Reviewerly/exHarmony huggingface dataset:
Visualization of AI Systems in Virtual Reality: A Comprehensive Review

This study provides a comprehensive review of the utilization of Virtual Reality (VR) in the context of Human-Computer Interaction (HCI) for visualizing Artificial Intelligence (AI) systems.Drawing from 18 selected studies, the results illuminate a complex interplay of tools, methods, and approaches, notably the prominence of VR engines like Unreal Engine and Unity.However, despite these tools, a universal solution for effective AI visualization remains elusive, reflecting the unique strengths and limitations of each technique.The application of VR for AI visualization across multiple domains is observed, despite challenges such as high data complexity and cognitive load.Moreover, it briefly discusses the emerging ethical considerations pertaining to the broad integration of these technologies.Despite these challenges, the field shows significant potential, emphasizing the need for dedicated research efforts to unlock the full potential of these immersive technologies.This review, therefore, outlines a roadmap for future research, encouraging innovation in visualization techniques, addressing identified challenges, and considering the ethical implications of VR and AI convergence.
Revolutionizing Foreign Exchange Market: A Critical Analysis of Blockchain's Opportunities and Challenges

The fast use of blockchain technology, cryptocurrencies, and electronic trading has had a profound influence on the world's foreign exchange market, creating both difficulties and potential for market regulation and exploitation. Blockchain has the capability to completely transform the foreign exchange business by bringing improved transparency, efficiency, and security. Blockchain allows for the construction of transparent, tamper-proof transaction records since it uses distributed ledger technology. Lately, Blockchain technology and Forex trading have experienced tremendous growth in popularity in recent years, but it's important to remember that they also include hazards that investors need to be aware of. The goal of this study is to determine how blockchain technology might be applied to boost the Forex market, highlighting the advantages of decentralization, efficiency, security, and transparency. This study aims to explore the potential contribution of blockchain technology to the foreign exchange market. The research employs a literature review and analysis approach to investigate the relationship between blockchain technology and the foreign exchange market. The findings of the study indicate that blockchain technology has the potential to revolutionize the foreign exchange market. The paper concludes that the use of distributed ledger technology enables the creation of transparent and tamper-proof transaction records, addressing existing challenges of transparency and security in the Forex market.
Effectiveness of Unified Computerized Reporting of Point-of-Care Glucose Meter Test

Sooin Choi, M.D., M.S., Dughyun Choi, M.D., Ph.D., Soo Jeong Choi, M.D., Ph.D., Jin Kuk Kim, M.D., Ph.D., You Kyoung Lee, M.D., Ph.D., and Yong-Wha Lee, M.D., Ph.D.. Ann Lab Med 2024;44:103-6. https://doi.org/10.3343/alm.2024.44.1.103
Editorial for the Special Issue on Advances in Augmented and Mixed Reality in Education

The Education Sciences Special Issue, Advances in Augmented and Mixed Reality in Education (Available online: https://www [...]
A ConvNet Structure Learning Spatiotemporal Features for Gesture Recognition

Gesture recognition makes Human-computer interaction more intuitive and natural, while recognizing complex dynamic gestures challenging.Building a powerful and efficient recognition model is very critical. In this paper, we propose a new network structure for dynamic gesture recognition: S3D + ConvLSTM + Mobilenet. Common RGB video frames are fed into this network, and then processed in three model sections sequentially. Our proposed methodology was rigorously evaluated using two prominent large-scale gesture recognition datasets, namely the Jester and IsoGD datasets. Experimental results demonstrated that our approach achieved performance that are comparable to cutting-edge methods, which significantly reduces the training and prediction calculation overhead by nearly 70%.
AN ANALYSIS OF DEIXIS IN CAMEROON SOCIAL MEDIA

This contribution focuses on the study of deictic expressions in Cameroon Social Media. It aims to find out the various types of deixis guided by Levinson’s (1983) theory of deixis composed of five types namely personal, temporal, spatial, social and discourse deixis. The study purposively uses a total of 464 francophone Cameroonian chats, 313 from WhatssAp, 118 from Facebook and 33 from. The data Messenger was collected through screenshots. To analyse the data obtained, the researcher applied a mixed method research .The findings show that the five types of deixis abound in all the selected social networks platforms. Besides, it is found that person deixis is predominant in the various posts and the that the second person ‘you’ represents the highest rate of deixis used by Cameroonians in WHAP, FBK and MSG platforms with an overall total of 104 (25.24%), 52 (42.98%) and 14 (48.28%) occurrences respectively. On the other hand, time or temporal deixis follows person deixis, being manifest through deictic words. Nonetheless, place or spatial deixis, attained via adverbs and demonstrative pronouns were also used so as to locate both writer and addressee in space. Finally, discourse deixis falls upon this, these and that, whereas, social deixis involves relational social deixis.
THE EVOLUTION OF SMART CONTRACT PLATFORMS: A LOOK AT CURRENT TRENDS AND FUTURE DIRECTIONS

