Reviewerly/exHarmony huggingface dataset:
Privacy-Aware Resource Sharing in Cross-Device Federated Model Training for Collaborative Predictive Maintenance

The proliferation of Industry 4.0 has made modern industrial assets a rich source of data that can be leveraged to optimise operations, ensure efficiency, and minimise maintenance costs.The availability of data is advantageous for asset management, however, attempts to maximise the value of this data often fall short due to additional constraints, such as privacy concerns and data stored in distributed silos that is difficult to access and share.Federated Learning (FL) has been explored to address these challenges and has been demonstrated to provide a mechanism that allows highly distributed data to be mined in a privacy-preserving manner and offering new opportunities for a collaborative approach to asset management.Despite the benefits, FL has some challenges that need to be overcome to make it fully compatible for asset management or more specifically predictive maintenance applications.FL requires a set of clients that participate in the model training process, however, orchestration, device heterogeneity and scalability can hinder the speed and accuracy in the context of collaborative predictive maintenance.To address this challenge, this work proposes a split-learning-based framework (SplitPred) that enables FL clients to maximise available resources within their local network without compromising the benefits of a FL approach (i.e., privacy and shared learning).Experiments performed on the benchmark C-MAPSS dataset demonstrate the advantage of applying SplitPred in the FL process in terms of efficient use of resources, i.e., model convergence time, accuracy, and network load.
Efficient Backcasting Search for Optical Quantum State Synthesis

Non-Gaussian states are essential for many optical quantum technologies. The so-called optical quantum state synthesizer (OQSS), consisting of Gaussian input states, linear optics, and photon-number resolving detectors, is a promising method for non-Gaussian state preparation. However, an inevitable and crucial problem is the complexity of the numerical simulation of the state preparation on a classical computer. This problem makes it very challenging to generate important non-Gaussian states required for advanced quantum information processing. Thus, an efficient method to design OQSS circuits is highly desirable. To circumvent the problem, we offer a scheme employing a backcasting approach, where the circuit of OQSS is divided into some sublayers, and we simulate the OQSS backwards from final to first layers. Moreover, our results show that the detected photon number by each detector is at most 2, which can significantly reduce the requirements for the photon-number resolving detector. By virtue of the potential for the preparation of a wide variety of non-Gaussian states, the proposed OQSS can be a key ingredient in general optical quantum information processing.
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning

While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications.We present a continual pre-training approach that equips PTLMs with targeted knowledge about event temporal relations.We design self-supervised learning objectives to recover masked-out event and temporal indicators and to discriminate sentences from their corrupted counterparts (where event or temporal indicators got replaced).By further pre-training a PTLM with these objectives jointly, we reinforce its attention to event and temporal information, yielding enhanced capability on event temporal reasoning.This Effective CONtinual pre-training framework for Event Temporal reasoning (ECONET) improves the PTLMs' fine-tuning performances across five relation extraction and question answering tasks and achieves new or on-par state-of-the-art performances in most of our downstream tasks. 1
Semantic Features with Contextual Knowledge-Based Web Page Categorization Using the GloVe Model and Stacked BiLSTM

Internet technologies are emerging very fast nowadays, due to which web pages are generated exponentially. Web page categorization is required for searching and exploring relevant web pages based on users’ queries and is a tedious task. The majority of web page categorization techniques ignore semantic features and the contextual knowledge of the web page. This paper proposes a web page categorization method that categorizes web pages based on semantic features and contextual knowledge. Initially, the GloVe model is applied to capture the semantic features of the web pages. Thereafter, a Stacked Bidirectional long short-term memory (BiLSTM) with symmetric structure is applied to extract the contextual and latent symmetry information from the semantic features for web page categorization. The performance of the proposed model has been evaluated on the publicly available WebKB dataset. The proposed model shows superiority over the existing state-of-the-art machine learning and deep learning methods.
SPAN: A Simple Predict &amp; Align Network for Handwritten Paragraph Recognition

