Reviewerly/exHarmony huggingface dataset:
Active Learning for Name Entity Recognition with External Knowledge

Named Entity Recognition (NER) is an important task in knowledge extraction, which targets extracting structural information from unstructured text. To fully employ the prior-knowledge of the pre-trained language models, some research works formulate the NER task into the machine reading comprehension form (MRC-form) to enhance their model generalization capability of commonsense knowledge. However, this transformation still faces the data-hungry issue with limited training data for the specific NER tasks. To address the low-resource issue in NER, we introduce a method named active multi-task-based NER (AMT-NER), which is a two-stage multi-task active learning training model. Specifically, A multi-task learning module is first introduced into AMT-NER to improve its representation capability in low-resource NER tasks. Then, a two-stage training strategy is proposed to optimize AMT-NER multi-task learning. An associated task of Natural Language Inference (NLI) is also employed to enhance its commonsense knowledge further. More importantly, AMT-NER introduces an active learning module, uncertainty selective, to actively filter training data to help the NER model learn efficiently. Besides, we also find different external supportive data under different pipelines improves model performance differently in the NER tasks. Extensive experiments are performed to show the superiority of our method, which also proves our findings that the introduction of external knowledge is significant and effective in the MRC-form NER tasks.
False perspectives on human language: Why statistics needs linguistics

A sharp tension exists about the nature of human language between two opposite parties: those who believe that statistical surface distributions, in particular using measures like surprisal, provide a better understanding of language processing, vs. those who believe that discrete hierarchical structures implementing linguistic information such as syntactic ones are a better tool. In this paper, we show that this dichotomy is a false one. Relying on the fact that statistical measures can be defined on the basis of either structural or non-structural models, we provide empirical evidence that only models of surprisal that reflect syntactic structure are able to account for language regularities. One-sentence summary Language processing does not only rely on some statistical surface distributions, but it needs to be integrated with syntactic information.
Meeting Your Virtual Twin: Effects of Photorealism and Personalization on Embodiment, Self-Identification and Perception of Self-Avatars in Virtual Reality

Embodying virtual twins – photorealistic and personalized avatars – will soon be easily achievable in consumer-grade VR. For the first time, we explored how photorealism and personalization impact self-identification, as well as embodiment, avatar perception and presence. Twenty participants were individually scanned and, in a two-hour session, embodied four avatars (high photorealism personalized, low photorealism personalized, high photorealism generic, low photorealism generic). Questionnaire responses revealed stronger mid-immersion body ownership for the high photorealism personalized avatars compared to all other avatar types, and stronger embodiment for high photorealism compared to low photorealism avatars and for personalized compared to generic avatars. In a self-other face distinction task, participants took significantly longer to pause the face morphing videos of high photorealism personalized avatars, suggesting a stronger self-identification bias with these avatars. Photorealism and personalization were perceptually positive features; how employing these avatars in VR applications impacts users over time requires longitudinal investigation.
Pair-Up: Prototyping Human-AI Co-orchestration of Dynamic Transitions between Individual and Collaborative Learning in the Classroom

Enabling students to dynamically transition between individual and collaborative learning activities has great potential to support better learning. We explore how technology can support teachers in orchestrating dynamic transitions during class. Working with five teachers and 199 students over 22 class sessions, we conducted classroom-based prototyping of a co-orchestration technology ecosystem that supports the dynamic pairing of students working with intelligent tutoring systems. Using mixed-methods data analysis, we study the resulting observed classroom dynamics, and how teachers and students perceived and experienced dynamic transitions as supported by our technology. We discover a potential tension between teachers’ and students’ preferred level of control: students prefer a degree of control over the dynamic transitions that teachers are hesitant to grant. Our study reveals design implications and challenges for future human-AI co-orchestration in classroom use, bringing us closer to realizing the vision of highly-personalized smart classrooms that address the unique needs of each student.
ALASCA: Reasoning in Quantified Linear Arithmetic

