Reviewerly/exHarmony huggingface dataset:
Contrastive Learning for Knowledge Tracing

Knowledge tracing is the task of understanding student’s knowledge acquisition processes by estimating whether to solve the next question correctly or not. Most deep learning-based methods tackle this problem by identifying hidden representations of knowledge states from learning histories. However, due to the sparse interactions between students and questions, the hidden representations can be easily over-fitted and often fail to capture student’s knowledge states accurately. This paper introduces a contrastive learning framework for knowledge tracing that reveals semantically similar or dissimilar examples of a learning history and stimulates to learn their relationships. To deal with the complexity of knowledge acquisition during learning, we carefully design the components of contrastive learning, such as architectures, data augmentation methods, and hard negatives, taking into account pedagogical rationales. Our extensive experiments on six benchmarks show statistically significant improvements from the previous methods. Further analysis shows how our methods contribute to improving knowledge tracing performances.
Recommendation Unlearning

Recommender systems provide essential web services by learning users’ personal preferences from collected data. However, in many cases, systems also need to forget some training data. From the perspective of privacy, users desire a tool to erase the impacts of their sensitive data from the trained models. From the perspective of utility, if a system’s utility is damaged by some bad data, the system needs to forget such data to regain utility. While unlearning is very important, it has not been well-considered in existing recommender systems. Although there are some researches have studied the problem of machine unlearning, existing methods can not be directly applied to recommendation as they are unable to consider the collaborative information.
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations

Abstract The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature—at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset—consisting of over 30 000 articles with manually reviewed topics—was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/
Attention in Attention: Modeling Context Correlation for Efficient Video Classification

Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current research on video attention generally focuses on adopting a specific aspect of contexts (e.g., channel, spatial/temporal, or global context) to refine the features and neglects their underlying correlation when computing attentions. This leads to incomplete context utilization and hence bears the weakness of limited performance improvement. To tackle the problem, this paper proposes an efficient attention-in-attention (AIA) method for element-wise feature refinement, which investigates the feasibility of inserting the channel context into the spatio-temporal attention learning module, referred to as CinST, and also its reverse variant, referred to as STinC. Specifically, we instantiate the video feature contexts as dynamics aggregated along a specific axis with global average and max pooling operations. The workflow of an AIA module is that the first attention block uses one kind of context information to guide the gating weights calculation of the second attention that targets at the other context. Moreover, all the computational operations in attention units act on the pooled dimension, which results in quite few computational cost increase (< 0.02%). To verify our method, we densely integrate it into two classical video network backbones and conduct extensive experiments on several standard video classification benchmarks. The source code of our AIA is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/haoyanbin918/Attention-in-Attention</uri> .
6Blocks: 6G-enabled trust management scheme for decentralized autonomous vehicles

In this paper, we propose a 6G-envisioned blockchain (BC)-based scheme for cellular vehicle-to-everything (C-V2X) ecosystems (termed as 6Blocks), which optimizes the edge-resource provisioning of 5G C-V2X nodes via network function virtualization (NFV)-enabled infrastructure. 6Blocks operates in three phases. In the first phase, a secure data aggregation through 6G sensors is performed at the data plane. Then, in the second phase, the aggregated data is sent to the 6G-enabled NFV control plane to leverage resource allocation to connected autonomous smart vehicles (CASVs) through edge nodes via service container operations. In the third phase, management plane operations are designed that includes registration and auction smart contracts (SC) for connected autonomous vehicle owner (CAVO) and CASVs over the BC with public/private pairs fetched from interplanetary file systems (IPFS). The proposed scheme is compared against existing NFV schemes like distant, edge, and SDN-based edge control. At 20 CASVs, 6Blocks achieves an edge-service latency of 0.05ms against 0.76ms, 0.27, and 0.15ms for cloud, edge, and SDN-edge schemes, respectively, with high connection density of 107 nodes, at maximum utilized IPFS bandwidth of 0.2 Mbps. Then, we validate the trust of our model through the automated validation of the internet security protocol and applications (AVISPA) tool and modeled the vehicle and sensor nodes. The obtained results show the efficacy of the proposed scheme against existing state-of-the-art approaches with respect to parameters such as network, security, transaction costs, and auction.
ScalAR: Authoring Semantically Adaptive Augmented Reality Experiences in Virtual Reality

