Reviewerly/exHarmony huggingface dataset:
Order based algorithms for the core maintenance problem on edge-weighted graphs

The cohesive subgraph k-core is a maximal connected subgraph with the minimum degree δ≥k of a simple graph, where integer k≥0. Define the core number of a vertex w as the maximum k such that w is contained in a k-core. The core decomposition problem which is calculating the core numbers of all vertices in static graphs, and the core maintenance problem which is updating the core numbers in dynamic graphs are our main concern. Although, core numbers can be updated by the core decomposition algorithms, only a small part of vertices' core numbers have changed after the change of a graph. Thus, it is necessary to update core numbers locally to reduce the cost. In this paper, we study the core maintenance problem on edge-weighted graphs by using the vertex sequence k-order which is ordered by the order that the core decomposition algorithm removes vertices. We design the core maintenance algorithms for inserting one edge at a time and the method of updating the k-order, which reduce the searching range and the time cost evidently. For the removing case, we use the existing subcore algorithm to do the core maintenance and modify it with the method of updating k-order we design. Finally, we do extensive experiments to evaluate the effectiveness and the efficiency of our algorithms, which shows that the order based algorithm has a better performance than the existing for the core maintenance on edge-weighted graphs.
Omitting types theorem in hybrid dynamic first-order logic with rigid symbols

In the present contribution, we prove an Omitting Types Theorem (OTT) for an arbitrary fragment of hybrid dynamic first-order logic with rigid symbols (i.e. symbols with fixed interpretations across worlds) closed under negation and retrieve. The logical framework can be regarded as a parameter and it is instantiated by some well-known hybrid and/or dynamic logics from the literature. We develop a forcing technique and then we study a forcing property based on local satisfiability, which lead to a refined proof of the OTT. For uncountable signatures, the result requires compactness, while for countable signatures, compactness is not necessary. We apply the OTT to obtain upwards and downwards Löwenheim-Skolem theorems for our logic, as well as a completeness theorem for its constructor-based variant.
BANGLA HANDWRITTEN NUMERAL RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK

The recognition of Bangla handwritten numerals (BHNR) has recently emerged as a very interesting area for machine learning and pattern recognition research. Recently, the technology for character or object recognition has also advanced. Bangla handwritten number recognition can serve as a foundation for creating an Optical Character Recognition (OCR) in the Bangla language. However, the lack of a sizable and accurate dataset makes Bangla's handwritten numeral recognition study insufficient in contrast to that of other well-known languages. Similar to MNIST for English digits, NumtaDB is by far the largest dataset collection for handwritten digits in the Bangla language. The most used datasets for the recognition of Bangla handwritten numerals in the past were NumtaDB, CMATERdb, and ISI. The majority of approaches now in use rely on feature extraction and a few outdated machine learning algorithms. Although some approaches operate quickly enough to meet practical demands, they are not always accurate. Even while certain techniques work quite well for languages other than Bangla, they still require improvement. Convolutional Neural Networks (CNN), in particular, are demonstrating excellent achievements in this discipline with higher accuracy. In this work, we’ve used custom CNN architectures to build our model to recognize digits using all the existing datasets with a high degree of accuracy. Our CNN model shows an average of 98% accuracy recognizing Bangla numeric in respect of above datasets. We have cross verified our model with mixed datasets and the result is also promising.
(Digital Presentation) A Battery Interface Ontology for Data Interoperability and Semantic Knowledge Representation

Data drives battery development. Despite the unprecedented volume of data being generated across both industry and research today, the battery data landscape remains a patchwork of heterogeneous sources with inconsistent metadata needed to correctly interpret its meaning. A new approach is needed to ensure that reported battery data is interoperable, reusable, and machine-readable. Furthermore, there is a growing need to share knowledge about batteries among groups from different backgrounds and organizations (e.g., experimentalists, modellers, engineers, technicians, etc.). The Battery Interface Ontology (BattINFO) is designed to meet these needs [1]. An ontology is a data model that represents domain knowledge as a map of concepts and the relations between them. This allows data to be mapped to common vocabulary terms, such that any two pieces of data mapped to the same term are also mapped to each other. Additionally, the network of concepts and relations allow expert knowledge to be expressed as graphs in a machine-readable form, which can then be queried to identify links between individual pieces of data and quickly share knowledge across organizations. BattINFO is a free, open-source domain ontology for electrochemistry and batteries that facilitates battery data interoperability and enables machine-readable semantic knowledge representation. A conceptual overview is shown in Figure 1. BattINFO was developed within the scope of the EU H2020 project BIG-MAP to facilitate data interoperability between more than 30 battery research institutes and companies in Europe. This contribution introduces BattINFO, demonstrates specific use cases for how it can be applied to accelerate battery data handling and development, and presents plans for future development and integration into the global battery data landscape. [1] S. Clark et al. , “Toward a Unified Description of Battery Data,” Adv. Energy Mater. , vol. 2102702, 2021. Figure 1
Analysis of Abstractive Text Summarization with Deep Learning Technique

