Reviewerly/exHarmony huggingface dataset:
A Survey on Existing Chatbots for Pregnant Women’s Healthcare

Pregnancy is a period of curiosity and questioning for many women, especially when it is their first experience. So most expectant mothers are allowed to get information via search engines or from those in their sphere of contact. Unfortunately, this information is not always medically true and may not meet women’s needs as each pregnancy is unique. Chatbots can be used to solve this problem by giving accurate and personalized advice in real-time and without considering geographical limits.The significant contribution of this paper is to review the existing chatbots for pregnant women’s healthcare, as well as the most commonly used deep learning methods. This review will provide new perspectives in the development of the pregnancy companion that will help pregnant women during pregnancy.
Analisis Kesalahan Berbahasa (Tulis) Pemelajar BIPA Level 7 dalam Pembuatan Karya Ilmiah

This study aims to describe written language errors of level 7 BIPA students in writing scientific papers, including errors in (1) conjunctions, (2) capital letters, (3) puntuations, and (4) sentences structure. The data used in this study were three BIPA level 7 scientific papers. The method used was descriptive-qualitative. Data analysis was carried out by (1) note things that are aspects of language errors, (2) classify language errors into certain groups, and (3) describe those language errors. The result of the study show that in the scientific papers of BIPA level 7 students there are still many errors, including errors in using conjunctions at the beginning of sentences, inaccuracies in the use of capital letters which are often placed in the middle of sentences, inaccuracies in the use of comma and full stop punctuation, and sentence ineffectiveness triggered by structural interference sentences in foreign languages, redudant words, and incomplete sentence patterns.
Building Event-Driven Data Pipelines with Spring Cloud Function

Spring Cloud Function plays a critical role in the hybrid cloud or on-premises/private cloud space. Building events/data pipelines that span across the local datacenter and the cloud increases complexity due to the firewall boundaries. Data pipelines play an important role when you want to acquire, move, or transform data as it comes (streaming) or when it’s offline (batch).
Extending Kubernetes API with Custom Resources Definitions

In the first part of this book, you learned that the Kubernetes API is organized in groups. The groups contain one or more resources, each of them being versioned.
Three-dimensional human pose estimation based on improved semantic graph convolution neural networks

Graph convolutional neural networks (GCN) have been widely applied to many real-world problems and have achieved better results. To improve the accuracy of GCN in the three-dimensional human pose estimation (3D HPE) task, improved semantic GCNs (Simpre-RxSkip-NonCam-SemGCN and SPRA-SemGCN) are proposed. For the problem of insufficiently utilizing effective information in 3D HPE tasks, the dual-layer attention mechanism module (NonCam-attention) of extracting information is proposed. It establishes a spatial and channel-based fusion structure model by capturing the distance dependencies between different joint nodes of human poses and the associated features between different mapping channels. To reduce the training time and GPU memory for the preaggregated semantic graph convolutional networks (Pre-SemGCN) model, the simplified Pre-SemGCN module (Simpre-SemGCN) is suggested, which can accelerate the training speed of the model by eliminating the nonlinear activation layer. Further, a nested recursive expansion-based residual connection module (RxSkip + LN) is proposed to adjust the ratio of input and output with fewer parameters. To verify the effectiveness of the proposed model, we conduct experiments on the dataset Human 3.6M. The results show that the SPRA-SemGCN model proposed can effectively reduce the error of the 3D HPE task, and the mean per joint position error is finally reduced to 37.20 mm.
Game Analytics

You have published your game and players have downloaded it. You even see in the server logs that they are joining multiplayer sessions. But you have almost no information about the players’ behavior. You should know if players leave the game after a while because of some nerve-racking challenge. Or, if most of your players cannot succeed to the next level.
The semantics of scalar equatives in Mandarin Chinese

Abstract The article presents a formal analysis of the xiang……yiyang scalar equative construction in Mandarin Chinese (MSEs). In the standard degree-based approach, scalar equatives are widely assumed to express an asymmetrical linear ordering between two degree-denoting descriptions such that the degree to which the comparee possesses is at least as great as the degree to which the standard possesses. However, this standard analysis would fall short of MSEs, which display a cluster of properties that are unexpected on the standard account: (i) MSEs disallow differentials; (ii) MSEs cannot take measure phrases as the standard; (iii) MSEs in general do not license NPIs in the standard phrases, and (iv) MSEs disallow factor phrases that express multiplication of numerical values. We propose that unlike scalar equatives in English (ESEs), where the comparison of equality is based on asymmetrical linear ordering of the degrees as points, MSEs recur to degrees as kinds, and consequently, the comparison of equality in the latter is based on instantiation of the degree-kinds, namely, equality of properties. The commonalities and differences between MSEs and ESEs suggest that, despite the fact that degrees and properties are semantic objects of distinct types, the underlying connection between them runs deep and fundamental.
31 Art Works from the First Russian Art Exhibition in the Krasnodar Regional Art Museum F.A. Kovalenko