Blockchain-based smart contracts are self-running computer programs that can automate a variety of commercial activities. Currently, the majority of these decentralized applications are developed using smart contract platforms like Polkadot, Cardano, and Ethereum. In addition to analyzing current technology developments and prospective future applications, this article provides a historical review of smart contract platforms. The study emphasizes the significance of smart contract platforms for supporting blockchain-based applications and enabling decentralized finance (DeFi). It also looks at the emergence of layer-2 scaling solutions, the introduction of non-fungible tokens (NFTs), and the growing need of interoperability among different smart contract platforms. The article also looks at the potential for multi-chain smart contracts, the effects of quantum computing, the integration of AI and ML technologies with smart contract platforms, and the potential for smart contract platforms to support decentralized autonomous organizations (DAOs). The difficulties of expanding smart contract platforms, the requirement for uniformity in the creation of smart contracts, and the potential for smart contract platforms to revolutionize sectors like healthcare, real estate, and supply chain management are also covered. The paper emphasizes the significance of ongoing innovation and development in smart contract platforms for the expansion of the blockchain ecosystem as it draws to a close.
Community-driven governance of FAIRness assessment: an open issue, an open discussion

<ns7:p>Although FAIR Research Data Principles are targeted at and implemented by different communities, research disciplines, and research stakeholders (data stewards, curators, etc.), there is no conclusive way to determine the level of FAIRness intended or required to make research artefacts (including, but not limited to, research data) Findable, Accessible, Interoperable, and Reusable. The FAIR Principles cover all types of digital objects, metadata, and infrastructures. However, they focus their narrative on data features that support their reusability. FAIR defines principles, not standards, and therefore they do not propose a mechanism to achieve the behaviours they describe in an attempt to be technology/implementation neutral. Various FAIR assessment metrics and tools have been designed to measure FAIRness. Unfortunately, the same digital objects assessed by different tools often exhibit widely different outcomes because of these independent interpretations of FAIR. This results in confusion among the publishers, the funders, and the users of digital research objects. Moreover, in the absence of a standard and transparent definition of what constitutes FAIR behaviours, there is a temptation to define existing approaches as being FAIR-compliant rather than having FAIR define the expected behaviours. This whitepaper identifies three high-level stakeholder categories -FAIR decision and policymakers, FAIR custodians, and FAIR practitioners - and provides examples outlining specific stakeholders' (hypothetical but anticipated) needs. It also examines possible models for governance based on the existing peer efforts, standardisation bodies, and other ways to acknowledge specifications and potential benefits. This whitepaper can serve as a starting point to foster an open discussion around FAIRness governance and the mechanism(s) that could be used to implement it, to be trusted, broadly representative, appropriately scoped, and sustainable. We invite engagement in this conversation in an open Google Group <ns7:ext-link xmlns:ns8="http://www.w3.org/1999/xlink" ext-link-type="uri" ns8:href="mailto:fair-assessment-governance@googlegroups.com">fair-assessment-governance@googlegroups.com</ns7:ext-link></ns7:p>
AdaDeepStream: streaming adaptation to concept evolution in deep neural networks

Abstract Typically, Deep Neural Networks (DNNs) are not responsive to changing data. Novel classes will be incorrectly labelled as a class on which the network was previously trained to recognise. Ideally, a DNN would be able to detect changing data and adapt rapidly with minimal true-labelled samples and without catastrophically forgetting previous classes. In the Online Class Incremental (OCI) field, research focuses on remembering all previously known classes. However, real-world systems are dynamic, and it is not essential to recall all classes forever. The Concept Evolution field studies the emergence of novel classes within a data stream. This paper aims to bring together these fields by analysing OCI Convolutional Neural Network (CNN) adaptation systems in a concept evolution setting by applying novel classes in patterns. Our system, termed AdaDeepStream , offers a dynamic concept evolution detection and CNN adaptation system using minimal true-labelled samples. We apply activations from within the CNN to fast streaming machine learning techniques. We compare two activation reduction techniques. We conduct a comprehensive experimental study and compare our novel adaptation method with four other state-of-the-art CNN adaptation methods. Our entire system is also compared to two other novel class detection and CNN adaptation methods. The results of the experiments are analysed based on accuracy, speed of inference and speed of adaptation. On accuracy, AdaDeepStream outperforms the next best adaptation method by 27% and the next best combined novel class detection/CNN adaptation method by 24%. On speed, AdaDeepStream is among the fastest to process instances and adapt.
Real Word Spelling Error Detection and Correction for Urdu Language

