Reviewerly/exHarmony huggingface dataset:
Implementing Blockchain Technology in Supply Chain Management

This paper is aimed at studying the factors influencing the implementation of blockchain in supply chain management to solve the current issues faced in the supply chain ecosystem. Supply chains are part and parcel of every business and have multiple inefficiencies in the system. Some of these inefficiencies can be managed by usage of blockchain Platform .Technology, intracompany synergies, intercompany collaboration, extrinsic factors, and innovation are critically evaluated for adoption of blockchain in supply chain. A pilot study is conducted in form survey for analysis of these factors. Hypotheses are derived for these factors for quantitative research. Subsequently these hypotheses are examined with the help of ADANCO2.3 for structural equation modelling. As an outcome, it is evident that Innovation and Extrinsic factors are significantly impacting the adoption of blockchain in supply chain management.
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization

The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain adaptation in summarization generally rely on large-scale pre-trainings.To explore the lightweight fine-tuning methods for domain adaptation of dialogue summarization, in this paper, we propose an efficient and generalizable Domain-Oriented Prefix-tuning model, which utilizes a domain word initialized prefix module to alleviate domain entanglement and adopts discrete prompts to guide the model to focus on key contents of dialogues and enhance model generalization.We conduct zero-shot experiments and build domain adaptation benchmarks on two multi-domain dialogue summarization datasets, TODSum and QMSum.Adequate experiments and qualitative analysis prove the effectiveness of our methods.* The first two authors contribute equally.
Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering

Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself.Answering complex questions that require multi-hop reasoning under weak supervision is considered as a challenging problem since i) no supervision is given to the reasoning process and ii) highorder semantics of multi-hop knowledge facts need to be captured.In this paper, we introduce a concept of hypergraph to encode highlevel semantics of a question and a knowledge base, and to learn high-order associations between them.The proposed model, Hypergraph Transformer, constructs a question hypergraph and a query-aware knowledge hypergraph, and infers an answer by encoding inter-associations between two hypergraphs and intra-associations in both hypergraph itself.Extensive experiments on two knowledgebased visual QA and two knowledge-based textual QA demonstrate the effectiveness of our method, especially for multi-hop reasoning problem.
ByT5 model for massively multilingual grapheme-to-phoneme conversion

In this study, we tackle massively multilingual grapheme-to-phoneme conversion through implementing G2P models based on ByT5. We have curated a G2P dataset from various sources that covers around 100 languages and trained large-scale multilingual G2P models based on ByT5. We found that ByT5 operating on byte-level inputs significantly outperformed the token-based mT5 model in terms of multilingual G2P. Pairwise comparison with monolingual models in these languages suggests that multilingual ByT5 models generally lower the phone error rate by jointly learning from a variety of languages. The pretrained model can further benefit low resource G2P through zero-shot prediction on unseen languages or provides pretrained weights for finetuning, which helps the model converge to a lower phone error rate than randomly initialized weights. To facilitate future research on multilingual G2P, we make available our code and pretrained multilingual G2P models at: https://github.com/lingjzhu/CharsiuG2P.
Improving Fairness in Speaker Verification via Group-Adapted Fusion Network

Modern speaker verification models use deep neural networks to encode utterance audio into discriminative embedding vectors. During the training process, these networks are typically optimized to differentiate arbitrary speakers. This learning process biases the learning of fine voice characteristics towards dominant demographic groups, which can lead to an unfair performance disparity across different groups. This is observed especially with underrepresented demographic groups sharing similar voice characteristics. In this work, we investigate the fairness of speaker verification models on controlled datasets with imbalanced gender distributions, providing direct evidence that model performance suffers for underrepresented groups. To mitigate this disparity we propose the group-adapted fusion network (GFN) architecture, a modular architecture based on group embedding adaptation and score fusion. We show that our method alleviates model unfairness by improving speaker verification both overall and for individual groups. Given imbalanced group representation in training, our proposed method achieves overall equal error rate (EER) reduction of 9.6% to 29.0% relative, reduces minority group EER by 13.7% to 18.6%, and results in 20.0% to 25.4% less EER disparity, compared to baselines. The approach is applicable to other types of training data skew in speaker recognition systems.
Designing Multi-Modal Embedding Fusion-Based Recommender