Unconstrained handwriting recognition is an essential task in document analysis. It is usually carried out in two steps. First, the document is segmented into text lines. Second, an Optical Character Recognition model is applied on these line images. We propose the Simple Predict & Align Network: an end-to-end recurrence-free Fully Convolutional Network performing OCR at paragraph level without any prior segmentation stage. The framework is as simple as the one used for the recognition of isolated lines and we achieve competitive results on three popular datasets: RIMES, IAM and READ 2016. The proposed model does not require any dataset adaptation and can be trained without line breaks in the transcription labels. Our code and trained model weights are available at https://github.com/FactoDeepLearning/SPAN.
Glassboxing Deep Learning to Enhance Aircraft Detection from SAR Imagery

Although deep learning has achieved great success in aircraft detection from SAR imagery, its blackbox behavior has been criticized for low comprehensibility and interpretability. Such challenges have impeded the trustworthiness and wide application of deep learning techniques in SAR image analytics. In this paper, we propose an innovative eXplainable Artificial Intelligence (XAI) framework to glassbox deep neural networks (DNN) by using aircraft detection as a case study. This framework is composed of three parts: hybrid global attribution mapping (HGAM) for backbone network selection, path aggregation network (PANet), and class-specific confidence scores mapping (CCSM) for visualization of the detector. HGAM integrates the local and global XAI techniques to evaluate the effectiveness of DNN feature extraction; PANet provides advanced feature fusion to generate multi-scale prediction feature maps; while CCSM relies on visualization methods to examine the detection performance with given DNN and input SAR images. This framework can select the optimal backbone DNN for aircraft detection and map the detection performance for better understanding of the DNN. We verify its effectiveness with experiments using Gaofen-3 imagery. Our XAI framework offers an explainable approach to design, develop, and deploy DNN for SAR image analytics.
Multi-scale iterative refinement network for RGB-D salient object detection

The extensive research leveraging RGB-D information has been exploited in salient object detection. However, salient visual cues appear in various scales and resolutions of RGB images due to semantic gaps at different feature levels. Meanwhile, similar salient patterns are available in cross-modal depth images as well as multi-scale versions. Cross-modal fusion and multi-scale refinement are still an open problem in RGB-D salient object detection task. In this paper, we begin by introducing top-down and bottom-up iterative refinement architecture to leverage multi-scale features, and then devise attention based fusion module (ABF) to address on cross-modal correlation. We conduct extensive experiments on seven public datasets. The experimental results show the effectiveness of our devised method.
Parallel Refinements for Lexically Constrained Text Generation with BART

Lexically constrained text generation aims to control the generated text by incorporating some pre-specified keywords into the output.Previous work injects lexical constraints into the output by controlling the decoding process or refining the candidate output iteratively, which tends to generate generic or ungrammatical sentences, and has high computational complexity.To address these challenges, we propose Constrained BART (CBART) for lexically constrained text generation.CBART leverages the pre-trained model BART and transfers part of the generation burden from the decoder to the encoder by decomposing this task into two sub-tasks, thereby improving the sentence quality.Concretely, we extend BART by adding a token-level classifier over the encoder, aiming at instructing the decoder where to replace and insert.Guided by the encoder, the decoder refines multiple tokens of the input in one step by inserting tokens before specific positions and re-predicting tokens with low confidence.To further reduce the inference latency, the decoder predicts all tokens in parallel.Experiment results on One-Billion-Word and Yelp show that CBART can generate plausible text with high quality and diversity while significantly accelerating inference.
Blockchain-Based Traceability for Anti-Counterfeit in Cross-Border E-Commerce Transactions

Cross-border e-commerce, involving international product transactions via online and mobile platforms, is growing at a dramatic rate around the globe. One of the main concerns of brand firms is preventing counterfeit products from being sold under their names on e-commerce platforms. Counterfeit goods not only create economic losses to both the supply and demand sides, but also undermine efforts to improve sustainability. Proliferating counterfeits harm the brands of supply firms and trust in selling e-commerce platforms. In addition, they discourage participants in the supply chain from investing in social and environmental sustainability. If end-customers have access to detailed and comprehensive product information with a traceability system that can help overcome information uncertainty and asymmetry, losses can be prevented. The result of the pilot test has shown that securely shared in-depth product information among supply chain stakeholders from the supply side to end-customers can help prevent counterfeit goods from proliferating further by enabling consumers to determine the authenticity of products and report forgeries before paying.
Action Recognition Algorithm of Spatio–Temporal Differential LSTM Based on Feature Enhancement