Non-word and real-word errors are generally two types of spelling errors. Non-word errors are misspelled words that are nonexistent in the lexicon while real-word errors are misspelled words that exist in the lexicon but are used out of context in a sentence. Lexicon-based lookup approach is widely used for non-word errors but it is incapable of handling real-word errors as they require contextual information. Contrary to the English language, real-word error detection and correction for low-resourced languages like Urdu is an unexplored area. This paper presents a real-word spelling error detection and correction approach for the Urdu language. We develop an extensive lexicon of 593,738 words and use this lexicon to develop a dataset for real-word errors comprising 125562 sentences and 2,552,735 words. Based on the developed lexicon and dataset, we then develop a contextual spell checker that detects and corrects real-word errors. For the real-word error detection phase, word-gram features are used along with five machine learning classifiers, achieving a precision, recall, and F1-score of 0.84,0.79, and 0.81 respectively. We also test the proposed approach with a 40% error density. For real-word error correction, the Damerau-Levenshtein distance is used along with the n-gram model for further ranking of the suggested candidate words, achieving an accuracy of up to 83.67%.
UNDERSTANDING CHALLENGES OF FOREIGN LANGUAGE TEACHERS: UKRAINIAN PERSPECTIVE

Challenges encountered by foreign language teachers remain an understudied issue in the context of Ukraine, awaiting a more systematic empirical response. The given paper aimed to probe for the challenges of Ukrainian language teachers and consider their possible causes. To this end, an exploratory study was carried out with the participation of 44 foreign language teachers. The qualitative data collected through an open interview were subjected to content analysis. A range of potentially challenging aspects of language teaching and their causes were derived from the data analyzed. The findings indicated that the respondents were inclined to identify the challenges of language teaching with the aspects inherent to the profession. In other words, they commonly referred to difficulties deriving from the characteristics of their competence and the nature of their professional activity. The interviewees repeatedly referred to the challenges of personalizing instruction, its planning and management, establishing rapport with learners, attending to discipline issues, motivating learners, and teaching mixed-ability classes. The teachers also reflected on the challenges of developing various language competencies, like explaining grammar in clear terms and teaching speaking. Meanwhile, the respondents rarely mentioned causes of difficulties external to them, for instance, language policy. However, they frequently commented on the problems caused by the lack of resources, in particular, indispensable in distance learning. Thus, the teachers’ overriding concern stemmed from having to deal with learners, which outweighed the significance of developing their language skills. Overall, the qualitative snapshots of the foreign language teaching challenges help discern areas deserving more specific attention in teacher education. One of the study’s implications is the need to embrace the views of practitioners in organizing effective teacher education. Otherwise, risks are high of furnishing them with preparation experiences divorced from their actual needs.
JobXMLC: EXtreme Multi-Label Classification of Job Skills with Graph Neural Networks

Writing a good job description is an important step in the online recruitment process to hire the best candidates. Most recruiters forget to include some relevant skills in the job description. These missing skills affect the performance of recruitment tasks such as job suggestions, job search, candidate recommendations, etc. Existing approaches are limited to contextual modelling, do not exploit inter-relational structures like job-job and job-skill relationships, and are not scalable. In this paper, we exploit these structural relationships using a graph-based approach. We propose a novel skill prediction framework called JobXMLC, which uses graph neural networks with skill attention to predict missing skills using job descriptions. JobXMLC enables joint learning over a job-skill graph consisting of 22.8K entities (jobs and skills) and 650K relationships. We experiment with real-world recruitment datasets to evaluate our proposed approach. We train JobXMLC on 20,298 job descriptions and 2,548 skills within 30 minutes on a single GPU machine. JobXMLC outperforms the state-of-the-art approaches by 6% in precision and 3% in recall. JobXMLC is 18X faster for training task and up to 634X faster in skill prediction on benchmark datasets enabling JobXMLC to scale up on larger datasets.
A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets

Low-quality data can cause downstream problems in high-stakes applications.Data-centric approach emphasizes on improving dataset quality to enhance model performance.Highquality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a large number of domain experts for their creation.Thus, it is vital to ensure high-quality domain-specific training data.In this paper, we propose a framework for enhancing the data quality of original datasets 1 .We applied the proposed framework to four biomedical datasets and showed relative improvement of up to 33%/40% for fine-tuning of retrieval/reader models on the BioASQ dataset when using back translation to enhance the original dataset quality.
Fine-Tuning Deteriorates General Textual Out-of-Distribution Detection by Distorting Task-Agnostic Features

