Reviewerly/exHarmony huggingface dataset:
AISHE-Onto: A Semantic Model for Public Higher Education Universities

The Electronic Government is a challenging field for the Semantic Web and the ontologies play a key role in the development of the Semantic Web. This paper explains the terms of the university through university ontology. We will focus on creating a university ontology. Here an ontology-based case study has been implemented for Public Higher Education (AISHE-Onto). SPARQL queries have been applied to make reasoning with the proposed ontology. As a result, a successful query interface has been provided to search academic data by the AISHE-Onto semantic portal.
Leveraging Tripartite Interaction Information from Live Stream E-Commerce for Improving Product Recommendation

Recently, a new form of online shopping becomes more and more popular, which combines live streaming with E-Commerce activity. The streamers introduce products and interact with their audiences, and hence greatly improve the performance of selling products. Despite of the successful applications in industries, the live stream E-commerce has not been well studied in the data science community. To fill this gap, we investigate this brand-new scenario and collect a real-world Live Stream E-Commerce (LSEC) dataset. Different from conventional E-commerce activities, the streamers play a pivotal role in the LSEC events. Hence, the key is to make full use of rich interaction information among streamers, users, and products. We first conduct data analysis on the tripartite interaction data and quantify the streamer's influence on users' purchase behavior. Based on the analysis results, we model the tripartite information as a heterogeneous graph, which can be decomposed to multiple bipartite graphs in order to better capture the influence. We propose a novel Live Stream E-Commerce Graph Neural Network framework (LSEC-GNN) to learn the node representations of each bipartite graph, and further design a multi-task learning approach to improve product recommendation. Extensive experiments on two real-world datasets with different scales show that our method can significantly outperform various baseline approaches.
With blockchain or not? Opportunities and challenges of self-sovereign identity implementation in public administration

Self-sovereign identity (SS) has become a trend within the digital identity circles. This new paradigm places a user in the centre and allows them to have more control over their identity information. Such a concept has swiftly found its enthusiasts, who also actively experiment on SSI implementation in the public sector. Although SSI does not imply having any technology at the backbone, it is usually discussed in the blockchain context. This article explores what opportunities this new identity concept can bring to the public sector and the associated challenges in this process. Also, we seek to examine the dependency of SSI on blockchain technology. For this, we study the existing knowledge on SSI from the literature and interview industry experts to elucidate this phenomenon. Using the thematic analysis technique, we propose a framework for assessing the opportunities and challenges, using the case of Belgium as an example, showcasing the potential of SSI and what barriers it still needs to overcome. We conclude that although blockchain does contribute to achieving SSI, it is not the silver bullet for it.
Contextual recommender system for E-commerce applications

Today's arena of global village organizations, social applications, and commercial websites provides huge information about products, individuals, and activities. This is leading to a plethora of content that requires effective handling to obtain the desired information. A recommendation system (RS) suggests relevant items to the user according to his/her desired preference. It processes various information related to users and items. However, RSs suffer from data sparsity. Generally, deep learning techniques are used in RSs for deep analysis of item contents to create precise recommendations. However, the effective handling of user reviews in parallel with item reviews is still an open research domain that can be further explored. In this paper, a hybrid model that handles both user and item metadata concurrently is proposed with the aim of solving the sparsity problem. To demonstrate the viability of the proposed methodology, a series of experiments was performed on three real-world datasets. The results show that the proposed model outperforms other state-of-the-art approaches to the best of our knowledge.
Semantic Modeling for Food Recommendation Explanations

With the increased use of AI methods to provide recommendations in the health, specifically dietary recommendation space, there is also an increased need for explainability of those recommendations. Such explanations would benefit users of recommendation systems by empowering them with justifications for following the system's suggestions. We present the Food Explanation Ontology (FEO) that provides a formalism for modeling explanations to users for food-related recommendations. FEO models food recommendations, using concepts from the explanation domain to create responses to user questions about food recommendations they receive from AI systems such as personalized knowledge base question answering systems. FEO uses a modular, extensible structure that lends itself to a variety of explanations while still preserving important semantic details to accurately represent explanations of food recommendations. In order to evaluate this system, we used a set of competency questions derived from explanation types present in literature that are relevant to food recommendations. Our motivation with the use of FEO is to empower users to make decisions about their health, fully equipped with an understanding of the AI recommender systems as they relate to user questions, by providing reasoning behind their recommendations in the form of explanations.
Enhancement of a multi-dialectal sentiment analysis system by the detection of the implied sarcastic features