Augmented Reality (AR) experiences tightly associate virtual contents with environmental entities. However, the dissimilarity of different environments limits the adaptive AR content behaviors under large-scale deployment. We propose ScalAR, an integrated workflow enabling designers to author semantically adaptive AR experiences in Virtual Reality (VR). First, potential AR consumers collect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer authors the AR contents’ semantic associations and validates the design while being immersed in the provided scenes. We adopt a decision-tree-based algorithm to fit the designer’s demonstrations as a semantic adaptation model to deploy the authored AR experience in a physical scene. We further showcase two application scenarios authored by ScalAR and conduct a two-session user study where the quantitative results prove the accuracy of the AR content rendering and the qualitative results show the usability of ScalAR.
Social media text analytics of Malayalam–English code-mixed using deep learning

Zigzag conversational patterns of contents in social media are often perceived as noisy or informal text. Unrestricted usage of vocabulary in social media communications complicates the processing of code-mixed text. This paper accentuates two major aspects of code mixed text: Offensive Language Identification and Sentiment Analysis for Malayalam-English code-mixed data set. The proffered framework addresses 3 key points apropos these tasks-dependencies among features created by embedding methods (Word2Vec and FastText), comparative analysis of deep learning algorithms (uni-/bi-directional models, hybrid models, and transformer approaches), relevance of selective translation and transliteration and hyper-parameter optimization-which ensued in F1-Scores (model's accuracy) of 0.76 for Forum for Information Retrieval Evaluation (FIRE) 2020 and 0.99 for European Chapter of the Association for Computational Linguistics (EACL) 2021 data sets. A detailed error analysis was also done to give meaningful insights. The submitted strategy turned in the best results among the benchmarked models dealing with Malayalam-English code-mixed messages and it serves as an important step towards societal good.
Logic Bonbon: Exploring Food as Computational Artifact

In recognition of food's significant experiential pleasures, culinary practitioners and designers are increasingly exploring novel combinations of computing technologies and food. However, despite much creative endeavors, proposals and prototypes have so far largely maintained a traditional divide, treating food and technology as separate entities. In contrast, we present a "Research through Design" exploration of the notion of food as computational artifact: wherein food itself is the material of computation. We describe the Logic Bonbon, a dessert that can hydrodynamically regulate its flavor via a fluidic logic system. Through a study of experiencing the Logic Bonbon and reflection on our design practice, we offer a provisional account of how food as computational artifact can mediate new interactions through a novel approach to food-computation integration, that promotes an enriched future of Human-Food Interaction.
Real-time identification and avoidance of simultaneous static and dynamic obstacles on point cloud for UAVs navigation

Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a more robust technique to distinguish and track dynamic obstacles from static ones with only point cloud input. Then, to achieve dynamic avoidance, we propose the forbidden pyramids method to solve the desired vehicle velocity with an efficient sampling-based method in iteration. The motion primitives are generated by solving a nonlinear optimization problem with the constraint of desired velocity and the waypoint. Furthermore, we present several techniques to deal with the position estimation error for close objects, the error for deformable objects, and the time gap between different submodules. The proposed approach is implemented to run onboard in real-time and validated extensively in simulation and real hardware tests, demonstrating our superiority in tracking robustness, energy cost, and calculating time.
Discovering the Syntax and Strategies of Natural Language Programming with Generative Language Models

