Reviewerly/exHarmony huggingface dataset:
Attention-Based Model for Accurate Stance Detection

Effective representation learning is an essential building block for achieving many natural language processing tasks such as stance detection as performed implicitly by humans. Stance detection can assist in understanding how individuals react to certain information by revealing the user’s stance on a particular topic. In this work, we propose a new attention-based model for learning feature representations and show its effectiveness in the task of stance detection. The proposed model is based on transfer learning and multi-head attention mechanisms. Specifically, we use BERT and word2vec models to learn text representation vectors from the data and pass both of them simultaneously to the multi-head attention layer to help focus on the best learning features. We present five variations of the model, each with a different combination of BERT and word2vec embeddings for the query and value parameters of the attention layer. The performance of the proposed model is evaluated against multiple baseline and state-of-the-art models. The best of the five proposed variations of the model improved the accuracy on average by 0.4% and achieved 68.4% accuracy for multi-classification, while the best accuracy for binary classification is 86.1% with a 1.3% improvement.
Empathy and Persona of English vs. Arabic Chatbots: A Survey and Future Directions

There is a high demand for chatbots across a wide range of sectors. Human-like chatbots engage meaningfully in dialogues while interpreting and expressing emotions and being consistent through understanding the user’s personality. Though substantial progress has been achieved in developing empathetic chatbots for English, work on Arabic chatbots is still in its early stages due to various challenges associated with the language constructs and dialects. This survey reviews recent literature on approaches to empathetic response generation, persona modelling and datasets for developing chatbots in the English language. In addition, it presents the challenges of applying these approaches to Arabic and outlines some solutions. We focus on open-domain chatbots developed as end-to-end generative systems due to their capabilities to learn and infer language and emotions. Accordingly, we create four open problems pertaining to gaps in Arabic and English work; namely, (1) feature representation learning based on multiple dialects; (2) modelling the various facets of a persona and emotions; (3) datasets; and (4) evaluation metrics.
Contrast-Free Liver Tumor Detection Using Ternary Knowledge Transferred Teacher-Student Deep Reinforcement Learning

Contrast-free liver tumor detection technology has a significant impact on clinics due to its ability to eliminate contrast agents (CAs) administration in the current tumor diagnosis. In this paper, we proposed a novel ternary knowledge transferred teacher-student DRL (Ts-DRL) as a safe, speedy, and inexpensive contrast-free technology for liver tumor detection. Ts-DRL leverages a teacher network to learn tumor knowledge after CAs administration, and create a pipeline to transfer teacher's knowledge to guide a student network learning of tumor without CAs, thereby realizing contrast-free liver tumor detection. Importantly, Ts-DRL possesses a new ternary knowledge set (actions, rewards, and features of driven actions), which for the first time, allows the teacher network to not only inform the student network what to do, but also teach the student network why to do. Moreover, Ts-DRL possesses a novel progressive hierarchy transferring strategy to progressively adjust the knowledge rationing between teachers and students during training to couple with knowledge smoothly and effectively transferring. Evaluation on 325 patients including different types of tumors from two MR scanners, Ts-DRL significantly improves performance (Dice by at least 7%) when comparing the five most recent state-of-the-art methods. The results proved that our Ts-DRL has greatly promoted the development and deployment of contrast-free liver tumor technology.
End-to-End Segmentation of Medical Images via Patch-Wise Polygons Prediction

The leading medical image segmentation methods represent the output map as a pixel grid. We present an alternative in which the object edges are modeled, per image patch, as a polygon with k vertices that is coupled with per-patch label probabilities. The vertices are optimized by employing a differentiable neural renderer to create a raster image. The delineated region is then compared with the ground truth segmentation. Our method obtains multiple state-of-the-art results for the Gland segmentation dataset (Glas), the Nucleus challenges (MoNuSeg), and multiple polyp segmentation datasets, as well as for non-medical benchmarks, including Cityscapes, CUB, and Vaihingen. Our code for training and reproducing these results is attached as a supplement.
Prostate Cancer Histology Synthesis Using StyleGAN Latent Space Annotation

The latent space of a generative adversarial network (GAN) may model pathologically-significant semantics with unsupervised learning. To explore this phenomenon, we trained and tested a StyleGAN2 on a high quality prostate histology dataset covering the prostate cancer (PCa) diagnostic spectrum. Our pathologist annotated synthetic images to identify learned PCa regions in the GAN latent space. New points were drawn from these regions, synthesized into images, and given to a pathologist for annotation. 77% of the new points received the same annotation, and 98% of the latent points received the same or adjacent diagnostic stage annotation. This confirms the GAN network can accurately disentangle and model PCa features without exposure to labels in the training process.
Statistical and Neural Methods for Cross-lingual Entity Label Mapping in Knowledge Graphs