Abstract Automated reasoning is routinely used in the rigorous construction and analysis of complex systems. Among different theories, arithmetic stands out as one of the most frequently used and at the same time one of the most challenging in the presence of quantifiers and uninterpreted function symbols. First-order theorem provers perform very well on quantified problems due to the efficient superposition calculus, but support for arithmetic reasoning is limited to heuristic axioms. In this paper, we introduce the $$\textsc {Alasca}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mstyle> <mml:mi>L</mml:mi> <mml:mi>A</mml:mi> <mml:mi>S</mml:mi> <mml:mi>C</mml:mi> <mml:mi>A</mml:mi> </mml:mstyle> </mml:mrow> </mml:math> calculus that lifts superposition reasoning to the linear arithmetic domain. We show that $$\textsc {Alasca}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mstyle> <mml:mi>L</mml:mi> <mml:mi>A</mml:mi> <mml:mi>S</mml:mi> <mml:mi>C</mml:mi> <mml:mi>A</mml:mi> </mml:mstyle> </mml:mrow> </mml:math> is both sound and complete with respect to an axiomatisation of linear arithmetic. We implemented and evaluated $$\textsc {Alasca}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> <mml:mstyle> <mml:mi>L</mml:mi> <mml:mi>A</mml:mi> <mml:mi>S</mml:mi> <mml:mi>C</mml:mi> <mml:mi>A</mml:mi> </mml:mstyle> </mml:mrow> </mml:math> using the Vampire theorem prover, solving many more challenging problems compared to state-of-the-art reasoners.
Siamese infrared and visible light fusion network for RGB-T tracking

Due to the different photosensitive properties of infrared and visible light, infrared and visible light images have individual features. However, since the registered RGB-T image pairs shot in the same scene, they also contain common features. This paper proposes a Siamese infrared and visible light fusion Network (SiamIVFN) for RBG-T image-based tracking. SiamIVFN contains two main subnetworks: a complementary-feature-fusion network (CFFN) and a contribution-aggregation network (CAN). CFFN utilizes a two-stream multilayer convolutional structure that separately extracts individual features, and filters in each layer are partially coupled to extract common features. CFFN is a feature-level fusion network, which can cope with the misalignment of the RGB-T image pairs. Through adaptively calculating the contributions of infrared and visible light features obtained from CFFN, CAN makes the tracker robust under various light conditions. Experiments show that compared to state-of-the-art techniques, SiamIVFN improves the PR/SR score with 1.5%/8.8% on RGBT234 and 2.1%/6.9% on GTOT. The tracking speed of SiamIVFN is 147.6FPS, the current fastest RGB-T fusion tracker. The source codes are available at https://github.com/PengJingchao/SiamIVFN .
A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving, trading decisions, and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One of the ways to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Furthermore, their main goal is to test the robustness of DRL agents rather than testing the compliance of the agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We rely on machine learning models and a dedicated genetic algorithm to narrow the search toward faulty episodes (i.e., sequences of states and actions produced by the DRL agent). We apply STARLA on Deep-Q-Learning agents trained on two different RL problems widely used as benchmarks and show that STARLA significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess the risks of deploying it.
Analyzing CARLA ’s performance for 2D object detection and monocular depth estimation based on deep learning approaches

Vehicle and pedestrian perception are key for autonomous vehicles, and camera images are a common part of the sensor suite. This study explored the use of synthetic datasets from the CARLA simulator and computer vision algorithms were trained and evaluated in WAYMO Open, a real-world dataset. An efficient automated pedestrian and vehicle identification and counting has been developed, which enables high-speed identification of target features among many images and can count and output formatted identification results. For 2D object detection was applied the Faster Regions with Convolutional Neural Network features combined with a Residual Neural Network 50 (ResNet50) together with a Feature Pyramid Network as the backbone. For monocular depth estimation, the monodepth2 model hybrid with a U-Net image segmentation and ResNet18 as the backbone was the best-evaluated dataset. The best-evaluated model uses the pre-trained weights from Common Objects in Context (COCO) obtaining 55.25 and 59.34 average precision performance metric for vehicles and pedestrians, respectively. For monocular depth estimation the inclusion of synthetic data aided model generalization. Thus, the best model presented a 1.277 absolute average error, and 0.211, 0.408, and 0.593 depth accuracy metrics on thresholds 1, 2, and 3, respectively. The data collection tool, object detection, and monocular depth estimation code are open source1.
An Improved Byzantine Fault-Tolerant Algorithm Based on Reputation Model