In today's era, data in textual format has got great importance and is used to extract useful information from this data to design various kinds of information systems such as Document Generation, Prediction systems, Report Generation, Recommendation Systems, and Language modeling, and many more. That is why such techniques are very important, which will reduce the amount of data while saving the information and various parameters concerning this information. One such technique is text summarization which retains essential and useful information. This technique is very simple and convenient as compared to other techniques of summarization. For processing data, the Apache tool of Kafka is used. This platform is useful for real-time streaming data pipelines and many applications related to it. With this, one can use APIs of native Apache Kafka to populate data lakes, stream variants to and from databases, and power machine learning and analytically carry out. The input portion in this situation is a spark base platform for analytics. For the fast development of workflows for complex machine learning systems, Tensorflow is evolved as a significant library of machine learning.
Introducing a New High-Resolution Handwritten Digits Data Set with Writer Characteristics

The contributions in this article are two-fold. First, we introduce a new handwritten digit data set that we collected. It contains high-resolution images of handwritten digits together with various writer characteristics which are not available in the well-known MNIST database. The multiple writer characteristics gathered are a novelty of our data set and create new research opportunities. The data set is publicly available online. Second, we analyse this new data set. We begin with simple supervised tasks. We assess the predictability of the writer characteristics gathered, the effect of using some of those characteristics as predictors in classification task and the effect of higher resolution images on classification accuracy. We also explore semi-supervised applications; we can leverage the high quantity of handwritten digits data sets already existing online to improve the accuracy of various classifications task with noticeable success. Finally, we also demonstrate the generative perspective offered by this new data set; we are able to generate images that mimics the writing style of specific writers. The data set has unique and distinct features and our analysis establishes benchmarks and showcases some of the new opportunities made possible with this new data set.
Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings

Recent years brought an increasing interest in the application of machine learning algorithms in e-commerce, om-nichannel marketing, and the sales industry. It is not only to the algorithmic advances but also to data availability, representing transactions, users, and background product information. Finding products related in different ways, i.e., substitutes and complements is essential for users' recommendations at the vendor's site and for the vendor - to perform efficient assortment optimization. The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm. We also provide its experimental evaluation with regards to the state-of-the-art Shopper algorithm, studying the relevance of recommendations with surveys from industry experts. It is concluded that the new approach presented here offers suitable choices of recommended products, requiring a minimal amount of additional information. The algorithm can be used in various enterprises, effectively identifying substitute and complementary product options.
THE STATE OF KNOWLEDGE OF TEACHERS AND EDUCATORS IN PRIMARY AND SECONDARY EDUCATION ON CYBER THREATS CHALLENGING STUDENTS ON A DAILY BASIS - PROPOSAL OF PREVENTION AND TRAINING AREAS

THE STATE OF KNOWLEDGE OF TEACHERS AND EDUCATORS IN PRIMARY AND SECONDARY EDUCATION ON CYBER THREATS CHALLENGING STUDENTS ON A DAILY BASIS - PROPOSAL OF PREVENTION AND TRAINING AREAS
Learning to recover lost details from the dark

Deep convolutional neural networks (CNNs) have achieved significant milestones in low-light image enhancement. We note that existing methods still struggle to recover lost details. (1) They employ image-to-image mapping for enhancement, which may excel at global adjustment but lack attention to detail recovery. (2) They train CNN models with low-light/normal-light (L/N) image pairs that are generally non-pixel-level fully aligned, which causes the enhanced result to be prone to the problem of blurred details. To this end, we propose a novel CNN framework for learning to recover lost details from the dark. Specifically, we adopt a point-to-point mapping for better recovering lost details by estimating color affine transformations. And we design an associated group-codec mechanism to decouple the alignment problem from the enhancing process, thus preventing blurred details. Besides, data scarcity is an outstanding problem in low-light image enhancement; although some unsupervised models may deal with it, they would produce artifacts that cover real details. Hence, we propose a dynamic data collection method to acquire L/N image pairs. And we have collected a large and diverse low-light dataset to supplement existing benchmarks. Experiments conducted on our and public datasets show that our method is very competitive compared with state-of-the-art methods.
Hide text in an image using Blowfish algorithm and development of least significant bit technique

