Reviewerly/exHarmony huggingface dataset:
Supporting Sustainable Development Using Multiple Criteria Decision Aid: Towards an Age-Friendly Smart Living Environment

This chapter aims to contribute to a better understanding of how sustainable development (SD) can be supported in the building of age-friendly smart living environments (SLEs) to meet the needs of an increasingly ageing population. The proposed holistic analysis framework enables regional stakeholders engaged in building age-friendly SLEs to analyse the identified conditions and practices facilitating and encouraging knowledge collaboration (KC) and knowledge sharing (KS) that are key determinants of knowledge management (KM) and decisive means in supporting SD. Drawing on multiple criteria decision-aid (MCDA) approach, the framework was developed by involving representatives of regional stakeholders, who are innovation actors of the Häme region’s (Finland) quadruple innovation helix model, Quadruple Helix, into a collaborative decision-making process within two empirical studies. The pilot study provided a substantial background for a deeper exploration of multidimensional, complex research questions and context in the main study which utilised problem structuring methods and techniques such as strategic options development and analysis (SODA), cognitive mapping, nominal group technique (NGT), and multi-voting. Assuming a constructivist, process-oriented stance, the main study enabled the development of a more realistic analysis framework through the sharing and aggregating of stakeholders’ expertise and experiences and the uncovering of the cause-and-effect relationships among factors related to the topic under study. Taking the form of a collective cognitive map, the framework was validated by both the regional stakeholders engaged in the decision-making process of the main study and two external experts, who represented business organisations building age-friendly SLEs. Both studies revealed senior citizens’ genuine enthusiasm and motivation to be engaged in building age-friendly SLEs and the vast potential they have in developing collaboration and sharing their knowledge and experience with other stakeholders.
Dynamic path planning over CG-Space of 10DOF Rover with static and randomly moving obstacles using RRT* rewiring

Abstract Dynamic path planning is a core research content for intelligent robots. This paper presents a CG-Space-based dynamic path planning and obstacle avoidance algorithm for 10 DOF wheeled mobile robot (Rover) traversing over 3D uneven terrains. CG-Space is the locus of the center of gravity location of Rover while moving on a 3D terrain. A CG-Space-based modified RRT* samples a random space tree structure. Dynamic rewiring this tree can handle the randomly moving obstacles and target in real time. Simulations demonstrate that the Rover can obtain the target location in 3D uneven dynamic environments with fixed and randomly moving obstacles.
You-Only-Look-Once-v5 Based Table Detection for Academic Papers

Tables are an important module for summarizing research results in academic papers. Quickly obtaining table information can improve the efficiency of scientific researchers in analyzing experimental data. This paper proposes a table detection method for academic paper based on YOLOv5 (You Only Look Once v5). First, the academic paper is preprocessed to fit the detection model, and then a YOLOv5 based neural network is used to detect tables quickly. Experiment results show that the detection accuracy and speed of the proposed approach are superior to the competitive approaches.
Transformer-based Hierarchical Encoder for Document Classification

Document Classification has a wide range of applications in various domains like Ontology Mapping, Sentiment Analysis, Topic Categorization and Document Clustering, to mention a few. Unlike Text Classification, Document Classification works with longer sequences that typically contain multiple paragraphs. Previous approaches for this task have achieved promising results, but have often relied on complex recurrence mechanisms that are expensive and time-consuming in nature. Recently, self-attention based models like Transformers and BERT have achieved state-of-the-art performance on several Natural Language Understanding (NLU) tasks, but owing to the quadratic computational complexity of the self-attention mechanism with respect to the input sequence length, these approaches are generally applied to shorter text sequences. In this paper, we address this issue, by proposing a new Transformer-based Hierarchical Encoder approach for the Document Classification task. The hierarchical framework we adopt helps us extend the self-attention mechanism to long-form text modelling thereby reducing the complexity considerably. We use the Bidirectional Transformer Encoder (BTE) at the sentence-level to generate a fixed-size sentence embedding for each sentence in the document. A document-level Transformer Encoder is then used to model the global document context and learn the inter-sentence dependencies. We also carry out experiments with the BTE in a feature-extraction and a fine-tuning setup, allowing us to evaluate the trade-off between computation power and accuracy. Furthermore, we also conduct ablation experiments, and evaluate the impact of different pre-training strategies on the overall performance. Experimental results demonstrate that our proposed model achieves state-of-the-art performance on two standard benchmark datasets.
Early detection of breast cancer using efficient image processing algorithms and prediagnostic techniques: A detailed approach