This study proposes an improved Byzantine fault-tolerant consensus RB-BFT based on the reputation model to address the problems of low reliability of primary nodes and high communication complexity in the practical Byzantine algorithm (PBFT). First, this algorithm establishes a dynamic reputation model of nodes to distinguish honest and malicious nodes in the system, lowering the likelihood of malicious nodes being chosen as primary nodes and increasing the dependability of primary nodes. Second, the algorithm introduces supervisory nodes for information supervision while reducing the centrality of the system. Finally, this algorithm improves the consistency protocol of the PBFT algorithm by optimizing the process of mutual communication between nodes in the preparation and commitment phases, which reduces the algorithm communication complexity from O(n2) to O(n). Theoretical and practical studies reveal that the RB-BFT algorithm enhances performance and reliability greatly.
Extensive characterization and implementation of a family of three-qubit gates at the coherence limit

While all quantum algorithms can be expressed in terms of single-qubit and two-qubit gates, more expressive gate sets can help reduce the algorithmic depth. This is important in the presence of gate errors, especially those due to decoherence. Using superconducting qubits, we have implemented a three-qubit gate by simultaneously applying two-qubit operations, thereby realizing a three-body interaction. This method straightforwardly extends to other quantum hardware architectures, requires only a "firmware" upgrade to implement, and is faster than its constituent two-qubit gates. The three-qubit gate represents an entire family of operations, creating flexibility in quantum-circuit compilation. We demonstrate a gate fidelity of $97.90\%$, which is near the coherence limit of our device. We then generate two classes of entangled states, the GHZ and W states, by applying the new gate only once; in comparison, decompositions into the standard gate set would have a two-qubit gate depth of two and three, respectively. Finally, we combine characterization methods and analyze the experimental and statistical errors on the fidelity of the gates and of the target states.
simpleNomo: A Python Package of Making Nomograms for Visualizable Calculation of Logistic Regression Models

Background Logistic regression models are widely used in clinical prediction, but their application in resource-poor settings or areas without internet access can be challenging. Nomograms can serve as a useful visualization tool to speed up the calculation procedure, but existing nomogram generators often require the input of raw data, inhibiting the transformation of established logistic regression models that only provide coefficients. Developing a tool that can generate nomograms directly from logistic regression coefficients would greatly increase usability and facilitate the translation of research findings into patient care. Methods We designed and developed simpleNomo, an open-source Python toolbox that enables the construction of nomograms for logistic regression models. Uniquely, simpleNomo allows for the creation of nomograms using only the coefficients of the model. Further, we also devoloped an online website for nomogram generation. Results simpleNomo properly maintains the predictive ability of the original logistic regression model and easy to follow. simpleNomo is compatible with Python 3 and can be installed through Python Package Index (PyPI) or https://github.com/Hhy096/nomogram Conclusion This paper presents simpleNomo, an open-source Python toolbox for generating nomograms for logistic regression models. It facilitates the process of transferring established logistic regression models to nomograms and can further convert more existing works into practical use.
Implementing the Bashayer chatbot in Saudi higher education: measuring the influence on students' motivation and learning strategies

Since the fourth industrial revolution, intelligent software and applications that attempt to mimic human behavior have become increasingly common. The chatbot is an example of an artificial intelligence-based computer program that simulates human behavior by having a conversation and interacting with users using natural language. The implementation of chatbot technology in the educational context is still in its nascent stage, and further investigation into measuring its effectiveness in supporting learning and teaching processes is required, particularly in the context of higher education. Thus, this study presents the design and implementation of a task-oriented chatbot, that is embedded into the WhatsApp application, called Bashayer. It aims at supporting postgraduate students' motivation and learning strategies in Saudi Arabia. A quasi-experimental design with a single-subject experimental approach was adopted with a sample of 60 Saudi postgraduate students. The descriptive analysis of the collected data showed promising results of postgraduate students utilized the Bashayer chatbot system. Participants in the experimental group that used Bashayer were more motivated to learn than those in the control group. Participants also practiced more cognitive and metacognitive learning strategies while utilizing the chatbot compared to the control group. The results of this study are encouraging for the development of chatbot systems similar to Bashayer to support postgraduate students' successful learning. These results contribute to bridging the research gap and adding to the literature on chatbots use in postgraduate educational contexts.
Multifaceted Relation-aware Meta-learning with Dual Customization for User Cold-start Recommendation