Knowledge bases such as Wikidata amass vast amounts of named entity information, such as multilingual labels, which can be extremely useful for various multilingual and cross-lingual applications. However, such labels are not guaranteed to match across languages from an information consistency standpoint, greatly compromising their usefulness for fields such as machine translation. In this work, we investigate the application of word and sentence alignment techniques coupled with a matching algorithm to align cross-lingual entity labels extracted from Wikidata in 10 languages. Our results indicate that mapping between Wikidata’s main labels stands to be considerably improved (up to 20 points in F1-score) by any of the employed methods. We show how methods relying on sentence embeddings outperform all others, even across different scripts. We believe the application of such techniques to measure the similarity of label pairs, coupled with a knowledge base rich in high-quality entity labels, to be an excellent asset to machine translation.
Fine-Tuning BERT for Generative Dialogue Domain Adaptation

Current data-driven Dialogue State Tracking (DST) models exhibit a poor capacity to adapt themselves to domain changes, resulting in a significant degradation in performance. We propose a methodology, called Generative Dialogue Domain Adaptation, which significantly simplifies the creation of training data when a number of changes (e.g., new slot-values or new instances) occur in a domain Knowledge Base. We start from dialogues for a source domain and apply generative methods based on language models such as BERT, fine-tuned on task-related data and generate slot-values substitutions for a target domain. We have experimented dialogue domain adaptation in a few-shot setting showing promising results, although the task is still very challenging. We provide a deep analysis of the quality of the generated data and of the features that affect this task, and we emphasise that DST models are very sensitive to the distribution of slot-values in the corpus.
Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising

Text sentiment transfer aims to flip the sentiment polarity of a sentence (positive to negative or vice versa) while preserving its sentiment-independent content. Although current models show good results at changing the sentiment, content preservation in transferred sentences is insufficient. In this paper, we present a sentiment transfer model based on polarity-aware denoising, which accurately controls the sentiment attributes in generated text, preserving the content to a great extent and helping to balance the style-content trade-off. Our proposed model is structured around two key stages in the sentiment transfer process: better representation learning using a shared encoder and sentiment-controlled generation using separate sentiment-specific decoders. Empirical results show that our methods outperforms state-of-the-art baselines in terms of content preservation while staying competitive in terms of style transfer accuracy and fluency. Source code, data, and all other related details are available on Github ( https://github.com/SOURO/polarity-denoising-sentiment-transfer ).
Improving Accessibility to Arabic ETDs Using Automatic Classification

Electronic Theses and Dissertations (ETDs) are documents rich in research information that provide many benefits to students and future generations of scholars in various disciplines. Therefore, research is taking place to extract data from ETDs and make them more accessible. However, much of the related research involved ETDs in the English language, while Arabic ETDs remain an untapped source of data, although the number of Arabic ETDs available digitally is growing. Therefore, the need to make them more browsable and accessible increases. Some ways to achieve this need include data annotation, indexing, translation, and classification. As the size of the data increases, manual subject classification becomes less feasible. Accordingly, automatic subject classification becomes essential for the searchability and management of data. There are two main roadblocks to performing automatic subject classification of Arabic ETDs. The first is the lack of a large public corpus of Arabic ETDs for training purposes, while the second is the Arabic language's linguistic complexity, especially in academic documents. This research aims to collect key metadata of Arabic ETDs, and apply different automatic subject classification methodologies. The first goal is aided by scraping data from the AskZad Digital Library. The second goal is achieved by exploring different machine learning and deep learning techniques. The experiments' results show that deep learning using pretrained language models yielded the highest accuracy of approximately 0.83, while classical machine learning techniques yielded approximately 0.41 and 0.70 for multiclass classification one-vs-all classification respectively. This indicates that using pretrained language models assists in understanding languages which is essential for the classification of text.
A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided Brain Tissue Segmentation

