terms of health and socioeconomic issues. This study was conducted to determine how Bangladeshi people are responding to and dealing with the circumstances of COVID-19. This study took into account the status and comments on those issues related to COVID-19 from a variety of Facebook pages and YouTube channels run by reputable Bangladeshi news organizations and health experts. Throughout the study, several machine learning methods were studied, ranging from classical algorithms which include SVM and Random Forest, while CNN and LSTM are the deep learning algorithms to conduct experiments on a classified data set that belongs to the authors, which contains 10,581 data points. While evaluating the efficiency of these models in terms of model assessment, the finding suggests that LSTM outperforms all others with an accuracy of 84.92.
Toward a Smart Approach of Migration from Relational System DataBase to NoSQL System: Transformation Rules of Structure

In the last few years, databases have become very important and very large because they play a strategic and important role in most organizations and because they receive a huge flow of information from multiple sources every moment in building BigData. This situation identified several limitations and weaknesses in relational database management systems (RDBMS), such as availability, real-time response, horizontal scalability, decision support, advanced data analysis, and especially the management of Bigdata which can reach zeta bytes in storage. This requires the storage and organization of this data in a new management system database not fixed by a rigid structure and resolves all problems associated with the storage of database in a relational system. In this view, organizations need a new NoSQL (not only SQL) system that overcomes the limitations of the relational system. The change of the database management system requires the migration of the databases from the relational system to another NoSQL, taking into consideration all stored data and keeping the majority of the possibilities and functionalities of the old system, with all the advantages of the new system. In this paper, we will identify the elements of relational databases that belong to nature: data, structure, and semantics, which we must migrate to a NoSQL system, such as a document-oriented system. Also in this paper, we will present the rules for transforming the structure of the relational system to another document-oriented NoSQL, according to the principal’s basics of a new approach to migration.
A Color Cast Image Enhancement Method Based on Affine Transform in Poor Visible Conditions

In this letter, a simple yet effective dehazing framework is proposed, which consists of a novel color correction and a contrast enhancement. Most of the existing dehazing works focus on enhancing the contrast of the degraded images, but rarely of them concern about the color cast, which is ubiquitous in the scattering medium. To address the color distortion, an affine transform model-based color correction method is first proposed to improve the appearance of the image while preserving the details, which is inspired by the traditional color transfer. The color transfer alters the color values of a source image by sharing the global color statistics of a reference image, which makes it unsuitable to address the locally variable color deviations encountered in highly color distorted images as in poor visibility conditions (sandstorms and underwater). To alter color correction locally, we add local color fidelity and gradient constraint to the proposed technique, which overcomes the limitation that the traditional method depends too much on the global color statistics of the reference image and encourages it to handle the degraded image with various color casts and light conditions. In addition, a multiscale gradient-domain processing is applied to enhance the contrast. In this procedure, by extracting the information of different layers, we can easily restore the contrast while limiting the significant amplification of noise. The extensive qualitative and quantitative experiments reveal that the color and the contrast can be significantly improved by the proposed technique.
Sentiment Analysis of E-Commerce Reviews Based on Long Short-Term Memory Networks with Dropout Layer and Optimization

Traditional neural networks cannot accurately analyze the sentiment tendency of the text according to the context of sentences. To address this issue, this paper proposes a recurrent neural network (RNN) model combined with long short-term memory (LTSM) networks. Our method solves the problem that traditional text analysis methods cannot efficiently analyze the emotional tendency of numerous online shopping comments. Firstly, a tokenizer is used to divide sentences into words for emotional analysis, a stop vocabulary table is used to filter out meaningless high-frequency vocabulary, and the Word2Vec is used to vectorize words. Then, the model combining LTSM and RNN is used to capture the emotional features contained in online texts. Finally, the model is optimized to the best performance by choosing different iterations. The experimental results show that the accuracy and F1 value of the proposed model have reached a high rate. The proposed method combining LTSM and RNN can effectively extract the emotional features of Internet text.
Resu-mizer: Hybrid Resume Information Retrieval System

In the modern era, their exists a need for efficient Information Retrieval (IR), from large number of documents; consequently, leading towards automation. In the past decade, we have observed an exponential increase in data influx, particularly in the case of unstructured data, which includes images, videos, and textual documents. When textual data sources are taken into consideration, like in the case of resumes, there is no standard format, and hence, are liable to subjective experience. On the other hand, current automated information extraction techniques assume a standard format for documents. Previous researchers have employed Rule-based methods, supervised methods and semantics-based methods to extract entities from the resumes. Though these methods heavily depend on large amounts of data, that is usually in an unstructured format. Furthermore, these techniques are time-intensive and are prone to some limitations. Our study includes the selection of a two-step hybrid Information Retrieval methodology. Sequentially it can be broken down into text block classification which employs Boolean Naive Bayes with Laplcian smoothing and a tri-gram approach followed by Entity recognition using BERT-cased. Our approach had an Average F1 Score of 0.80 for Text Block Classification and an average F1 score of 0.52 for Named Entity Recognition.
RF Fingerprinting with Dilated Causal Convolutions–An Inherently Explainable Architecture