User cold-start scenarios pose great challenges to recommendation systems in accurately capturing user preferences with sparse interaction records. Besides incorporating auxiliary information to enrich user/item representations, recent studies under the schema of meta-learning focus on quickly adapting personalized recommendation models based on cold-start users’ scarce interactions. The majority of meta-learning based recommendation methods follow a bi-level optimization paradigm and learn globally shared initialization across all cold-start recommendation tasks. In addition, to further facilitate the ability of fast adaptation, existing methods have made efforts to tailor task-specific prior knowledge by identifying the individual characteristics of each task. However, we argue that multi-view commonalities between existing users and cold-start users are also essential for precisely distinguishing new tasks, but not comprehensively modeled in previous studies. In this article, we propose a multifaceted relation-aware meta-learning approach namely MeCM for user cold-start recommendation, which enhances task-adaptive initialization customization by extracting multiple views of task relevance. We design a dual customization framework consisting of two successive phases including cluster-level customization and task-level customization. Specifically, MeCM first extracts multifaceted semantic relations between tasks and refines task commonalities into task clusters maintained with memory networks (MNs). Globally learned fast weights corresponding to task clusters are queried to perform cluster-level customization. Then task-level customization is triggered based on contextual information of the target task via interaction-wise encoding. Extensive experiments on real-world datasets demonstrate the superior performance of our model over state-of-the-art meta-learning-based recommendation methods.
A Novel Curated Scholarly Graph Connecting Textual and Data Publications

In the last decade, scholarly graphs became fundamental to storing and managing scholarly knowledge in a structured and machine-readable way. Methods and tools for discovery and impact assessment of science rely on such graphs and their quality to serve scientists, policymakers, and publishers. Since research data became very important in scholarly communication, scholarly graphs started including dataset metadata and their relationships to publications. Such graphs are the foundations for Open Science investigations, data-article publishing workflows, discovery, and assessment indicators. However, due to the heterogeneity of practices (FAIRness is indeed in the making), they often lack the complete and reliable metadata necessary to perform accurate data analysis; e.g., dataset metadata is inaccurate, author names are not uniform, and the semantics of the relationships is unknown, ambiguous or incomplete. This work describes an open and curated scholarly graph we built and published as a training and test set for data discovery, data connection, author disambiguation, and link prediction tasks. Overall the graph contains 4,047 publications, 5,488 datasets, 22 software, 21,561 authors; 9,692 edges interconnect publications to datasets and software and are labeled with semantics that outline whether a publication is citing, referencing, documenting , supplementing another product. To ensure high-quality metadata and semantics, we relied on the information extracted from PDFs of the publications and the datasets and software webpages to curate and enrich nodes metadata and edges semantics. To the best of our knowledge, this is the first ever published resource, including publications and datasets with manually validated and curated metadata.
Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem

This paper presents a new hybrid algorithm that combines genetic algorithms (GAs) and the optimizing spotted hyena algorithm (SHOA) to solve the production shop scheduling problem. The proposed GA-SHOA algorithm incorporates genetic operators, such as uniform crossover and mutation, into the SHOA algorithm to improve its performance. We evaluated the algorithm on a set of OR library instances and compared it to other state-of-the-art optimization algorithms, including SSO, SCE-OBL, CLS-BFO and ACGA. The experimental results show that the GA-SHOA algorithm consistently finds optimal or near-optimal solutions for all tested instances, outperforming the other algorithms. Our paper contributes to the field in several ways. First, we propose a hybrid algorithm that effectively combines the exploration and exploitation capabilities of SHO and GA, resulting in a balanced and efficient search process for finding near-optimal solutions for the FSSP. Second, we tailor the SHO and GA methods to the specific requirements of the FSSP, including encoding schemes, objective function evaluation and constraint handling, which ensures that the hybrid algorithm is well suited to address the challenges posed by the FSSP. Third, we perform a comprehensive performance evaluation of the proposed hybrid algorithm, demonstrating its effectiveness in terms of solution quality and computational efficiency. Finally, we provide an in-depth analysis of the behavior of the hybrid algorithm, discussing the roles of the SHO and GA components and their interactions during the search process, which can help understand the factors contributing to the success of the algorithm and provide insight into potential improvements or adaptations to other combinatorial optimization problems.