The Long Short-Term Memory (LSTM) network is a classic action recognition method because of its ability to extract time information. Researchers proposed many hybrid algorithms based on LSTM for human action recognition. In this paper, an improved Spatio–Temporal Differential Long Short-Term Memory (ST-D LSTM) network is proposed, an enhanced input differential feature module and a spatial memory state differential module are added to the network. Furthermore, a transmission mode of ST-D LSTM is proposed; this mode enables ST-D LSTM units to transmit the spatial memory state horizontally. Finally, these improvements are added into classical Long-term Recurrent Convolutional Networks (LRCN) to test the new network’s performance. Experimental results show that ST-D LSTM can effectively improve the accuracy of LRCN.
Presence and Communication in Hybrid Virtual and Augmented Reality Environments

Abstract The use of virtual reality (VR) and augmented reality (AR) in connected environments is rarely explored but may become a necessary channel of communication in the future. Such environments would allow multiple users to interact, engage, and share multidimensional data across devices and between the spectrum of realities. However, communication between the two realities within a hybrid environment is barely understood. We carried out an experiment with 52 participants in 26 pairs, within two environments of 3D cultural artifacts: (1) a Hybrid VR and AR environment (HVAR) and (2) a Shared VR environment (SVR). We explored the differences in perceived spatial presence, copresence, and social presence between the environments and between users. We demonstrated that greater presence is perceived in SVR when compared with HVAR, and greater spatial presence is perceived for VR users. Social presence is perceived greater for AR users, possibly because they have line of sight of their partners within HVAR. We found positive correlations between shared activity time and perceived social presence. While acquainted pairs reported significantly greater presence than unacquainted pairs in SVR, there were no significant differences in perceived presence between them in HVAR.
An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation

Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart meters has produced an enormous volume of data, which supports the data-driven models for PVPG forecasting. Although emerging deep learning (DL) models, such as the long short-term memory (LSTM), based on historical data, have provided effective solutions for PVPG forecasting with great successes, these models utilize offline learning. As a result, DL models cannot take advantage of the opportunity to learn from newly-arrived data, and are unable to handle concept drift caused by installing extra PV units and unforeseen PV unit failures. Consequently, to improve day-ahead PVPG forecasting accuracy, as well as eliminate the impacts of concept drift, this paper proposes an adaptive LSTM (AD-LSTM), which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. A two-phase adaptive learning strategy (TP-ALS) is integrated into AD-LSTM, and a sliding window (SDWIN) algorithm is proposed, to detect concept drift in PV systems. Multiple datasets from PV systems are utilized to assess the feasibility and effectiveness of the proposed approaches. The developed AD-LSTM model demonstrates greater forecasting capability than the conventional offline LSTM model, particularly in the presence of concept drift. The forecasting skill of AD-LSTM can be improved up to 73.11%. Additionally, the proposed AD-LSTM model also achieves superior performance in terms of day-ahead PVPG forecasting for individual days and multiple days to other reference models in the literature.
Kori: Interactive Synthesis of Text and Charts in Data Documents

Charts go hand in hand with text to communicate complex data and are widely adopted in news articles, online blogs, and academic papers. They provide graphical summaries of the data, while text explains the message and context. However, synthesizing information across text and charts is difficult; it requires readers to frequently shift their attention. We investigated ways to support the tight coupling of text and charts in data documents. To understand their interplay, we analyzed the design space of chart-text references through news articles and scientific papers. Informed by the analysis, we developed a mixed-initiative interface enabling users to construct interactive references between text and charts. It leverages natural language processing to automatically suggest references as well as allows users to manually construct other references effortlessly. A user study complemented with algorithmic evaluation of the system suggests that the interface provides an effective way to compose interactive data documents.
The Convergence Model of Education for Sustainability in the Transition to Digital Economy

The paper studies the problem of updating educational programs and content, taking into account changing requirements of standards and employers for qualifications and competencies of specialists. First, we show that the updating process in transition to digital economy is associated with the transition to the convergence model of education providing its sustainability perspective. Furthermore, we propose the technique and develop basic models and methods for updating based on the convergence approach, for assessing the degree of convergence of competency models and educational content. The problems of collecting, consolidating, and extracting information on the required competencies from vacancy advertisements in open sources on the Internet are also considered. The convergence approach to updating educational programs is being introduced and tested in the information and learning environment of Penza State University (Penza, Russia).
Intelligent Hybrid Feature Selection for Textual Sentiment Classification

