Reviewerly/exHarmony huggingface dataset:
XAI with Machine Teaching When Humans Are (Not) Informed About the Irrelevant Features

Exemplar-based explainable artificial intelligence (XAI) aims at creating human understanding about the behaviour of an AI system, usually a machine learning model, through examples. The advantage of this approach is that the human creates their own explanation in their own internal language. However, what examples should be chosen? Existing frameworks fall short in capturing all the elements that contribute to this process. In this paper, we propose a comprehensive XAI framework based on machine teaching. The traditional trade-off between the fidelity and the complexity of the explanation is transformed here into a trade-off between the complexity of the examples and the fidelity the human achieves about the behaviour of the ML system to be explained. We analyse a concept class of Boolean functions that is learned by a convolutional neural network classifier over a dataset of images of possibly rotated and resized letters. We assume the human learner has a strong prior (Karnaugh maps over Boolean functions). Our explanation procedure then behaves like a machine teaching session optimising the trade-off between examples and fidelity. We include an experimental evaluation and several human studies where we analyse the capacity of teaching humans these Boolean function by means of the explanatory examples generated by our framework. We explore the effect of telling the essential features to the human and the priors, and see that the identification is more successful than by randomly sampling the examples.
KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation

Existing knowledge-grounded conversation systems generate responses typically in a retrieve-then-generate manner. They require a large knowledge base and a strong knowledge retrieval component, which is time- and resource-consuming. In this paper, we address the challenge by leveraging the inherent knowledge encoded in the pre-trained language models (PLMs). We propose Knowledgeable Prefix Tuning (KnowPrefix-Tuning), a two-stage tuning framework, bypassing the retrieval process in a knowledge-grounded conversation system by injecting prior knowledge into the lightweight knowledge prefix. The knowledge prefix is a sequence of continuous knowledge-specific vectors that can be learned during training. In addition, we propose a novel interactive re-parameterization mechanism that allows the prefix to interact fully with the PLM during the optimization of response generation. Experimental results demonstrate that KnowPrefix-Tuning outperforms fine-tuning and other lightweight tuning approaches, and performs comparably with strong retrieval-based baselines while being $$3\times $$ faster during inference (The code is available at https://github.com/fantast4ever/KnowPrefix-Tuning .)
An Experiment on University English Translation Teaching Model Based on Big Data-Driven Learning

Abstract Exploring the teaching mode of English translation in college to stimulate students’ learning initiative better and improve their English translation ability. In this paper, the basic principles of the plain Bayesian classification algorithm are introduced under big data technology-driven learning, and the conditional independence of plain Bayesian is illustrated. The algorithm and process of probabilistic classification of samples to be judged using great likelihood estimation are introduced, and the advantages and disadvantages of the algorithm are illustrated. Based on the disadvantages of this algorithm, the INB-MI classification algorithm is induced by adjusting the conditional probability after the number of smoothing using mutual information, and the validity is verified for this algorithm. For the university, English translation teaching was analyzed, a task-based English translation teaching model based on a blended environment was proposed, and the model was quantitatively analyzed with the INB-MI classification algorithm for the relevant data. In terms of cognitive types, the overall learning rate of this teaching model was 43.11%, 41.21%, 40.46%, and 38.3% for factual knowledge, conceptual knowledge, procedural knowledge, and metacognitive knowledge types, respectively. Regarding instructional evaluation, the percentage of those who agreed was 84.26%. Thus, the task-based English translation teaching model under big data-driven learning can help students integrate their existing knowledge and enhance their learning initiatives.
A Comprehensive Review of Ontologies in the Hydrology Towards Guiding Next Generation Artificial Intelligence Applications

Big data generated by remote sensing, ground-based measurements, models and simulations, social media and crowdsourcing, and a wide range of structured and unstructured sources necessitates significant data and knowledge management efforts. Innovations and developments in information technology over the last couple of decades have made data and knowledge management possible for an insurmountable amount of data collected and generated over the last decades. This enabled open knowledge networks to be built that led to new ideas in scientific research and the business world. To design and develop open knowledge networks, ontologies are essential since they form the backbone of conceptualization of a given knowledge domain. A systematic literature review was conducted to examine research involving ontologies related to hydrological processes and water resource management. Ontologies in the hydrology domain support the comprehension, monitoring, and representation of the hydrologic cycle’s complex structure, as well as the predictions of its processes. They contribute to the development of ontology-based information and decision support systems; understanding of environmental and atmospheric phenomena; development of climate and water resiliency concepts; creation of educational tools with artificial intelligence; and strengthening of related cyberinfrastructures. This review provides an explanation of key issues and challenges in ontology development based on hydrologic processes to guide the development of next generation artificial intelligence applications. The study also discusses future research prospects in combination with artificial intelligence and hydroscience.
Error Analysis Students’ Ability in Using Regular and Irregular Verb at the Second Grade of SMP Satria Dharma Perbaungan

This research aims to analyze student errors made by second grade students of SMP Satria Dharma Perbaungan in using regular and irregular verbs. This research aims to obtain the types of errors in identifying regular and irregular verbs in the past form and to find out the causes of why students make mistakes. This type of research is research with qualitative methods. This research was conducted by following error analysis procedures, starting with sample selection, identification, elaboration, explanation and assessment of errors. The data sources in this research were 10 students from the second grade of SMP Satria Dharma Perbaungan. The research data was obtained by doing observations, giving questionnaires and giving tests to students. From the error analysis process, it was found that students made three types of errors, namely ommission, addition, and misformation. The results of omission errors were 23.34%, addition errors were 26.66% and the most errors occurred at 50% misformation errors. According to the theory of Ellis and Barkhuizen, students' errors are caused by interlingual transfer and intralingual transfer.
Future projection of photovoltaic potential over South Korea based on high-resolution shared socioeconomic pathways (SSP) scenarios