Recently, there is increasing interest in data transfer between many different devices. Data must be encrypted before being sent so that the intended receiver can only read it to prevent unauthorized access and processing of a secret message. This paper suggests two levels of security to secure import messages that are sent via the internet. A secret text is encrypted by a Blowfish algorithm, then the secret text is hidden in an image by the least significant bit (LSB) technique. The LSB method of concealing important information was developed to conceal at least the first of at least two bits, depending on the cover data. The resolution of an image is raised in our work where the sender selects a stego image with a high peak signal to noise ratio (PSNR). The receiver knows all the necessary information for decoding by the first nine pixels. The PSNR is used for evaluating the resolution of the image to check robustness at sending. The number of PSNR illustrates in our proposal that the resolution of the image is near the original LSB technique, but the embedding is more randomly robust in steganography.
A Multi-Dimensional Matrix Product—A Natural Tool for Parameterized Graph Algorithms

We introduce the concept of a k-dimensional matrix product D of k matrices A1,…,Ak of sizes n1×n,…,nk×n, respectively, where D[i1,…,ik] is equal to ∑ℓ=1nA1[i1,ℓ]×…×Ak[ik,ℓ]. We provide upper bounds on the time complexity of computing the product and solving related problems of computing witnesses and maximum witnesses of the Boolean version of the product in terms of the time complexity of rectangular matrix multiplication. The multi-dimensional matrix product framework is useful in the design of parameterized graph algorithms. First, we apply our results on the multi-dimensional matrix product to the fundamental problem of detecting/counting copies of a fixed pattern graph in a host graph. The recent progress on this problem has not included complete pattern graphs, i.e., cliques (and their complements, i.e., edge-free pattern graphs, in the induced setting). The fastest algorithms for the aforementioned patterns are based on a reduction to triangle detection/counting. We provide an alternative simple method of detection/counting copies of fixed size cliques based on the multi-dimensional matrix product. It is at least as time efficient as the triangle method in cases of K4 and K5. Next, we show an immediate reduction of the k-dominating set problem to the multi-dimensional matrix product. It implies the W[2] hardness of the problem of computing the k-dimensional Boolean matrix product. Finally, we provide an efficient reduction of the problem of finding the lowest common ancestors for all k-tuples of vertices in a directed acyclic graph to the problem of finding witnesses of the Boolean variant of the multi-dimensional matrix product. Although the time complexities of the algorithms resulting from the aforementioned reductions solely match those of the known algorithms, the advantage of our algorithms is simplicity. Our algorithms also demonstrate the versatility of the multi-dimensional matrix product framework.
A Character-based Model for Interactive Storytelling in Games

In this paper, we present a novel character-based model for interactive storytelling in games, which combines multi-agent planning with a drama management strategy based on production rules to guide the narrative generation process. The underlying proposed method is capable of generating and controlling the enactment of character-based narratives in highly interactive game worlds. After describing the model, the paper proceeds to assess its applicability and performance. A fully implemented prototype game is used to demonstrate how the model works in practice.
Diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients: A systematic review