Free Access31 Art Works from the First Russian Art Exhibition in the Krasnodar Regional Art Museum F.A. KovalenkoDilyara SadykovaDilyara SadykovaSearch for more papers by this authorhttps://doi.org/10.7788/9783412525668.204SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1st editionISBN: 978-3-412-52564-4 eISBN: 978-3-412-52566-8HistoryPublished online:December 2022 PDF download
An Alternative Metric to Support Case Base Maintenance in Case-Based Reasoning Processes

Retention is one of the final steps of Case-Based Reasoning approach. Defining the criteria for case retention and maintaining the case base is a complex process. New studies have emerged in the area of nutrition and physical activity recommendation systems following the case-based reasoning cycle. However, the retention phase has only been superficially analysed. Typically, the success of recommendations is evaluated using subjective criteria, such as experts or users’ opinions. Moreover, there are few established processes to prevent uncontrolled growth of a case base. The main innovation of our work lies in the fact that an objective and scientifically approved metric is used for quantifying the success of the generated recommendations. Our research shows that by using well-being indexes, it is possible to define reliable mechanisms for deciding about the inclusion of new cases in the case base, without the need for external intervention. On the other hand, we also analysed the probability with which each case from the case base leads to successful solutions. Thus, solutions that have a low probability can be eliminated. This approach ensures continuous learning of the system without significantly affecting memory, and the cases that contribute most to cardiovascular well-being are kept in the case base.
Multi-party Computation on Multi-agent Architecture to Anonymize Matrix Data Structures

The multiplication of two matrices in n-modular arithmetic it is a process that which in knowledge discovery can be associated with the knapsack problem. The knapsack problem is an NP-Complete problem. This characteristic can be used to hide information through the operation of matrix multiplication. In this paper we show how this frame can be done in Multi-party computation using a Multi-agent architecture. These features provide a very robust methodology to anonymize matrix data structure.
Natural Language Processing Based on a Text Graph Convolutional Network

Deep Learning (DL) has been one of the preferred techniques for Natural Language Processing (NLP) applications. Due to its nature, a text can be better represented in a graph structure, when compared with the classical feature-based representations. Therefore, several researchers have explored the use of Graph Neural Networks (GNN) for text analysis. GNNs show excellent results in text classification tasks, given their property of capturing contextual and global information in a corpus. The Text Graph Convolutional Network (TGCN) showed the ability to outperform traditional NLP methods in benchmark classification tasks. However, this method has a very high memory cost for the text graph construction. By exploring the results of text representations, we propose a new method to generate a text graph, capable of influencing the result of the TGCN, leading to a reduced use of memory.
Impacts of Data Synthesis: A Metric for Quantifiable Data Standards and Performances

Clinical data analysis could lead to breakthroughs. However, clinical data contain sensitive information about participants that could be utilized for unethical activities, such as blackmailing, identity theft, mass surveillance, or social engineering. Data anonymization is a standard step during data collection, before sharing, to overcome the risk of disclosure. However, conventional data anonymization techniques are not foolproof and also hinder the opportunity for personalized evaluations. Much research has been done for synthetic data generation using generative adversarial networks and many other machine learning methods; however, these methods are either not free to use or are limited in capacity. This study evaluates the performance of an emerging tool named synthpop, an R package producing synthetic data as an alternative approach for data anonymization. This paper establishes data standards derived from the original data set based on the utilities and quality of information and measures variations in the synthetic data set to evaluate the performance of the data synthesis process. The methods to assess the utility of the synthetic data set can be broadly divided into two approaches: general utility and specific utility. General utility assesses whether synthetic data have overall similarities in the statistical properties and multivariate relationships with the original data set. Simultaneously, the specific utility assesses the similarity of a fitted model’s performance on the synthetic data to its performance on the original data. The quality of information is assessed by comparing variations in entropy bits and mutual information to response variables within the original and synthetic data sets. The study reveals that synthetic data succeeded at all utility tests with a statistically non-significant difference and not only preserved the utilities but also preserved the complexity of the original data set according to the data standard established in this study. Therefore, synthpop fulfills all the necessities and unfolds a wide range of opportunities for the research community, including easy data sharing and information protection.
K Nearest Neighbour Collaborative Filtering for Expertise Recommendation Systems

