Reviewerly/exHarmony huggingface dataset:
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning

Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names.Generative methods achieve remarkable performances in general domain EL with less memory usage while requiring expensive pre-training.Previous biomedical EL methods leverage synonyms from knowledge bases (KB) which is not trivial to inject into a generative method.In this work, we use a generative approach to model biomedical EL and propose to inject synonyms knowledge in it.We propose KBguided pre-training by constructing synthetic samples with synonyms and definitions from KB and require the model to recover concept names.We also propose synonyms-aware fine-tuning to select concept names for training, and propose decoder prompt and multisynonyms constrained prefix tree for inference.Our method achieves state-of-the-art results on several biomedical EL tasks without candidate selection which displays the effectiveness of proposed pre-training and fine-tuning strategies.
Mask removal : Face inpainting via attributes

Due to the outbreak of the COVID-19 pandemic, wearing masks in public areas has become an effective way to slow the spread of disease. However, it also brings some challenges to applications in daily life as half of the face is occluded. Therefore, the idea of removing masks by face inpainting appeared. Face inpainting has achieved promising performance but always fails to guarantee high-fidelity. In this paper, we present a novel mask removal inpainting network based on face attributes known in advance including nose, chubby, makeup, gender, mouth, beard and young, aiming to ensure the repaired face image is closer to ground truth. To achieve this, a dual pipeline network based on GANs has been proposed, one of which is a reconstructive path used in training that utilizes missing regions in ground truth to get prior distribution, while the other is a generative path for predicting information in the masked region. To establish the process of mask removal, we build a synthetic facial occlusion that mimics the real mask. Experiments show that our method not only generates faces more similarly aligned with real attributes, but also ensures semantic and structural rationality compared with state-of-the-art methods.
WikiDiverse: A Multimodal Entity Linking Dataset with Diversified Contextual Topics and Entity Types

Multimodal Entity Linking (MEL) which aims at linking mentions with multimodal contexts to the referent entities from a knowledge base (e.g., Wikipedia), is an essential task for many multimodal applications.Although much attention has been paid to MEL, the shortcomings of existing MEL datasets including limited contextual topics and entity types, simplified mention ambiguity, and restricted availability, have caused great obstacles to the research and application of MEL.In this paper, we present WIKIDiverse, a high-quality human-annotated MEL dataset with diversified contextual topics and entity types from Wikinews, which uses Wikipedia as the corresponding knowledge base.A well-tailored annotation procedure is adopted to ensure the quality of the dataset.Based on WIKIDiverse, a sequence of welldesigned MEL models with intra-modality and inter-modality attentions are implemented, which utilize the visual information of images more adequately than existing MEL models do.Extensive experimental analyses are conducted to investigate the contributions of different modalities in terms of MEL, facilitating the future research on this task.The dataset and baseline models are available at https://github.com/wangxw5/wikiDiverse.
A Novel Anchor-Free Method Based on FCOS + ATSS for Ship Detection in SAR Images

Ship detection in synthetic aperture radar (SAR) images has been widely applied in maritime management and surveillance. However, some issues still exist in SAR ship detection due to the complex surroundings, scattering interferences, and diversity of the scales. To address these issues, an improved anchor-free method based on FCOS + ATSS is proposed for ship detection in SAR images. First, FCOS + ATSS is applied as the baseline to detect ships pixel by pixel, which can eliminate the effect of anchors and avoid missing detections. Then, an improved residual module (IRM) and a deformable convolution (Dconv) are embedded into the feature extraction network (FEN) to improve accuracy. Next, a joint representation of the classification score and localization quality is used to address the inconsistent classification and localization of the FCOS + ATSS network. Finally, the detection head is redesigned to improve positioning performance. Experimental simulation results show that the proposed method achieves 68.5% average precision (AP), which outperforms other methods, such as single shot multibox detector (SSD), faster region CNN (Faster R-CNN), RetinaNet, representative points (RepPoints), and FoveaBox. In addition, the proposed method achieves 60.8 frames per second (FPS), which meets the real-time requirement.
Chaotic physical layer encryption scheme based on phase ambiguity for a DMT system

