Reviewerly/exHarmony huggingface dataset:
Learning to Model Editing Processes

Most existing sequence generation models produce outputs in one pass, usually left-to-right.However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing.Recent work has introduced edit-based models for various tasks (such as neural machine translation and text style transfer), but these generally model a single edit step.In this work, we propose modeling editing processes, modeling the whole process of iteratively generating sequences.We form a conceptual framework to describe the likelihood of multi-step edits, and describe neural models that can learn a generative model of sequences based on these multi-step edits.We introduce baseline results and metrics on this task, finding that modeling editing processes improves performance on a variety of axes on both our proposed task and related downstream tasks compared to previous single-step models of edits.
Weakly Supervised Context-based Interview Question Generation

We explore the task of automated generation of technical interview questions from a given textbook.Such questions are different from those for reading comprehension studied in question generation literature.We curate a context-based interview questions data set for Machine Learning and Deep Learning from two popular textbooks.We first explore the possibility of using a large generative language model (GPT-3) for this task in a zero shot setting.We then evaluate the performance of smaller generative models such as BART fine-tuned on weakly supervised data obtained using GPT-3 and hand-crafted templates.We deploy an automatic question importance assignment technique to figure out suitability of a question in a technical interview.It improves the evaluation results in many dimensions.We dissect the performance of these models for this task and also scrutinize the suitability of questions generated by them for use in technical interviews.
Structural Contrastive Representation Learning for Zero-shot Multi-label Text Classification

Zero-shot multi-label text classification (ZMTC) is a fundamental task in natural language processing with applications in the cold start problem of recommendation systems.Ideally, one would learn an expressive representation of both input text and label features so that ZMTC is transformed into a nearest neighbor search problem.However, the existing representation learning approaches for ZMTC struggle with accuracy as well as poor training efficiency.Firstly, the input text is structural, consisting of both short title sentences and long content paragraphs.It is challenging to model the correlation between short label descriptions and long structural input documents.Secondly, the enormous label space in ZMTC forces the existing approaches to perform multi-stage learning with label engineering.As a result, the training overhead is significant.In this paper, we address both problems by introducing an end-to-end structural contrastive representation learning approach.We propose a randomized text segmentation (RTS) technique to generate high-quality contrastive pairs.This RTS technique allows us to model title-content correlation.Additionally, we simplify the multi-stage ZMTC learning strategy by avoiding label engineering.Extensive experiments demonstrate that our approach leads to up to 2.33% improvement in precision@1 and 5.94× speedup in training time on publicly available datasets.
Representation Learning for Resource-Constrained Keyphrase Generation

State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data.To overcome this challenge, we design a data-oriented approach that first identifies salient information using retrieval-based corpus-level statistics, and then learns a task-specific intermediate representation based on a pre-trained language model using large-scale unlabeled documents.We introduce salient span recovery and salient span prediction as denoising training objectives that condense the intra-article and inter-article knowledge essential for keyphrase generation.Through experiments on multiple keyphrase generation benchmarks, we show the effectiveness of the proposed approach for facilitating low-resource keyphrase generation and zeroshot domain adaptation.Our method especially benefits the generation of absent keyphrases, approaching the performance of models trained with large training sets.
META-GUI: Towards Multi-modal Conversational Agents on Mobile GUI

Task-oriented dialogue (TOD) systems have been widely used by mobile phone intelligent assistants to accomplish tasks such as calendar scheduling or hotel reservation.Current TOD systems usually focus on multi-turn text/speech interaction, then they would call back-end APIs designed for TODs to perform the task.However, this API-based architecture greatly limits the information-searching capability of intelligent assistants and may even lead to task failure if TOD-specific APIs are not available or the task is too complicated to be executed by the provided APIs.In this paper, we propose a new TOD architecture: GUI-based task-oriented dialogue system (GUI-TOD).A GUI-TOD system can directly perform GUI operations on real APPs and execute tasks without invoking TOD-specific backend APIs.Furthermore, we release META-GUI, a dataset for training a Multi-modal convErsaTional Agent on mobile GUI.We also propose a multi-model action prediction and response model, which show promising results on META-GUI.The dataset, codes and leaderboard are publicly available ‡ .
Efficient Pre-training of Masked Language Model via Concept-based Curriculum Masking