Sentiment analysis is an NLP task that gained the interest of many researchers in various languages and recently in the Arabic language. We have encountered several challenges when dealing with this task, including sarcasm detection. In this article, we aim to exploit sarcastic characteristics to improve the accuracy of the sentiment analysis system. Sarcasm is difficult to detect because it is implicit and characterized by the presence of positive words in a negative context. We have then extracted a variety of features to define context incongruity and the opposition between the objective and subjective sentences. Offensive language and hate speech correspond to expressions that hurt others. The detection of offensive language is based on identifying offensive terms that are strongly negative and helpful to detect negative expressions. Thus, we have manually and automatically constructed sentimental, offensive and sarcastic lexicons and collected others. In the same way, many corpora either ironic (sarcastic, offensive) or sentimental (positive, negative) were collected. As sarcasm is a major challenge for the sentiment analysis system, we have built a balanced system that contains positive and negative (sarcastic, offensive) tweets. Since the analyzed corpus is multidialectal, we have used a cross dialect lexicon that retains meaning when passing from one dialect to another. Besides the Arabic dialect common characteristics, the classification was enhanced by the detection of the specificities of some dialects that use negation clitics as well as negation words to negate a term. The experiments prove that the enhancement of a sentiment analysis system by sarcastic features improved the results by 8% to reach 84.17% of accuracy using a classical machine learning approach and 80.36% using a Deep learning approach. The classical machine learning approach is improved afterward based on the expansion of the BOW lexicon and the reduction of the characteristic vector to reach an accuracy of 89.24%. This method is multilingual because the built model can be language independent. Indeed, it is enough to have the corresponding resources to apply the system to other languages.
Wavelet Multi-Level Attention Capsule Network for Texture Classification

Texture classification is one of the essential problems in computer vision. Due to the powerful feature extraction ability, convolutional neural network (CNN) based texture classification methods have attracted extensive attention in recent years. However, there are still some challenges, such as the extraction of multi-level texture features and their relationships. To address these problems, this letter proposes the wavelet multi-level attention capsule network (WMACapsNet), which integrates multi-scale wavelet decomposition and multi-level attention blocks into the capsule network. Specifically, multi-scale spectral features in frequency domain are extracted by multi-level wavelet transform; and then the self-attention block explores the dependencies of capsule features within each scale; finally, the cross-attention block refines capsule features and their relationships with attention mechanism across different scales. The proposed WMACapsNet provides an efficient way to explore spatial domain features, frequency domain features and their dependencies, useful for most texture classification tasks. Experimental results on several texture datasets show that the proposed WMACapsNet outperforms the state-of-the-art texture classification methods not only in accuracy but also in robustness.
Comparative Assessment of Digital Pathology Systems for Primary Diagnosis

Despite increasing interest in whole-slide imaging (WSI) over optical microscopy (OM), limited information on comparative assessment of various digital pathology systems (DPSs) is available.A comprehensive evaluation was undertaken to investigate the technical performance-assessment and diagnostic accuracy of four DPSs with an objective to establish the noninferiority of WSI over OM and find out the best possible DPS for clinical workflow.A total of 2376 digital images, 15,775 image reads (OM - 3171 + WSI - 12,404), and 6100 diagnostic reads (OM - 1245, WSI - 4855) were generated across four DPSs (coded as DPS: 1, 2, 3, and 4) using a total 240 cases (604 slides). Onsite technical evaluation revealed successful scan rate: DPS3 < DPS2 < DPS4 < DPS1; mean scanning time: DPS4 < DPS1 < DPS2 < DPS3; and average storage space: DPS3 < DPS2 < DPS1 < DPS4. Overall diagnostic accuracy, when compared with the reference standard for OM and WSI, was 95.44% (including 2.48% minor and 2.08% major discordances) and 93.32% (including 4.28% minor and 2.4% major discordances), respectively. The difference between the clinically significant discordances by WSI versus OM was 0.32%. Major discordances were observed mostly using DPS4 and least in DPS1; however, the difference was statistically insignificant. Almost perfect (κ ≥ 0.8)/substantial (κ = 0.6-0.8) inter/intra-observer agreement between WSI and OM was observed for all specimen types, except cytology. Overall image quality was best for DPS1 followed by DPS4. Mean digital artifact rate was 6.8% (163/2376 digital images) and maximum artifacts were noted in DPS2 (n = 77) followed by DPS3 (n = 36). Most pathologists preferred viewing software of DPS1 and DPS2.WSI was noninferior to OM for all specimen types, except for cytology. Each DPS has its own pros and cons; however, DPS1 closely emulated the real-world clinical environment. This evaluation is intended to provide a roadmap to pathologists for the selection of the appropriate DPSs while adopting WSI.
Quad division prototype selection-based k-nearest neighbor classifier for click fraud detection from highly skewed user click dataset