Breast cancer is one of the most common cancers today. Numerous studies have been conducted on the identification of breast cancer by utilizing different image processing algorithms and characterization procedures. Regardless, breast cancer remains a deadly illness. It affects one out of six women. Since the reason for breast cancer remains unclear, counteraction has gotten outlandish. Consequently, prediagnosis of tumors in the breast is the best way to fix breast cancer. Using CAD on mammography images is the most proficient and least challenging method for diagnosing breast cancer. Exact disclosure can viably reduce the death rate achieved by utilizing mamma malignancy. Masses and micro-calcification bunches are significant early side effects of conceivable breast cancers. They can help foresee breast cancer early. In the cognitive-based proposed model, we quantitatively portray the suitable image processing algorithm-based examination strategies utilized for surface highlights for recognition of malignancy with more accuracy. These surface highlights are extracted from the region of interest of the mammogram to portray the micro-calcifications as innocuous, customary, or undermining.
Word Sense Disambiguation Models Emerging Trends: A Comparative Analysis

Abstract Word Sense Disambiguation (WSD) arises due to the presence of ambiguity in the text during the semantic analysis of natural languages. It is a major unsolved problem in the area of Natural Language Processing (NLP) and its applications. This paper explores and reviews WSD algorithms that have contributed to, or created state-of-art solutions in recent years. Moreover, this paper also aims to analyze the recent technological trends in the domain of WSD which can give us leverage to identify the possible future trajectory of the search for better WSD solutions.
Towards Real-Time House Detection in Aerial Images Using Faster Region-Based Convolutional Neural Network

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Comparative Study of Image to Image Translation Models for Underwater Image Enhancement

Understanding the underwater environment is important for navigation, surveillance, and exploration. The aim is to restore underwater images degraded by light absorption and scattering in water medium resulting in poor visibility, low brightness, and low contrast of the scene. In this paper, we introduce a network based on the image to image translation that is Contextual Conditional Generative Adversarial Network (CCGAN) and compare its performance with the available image to image translation models namely, Unsupervised Image Translation (UNIT), cycleGAN, Contrastive Unpaired Translation (CUT) and fastCUT qualitatively and quantitatively. Both supervised and unsupervised models are analyzed and we show that the proposed model outrun the alternatives.
Hybridising Self-Organising Maps with Genetic Algorithms

The aim of this work is to develop a hybridised approach consisting of the Self-Organising Map (SOM), which is an artificial neural network, and a Genetic Algorithm (GA) to tackle large and complex optimisation problems by decomposition. The approach is tested on the travelling salesman problem (TSP), which is a very important problem in combinatorial optimisation. The SOM clusters the nodes into a specified number of groups. Then, the resulting smaller TSPs within each cluster are solved, and finally, the solutions of the clusters are connected to build a high quality solution for the whole TSP. In the two latter steps, a GA is used. Our approach is examined on some random examples including 100 to 10000 nodes as well as 12 benchmark instances. The results with various numbers of clusters are compared to each other and also to the case of solving the TSP with all nodes and without any clustering. Furthermore, the effect of each component algorithm in the hybridised approach is evaluated by some complementary experiments in which the clustering and the optimisation algorithm are replaced by other methods.
READINESS OF UNIVERSITY TEACHERS TO PARTICIPATE IN EMPIRICAL DATA EXCHANGE: PILOT STUDY IN UKRAINE

The article offers the description of the pilot study results on university teachers’ engagement in exchange of empirical data on distance learning. It was suggested that they have a shaped intention to exchange; however, the specifics of their efforts make it difficult to integrate the collected data into the body of open empiricism. The study identifies the meaningful constructs of teachers’ engagement in data exchange. The research findings can be used in the practice of knowledge management in the system of higher education in order to optimize the engagement of teachers in the exchange of empirical data.
Privacy-Preserving Edge Intelligent Computing Based on Federated Learning