With digitization the number of internet users are increasing and a wide variety of businesses based on the internet have emerged and modern ecommerce is one of them. In modern ecommerce there are a large number of products and a huge number of users accessing those to fulfill their needs. Searching for products is a time consuming task and a user may not have knowledge about every category of products listed. The recommendation system is important in the digital space and e-commerce because it suggests content to users based on their preferences. Modern recommendation systems based on machine learning models can more precisely recommend items to a user which they actually like. Collaborative filtering is one of them. It works by recommending items to a user by finding similar users preferences based on the predicting ratings of unknown items. In this work we have applied the k nearest neighbor (KNN) collaborative filtering algorithm on the Amazon Kindle Store Book review dataset which is the largest online e-book store on the internet. We have also proposed a modified version of this algorithm by considering expert users on the system to generate more precious recommendations for a user. Finally, We have evaluated our models using RMSE, MAE, hit rate and coverage and achieved outstanding results compared to the baseline algorithm.
A Proposal of Printed Table Digitization Algorithm with Image Processing

Nowadays, digital transformation (DX) is the key concept to change and improve the operations in governments, companies, and schools. Therefore, any data should be digitized for processing by computers. Unfortunately, a lot of data and information are printed and handled on paper, although they may originally come from digital sources. Data on paper can be digitized using an optical character recognition (OCR) software. However, if the paper contains a table, it becomes difficult because of the separated characters by rows and columns there. It is necessary to solve the research question of “how to convert a printed table on paper into an Excel table while keeping the relationships between the cells?” In this paper, we propose a printed table digitization algorithm using image processing techniques and OCR software for it. First, the target paper is scanned into an image file. Second, each table is divided into a collection of cells where the topology information is obtained. Third, the characters in each cell are digitized by OCR software. Finally, the digitalized data are arranged in an Excel file using the topology information. We implement the algorithm on Python using OpenCV for the image processing library and Tesseract for the OCR software. For evaluations, we applied the proposal to 19 scanned and 17 screenshotted table images. The results show that for any image, the Excel file is generated with the correct structure, and some characters are misrecognized by OCR software. The improvement will be in future works.
‘Mixed Blood’ and Aesthetic Evolution in China’s Electroacoustic Music Today

Current trends in the works of Chinese composers and their electroacoustic music today embrace increased abstraction and individual self-expression, while others celebrate the importance of clear communication and a unified ‘Chinese model’. How is one to derive the current state of a Western art form imported by China, in the context of unparalleled sharing and convergence of ideas made possible by the internet and extensive intercultural exchange in today’s globalised world? And how important is it to identify and measure the level of Chineseness in the artistic output of today’s electroacoustic music composers that embrace Chinese elements – whether born inside or outside of China? This article seeks to unpack some of the current thinking shared by institutional leaders, such as Yu Feng, president of the Central Conservatory of Music; scholars of Chinese electroacoustic music, such as Marc Battier, Ken Fields, Leigh Landy, Yang Yinuo and Annie Yen-Ling Liu; and several composers and thought leaders in the most recent generations of Chinese electroacoustic music.
Towards Real-Time Warning and Defense Strategy AI Planning for Cyber Security Systems Aided by Security Ontology

Cyber security systems generally have the phenomena of passive defense and low-efficiency early warnings. Aiming at the above problems, this study proposes a real-time warning and plans an AI defense strategy for a cyber security system aided by a security ontology. First, we design a security defense ontology that integrates attack graphs, general purpose and domain-specific knowledge bases, and on this basis, we (1) develop an ontology-driven method of early warnings of real-time attacks, which supports non-intrusive scanning attack detection and (2) combine artificial intelligence planning and bounded rationality to recommend and automatically execute defense strategies in conventional defense scenarios. A case study has been performed, and the results indicate that: (1) the proposed method can quickly analyze network traffic data for real-time warnings, (2) the proposed method is highly feasible and has the ability to implement defense strategies autonomously, and (3) the proposed method performs the best, with a 5.4–11.4% increase in defense effectiveness against the state-of-the-art counterparts considering the APT29 attack. Overall, the proposed method holds the potential to increase the defense effectiveness against cyberattacks under high computing resource constraints.
Webly Supervised Fine-Grained Image Recognition with Graph Representation and Metric Learning