Detecting out-of-distribution (OOD) inputs is crucial for the safe deployment of natural language processing (NLP) models. Though existing methods, especially those based on the statistics in the feature space of fine-tuned pre-trained language models (PLMs), are claimed to be effective, their effectiveness on different types of distribution shifts remains underexplored. In this work, we take the first step to comprehensively evaluate the mainstream textual OOD detection methods for detecting semantic and non-semantic shifts. We find that: (1) no existing method behaves well in both settings; (2) fine-tuning PLMs on in-distribution data benefits detecting semantic shifts but severely deteriorates detecting non-semantic shifts, which can be attributed to the distortion of task-agnostic features. To alleviate the issue, we present a simple yet effective general OOD score named GNOME that integrates the confidence scores derived from the task-agnostic and task-specific representations. Experiments show that GNOME works well in both semantic and non-semantic shift scenarios, and further brings significant improvement on two cross-task benchmarks where both kinds of shifts simultaneously take place. Our code is available at https://github.com/lancopku/GNOME.
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training

Large-scale vision-language pre-trained (VLP) models are prone to hallucinate non-existent visual objects when generating text based on visual information. In this paper, we systematically study the object hallucination problem from three aspects. First, we examine recent state-of-the-art VLP models, showing that they still hallucinate frequently and models achieving better scores on standard metrics (e.g., CIDEr) could be more unfaithful. Second, we investigate how different types of image encoding in VLP influence hallucination, including region-based, grid-based, and patch-based. Surprisingly, we find that patch-based features perform the best and smaller patch resolution yields a non-trivial reduction in object hallucination. Third, we decouple various VLP objectives and demonstrate that token-level image-text alignment and controlled generation are crucial to reducing hallucination. Based on that, we propose a simple yet effective VLP loss named ObjMLM to further mitigate object hallucination. Results show that it reduces object hallucination by up to 17.4% when tested on two benchmarks (COCO Caption for in-domain and NoCaps for out-of-domain evaluation).
Combining Parameter-efficient Modules for Task-level Generalisation

A modular design encourages neural models to disentangle and recombine different facets of knowledge to generalise more systematically to new tasks. In this work, we assume that each task is associated with a subset of latent skills from an (arbitrary size) inventory. In turn, each skill corresponds to a parameter-efficient (sparse / low-rank) model adapter. By jointly learning adapters and a routing function that allocates skills to each task, the full network is instantiated as the average of the parameters of active skills. We propose several inductive biases that encourage re-usage and composition of the skills, including variable-size skill allocation and a dual-speed learning rate. We evaluate our latent-skill model in two main settings: 1) multitask reinforcement learning for instruction following on 8 levels of the BabyAI platform; and 2) few-shot fine-tuning of language models on 160 NLP tasks of the CrossFit benchmark. We find that the modular design of our network enhances sample efficiency in reinforcement learning and few-shot generalisation in supervised learning, compared to a series of baselines. These include models where parameters are fully shared, task-specific, conditionally generated (HyperFormer), or sparse mixture-of-experts (TaskMoE).
BEVERS: A General, Simple, and Performant Framework for Automatic Fact Verification

Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final claim classification, however, we spend considerable effort ensuring optimal performance for each component. The results are that BEVERS achieves the highest FEVER score and label accuracy among all systems, published or unpublished. We also apply this pipeline to another fact verification dataset, Scifact, and achieve the highest label accuracy among all systems on that dataset as well. We also make our full code available.
Overview of PAN 2023: Authorship Verification, Multi-Author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection

The paper gives a brief overview of three shared tasks which have been organized at the PAN 2023 lab on digital text forensics and stylometry hosted at the CLEF 2023 conference. The tasks include authorship verification across discourse types, multi-author writing style analysis, profiling cryptocurrency influencers with few-shot learning, and trigger detection. Authorship verification and multi-author analysis continue and advance from past editions of PAN and influencer profiling and trigger detection are new tasks with novel research questions and evaluation resources. All four tasks alilgn with the goals of all shared tasks at PAN: to advance the state of the art in text forensics and stylometry while ensuring objective evaluation on newly developed benchmark datasets.
SDAT-Former: Foggy Scene Semantic Segmentation Via A Strong Domain Adaptation Teacher

Semantic Segmentation in the Foggy Scenes (SSFS) remains a difficult problem due to uncertainties caused by imperfect observations. Considering the success of domain adaptive semantic segmentation in the clear scenes, we believe it is reasonable to transfer the knowledge from the clear images to the foggy images. Different from the previous methods which mainly focus on narrowing the domain gap caused by fog, we try to transfer both the knowledge of fog factors and style factors between different domains to a "teacher" seg-mentor, thus the latter can generate better pseudo labels to supervise the student segmentor (main segmentor) to close the domain gap. Our method achieved better performance on ACDC and Foggy Zurich benchmark compared with state-of-the-art works.