In this paper, we present a natural language code synthesis tool, GenLine, backed by 1) a large generative language model and 2) a set of task-specific prompts that create or change code. To understand the user experience of natural language code synthesis with these new types of models, we conducted a user study in which participants applied GenLine to two programming tasks. Our results indicate that while natural language code synthesis can sometimes provide a magical experience, participants still faced challenges. In particular, participants felt that they needed to learn the model’s “syntax,” despite their input being natural language. Participants also struggled to form an accurate mental model of the types of requests the model can reliably translate and developed a set of strategies to debug model input. From these findings, we discuss design implications for future natural language code synthesis tools built using large generative language models.
Defining a Knowledge Graph Development Process Through a Systematic Review

Knowledge graphs are widely used in industry and studied within the academic community. However, the models applied in the development of knowledge graphs vary. Analysing and providing a synthesis of the commonly used approaches to knowledge graph development would provide researchers and practitioners a better understanding of the overall process and methods involved. Hence, this article aims at defining the overall process of knowledge graph development and its key constituent steps. For this purpose, a systematic review and a conceptual analysis of the literature was conducted. The resulting process was compared to case studies to evaluate its applicability. The proposed process suggests a unified approach and provides guidance for both researchers and practitioners when constructing and managing knowledge graphs.
Symmetric transformer-based network for unsupervised image registration

Medical image registration is a fundamental and critical task in medical image analysis. With the rapid development of deep learning, convolutional neural networks (CNNs) have dominated the medical image registration field. Due to the disadvantage of the local receptive field of CNNs, some recent registration methods have focused on using transformers for nonlocal registration. However, the standard transformer has a vast number of parameters and high computational complexity, which means that it can only be applied at the bottom of registration models. As a result, only coarse information is available at the lowest resolution, limiting the contribution of the transformer in these models. To address these challenges, we propose a convolution-based efficient multihead self-attention (CEMSA) block, which reduces the number of parameters of the traditional transformer and captures local spatial context information to reduce semantic ambiguity in the attention mechanism. Based on the proposed CEMSA, we present a novel symmetric transformer-based model (SymTrans). SymTrans employs the transformer blocks in the encoder and the decoder to model the long-range spatial cross-image relevance. We apply SymTrans to the displacement field and diffeomorphic registration. Experimental results show that our proposed method achieves state-of-the-art performance in image registration. Our code is publicly available at https://github.com/MingR-Ma/SymTrans .
Supporting Accessible Data Visualization Through Audio Data Narratives

Online data visualizations play an important role in informing public opinion but are often inaccessible to screen reader users. To address the need for accessible data representations on the web that provide direct, multimodal, and up-to-date access to the data, we investigate audio data narratives –which combine textual descriptions and sonification (the mapping of data to non-speech sounds). We conduct two co-design workshops with screen reader users to define design principles that guide the structure, content, and duration of a data narrative. Based on these principles and relevant auditory processing characteristics, we propose a dynamic programming approach to automatically generate an audio data narrative from a given dataset. We evaluate our approach with 16 screen reader users. Findings show with audio narratives, users gain significantly more insights from the data. Users describe data narratives help them better extract and comprehend the information in both the sonification and description.
URLTran: Improving Phishing URL Detection Using Transformers

Browsers often include security features to detect phishing web pages. In the past, some browsers evaluated an unknown URL for inclusion in a list of known phishing pages. However, as the number of URLs and known phishing pages continued to increase at a rapid pace, browsers started to include one or more machine learning classifiers as part of their security services that aim to better protect end users from harm. While additional information could be used, browsers typically evaluate every unknown URL using some classifier in order to quickly detect these phishing pages. Early phishing detection used standard machine learning classifiers, but recent research has instead proposed the use of deep learning models for the phishing URL detection task. Concurrently, text embedding research using transformers has led to state-of-the-art results in many natural language processing tasks. In this work, we perform a comprehensive analysis of transformer models on the phishing URL detection task. We consider standard masked language modeling and additional domain-specific pre-training tasks, and compare these models to fine-tuned BERT and RoBERTa models. Combining the insights from these experiments, we propose URLTran which uses transformers to significantly improve the performance of phishing URL detection over a wide range of very low false positive rates (FPRs) compared to other deep learning-based methods. For example, URLTran yields a true positive rate (TPR) of 86.80&#x0025; compared to 71.20&#x0025; for the next best baseline at an FPR of 0.01&#x0025;, resulting in a relative improvement of over 21.9&#x0025;. Further, we consider some classical adversarial black-box phishing attacks such as those based on homoglyphs and compound word splits to improve the robustness of URLTran. We consider additional fine-tuning with these adversarial samples and demonstrate that URLTran can maintain low FPRs under these scenarios.
Toward Efficient Safety Helmet Detection Based on YoloV5 With Hierarchical Positive Sample Selection and Box Density Filtering