Chaotic encryption is a promising scheme for physical layer security. By solving the multi-dimensional chaotic equations and transforming the obtained results, both bit-level and symbol-level encryption can be realized. One of the mainstream symbol-level encryption solutions is the constellation shifting (CS) scheme, which treats the chaotic sequence as artificial noise and adds it to the QAM signal sequence to achieve encryption. However, this scheme has several technical flaws in practical application, in terms of computational complexity and coexistence with blind equalization algorithm and the probabilistic shaping (PS) technique. In this paper, we propose a novel symbol-level encryption scheme based on phase ambiguity (PA), which converts the two sequences originally used to generate artificial noise into a set of phase rotation keys and complex conjugate keys, so that the encrypted symbols are still on the ideal constellation point coordinates. Simulation verification is carried out in a discrete multi-tone (DMT) system with 64QAM modulation. Results show that the proposed scheme can fully retain the shaping gain brought by the PS technique and avoid the error convergence of the blind equalizer. Moreover, the time required to solve the chaotic equations is only 38% of the CS scheme. Experimental verification is carried out, and the obtained results once again prove the superiority of the proposed encryption algorithm, which is a practical alternative for future physical layer secure optical communications.
Metaverse: The Vision for the Future

In recent years, the notion of the Metaverse has become the focus of a growing body of work in the industry. However, there is no consensus on the conceptualization in academia. To date, much of this attention has revolved around technological challenges. However, what is notably missing from these discussions is a consideration of the human factors and social aspects that are considered more critical challenges within HCI. The aims of this SIG are as follows: Firstly, to provide a platform for researchers and practitioners to engage with the various definitions and the ways in which the Metaverse is developing. Secondly, to discuss the opportunities, challenges, and future possibilities in the context of HCI. This will lay the foundations to build a network for academics interested in the field for future multidisciplinary research relating to the Metaverse.
Language Learning Strategies Used by ESL Undergraduate Students

English is considered a second language (ESL) in Malaysia and is one of the prerequisite subjects at private universities.Students are able to identify the strategies yet still some struggle in learning the language.In order to master a language, students must be able to develop their own language learning techniques.The goal of this research is to find out more about the language learning strategies (LLS) used by students of a private university in Selangor.It involved 200 freshmen from five different faculties at a university.This quantitative survey used a questionnaire adapted from Strategy Inventory of Language Learning (SILL) by Oxford (1990) as the instrument.Descriptive statistics were administered to investigate the usage of LLS among the respondents.The findings showed that respondents tend to use their preferred LLS in improving their language skills.It is hoped that language educators would use the findings to focus their attention on facilitating English language learning programs that are appropriate for students' LLS, thus assisting them in developing their English language skills.
Sentiment Analysis of Customer Feedback in Online Food Ordering Services

Abstract Background: E-commerce websites have been established expressly as useful online communication platforms, which is rather significant. Through them, users can easily perform online transactions such as shopping or ordering food and sharing their experiences or feedback. Objectives: Customers’ views and sentiments are also analyzed by businesses to assess consumer behavior or a point of view on certain products or services. Methods/Approach: This research proposes a method to extract customers’ opinions and analyse sentiment based on a collected dataset, including 236,867 online Vietnamese reviews published from 2011 to 2020 on foody.vn and diadiemanuong.com. Then, machine learning models were applied and assessed to choose the optimal model. Results: The proposed approach has an accuracy of up to 91.5 percent, according to experimental study findings. Conclusions: The research results can help enterprise managers and service providers get insight into customers’ satisfaction with their products or services and understand their feelings so that they can make adjustments and correct business decisions. It also helps food e-commerce managers ensure a better e-commerce service design and delivery.
Transforming the Capabilities of Artificial Intelligence in GCC Financial Sector: A Systematic Literature Review