Deep learning approaches have been shown to be functional approximators, with radio frequency signals being no exception. This, however, typically comes at the cost of explainability in how they arrive at answers. In this paper, we dive into our RiftNet <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> algorithm that is based on dilated causal convolutions and has shown over 95% accuracy for fingerprinting 10,000 open air real-world devices. We explore how core elements of the design are particularly suited to pulling out generic signal features and shed some light on the inner workings of the algorithms by exploring transparent aspects that are inherently explainable.
A GA-Based Energy Aware Virtual Machine Placement Algorithm for Cloud Data Centers

Virtual machine placement (VMP) is among the important techniques to improve the energy efficiency of green cloud data centers. To address this issue, bionic optimization algorithms have been widely used. One of the most effective methods is to use the genetic algorithm (GA) to find the best or near-optimal solution for the VMP problem. In this paper, a GGA-based algorithm is proposed to solve the energy-aware VMP problem. The algorithm regards the VMP problem as BPP and strives to reduce the number of active hosts in order to reduce the system energy consumption. Further, since it is important to find the solution of VMP in a short time especially in the online scenario, several optimization strategies are developed and applied in the proposed algorithm to improve the convergence speed of the algorithm and reduce the execution time of the algorithm. The experimental results demonstrate that compared with the traditional genetic algorithm, our method can effectively decrease the number of iterations and improve the speed of seeking optimal solutions.
GasBotty: Multi-Metric Extraction in the Wild

The lived environment, particularly when proximal to roadways, is filled with multi-digit and multi-numbered values corresponding to advertised commodities. The reliable detection of single multi-digit values from natural imagery has been widely studied through the last decade (e.g. Street View House Numbers [SVHN]); however, extraction and assignment of the contextual meaning of those values are far more difficult given the diversity and unstructured nature of advertisements. To operationalize information extracted from detected values in the wild, the contextual meaning that those values represent is critical. To our knowledge, no large-scale visual dataset comprising multi-digit, multi-number values with associated context labels exists; we denote this class of problems as &#x201C;multi-metric extraction in the wild&#x201D;. In this work, we focus on the accurate detection and reading of gas prices, and their contextual association to gas grade and payment type. We provide complete annotations for the Gas Prices of America (GPA) dataset, comprising 2,048 training and 512 test images sampled across the United States including over 2,600 signs, 6,000 prices, 27,000 digits, and 7,800 gas grade and payment type labels. With these data, we develop the GasBotty predictor, a composite neural network model, and evaluate it over eight benchmark tasks of increasing difficulty. Finally, we define a new highly stringent, binary-type metric, denoted &#x201C;All-or-Nothing Accuracy&#x201D; (ANA), requiring that a predictor perfectly extract and correctly associate all information in a gas sign. Our proposed model achieves 72.9&#x0025; ANA over the independent test set of 512 images, where conventional state-of-the-art models object detection models and both a uniform random and biased random predictor would all tend towards 0&#x0025; ANA. GasBotty and the GPA dataset will serve as a valuable benchmark for the development of future multi-metric extraction in the wild systems.
Analysis of the Proposed CNN Model for the Recognition of Gurmukhi Handwritten City Names of Punjab

A holistic approach employing different learning rates is proposed for the recognition of handwritten district names of Punjab state which are written in Gurmukhi Script. Learning rate (LR) is a hyperparameter that specifies how much the model can change when the model parameters are changed in terms of error. For the purpose of recognition, a convolutional neural network (CNN) using deep learning is proposed. Initially, the dataset of 4000 of images is prepared for ten different city names of Punjab state, and later, a CNN is employed. The proposed CNN architecture having 12 layers is developed and employed using five different learning rates: 0.00001, 0.0001, 0.001, 0.01 and 0.1, and their performance is analysed for the purpose of text recognition. Best average validation accuracy achieved for the proposed CNN architecture is 93%, and maximum achieved validation accuracy is 99% by employing LR of 0.0001 for the prepared dataset.
Enumerating Minimal Generators from Closed Itemsets – Toward Effective Compression of Negative Association Rules

Negative association rules are valuable and essential for expressing various latent properties which hide in big data. The number of valid negative association rules, however, always becomes so huge, thus an effective compression method of the set of negative rules is quite important. Minimal generators are very useful for compressing the set of valid negative rules. In this paper, we study several efficient algorithms for enumerating minimal generators from given closed itemsets. Especially we propose a novel enumeration algorithm which does not use any support computation, but uses an eager hash search in a top-down tree search. We show experimental results for evaluating our enumeration algorithms, and confirm very good performance of the enumeration method without support computation.