In online advertising, the user-clicks dataset based fraudulent publishers’ classification models exhibit poor performance due to high skewness in class distribution of the publishers. The nearest-neighbor based classification techniques are popularly used to reduce the impact of class skewness on performance. The Nearest-Neighbor techniques use Prototype Selection (PS) methods to select promising samples before classifying them for reducing the size of training data. Although Nearest-Neighbor techniques are simple to use and reduce the negative impact of the loss of potential information, they suffer from higher storage requirements and slower classification speed when applied on datasets with skewed class distributions. In this paper, we propose a Quad Division Prototype Selection-based k-Nearest Neighbor classifier (QDPSKNN) by introducing quad division method for handling uneven class distribution. The quad-division divides the data into four quartiles (groups) and performs controlled under-sampling for balancing class distribution. It reduces the size of the training dataset by selecting only the relevant prototypes in the form of nearest-neighbors. The performance of QDPSKNN is evaluated on Fraud Detection in Mobile Advertising (FDMA) user-click dataset and fifteen other benchmark imbalanced datasets to test its generalizing behaviour. The performance is also compared with one baseline model (k-NN) and four other prototype selection methods such as NearMiss-1, NearMiss-2, NearMiss-3, and Condensed Nearest-Neighbor. The results show improved classification performance with QDPSKNN in terms of precision, recall, f-measure, g-mean, reduction rate and execution time, compared to existing prototype selection methods in the classification of fraudulent publishers as well as on other benchmark imbalanced datasets. Wilcoxon signed ranked test is conducted to demonstrate significant differences amid QDPSKNN and state-of-the-art methods.
MHSU-Net: A more versatile neural network for medical image segmentation

• An improved convolutional module MCB is proposed. • A hybrid down-sampling module HDSB is proposed to reduce the feature loss. • The Skip Connection+ structure is proposed to capture more context information. Background and objective: Medical image segmentation plays an important role in clinic. Recently, with the development of deep learning, many convolutional neural network (CNN)-based medical image segmentation algorithms have been proposed. Among them, U-Net is one of the most famous networks. However, the standard convolutional layers used by U-Net limit its capability to capture abundant features. Additionally, the consecutive maximum pooling operations in U-Net cause certain features to be lost. This paper aims to improve the feature extraction capability of U-Net and reduce the feature loss during the segmentation process. Meanwhile, the paper also focuses on improving the versatility of the proposed segmentation model. Methods: Firstly, in order to enable the model to capture richer features, we have proposed a novel multiscale convolutional block (MCB). MCB adopts a wider and deeper structure, which can be applied to different types of segmentation tasks. Secondly, a hybrid down-sampling block (HDSB) has been proposed to reduce the feature loss via replacing the maximum pooling layer. Thirdly, we have proposed a context module (CIF) based on atrous convolution and SKNet to extract sufficient context information. Finally, we combined the CIF module with Skip Connection of U-Net, and further proposed the Skip Connection+ structure. Results: We name the proposed network MHSU-Net. MHSU-Net has been evaluated on three different datasets, including lung, cell contour, and pancreas. Experimental results demonstrate that MHSU-Net outperforms U-Net and other state-of-the-art models under various evaluation metrics, and owns greater potential in clinical applications. Conclusions: The proposed modules can greatly improve the feature extraction capability of the segmentation model and effectively reduce the feature loss during the segmentation process. MHSU-Net can also be applied to different types of medical image segmentation tasks.
Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders

Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In this paper, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of un-labeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing auto-encoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations. Codes are available at https://github.com/junhocho/HGCAE.
Block-Wise Training Residual Networks on Multi-Channel Time Series for Human Activity Recognition

Recently, human activity recognition (HAR) has become an active research area in wearable computing scenario. On the other hand, residual nets have continued to push the state-of-the-art of computer vision and natural language processing. However, residual nets have rarely been considered in the HAR field. As residual nets grow deeper, memory footprint limit its wide use for a variety of HAR tasks. In this paper, we present a novel block-wise training residual nets that use local loss functions for HAR applications. Instead of global backprop, the local cross-entropy loss together with a supervised local similarity matching loss is utilized to train each residual block independently, in which gradient need not to be propagated down the network. As a result, the gradient and activations do not have to be kept in memory any more, which alleviates the memory requirements and is more beneficial for wearable HAR computing. We demonstrate the effectiveness of block-wise training residual nets on OPPORTUNITY, WISDM, UNIMIB SHAR and PAMAP2 datasets, which establishes obvious better classification accuracy compared to equally-sized residual nets, even though memory requirement is much smaller.
Challenges and barriers to Austrian early childhood educators' participation in online professional development programmes

Abstract Online professional development programmes have a long tradition in adult education. However, in early childhood education, such programmes are only just beginning to be developed. Before online professional development courses can be made inclusive, accessible and widely available to early childhood educators, they must meet a few basic requirements. The present study provides insights into essential aspects that need to be considered when setting up online professional development programmes, a field which is still in its infancy in Austria. The results of a representative survey ( n = 317) demonstrated that early childhood educators' digital competencies are highly variable and cannot be taken for granted. The survey results also stress the need to provide educators with functional digital devices appropriate to their work environment. Early childhood educators' interest in online professional development programmes is very high, in recognition of the advantages afforded by flexible participation options independent of users' time constraints or location. The development of attractive, compelling and accessible online professional development courses can contribute to current professionalisation efforts in the field of early childhood education. Practitioner notes What is already known about this topic Online professional development has already a very long tradition in the field of adult vocational training but not in Early Childhood Education. The possibility of attending online professional development programmes independent of a participant's time constraints or location is viewed as a particularly beneficial advantage. Online professional development programmes positively affect the professionalisation of educators. For this reason, they must increasingly be integrated into the field of early childhood education. What this paper adds In the field of early childhood education in German‐speaking regions, online professional development training courses are only beginning to be developed. This paper examines the challenges and barriers for early childhood educators associated with implementing online professional development programmes for early childhood educators. The survey was conducted during the COVID‐19 pandemic and includes current data on the ongoing digitalisation boom. Implications for practise and/or policy Online professional development courses should not be understood as competition for conventional face‐to‐face programmes. Instead, they act as a useful supplement. Media competencies are a fundamental prerequisite for everyday professional life—early childhood educators need functional media devices, stable internet connections, and support structures in IT and computer literacy. Early childhood educators require effective instruction in using online professional development programmes to expand online professional development programmes in their field. They must also address compelling topics in early childhood education relevant to educators' practise.
Universal Adversarial Training with Class-Wise Perturbations

Despite their overwhelming success on a wide range of applications, convolutional neural networks (CNNs) are widely recognized to be vulnerable to adversarial examples. This intriguing phenomenon led to a competition between adversarial attacks and defense techniques. So far, adversarial training is the most widely used method for defending against adversarial attacks. It has also been extended to defend against universal adversarial perturbations (UAPs). The SOTA universal adversarial training (UAT) method optimizes a single perturbation for all training samples in the mini-batch. In this work, we find that a UAP does not attack all classes equally. Inspired by this observation, we identify it as the source of the model having unbalanced robustness. To this end, we improve the SOTA UAT by proposing to utilize class-wise UAPs during adversarial training. On multiple benchmark datasets, our class-wise UAT leads superior performance for both clean accuracy and adversarial robustness against universal attack.