An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clinical practices due to the high cost and low accessibility. In this paper, we propose a novel knowledge keeper network (KKN) to guide brain tissue segmentation by taking advantage of 7T representations without explicitly using 7T images. By extracting features of a 3T input image substantially and then transforming them to 7T features via knowledge distillation (KD), our method achieves deriving 7T-like representations from a given 3T image and exploits them for tissue segmentation. On two independent datasets, we evaluated our method's validity in qualitative and quantitative manners on 7T-like image synthesis and 7T-guided tissue segmentation by comparing with the comparative methods in the literature.
A Novel Hybrid Framework to Enhance Zero-shot Classification

As manually labelling data can be error-prone and labour-intensive, some recent studies automatically classify documents without any training on labelled data and directly exploit pre-trained language models (PLMs) for many downstream tasks, also known as zero-shot text classification. In the same vein, we propose a novel framework aims at improving zero-short learning and enriching domain specific information required by PLMs with transformer models. To unleash the power of PLMs pre-trained on massive cross-section corpus, the framework unifies two transformers for different purposes: 1) expanding categorical labels required by PLMs by creating coherent representative samples with GPT2, which is a language model acclaimed for generating sensical text outputs, and 2) augmenting documents with T5, which has the virtue of synthesizing high quality new samples similar to the original text. The proposed framework can be easily integrated into different general testbeds. Extensive experiments on two popular topic classification datasets have proved its effectiveness.
The Knowledge Trust: A Proposal for a Blockchain Consortium for Digital Archives

We present a modest proposal for a blockchain-based consortium to preserve and provide a consistent view of the global integrity of digital information currently scattered across various archives. Based on a number of real-world threats to centralized archives, our design provides increased and frequent integrity checks that can help detect and ameliorate data loss in digital archives.
Analysis of the Deletions of DOIs

Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what causes them. We identified deleted DOIs by comparing the datasets of all Crossref DOIs on two different dates, investigated the number of deleted DOIs in the scholarly content along with the corresponding document types, and analyzed the factors that cause deleted DOIs. Using the proposed method, 708,282 deleted DOIs were identified. The majority corresponded to individual scholarly articles such as journal articles, proceedings articles, and book chapters. There were cases of many DOIs assigned to the same content, e.g., retracted journal articles and abstracts of international conferences. We show the publishers and academic societies which are the most common in deleted DOIs. In addition, the top cases of single scholarly content with a large number of deleted DOIs were revealed. The findings of this study are useful for citation analysis and altmetrics, as well as for avoiding deleted DOIs.
Machine Creativity

In this chapter, the theme of machine creativity is examined. The main questions underlying this topic are illustrated, reporting and discussing the views of authors involved in early artificial intelligence (AI) research. These were the scientists engaged in experiments and theoretical studies in expert systems, logical reasoning, logical programming, machine learning, and philosophy of mind. In addition, the issue of machine intelligence is raised in reference to Alan Turing's question: Can machines think? Knowledge acquisition and representation as well as machine learning have also briefly been introduced, so as to prepare the ground for discussing the question: Can machines be creative? Finally, a problem that worries many journalists, socio-philosophers, and people in general is briefly explored, namely: Will technology grow out of human control? Will there be a moment when this transformation becomes irreversible?
Interactive Virtual Participation for Opera and Theatre Using New Digitization Information Systems

Society 5.0 has implemented the use of new digital technologies, overcoming traditional active learning systems with means and methodologies that extend the involvement of the digitized user. This trend has revolutionized how organizations and companies deliver their services through interconnected and interoperable platforms. The prevalence of new media has led to the adoption of applications that exploit gamification techniques and serious games to transfer reality into new virtuality. The contribution analyses procedures and methodologies that can be adapted to digitalize cultural heritage, focusing on the theatrical and musical entertainment sector (i.e., opera and theatre). During the COVID-19 pandemic, cultural organizations received significant containment measures to cancel events and openings. Therefore, investing inaccessible and reality-like digital applications through advanced participatory techniques reduced financial and target losses. In this way, the shift from the digital model to the interactive service model for sensory experiences skills the Citizen 5.0.
Modified algorithm of unimodal thresholding for FPGA implementation

Thresholding is one of the basic digital image processing procedures that is commonly used for segmentation or edge detection. The final result of thresholding is significantly affected by the used threshold value. The several methods for determining the optimal threshold have been developed. Otsu's and Rosin's methods are the most used algorithms for computing the threshold value in non-real time image processing on a PC. These methods have not been used in FPGA circuits so far because of complicated calculation algorithm. In this paper we propose modification of Rosin's algorithm based on the geometric transformation of the histogram. Our approach simplifies calculations and allows FPGA implementation of Rosin's method. The simulations with three different images confirmed, that the proposed algorithm gives the same results as the original.
Resolution independent person re‐identification network