Masked language modeling (MLM) has been widely used for pre-training effective bidirectional representations, but incurs substantial training costs.In this paper, we propose a novel concept-based curriculum masking (CCM) method to efficiently pre-train a language model.CCM has two key differences from existing curriculum learning approaches to effectively reflect the nature of MLM.First, we introduce a carefully-designed linguistic difficulty criterion that evaluates the MLM difficulty of each token.Second, we construct a curriculum that gradually masks words related to the previously masked words by retrieving a knowledge graph.Experimental results show that CCM significantly improves pre-training efficiency.Specifically, the model trained with CCM shows comparative performance with the original BERT on the General Language Understanding Evaluation benchmark at half of the training cost.Code is available at https://github.com/KoreaMGLEE/Conceptbased-curriculum-masking.
Translating Hanja Historical Documents to Contemporary Korean and English

The Annals of Joseon Dynasty (AJD) contain the daily records of the Kings of Joseon, the 500-year kingdom preceding the modern nation of Korea.The Annals were originally written in an archaic Korean writing system, 'Hanja', and were translated into Korean from 1968 to 1993.The resulting translation was however too literal and contained many archaic Korean words; thus, a new expert translation effort began in 2012.Since then, the records of only one king have been completed in a decade.In parallel, expert translators are working on English translation, also at a slow pace and produced only one king's records in English so far.Thus, we propose H2KE, a neural machine translation model, that translates historical documents in Hanja to more easily understandable Korean and to English.Built on top of multilingual neural machine translation, H2KE learns to translate a historical document written in Hanja, from both a full dataset of outdated Korean translation and a small dataset of more recently translated contemporary Korean and English.We compare our method against two baselines: a recent model that simultaneously learns to restore and translate Hanja historical document and a Transformer based model trained only on newly translated corpora.The experiments reveal that our method significantly outperforms the baselines in terms of BLEU scores for both contemporary Korean and English translations.We further conduct extensive human evaluation which shows that our translation is preferred over the original expert translations by both experts and non-expert Korean speakers.
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation

Sentence representations are essential in many NLP tasks operating at the sentence level.Recently, research attention has shifted towards learning how to represent sentences without any annotations, i.e., unsupervised representation learning.Despite the benefit of training without supervised data, there is still a performance penalty compared to supervised methods.Furthermore, the supervised-unsupervised performance gap widens as we reduce the model size.In this paper, we propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap, especially for smaller models.Utilizing the concept for knowledge distillation, we derive a distillation framework comprising two training objectives, control and generalize, called ConGen.Experiments on semantic textual similarity (STS), text classification (transfer), and natural language inference (NLI) tasks show that ConGen is on par with supervised training even on smaller models.Furthermore, our method consistently outperformed competitors on multilingual STS.
Deep Learning-based Binary Classification for Spam Detection in SMS Data: Addressing Imbalanced Data with Sampling Techniques

This research paper presents a deep learning-based approach for detecting spam in SMS (text) data. The study uses various models namely Dense, LSTM, Bi-LSTM, and GRU to conduct binary classification and predict spam text messages. To address the imbalanced data problem, the study employs undersampling, downsampling, and SMOTE sampling techniques on a public dataset of SMS messages from UCL datasets. The paper presents a study on detecting spam messages in SMS using a dense model. The researchers visualize the commonly used words in spam and non-spam messages and analyze their impact on the model's performance. The findings from this study demonstrate that the proposed dense model exhibits high accuracy in detecting spam messages on the test dataset. This suggests that the model can be useful in identifying spam messages in SMS.
Investigation of Professional, Spiritual and Emotional Intelligence on Organizational Learning

Continuous learning is the norm of the learning organizations. It works like for blood for knowledge workers to proceed and succeed. Moreover, it keeps employees’ firms, optimistic and enthusiastic. This study focused on the professional, spiritual, and emotional intelligence and their incorporation for organizational learning effectiveness and development. The study is based on the cross sectional quantitative data, which was collected through structured questionnaire using survey method. The questionnaire was adopted from previous studies. The study adopted convenient sampling technique for the collection of data. A total of 500 questionnaires were distributed, however, only 418 questions were received back, which were complete and considered for the inclusion in the study. Moreover, the study used SPSS for the data analysis. The study results admit that professional, spiritual and emotional intelligences have significant impact on organizational learning and effectiveness. Moreover, the study confirmed that learning organizations give better support to their and have higher level of support for their knowledge worker. The study recommends that all the learning organizations should develop a mechanism for the professional, spiritual and emotional development of their knowledge worker for their better throughput, productivity and efficiency of the organization.
Detection and Investigation Model for the Hard Disk Drive Attacks using FTK Imager