Identity and access management is a business process framework that makes it easier to maintain genuine user identities and regulate access to sensitive assets. The word “access control” refers to an organization’s policy for authorizing access procedures, the mechanisms that implement and enforce the policy, and the model that the policy and procedures are built on. Adopting new technology may give rise to specific cyber threats that decrease or degrade business operations. The paper has designed to discuss the artificial intelligence-based access control system as a necessary component of governing and safeguarding the financial sector’s information assets in the Gulf Cooperation Council (GCC) region. Due to the dynamic and complicated nature of security rules for access control, organizations that employ web-enabled remote access in conjunction with applications access deployed over several networks face various obstacles, including increased operational complexity and monitoring concerns. Organizations spend a vast budget on securing their business. As the industry trend has shifted to intelligent internet-based companies on the same side, the cyber threat has become a challenge for the researcher to find the solution. A systematic research is conducted to fill the gaps in the existing literature by picking the most relevant research papers (126) from the four most reputable online repositories based on the four research questions specified. These research topics aim to evaluate the current situation from many perspectives and provide new avenues for future study to be studied soon to maintain high security and authenticity inside financial sectors of the GCC’s countries.
LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking

Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history.Existing methods generally exploit the utterances of all dialogue turns to assign value for each slot.This could lead to suboptimal results due to the information introduced from irrelevant utterances in the dialogue history, which may be useless and can even cause confusion.To address this problem, we propose LUNA, a SLot-TUrN Alignment enhanced approach.It first explicitly aligns each slot with its most relevant utterance, then further predicts the corresponding value based on this aligned utterance instead of all dialogue utterances.Furthermore, we design a slot ranking auxiliary task to learn the temporal correlation among slots which could facilitate the alignment.Comprehensive experiments are conducted on multi-domain task-oriented dialogue datasets, i.e., MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2.The results show that LUNA achieves new state-ofthe-art results on these datasets.
Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies

The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%.
Logic-level Evidence Retrieval and Graph-based Verification Network for Table-based Fact Verification

Table-based fact verification task aims to verify whether the given statement is supported by the given semi-structured table.Symbolic reasoning with logical operations plays a crucial role in this task.Existing methods leverage programs that contain rich logical information to enhance the verification process.However, due to the lack of fully supervised signals in the program generation process, spurious programs can be derived and employed, which leads to the inability of the model to catch helpful logical operations.To address the aforementioned problems, in this work, we formulate the tablebased fact verification task as an evidence retrieval and reasoning framework, proposing the Logic-level Evidence Retrieval and Graphbased Verification network (LERGV).Specifically, we first retrieve logic-level program-like evidence from the given table and statement as supplementary evidence for the table.After that, we construct a logic-level graph to capture the logical relations between entities and functions in the retrieved evidence, and design a graph-based verification network to perform logic-level graph-based reasoning based on the constructed graph to classify the final entailment relation.Experimental results on the large-scale benchmark TABFACT show the effectiveness of the proposed approach 1 .
Subsidy and pricing strategies of an agri-food supply chain considering the application of Big Data and blockchain

Based on the advantages of Big Data and blockchain in food traceability area and information sharing area, it has attracted widespread attentions. However, it is not so popular in agricultural field, a vital reason is the scarcity of effective incentives. Government incentive as an important incentive measure is thought to be useful. To study the subsidy rules in the new background, we chose an agri-food supply chain with one producer and one retailer as research object and divided government incentive into direct incentive and indirect incentive. Then, considering the changes of consumer perceived safety on agri-food in the new environment, the demand function was revised. Furthermore, we proposed and analyzed three subsidy models and their benefit functions considering the information service inputs based on Big Data and blockchain (BBIS). Findings: (1) The subsidy models will not change the variation tendency of prices and benefits with the BBIS optimization coefficient, the BBIS investment costs from the producer and the retailer, the ascension of the unreliability coefficient of quality safety and the agri-food quality level. (2) When the subsidy coefficients about direct and indirect subsidies can meet a relationship, benefits of chain members in the indirect subsidy model are higher than them in the direct subsidy model. Findings offer a theoretical guidance for government departments to make and implement the subsidy strategies. In addition, for company, it can provide a theoretical guidance on setting pricing strategies in the new technology background.
Contagion effect of cryptocurrency on the securities market: a study of Bitcoin volatility using diagonal BEKK and DCC GARCH models