The aim of webly supervised fine-grained image recognition (FGIR) is to distinguish sub-ordinate categories based on data retrieved from the Internet, which can significantly mitigate the dependence of deep learning on manually annotated labels. Most current fine-grained image recognition algorithms use a large-scale data-driven deep learning paradigm, which relies heavily on manually annotated labels. However, there is a large amount of weakly labeled free data on the Internet. To utilize fine-grained web data effectively, this paper proposes a Graph Representation and Metric Learning (GRML) framework to learn discriminative and effective holistic–local features by graph representation for web fine-grained images and to handle noisy labels simultaneously, thus effectively using webly supervised data for training. Specifically, we first design an attention-focused module to locate the most discriminative region with different spatial aspects and sizes. Next, a structured instance graph is constructed to correlate holistic and local features to model the holistic–local information interaction, while a graph prototype that contains both holistic and local information for each category is introduced to learn category-level graph representation to assist in processing the noisy labels. Finally, a graph matching module is further employed to explore the holistic–local information interaction through intra-graph node information propagation as well as to evaluate the similarity score between each instance graph and its corresponding category-level graph prototype through inter-graph node information propagation. Extensive experiments were conducted on three webly supervised FGIR benchmark datasets, Web-Bird, Web-Aircraft and Web-Car, with classification accuracy of 76.62%, 85.79% and 82.99%, respectively. In comparison with Peer-learning, the classification accuracies of the three datasets separately improved 2.47%, 4.72% and 1.59%.
TM-Net: A Neural Net Architecture for Tone Mapping

Tone mapping functions are applied to images to compress the dynamic range of an image, to make image details more conspicuous, and most importantly, to produce a pleasing reproduction. Contrast Limited Histogram Equalization (CLHE) is one of the simplest and most widely deployed tone mapping algorithms. CLHE works by iteratively refining an input histogram (to meet certain conditions) until convergence, then the cumulative histogram of the result is used to define the tone map that is used to enhance the image. This paper makes three contributions. First, we show that CLHE can be exactly formulated as a deep tone mapping neural network (which we call the TM-Net). The TM-Net has as many layers as there are refinements in CLHE (i.e., 60+ layers since CLHE can take up to 60 refinements to converge). Second, we show that we can train a fixed 2-layer TM-Net to compute CLHE, thereby making CLHE up to 30× faster to compute. Thirdly, we take a more complex tone-mapper (that uses quadratic programming) and show that it too can also be implemented - without loss of visual accuracy-using a bespoke trained 2-layer TM-Net. Experiments on a large corpus of 40,000+ images validate our methods.
Deployment of the Fed4FIRE+ testbed for forensics visualization purposes

A security incident or rule violation can be detected and documented using forensic analysis, which is made easier by preconfigured views that are enhanced with crucial data. In this paper, we present an advanced visualization mechanism for digital forensics that increases the situational awareness of a security expert by analysing and presenting security events, alarms and critical performance indicators. Using testbeds made available by Fed4FIRE+, we demonstrate an experimentation setup that simulates genuine client settings, including their varying needs and differences in size and requirements. These tests allowed for the parameterization of the variables, which led to rapid and well-documented results that could only be reached by trial and error with potential financial repercussions.
FPGA-based point processing for denoising of the show-through effect in scanned document images

Document Image Binarization is one of the image enhancement techniques and has potential applications in the field of Document Image Analysis and Recognition (DIAR). During the DIAR process, different types of noise such as uneven illumination, clutter, margin, skew, bleed-through and show-through are removed. However, the removal of show-through noise is a challenging task because it affects mostly the document image quality and readability. In this work, we have designed and developed a model-based point processing operation, which is implemented in the Altera Cyclone® V DE1-SoC for removing the show-through noise and enhancing the degraded document image quality. The Altera Cyclone® V DE1-SoC board was chosen for this study due to its performance, size compatibility, and real-time implementation, which are better than the programmable DSP chips. The Pixel/Point processing operations were implemented using the DSP builder tool, which boost-up better execution of computational operations involved in complex algorithms. To implement the proposed algorithm, we have developed the board library file for Altera Cyclone® V DE1-SoC series 5CSEMA5F31C6 board in DSP builder and tested on various document images. The calculated quality measures of the proposed algorithm tested on various document images in the FPGA board exhibited better results in terms of document image readability, quality, processing speed, and memory size. Further, the performance of the proposed algorithms was studied in different scanned document images dataset as well as with the other traditional techniques. The results of this study witnessed that the proposed model-based point processing methods performs very well than the other methods.