Recommendation systems have lately been popularised globally. However, often they need to be adapted to particular data and the use case. We have developed a machine learning-based recommendation system, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our system supports multiple types of interaction data with various modalities of metadata through a multi-modal fusion of different data representations. We deployed the system into numerous e-commerce stores, e.g., food and beverages, shoes, fashion items, and telecom operators. We present our system and its main algorithms for data representations and multi-modal fusion. We show benchmark results on open datasets that outperform the state-of-the-art prior work. We also demonstrate use cases for different e-commerce sites.
Factors Influencing the Implementation of Gamification for Learning in Information Systems Education

Current technological advancements urge institutions to generate graduates capable of contributing to the industrial world. Information Systems is one of the scientific subjects whose graduates play a significant part in the advancement of modern technology. As a result, lecturers in Information Systems education, particularly at the undergraduate level, must be able to retain and even improve students' interest in studying this field. Gamification is one method that can be used to accomplish this purpose. The goal of this study is to identify factors that determine the successful implementation of gamification for learning, particularly in the context of Information Systems Education at the undergraduate level. This study's methodology is based on a systematic literature review technique from previous studies. Motivation, engagement, perceived utility, game design, and student perspective were identified as elements influencing the effective use of gamification for learning, particularly in the field of Information Systems education.
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language

More than 43% of the languages spoken in the world are endangered, and language loss currently occurs at an accelerated rate because of globalization and neocolonialism.Saving and revitalizing endangered languages has become very important for maintaining the cultural diversity on our planet.In this work, we focus on discussing how NLP can help revitalize endangered languages.We first suggest three principles that may help NLP practitioners to foster mutual understanding and collaboration with language communities, and we discuss three ways in which NLP can potentially assist in language education.We then take Cherokee, a severely-endangered Native American language, as a case study.After reviewing the language's history, linguistic features, and existing resources, we (in collaboration with Cherokee community members) arrive at a few meaningful ways NLP practitioners can collaborate with community partners.We suggest two approaches to enrich the Cherokee language's resources with machine-in-the-loop processing, and discuss several NLP tools that people from the Cherokee community have shown interest in.We hope that our work serves not only to inform the NLP community about Cherokee, but also to provide inspiration for future work on endangered languages in general. 1
Use of GeoGebra in Teaching and Learning Geometric Transformation in School Mathematics

The use of GeoGebra in teaching geometric transformations was investigated in this study. GeoGebra is a math software available in over 100 plus languages, both online and offline. GeoGebra is a useful application to improve and enrich mathematics teaching and learning by allowing students to visualize mathematical concepts, which is extremely useful for mathematical experiments and discoveries at all educational levels, from elementary school to university. The theoretical referents used in this article are cognitive learning theory and Vygotsky's social learning theory. Twenty students (twelve boys and eight girls) in grade IX from a private school in Kathmandu Valley, Nepal, were taught mathematics using a variety of specific instances of transformation highlighted in this study, including reflection, rotation, translation, and dilation. This research used a qualitative research method called a teaching experiment to examine the use of GeoGebra in eleven episodes. Students were aided in visualizing abstract concepts of change by using relevant images, photos, and animations of GeoGebra-created objects. The findings of a classroom experiment are GeoGebra is an easy-to-use application, GeoGebra allows for discovery learning, GeoGebra encourages collaborative learning, and GeoGebra to visualize geometric transformations. Likewise, GeoGebra aids in the teaching and comprehension of abstract transformation concepts. These findings show how students can develop into active knowledge builders when GeoGebra is used in mathematics classes. They also communicate with one another, keep track of the change process, and respect their instructors' authority in such classes. It is an important instructional tool that supports the educational system's transition from a teacher-centered to a learner-centered approach by complementing the traditional lecture method of teaching mathematics.
LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval

In this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training.Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively trains the query and document encoders with a cache mechanism.ICoL not only enlarges the number of negative instances but also keeps representations of cached examples in the same hidden space.We then propose Lexicon-Enhanced Dense Retrieval (LEDR) as a simple yet effective way to enhance dense retrieval with lexical matching.We evaluate LaPraDoR on the recently proposed BEIR benchmark, including 18 datasets of 9 zeroshot text retrieval tasks.Experimental results show that LaPraDoR achieves state-of-the-art performance compared with supervised dense retrieval models, and further analysis reveals the effectiveness of our training strategy and objectives.Compared to re-ranking, our lexiconenhanced approach can be run in milliseconds (22.5× faster) while achieving superior performance.
Plane Geometry Diagram Parsing

Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship. In this paper, we propose a powerful diagram parser based on deep learning and graph reasoning. Specifically, a modified instance segmentation method is proposed to extract geometric primitives, and the graph neural network (GNN) is leveraged to realize relation parsing and primitive classification incorporating geometric features and prior knowledge. All the modules are integrated into an end-to-end model called PGDPNet to perform all the sub-tasks simultaneously. In addition, we build a new large-scale geometry diagram dataset named PGDP5K with primitive level annotations. Experiments on PGDP5K and an existing dataset IMP-Geometry3K show that our model outperforms state-of-the-art methods in four sub-tasks remarkably. Our code, dataset and appendix material are available at https://github.com/mingliangzhang2018/PGDP.
Raising the Bar in Graph-level Anomaly Detection

Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks. While most research has focused on anomaly detection for visual data such as images, where high detection accuracies have been obtained, existing deep learning approaches for graphs currently show considerably worse performance. This paper raises the bar on graph-level anomaly detection, i.e., the task of detecting abnormal graphs in a set of graphs. By drawing on ideas from self-supervised learning and transformation learning, we present a new deep learning approach that significantly improves existing deep one-class approaches by fixing some of their known problems, including hypersphere collapse and performance flip. Experiments on nine real-world data sets involving nine techniques reveal that our method achieves an average performance improvement of 11.8% AUC compared to the best existing approach.
Errors, Irregularities, and Misdirection: Cue Utilisation and Cognitive Reflection in the Diagnosis of Phishing Emails

The study aimed to examine the role of, and potential interplay between, cue utilisation and cognitive reflection in email users’ ability to accurately (and efficiently) differentiate between phishing and genuine emails. 145 participants completed the Cognitive Reflection Test (CRT), a phishing diagnostic task, and the Expert Intensive Skill Evaluation (EXPERTise 2.0) battery, which provided a gauge of users’ cue utilisation in the domain. The results revealed an interaction between users’ cognitive utilisation and cue reflection, whereby users low in both facets performed significantly worse in diagnosing phishing emails than all other groups. Further, those participants with both higher cue utilisation and cognitive reflection took significantly longer to make their diagnosis. It is concluded that a high level of cognitive reflection was able to compensate for a lower level of cue utilisation, and vice versa. Participants reported using seven types of cue during diagnosis, however, there was no significant relationship between the types of cues used and users’ level of cue utilisation. Taken together, the findings have implications to the design of user-level interventions in relation to the identification of vulnerable users, as well as the need to consider training approaches that extend beyond the use of simple cue inventories.
Multiscale and Hierarchical Feature-Aggregation Network for Segmenting Medical Images

We propose an encoder-decoder architecture using wide and deep convolutional layers combined with different aggregation modules for the segmentation of medical images. Initially, we obtain a rich representation of features that span from low to high levels and from small to large scales by stacking multiple k × k kernels, where each k × k kernel operation is split into k × 1 and 1 × k convolutions. In addition, we introduce two feature-aggregation modules-multiscale feature aggregation (MFA) and hierarchical feature aggregation (HFA)-to better fuse information across end-to-end network layers. The MFA module progressively aggregates features and enriches feature representation, whereas the HFA module merges the features iteratively and hierarchically to learn richer combinations of the feature hierarchy. Furthermore, because residual connections are advantageous for assembling very deep networks, we employ an MFA-based long residual connections to avoid vanishing gradients along the aggregation paths. In addition, a guided block with multilevel convolution provides effective attention to the features that were copied from the encoder to the decoder to recover spatial information. Thus, the proposed method using feature-aggregation modules combined with a guided skip connection improves the segmentation accuracy, achieving a high similarity index for ground-truth segmentation maps. Experimental results indicate that the proposed model achieves a superior segmentation performance to that obtained by conventional methods for skin-lesion segmentation, with an average accuracy score of 0.97 on the ISIC-2018, PH2, and UFBA-UESC datasets.
Clues Before Answers: Generation-Enhanced Multiple-Choice QA