Background: Nursing data consist of observations of patients' conditions and information on nurses' clinical judgment based on critically ill patients' behavior and physiological signs.Nursing data in electronic health records were recently emphasized as important predictors of patients' deterioration but have not been systematically reviewed.Objective: We conducted a systematic review of prediction models using nursing data for clinical outcomes, such as prolonged hospital stay, readmission, and mortality in intensive care patients, compared to physiological data only.In addition, the type of nursing data used in prediction model developments was investigated.Design: A systematic review.Methods: PubMed, CINAHL, Cochrane CENTRAL, EMBASE, IEEE Xplore Digital Library, Web of Science, and Scopus were searched.Clinical outcome prediction models using nursing data for intensive care patients were included.Clinical outcomes were prolonged hospital stay, readmission, and mortality.Data were extracted from selected studies such as study design, data source, outcome definition, sample size, predictors, reference test, model development, model performance, and evaluation.The risk of bias and applicability was assessed using the Prediction model Risk of Bias Assessment Tool checklist.Descriptive summaries were produced based on paired forest plots and summary receiver operating characteristic curves.Results: Sixteen studies were included in the systematic review.The data types of predictors used in prediction models were categorized as physiological data, nursing data, and clinical notes.The types of nursing data consisted of nursing notes, assessments, documentation frequency, and flowsheet comments.The studies using physiological data as a reference test showed higher predictive performance in combined data or nursing data than in physiological data.The overall risk of bias indicated that most of the included studies have a high risk.Conclusions: This study was conducted to identify and review the diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients.Most of the included studies developed models using nursing notes, and other studies used nursing assessments, documentation frequency, and flowsheet comments.Although the findings need careful interpretation due to the high risk of bias, the area under the curve scores of nursing data and combined data were higher than physiological data alone.It is necessary to establish a strategy in prediction modeling to utilize nursing data, clinical notes, and physiological data as predictors, considering the clinical context rather than physiological data alone.
Improving Radiology Report Generation with Adaptive Attention

To avoid the tedious and laborious radiology report writing, automatic radiology reports generation has drawn great attention in recent years. As vision to language task, visual features and language features are equally important for radiology report generation. However, previous methods mainly pay attention to generating fluent reports, which neglects the eminent importance of how to better extract and utilize vision information. Keeping this in mind, we propose a novel architecture with a CLIP-based visual extractor and Multi-Head Adaptive Attention (MHAA) module to address the above two issues: through the vision-language pretrained encoders, more sufficient visual information has been explored, then during report generation, MHAA controls the visual information participating in the generation of each word. Experiments conducted on two public datasets demonstrate that our method outperforms state-of-the-art methods on all the metrics.
Multimodality pedestrian detection based on YOLOv5

A pedestrian detection method based on YoloV5 RGB and thermal image data fusion is proposed in this paper. This method uses two yolov5 branch structures to learn the features of RGB and thermal image data respectively, and finally uses multimodal features for fusion detection. It includes the following stages. Firstly, we use yolov5 network as a branch network to learn features from paired RGB and thermal image data. The two yoloV5-based backbone networks extract the features of the two modes for preliminary fusion, and then obtain the importance parameters of the two modes through feature compression and extraction. The effective information is enhanced, and redundant information is eliminated by multiplying the initial features. Finally, the fusion feature is used for target detection. Through this method, the detection effect is improved. We have done a lot of experiments on the public KAIST and VOT2019 pedestrian data set, and the experiments show that our method is better than the advanced method.
Energy Storage for Hourly Dispatching Utility-Scale Solar PV Power using HOMER Pro - A Cost Investigation

The objective of this study is to investigate the expenditure of different kinds of energy storage systems (ESSs) for the economical dispatching of solar power at one-hour increments for an entire day for megawatt-scale grid-connected photovoltaic (PV) arrays. Accurate forecasting of PV power is vital for generation scheduling and cost-effective operation. A multilayer perceptron Artificial Neural Network (ANN) is utilized to predict PV irradiance one hour ahead of time, which performs well with good convergence mapping between input and target output data. Moreover, this research proposes a state of charge (SOC) control algorithm based on an adaptive neuro-fuzzy inference system (ANFIS) that can accurately estimate the grid reference power for each one-hour dispatching period, which is necessary for ensuring the ESS completes each dispatching period with its starting SOC and has sufficient capacity for next-day operation. Finally, an economic comparison is presented utilizing the Hybrid Optimization of Multiple Energy Resources (HOMER Pro) software to develop a cost-effective ESS for an hourly PV power dispatching scenario.
A survey of medical image watermarking: state-of-the-art and research directions

With the growth and popularity of the utilization of medical images in smart health care, the large amount of health records and related images, which needs to be transmitted, is consistently increasing. However, exchanging these medical records among hospitals, doctors, and medical team faces many challenges associated to ownership conflicts, data security, and privacy. Medical image watermarking is an effective copyright protection tool by embedding copyright/secret information/logo into the carrier image. This chapter provides a detailed overview of watermarking along with its characteristics and current applications. We then provide a comparative survey of the different state-of-the-art approaches along with their merits and limitations. We further summarized each of the state-of-the-art approaches in detail, including objectives, goals, dataset used, evaluation metrics, and weaknesses, and discussed the recent challenges and their possible solutions. We believe this chapter will provide the readers a comprehensive insight to the field of watermarking research in the health care domain.