A computer hard disk drive (HDD) is a device that stores, organizes, and manages computer data.In general, it is used for system storage, in which the computer maintains its operating system and other programs.A hard disk drive can, however, be physically damaged as well as affected by software errors, data corruption, and viruses that are used by attackers to cause damage.This study aims to develop a detection and investigation model (DIM) for HDD to detect and investigate HDD attacks using the FTK Imager forensic tool.The design science method is adapted to develop and evaluate the DIM.The developed DIM consists of three main phases: detection, gathering, and analysis.In order to evaluate the capabilities of the developed DIM for HDD, a real scenario was used.According to the results, the DIM can detect and investigate the HDD easily using FTK Imager.Thus, organizations can use the developed DIM to detect, investigate, mitigate, or avoid HDD threats.
Whale Optimization-Driven Generative Convolutional Neural Network Framework for Anaemia Detection from Blood Smear Images

Anaemia is a frequent blood disorder marked by a reduction in the quantity of haemoglobin or the number of red blood cells in the blood.Quick and accurate anaemia detection is crucial for fast action and effective treatment.In this research, we provide a new structure called Whale Optimization-Driven Generative Convolutional Neural Network (WO-GCNN) for the detection of anaemia using blood smear pictures.To increase anaemia detection accuracy, the WO-GCNN system combines the strength of generative models and convolutional neural networks (CNNs).In order to create artificial blood smear images and learn the underlying data distribution, generative models, such as Generative Adversarial Networks (GANs), are used.Improve the functionality of the WO-GCNN system by applying the Whale Optimisation Algorithm (WOA), which is based on the hunting behaviours of humpback whales.To create the optimal set of CNN weights, the WOA effectively achieves a compromise between exploitation and exploration.The WO-GCNN framework accelerates convergence speed and increases overall performance of anaemia detection by incorporating the WOA into the training process.On a sizable dataset of blood smear pictures obtained from clinical settings, we assess the suggested WO-GCNN system.A highly accurate and effective approach for the early identification of anaemia is produced by combining generative models and CNNs with the WOA optimisation.By enabling early anaemia identification, the proposed WO-GCNN framework has the potential to have a substantial impact on the field of medical image analysis and enhance patient care.It can be a useful tool for medical personnel, supporting them in making decisions and giving anaemia patients urgent interventions.
Blockchain in Enterprise Application for Pharmaceutical Drug Traceability

A promising approach to building a distributed platform for shared data that is unchangeable, trustworthy, dependable, and accessible is blockchainbased drug traceability. The Hyperledger fabric and Hyperledger Besu methodologies are used in this article to provide a complete study and summary of the present state of drug traceability distribution research on the blockchain technology in enterprise platform. The Hyperledger Fabric and Besu blockchainbased platforms satisfy critical requirements for medication traceability, including privacy, reliability, transparency, security, authorisation, authentication, and scalability. Drug supply chain transactions are carried out skill fully and securely by the Hyperledger Fabric blockchain platform among a dispersed network of stakeholders. Various pharmaceutical stakeholder groups comprise this fabricenabled private, permissioned distributed network, which supports the efficient and secure execution of medication in supply chain transactions in enterprise system. This study also explains how blockchain technology is benefiting the healthcare industry while showcasing some of its disruptive aspects that have the potential to alter current enterprise application in drug tracking procedures.
Image Segmentation of Normal Pap Smear Thinprep using U-Net with Mobilenetv2 Encoder

Pap smear is a technique to detect changes in the cells in the uterine wall. With a Pap smear, a woman can be known to have cervical cancer or not. However, the problem of cancer screening on pap smear images is largely hindered by improper cell staining and overlapping cell images. For accurate Pap smear image segmentation, this study uses the U-Net method which is better for Pap smear image segmentation. This method integrates the MobilenetV2 network and converts ordinary convolution into deep split convolution to improve transmission and feature utilization by the network, and at the same time increase the speed of feature extraction. Then the segmentation results from MobilenetV2 produce accuracy in distinguishing the nucleus, cytoplasm, and background on the pap smear image. The dataset used in this study is a normal analogue image of the Pap smear image obtained from the RepoMedUNM Database. Initial data processing is done by digitizing the image, where analog data from the Pap smear is transformed into a digital image. Based on the results of research that has been carried out, namely segmentation of Pap smear images using U-Net with MobilenetV2 encoder, the accuracy value on differences in nucleus, cytoplasm, and background cells is 98%.
Construction of an Ontology-based Document Collection for the IT Job Offer in Morocco