A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework.By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal.However, a side effect of twisting a generation target to fit the classification nature of MCQA is the underutilization of the decoder and the knowledge that can be decoded.To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC.It generates a clue from the question and then leverages the clue to enhance a reader for MCQA.It outperforms textto-text models on multiple MCQA datasets.
Co-Designing the User Experience of Location-Based Games for a Network of Museums: Involving Cultural Heritage Professionals and Local Communities

The design of location-based games (LBGs) for cultural heritage should ensure the active participation and contribution of local communities and heritage professionals to achieve contextual relevance, importance, and content validity. This paper presents an approach and methods of the participatory and co-design of LBGs that promote awareness and learning about the intangible cultural heritage of craftsmanship and artisanal technology throughout a long-term project from sensitization to implementation. Following the design thinking process, we outline the participatory methods (and reflect on results and lessons learnt) of involving cultural heritage professionals, local communities, and visitors (users) of museums and cultural settlements, mainly: field visits, design workshops, field playtesting, and field studies. We discuss issues of participatory design that we experienced throughout the project such as participant centrality and representativeness, producing tangible output from meetings, co-creation of content via playtesting, and implications from the pandemic. This work contributes a case of participatory and co-design of LBGs for cultural heritage that is characterized by longevity and engagement throughout the design process for three LBGs of a museum network in different cultural sites.
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation

Token-level adaptive training approaches can alleviate the token imbalance problem and thus improve neural machine translation, through re-weighting the losses of different target tokens based on specific statistical metrics (e.g., token frequency or mutual information).Given that standard translation models make predictions on the condition of previous target contexts, we argue that the above statistical metrics ignore target context information and may assign inappropriate weights to target tokens.While one possible solution is to directly take target contexts into these statistical metrics, the target-context-aware statistical computing is extremely expensive, and the corresponding storage overhead is unrealistic.To solve the above issues, we propose a target-context-aware metric, named conditional bilingual mutual information (CBMI), which makes it feasible to supplement target context information for statistical metrics.Particularly, our CBMI can be formalized as the log quotient of the translation model probability and language model probability by decomposing the conditional joint distribution.Thus CBMI can be efficiently calculated during model training without any pre-specific statistical calculations and large storage overhead.Furthermore, we propose an effective adaptive training approach based on both the token-and sentence-level CBMI.Experimental results on WMT14 English-German and WMT19 Chinese-English tasks show our approach can significantly outperform the Transformer baseline and other related methods.
Detection of Adversarial DDoS Attacks Using Generative Adversarial Networks with Dual Discriminators

DDoS (Distributed Denial of Service) has emerged as a serious and challenging threat to computer networks and information systems’ security and integrity. Before any remedial measures can be implemented, DDoS assaults must first be detected. DDoS attacks can be identified and characterized with satisfactory achievement employing ML (Machine Learning) and DL (Deep Learning). However, new varieties of aggression arise as the technology for DDoS attacks keep evolving. This research explores the impact of a new incarnation of DDoS attack–adversarial DDoS attack. There are established works on ML-based DDoS detection and GAN (Generative Adversarial Network) based adversarial DDoS synthesis. We confirm these findings in our experiments. Experiments in this study involve the extension and application of the GAN, a machine learning framework with symmetric form having two contending neural networks. We synthesize adversarial DDoS attacks utilizing Wasserstein Generative Adversarial Networks featuring Gradient Penalty (GP-WGAN). Experiment results indicate that the synthesized traffic can traverse the detection systems such as k-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP) and Random Forest (RF) without being identified. This observation is a sobering and pessimistic wake-up call, implying that countermeasures to adversarial DDoS attacks are urgently needed. To this problem, we propose a novel DDoS detection framework featuring GAN with Dual Discriminators (GANDD). The additional discriminator is designed to identify adversary DDoS traffic. The proposed GANDD can be an effective solution to adversarial DDoS attacks, as evidenced by the experimental results. We use adversarial DDoS traffic synthesized by GP-WGAN to train GANDD and validate it alongside three other DL technologies: DNN (Deep Neural Network), LSTM (Long Short-Term Memory) and GAN. GANDD outperformed the other DL models, demonstrating its protection with a TPR of 84.3%. A more sophisticated test was also conducted to examine GANDD’s ability to handle unseen adversarial attacks. GANDD was evaluated with adversarial traffic not generated from its training data. GANDD still proved effective with a TPR around 71.3% compared to 7.4% of LSTM.