Object detectors based on convolutional neural networks (CNNs) have been widely deployed in industrial production for safety detection to guarantee the security of workers, and safety helmet detection is one of the most crucial application scenarios. In this article, we first consider the insufficiency of the existing largest open-source helmet detection dataset SHWD and introduce our safety helmet detection dataset which contains various industrial scenarios that are lacking in the former dataset. In addition, we reconsider the sample selection method of the Yolo series and propose a hierarchical positive sample selection (HPSS) mechanism in the training process, which improves the fitting ability of YoloV5. Furthermore, inspired by object detection in continuous frames from videos, we propose a post-processing algorithm based on box density to effectively suppress the appearance of false detection. Under the confidence threshold is 0.1, the combination of the two optimization strategies improves the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -dscore of YoloV5s by 12.47% without increasing any calculation. The dataset will be open-source in the near future.
Gender and digital competence: Analysis of pre-service teachers’ educational needs and its implications

The purpose of this study was to analyze educational needs of pre-service teachers’ digital competence by gender. 157 pre-service teachers at a university in South Korea responded to the on-line survey. For data analyses, the researcher used the paired-samples t-test, Borich needs assessment analysis, and the locus for focus model. Results revealed that male and female pre-service teachers prioritized sub-competences differently. Male pre-service teachers recognized five sub-competences in four areas of digital competence. Female pre-service teachers identified seven sub-competences in five areas as a priority. Three sub-competences such as reflective practice, guidance, and assessment strategies were included in educational needs for both males and females. The researcher discusses implications for improvement of digital competence and pre-service teacher education.
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains

Abstract Natural Language Processing algorithms have made incredible progress, but they still struggle when applied to out-of-distribution examples. We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on several source domains, and then applied to examples from unseen domains that are unknown at training time. Particularly, no examples, labeled or unlabeled, or any other knowledge about the target domain are available to the algorithm at training time. We present PADA: An example-based autoregressive Prompt learning algorithm for on-the-fly Any-Domain Adaptation, based on the T5 language model. Given a test example, PADA first generates a unique prompt for it and then, conditioned on this prompt, labels the example with respect to the NLP prediction task. PADA is trained to generate a prompt that is a token sequence of unrestricted length, consisting of Domain Related Features (DRFs) that characterize each of the source domains. Intuitively, the generated prompt is a unique signature that maps the test example to a semantic space spanned by the source domains. In experiments with 3 tasks (text classification and sequence tagging), for a total of 14 multi-source adaptation scenarios, PADA substantially outperforms strong baselines.1
Understanding augmented reality marketing in world cultural heritage site, the lens of authenticity perspective

With the development of the AR tourism experience, destination managers are starting to pay attention to the essential role of AR in tourism marketing, while at the same time, tourists’ demand for authenticity in AR is also increasing. The purpose of this study is to identify the relationships between Objective Authenticity, Constructive Authenticity and Existential Authenticity and their impact on tourist experience and behavioral intention during AR-based World Cultural Heritage site tourism experience. Results indicated that Constructive and Existential Authenticity positively predicted tourist satisfaction, and hence tourist intention to visit. However, Objective Authenticity has no significant impact on tourist satisfaction in the AR tourism experience. Theoretical and managerial implications are presented.