Information Technology (IT) job offers are available on the web in a heterogeneous way.It is difficult for a candidate looking for an IT job to retrieve the exact information they need to locate the ideal match for their profile, without wasting time on useless searches.Traditional IT job search systems are based on simple keywords that are generally not adapted to provide detailed answers because they do not take into account semantic links.In this article, an ontology is developed to meet the expectations of IT profiles from the IT job descriptions accumulated and pre-annotated using the UBIAI tool.The classes and subclasses of the ontology are designed using the Protégé 5.5.0 editor.Then the properties of objects and data are defined to improve the ontology.The ontology results are validated using DL queries by asking a number of questions to retrieve the requested information for each IT profile, and the ontology answers all these questions adequately.Finally, various plugins are used to display an ontology in a graphical representation.
Cross-cultural consistency of image memorability

Abstract Memorability refers to the intrinsic property of an image that determines how well it is remembered or forgotten. Recent studies have found that memorability is highly consistent across individuals. However, most studies on memorability were conducted with participants from Western cultures, and the images used in memorability studies were culturally biased. Previous studies implicitly assumed that memorability would be held constant across different cultural groups; however, to the best of our knowledge, this has not yet been empirically investigated. In the current study, we recruited participants from South Korea and the US and examined whether image memorability was consistent across these two cultures. We found that South Korean participants showed greater memory performance for images rated highly memorable by US participants. The current findings provide converging evidence that image memorability is not fully accounted for by individual differences, and suggest the possibility of cross-cultural consistency in image memorability.
Integration of Blockchain and Edge Computing in Healthcare: Accountability and Collaboration

A decentralized, safe, and effective ecosystem is created in the healthcare industry through the integration of blockchain and edge computing. Secure data interchange, real-time analytics, enhanced privacy, and patient-centered treatment are all made possible. Realizing the full potential of integrating blockchain and edge computing for health care will need accountability and collaboration. It will make it possible to create reliable, secure, and cooperative healthcare organizations that will increase patient care, protect the confidentiality of information, and support cutting-edge applications for healthcare. Our Solution is to share data safely and cooperatively, improve patient confidentiality, and support healthcare data's ethical and accountable use. In this paper, we propose that combining blockchain technology with edge computing in healthcare is intended to improve accountability and teamwork. The methodologies used in integrating deep learning deploy various models on edge devices such as Q-Learning and Deep Q-Networks (DQN), SVM, etc. In conclusion, the application of edge computing and blockchain in the healthcare sector offers fascinating possibilities for cooperation and accountability. Healthcare systems may improve data security, privacy, interoperability, and real-time analytics by combining the advantages of the two technologies. The delivery of healthcare might change as a result of this integration, which could also foster cooperative research and eventually enhance patient outcomes.&#x0D;
An application of stereo matching algorithm based on transfer learning on robots in multiple scenes

Abstract Robot vision technology based on binocular vision holds tremendous potential for development in various fields, including 3D scene reconstruction, target detection, and autonomous driving. However, current binocular vision methods used in robotics engineering have limitations such as high costs, complex algorithms, and low reliability of the generated disparity map in different scenes. To overcome these challenges, a cross-domain stereo matching algorithm for binocular vision based on transfer learning was proposed in this paper, named Cross-Domain Adaptation and Transfer Learning Network (Ct-Net), which has shown valuable results in multiple robot scenes. First, this paper introduces a General Feature Extractor to extract rich general feature information for domain adaptive stereo matching tasks. Then, a feature adapter is used to adapt the general features to the stereo matching network. Furthermore, a Domain Adaptive Cost Optimization Module is designed to optimize the matching cost. A disparity score prediction module was also embedded to adaptively adjust the search range of disparity and optimize the cost distribution. The overall framework was trained using a phased strategy, and ablation experiments were conducted to verify the effectiveness of the training strategy. Compared with the prototype PSMNet, on KITTI 2015 benchmark, the 3 PE - fg of Ct-Net in all regions and non-occluded regions decreased by 19.3 and 21.1% respectively, meanwhile, on the Middlebury dataset, the proposed algorithm improves the sample error rate at least 28.4%, which is the Staircase sample. The quantitative and qualitative results obtained from Middlebury, Apollo, and other datasets demonstrate that Ct-Net significantly improves the cross-domain performance of stereo matching. Stereo matching experiments in real-world scenes have shown that it can effectively address visual tasks in multiple scenes.