The rapid development of the Internet of things industry makes the application of edge intelligent computing more and more widely, but it also exposes serious problems of data security and privacy disclosure. Federated learning can cooperate with multiple terminals to complete collaborative training without local data, which can reduce the risk of privacy disclosure of edge intelligent computing. However, it is difficult to resist the latest privacy attacks such as model extraction and model inversion, and the traditional privacy protection protocol can not be applied to edge devices with limited computing and communication resources. Therefore, this paper proposes an edge intelligent computing privacy protection method based on federated learning. Based on secret sharing and gradient mask, a lightweight privacy protection protocol is designed to protect the privacy of device data with less computing overhead. A periodic update strategy is proposed to reduce the communication overhead. Compared with similar methods on public data sets, the results show that this method performs better in model accuracy, communication overhead and operation efficiency, and can effectively resist gradient disclosure attack.
Curiosity-driven 3D Object Detection Without Labels

In this paper we set out to solve the task of 6-DOF 3D object detection from 2D images, where the only supervision is a geometric representation of the objects we aim to find. In doing so, we remove the need for 6-DOF labels (i.e. position, orientation etc.), allowing our network to be trained on unlabeled images in a self-supervised manner. We achieve this through a neural network which learns an explicit scene parameterization which is subsequently passed into a differentiable renderer. We analyze why analysis-by-synthesis-like losses for supervision of 3D scene structure using differentiable rendering is not practical, as it almost always gets stuck in local minima of visual ambiguities. This can be overcome by a novel form of training, where an additional network is employed to steer the optimization itself to explore the entire parameter space i.e. to be curious, and hence, to resolve those ambiguities and find workable minima.
Hierarchical Cyber Troll Detection with Text and User Behavior

The cyber trolls in social media have threatened users' personal rights and social order. By publishing offensive and disgusting comments on social media, cyber trolls try to shift the focus of the discussion, provoke others, and even trigger antagonistic behaviors among groups. Most of existing studies were based on English scenes. These methods mainly distinguished the cyber trolls from ordinary users according to whether the comments were offensive or not. But the studies ignored the diversity and concealment of cyber trolls, so it was difficult to identify them pertinently and finely. This paper builds a new Chinese cyber troll dataset and presents a hierarchical cyber troll detection method based on text and user behavior. Starting from the behavior motivation of cyber trolls, we divide users into two levels: inactive and active. For each level of users, this paper proposes some new behavior indicators based on the user statistical features, and selects the text features with significant influence from the comments. Next, these two types of features are input into the XGBoost model for detection. Finally, the detected cyber trolls at each level are combined as the final detection result. Experiments on our dataset show that our method is superior to other baseline methods.
A Combined Model of NLP with Business Process Modelling for Sentiment Analysis

Natural language processing is one of the main sub-field of artificial intelligence, where all end users of internet in real time use their native language to extract the required information. This work concentrates on the sentiment analysis on social media, which comprises of text, numbers, hashtags, symbolic representations and much more. It becomes tedious in handling these unstructured data. Hence, the solution is to makes use of basic NLP tools, then implement Markov decision process in order to process the translation of the native language input to a SQL query. An implementation idea for business process models is also incorporated to have different analysis states of the input data. The accuracy achieved through Deep learning models in this proposed work are greater compared to the other machine learning and normal corpus methodologies.
Blood Cancer Detection using Machine Learning

Cancer is a deadly disease. Initial detection of cancer is the best way to cure the disease. Medical Image Processing plays an essential part in the detection of disease. Leukemia is a kind of blood cancer that happens due to irregular or immature White Blood Cell (WBC) s. In general, WBC is the fighter to fight against infectious cells in the human body. Abnormal growth of WBC from bone marrow will destroy the other cells and affect bone marrow and lymphatic tissues. These cells do not function properly and leads to leukemia. In olden days, identification of disease and cell counting in the blood were very complicated. In the medical sector, they use a device called Haemocytometer which counts the amount of cells in the blood manually. But it takes more time for counting and gives inaccurate results. To overcome these issues, a software based solution is given with the help of microscopic images. With this image processing technique, the number of RBCs, WBCs and platelets are calculated and also whether a person can be affected by leukemia or not is identified.
Library documentology