Abstract Does a query image with much higher resolution than that of the gallery image also affect the pedestrian re‐identification performance? If so, and how does it affect performance? The study proposes a novel framework for performing high‐resolution image reconstruction and pedestrian re‐identification tasks, independent of the query image resolution. More precisely, an end‐to‐end trainable Resolution Independent person Re‐identification network is proposed. It is composed of our designed Cross‐Resolution GAN and Embedding Batch Normalisation layers. The model is then compared with the traditional low‐resolution pedestrian recognition algorithm and the hybrid method of high‐resolution reconstruction and pedestrian re‐identification. The results demonstrate that the proposed method outperforms the state‐of‐the‐art methods in the pedestrian re‐identification task based on our expanded benchmark dataset. It also reaches an equivalent performance to the existing methods in the high‐resolution image reconstruction task.
Software Reuse Practices among Malaysian Freelance Developers: A Conceptual Framework

Software reuse development practices have been proven to benefit software development in terms of a quicker time to market and quality. Although being aware of the benefits, software developers tend to overlook this method due to various reasons. Impacted by the advancement of technology, the software development scenario has been dramatically altered as the popularity of software development organizations hiring third party developers acutely rises. Freelance developers enable software development organizations to obtain high expertise in development specific areas on a project-by-project basis that gives development cost flexibility. However, information regarding freelance developer development practice is found to be limited in the related literature. By applying the systematic literature review approach, this study&rsquo;s main objective is to develop a conceptual framework to investigate software reuse practices among Malaysian freelance developers. The framework consists of individual characteristics, project characteristics, technological characteristics, software reuse development practices, quality, and achievement goals as constructs. The framework would be a basis for future empirical studies.
The Moderating Role of Information Security Behaviour (ISB) on the Relationship between Digital Literacy (DL) and Information Security Culture (ISC): A Proposed Research Framework

Information Security Culture (ISC) has been suggested as a means of strengthening employees&rsquo; information security in the workplace. However, the role of employees and their digital skills in the process has been largely overlooked. Establishing an information security culture that strives to secure information by having employees who are digitally literate, as well as exerting influence on the employees&rsquo; behaviour regarding security, is one of the measures that may be utilised to mitigate risks provided by humans. Therefore, the purpose of this paper is to establish a research framework that moderates the effect of information security behaviour (ISB) on the relationship between digital literacy (DL) and information security culture (ISC). The study will improve DL and ISCB among employees who implement the country&rsquo;s development plans, such as enhancing administrative functions, social infrastructure, and economic growth performance in accordance with MyDigital initiatives, as well as the government&rsquo;s ISC.
Machine Learning Methods to Identify Aggressive Behavior in Social Media

With the more usage of Internet and online social media, platforms creep with lot of cybercrimes. Texts in the online platforms and chat rooms are aggressive. In few instances, people target and humiliate them with the text. It affects victim mental health. Therefore, there is a need of detecting the abuse words in the text. In this paper, a study of machine learning methods is done to identify the aggressive behavior. Accuracy can be improved by incorporating additional features.
Redundant Label Learning via Subspace Representation and Global Disambiguation

Redundant Label Learning (RLL) aims at inducing a robust model from training data, where each example is associated with a set of candidate labels, among which some of them are incorrect. Most existing approaches deal with such problem by disambiguating the candidate labels first and then inducing the predictive model from the disambiguated data. However, these approaches only focus on disambiguation for each instance’ candidate label set, while the global label context tends to be ignored. Meanwhile, these approaches usually induce the objective model by directly utilizing the original feature information, which may lead to the model overfitting due to high-dimensional redundant features. To tackle the above issues, we propose a novel feature S ubspac E R epresentation and label G lobal Disambiguat IO n ( SERGIO ) approach, which improves the generalization ability of the learning system from the perspective of both feature space and label space. Specifically, we project the original high-dimensional feature space into a low-dimensional subspace, where the projection matrix is regularized with an orthogonality constraint to make the subspace more compact. Meanwhile, we introduce a label confidence matrix and constrain it with ℓ 1 -norm and trace-norm regularization simultaneously, which are utilized to explore global label correlations and further well in accordance with the nature of single-label classification and multi-label classification problem, respectively. Extensive experiments on both single-label and multi-label RLL datasets demonstrate that our proposed method achieves competitive performance against state-of-the-art approaches.