The fundamental aim of this study is to examine the contagion effect of Bitcoin on the National Securities Exchange, Shanghai Stock Exchange, London Stock Exchange, and Dow Jones Industrial Average by analyzing the volatility spillover and correlation between these markets to understand the short-term and long-term impact of this volatility ranging from the shocks during the period March 2017-May 2021. Irrespective of ups and downs happening in the cryptocurrency market, more investors are investing their money in the cryptocurrency market. This paper will contribute to the existing literature by studying volatility spillover in the market, its contagion effect, and to identify if there is a long-term and short-term impact between the Bitcoin and stock markets facilitating the transmission of volatility spillover. We employed the Diagonal BEKK and DCC MGARCH models to investigate the integration between Bitcoin and the stock markets. From the empirical analyses, we find the overall time-varying correlation between Bitcoin and the stock markets is low, indicating that Bitcoin can be taken as an asset to hedge against the risk of these stock markets. It was also evident that these stock markets responded more to the negative shocks during 2018 and 2021 than the positive shocks in the Bitcoin market. Our study may be helpful for investment decisions, academia, and policymakers.
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit

PaddleSpeech is an open-source all-in-one speech toolkit.It aims at facilitating the development and research of speech processing technologies by providing an easy-to-use commandline interface and a simple code structure.This paper describes the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to-speech tasks.PaddleSpeech achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods.It also provides recipes and pretrained models to quickly reproduce the experimental results in this paper.
DriNet: Dynamic Backdoor Attack against Automatic Speech Recognization Models

Automatic speech recognition (ASR) is popular in our daily lives (e.g., via voice assistants or voice input). Once its security attributes are destroyed, it poses as a severe threat to a user’s life and ‘property safety’. Prior research has demonstrated that ASR systems are vulnerable to backdoor attacks. A model embedded with a backdoor behaves normally on clean samples yet misclassifies malicious samples that contain triggers. Existing backdoor attacks have mostly been conducted in the image domain. However, they can not be applied in the audio domain because of poor transferability. This paper proposes a dynamic backdoor attack method against ASR models, named DriNet. Significantly, we designed a dynamic trigger generation network to craft a variety of audio triggers. It is trained jointly with the discriminative model incorporated with an attack success rate on poisoned samples and accuracy on clean samples. We demonstrate that DriNet achieves an attack success rate of 86.4% when infecting only 0.5% of the training set without reducing its accuracy. DriNet can still achieve comparable attack performance to backdoor attacks using static triggers, further enjoying richer attack patterns. We further evaluated DriNet’s resistance to a current state-of-the-art defense mechanism. The anomaly index of DriNet is more than 37.4% smaller than that of BadNets method. The triggers generated by DriNet are hard reverse, keeping DriNet from the detectors.
Dental Research Data Availability and Quality According to the FAIR Principles

According to the FAIR principles, data produced by scientific research should be findable, accessible, interoperable, and reusable-for instance, to be used in machine learning algorithms. However, to date, there is no estimate of the quantity or quality of dental research data evaluated via the FAIR principles. We aimed to determine the availability of open data in dental research and to assess compliance with the FAIR principles (or FAIRness) of shared dental research data. We downloaded all available articles published in PubMed-indexed dental journals from 2016 to 2021 as open access from Europe PubMed Central. In addition, we took a random sample of 500 dental articles that were not open access through Europe PubMed Central. We assessed data sharing in the articles and compliance of shared data to the FAIR principles programmatically. Results showed that of 7,509 investigated articles, 112 (1.5%) shared data. The average (SD) level of compliance with the FAIR metrics was 32.6% (31.9%). The average for each metric was as follows: findability, 3.4 (2.7) of 7; accessibility, 1.0 (1.0) of 3; interoperability, 1.1 (1.2) of 4; and reusability, 2.4 (2.6) of 10. No considerable changes in data sharing or quality of shared data occurred over the years. Our findings indicated that dental researchers rarely shared data, and when they did share, the FAIR quality was suboptimal. Machine learning algorithms could understand 1% of available dental research data. These undermine the reproducibility of dental research and hinder gaining the knowledge that can be gleaned from machine learning algorithms and applications.