The article is a follow-up of the previous publications by Yu. N. Stolyarov “Documentology: The backgrould and development” and “The basic postulates of documentology as the general theory of documents”. The author substantiates the need for developing the library documentology as an individual discipline with its specific subject, specific document definition, and specific content. The subjects of library document studies are the primary and secondary document; technological document; accompanying document; and administrative document (record). The library workflows are based on procedural documents that form the class of administrative documents that ensure the optimal functioning of the library. Administrative documents in the library, as in other legal entities, are grouped into large subclasses: planning, accounting, reporting, personnel, financial documents, etc. Among them, the managerial documents are mostly important as they are to support the entire management process. Beside these subclasses, the accompanying documentation forms the subclass that makes an essential part of the collection, namely technological, and administrative documentation. To separate the fundamental library concepts of the document, the author proposes to adopt a specific definition for each document process. The library collection document is information specific for the library that is fixed or to be fixed. Fixed information refers to the diachronous, discrete documents, information to be fixed (continuously and to be continuously updated) refers to synchronous (existing at the time of retrieval), continuous (having only the beginning) document. The author defines library documentation as the systematized collection of internal documents. The tasks and purpose of these documents are determined by the specific of library technological tasks and functions. The author insists on developing the key provisions and, above all, the terms of library documentology.
Local Density Estimation in High Dimensions

An important question that arises in the study of high-dimensional vector representations learned from data are, given a set [Formula: see text] of vectors and a query q, estimate the number of points within a specified distance threshold of q. We develop two estimators, LSH count and multiprobe count that use locality-sensitive hashing to preprocess the data to accurately and efficiently estimate the answers to such questions via importance sampling. A key innovation is the ability to maintain a small number of hash tables via preprocessing data structures and algorithms that sample from multiple buckets in each hash table. We give bounds on the space requirements and sample complexity of our schemes and demonstrate their effectiveness in experiments on a standard word embedding data set.
Entity Representation Learning with Multimodal Neighbors for Link Prediction in Knowledge Graph

Entity representation learning is both fundamental and crucial for link prediction in knowledge graph. Existing entity representation learning approaches mainly focus on learning from knowledge triples with single modal text data which could only describe the things or concepts in words. However, multimodal data commonly exists in the real worlds and may describe things or concepts from different aspects. Therefore, multimodal entities have emerged in knowledge graph in recent years, which greatly enrich the knowledge graph by different information. This paper focuses on how to learn more robust and comprehensive representations of multimodal entities for better link prediction. A new representation learning method for multimodal entities is proposed, called Graph Attention with Bi-pooling Representation Learning (GBRL), where multimodal fusion and knowledge embedding are combined for learning representations of an entity with consideration of its multimodal neighbors. Firstly, feature vectors are obtained by pre-trained models for each modality. Then, the bilinear pooling is utilized for combining feature vectors of multimodal entities into multimodal feature vectors. A two-layer graph attention module is followed to generate multimodal representations of an entity from its multimodal neighbors. Finally, the inception decoder uses multimodal knowledge embedding to calculate scores for link prediction. The experimental results have shown that the proposed method performs well on link prediction by consideration of multimodal entities comparing with traditional methods that using only single modal text data.
The Dataset and Baseline Models to Detect Human Postural States Robustly against Irregular Postures

In many visual applications, we often encounter people with irregular postures, such as lying down. Many approaches adopted two-step methods to handle a person with irregular postures: 1) person detection and 2) posture prediction based on the detected person. However, it is challenging to detect irregular postures because the existing detectors were trained with datasets consisting of most upright postures. Therefore, we propose a new Irregular Human Posture (IHP) dataset to handle various postures captured from real-world surveillance cameras. The IHP dataset provides sufficient annotations to understand the posture of person, including segmentation, keypoints, and postural states. This paper also provides two baseline net-works for postural state estimation of the people trained on the IHP dataset. Moreover, we show that our baseline networks effectively detect the people with irregular postures that may be in an urgent situation in a surveillance environment.