Sentiment Analysis (SA) aims to extract useful information from online Unstructured User-Generated Contents (UUGC) and classify them into positive and negative classes. State-of-the-art techniques for SA suffer a high dimensional feature space because of noisy and irrelevant features from the UUGC. Researchers have also proposed feature extraction and selection techniques to reduce high dimensional feature space, but they fall short in extracting and selecting the most effective sentiment features for sentiment model learning. Effective feature extraction and selection are significant for the SA because they can boost the learning algorithm’s predictive performance while reducing the high-dimensional feature space. To address these concerns, we propose an Intelligent Hybrid Feature Selection for Sentiment Analysis (IHFSSA) based on ensemble learning methods. IHFSSA first identifies sentiment features in the review text utilizing Penn Treebank part-of-speech tagset and integrated Wide Coverage Sentiment Lexicons (WCSL). The sentiment features subset is then selected employing a fast and simple rank-based ensemble of multiple filters feature selection method. The selected sentiment features are further refined by applying a wrapper-based backward feature selection method. Finally, for textual sentiment classification, the well-known classification algorithms Support Vector Machine (SVM), Naive Bayes (NB), Generalized Linear Model (GLM) are trained in the ensemble model on the refined sentiment feature set. The in-depth evaluation using heterogeneous domain benchmark datasets demonstrates that IHFSSA outperforms existing SA techniques.
Digital Transformation in Education: Teacher Candidate Views on Mobile Learning

The number of technological tools used in education is increasing day by day. Studies on mobile learning have increased in recent years. Future teachers’ views on mobile learning are very important. In line with this purpose, the opinions of teacher candidates on mobile learning were taken by using qualitative research method in order to determine in detail their views on mobile learning. The participants of the study consists of 61 teacher candidates studying at Plekhanov Russian University of Economics, I.M. Sechenov First Moscow State Medical University, Gubkin Russian State University of Oil and Gas, Kazan National Research Technological University and Kazan (Volga region) Federal University. The opinions of pre-service teachers selected on the basis of voluntarism were analysed by content analysis method. The research questions were prepared by the researchers by taking expert opinion. The findings obtained as a result of the research are given in detail in the findings and results section.
Indian Sign Language Recognition using Convolutional Neural Network

Communicating with the person having hearing disability is always a major challenge. The work presented in paper is an exertion(extension) towards examining the difficulties in classification of characters in Indian Sign Language(ISL). Sign language is not enough for communication of people with hearing ability or people with speech disability. The gestures made by the people with disability gets mixed or disordered for someone who has never learnt this language. Communication should be in both ways. In this paper, we introduce a Sign Language recognition using Indian Sign Language.The user must be able to capture images of hand gestures using a web camera in this analysis, and the system must predict and show the name of the captured image. The captured image undergoes series of processing steps which include various Computer vision techniques such as the conversion to gray-scale, dilation and mask operation. Convolutional Neural Network (CNN) is used to train our model and identify the pictures. Our model has achieved accuracy about 95%
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark

Dialogue safety problems severely limit the real-world deployment of neural conversational models and have attracted great research interests recently.However, dialogue safety problems remain under-defined and the corresponding dataset is scarce.We propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in humanbot dialogue settings, with focuses on contextsensitive unsafety, which is under-explored in prior works.To spur research in this direction, we compile DIASAFETY, a dataset with rich context-sensitive unsafe examples.Experiments show that existing safety guarding tools fail severely on our dataset.As a remedy, we train a dialogue safety classifier to provide a strong baseline for context-sensitive dialogue unsafety detection.With our classifier, we perform safety evaluations on popular conversational models and show that existing dialogue systems still exhibit concerning contextsensitive safety problems.
Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding

Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols.These approaches are usually limited to a set of pre-defined types.We propose a novel event extraction framework that uses event types and argument roles as natural language queries to extract candidate triggers and arguments from the input text.With the rich semantics in the queries, our framework benefits from the attention mechanisms to better capture the semantic correlation between the event types or argument roles and the input text.Furthermore, the query-and-extract formulation allows our approach to leverage all available event annotations from various ontologies as a unified model.Experiments on ACE and ERE demonstrate that our approach achieves state-of-the-art performance on each dataset and significantly outperforms existing methods on zero-shot event extraction. 1