The study predicts the future photovoltaic power generation potential (PVpot) changes over South Korea based on high-resolution climate change scenarios. According to the four Shared Socioeconomic Pathways (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5), at the late 21st century, surface down-welling shortwave radiation, which has a major impact on PVpot, is expected to increase (by about +0.7 ∼ +3.5 W/m²) compared to the present-day. However, the increase or decrease of PVpot depends on the level of emissions in the future. In the low-emissions scenario, PVpot is expected to increase (by about +1.3%), while that in the high-emissions scenarios, PVpot isare expected to decrease (by about -0.7 ∼ -2.0%). As the temperature increases significantly, the temperature of the solar cell panels increase, and the performance ratio of PVpot decreases. This means that, even though the surface down-welling shortwave radiation increases, the increase in temperature may offset the positive impact on PVpot. Seasonally, PVpot is expected to increase (by about +2.3 ∼ +4.0%) in summer and decrease (by about -3.6 ∼ -7.1%) in winter. Overall, this study highlights the importance of considering the impact of climate change on solar energy. It also emphasizes the need to adopt low-emissions policies to mitigate the negative effects of climate change on renewable energy generation potential.
Unsupervised Deep Cross-Language Entity Alignment

Cross-lingual entity alignment is the task of finding the same semantic entities from different language knowledge graphs. In this paper, we propose a simple and novel unsupervised method for cross-language entity alignment. We utilize the deep learning multi-language encoder combined with a machine translator to encode knowledge graph text, which reduces the reliance on label data. Unlike traditional methods that only emphasize global or local alignment, our method simultaneously considers both alignment strategies. We first view the alignment task as a bipartite matching problem and then adopt the re-exchanging idea to accomplish alignment. Compared with the traditional bipartite matching algorithm that only gives one optimal solution, our algorithm generates ranked matching results which enabled many potentials downstream tasks. Additionally, our method can adapt two different types of optimization (minimal and maximal) in the bipartite matching process, which provides more flexibility. Our evaluation shows, we each scored 0.966, 0.990, and 0.996 Hits@1 rates on the $$\textrm{DBP15K}$$ dataset in Chinese, Japanese, and French to English alignment tasks. We outperformed the state-of-the-art method in unsupervised and semi-supervised categories. Compared with the state-of-the-art supervised method, our method outperforms 2.6% and 0.4% in Ja-En and Fr-En alignment tasks while marginally lower by 0.2% in the Zh-En alignment task.
News Recommendation via Jointly Modeling Event Matching and Style Matching

News recommendation is a valuable technology that helps users effectively and efficiently find news articles that interest them. However, most of existing approaches for news recommendation often model users’ preferences by simply mixing all different information from news content together without in-depth analysis on news content. Such a practice often leads to significant information loss and thus impedes the recommendation performance. In practice, two factors which may significantly determine users’ preferences towards news are news event and news style since users tend to read news articles that report events they are interested in, and they also prefer articles that are written in their preferred style. Such two factors are often overlooked by existing approaches. To address this issue, we propose a novel Event and Style Matching (ESM) model for improving the performance of news recommendation. The ESM model first uses an event-style disentangler to extract event and style information from news articles respectively. Then, a novel event matching module and a novel style matching module are designed to match the candidate news with users’ preference from the event perspective and style perspective respectively. Finally, a unified score is calculated by aggregating the event matching score and style matching score for next news recommendation. Extensive experiments on real-world datasets demonstrate the superiority of ESM model and the rationality of our design (The source code and the splitted datasets are publicly available at https://github.com/ZQpengyu/ESM ).
Lightweight Mask Wearing Detection Algorithm Based on Improved YOLOv5

To prevent the spread of the epidemic, a lightweight mask-wearing detection algorithm based on the modified YOLOv5 is proposed. The three categories of mask-wearing, mask-less, and non-standard mask-wearing were studied, and images of faces covered with hands and clothing were added as interference items to make the study more realistic. Using YOLOv5 as the base model, the modified deformable convolution is introduced into the CSP-Darknet53 module of the backbone network, which effectively enhances the model's ability to extract key information such as faces and masks and improves the detection accuracy of the model. In addition, image defogging based on dark channel prior is introduced to make the model adapt to detect whether pedestrians wear a mask correctly in special weather. Finally, the knowledge distillation method is used to perform light-weight compression on the model to solve the problem of slow detection speed in practical applications. Training and testing on the self-made T-Mask dataset, the mAP of the model reaches 93.6%, which is about 3% points higher than the original YOLOv5m algorithm, and the number of parameters is reduced to 1/3 of the original one, which has a higher practical value.
Abductive Explanations of Classifiers Under Constraints: Complexity and Properties

Abductive explanations (AXp’s) are widely used for understanding decisions of classifiers. Existing definitions are suitable when features are independent. However, we show that ignoring constraints when they exist between features may lead to an explosion in the number of redundant or superfluous AXp’s. We propose three new types of explanations that take into account constraints and that can be generated from the whole feature space or from a sample (such as a dataset). They are based on a key notion of coverage of an explanation, the set of instances it explains. We show that coverage is powerful enough to discard redundant and superfluous AXp’s. For each type, we analyse the complexity of finding an explanation and investigate its formal properties. The final result is a catalogue of different forms of AXp’s with different complexities and different formal guarantees.
Review of Knowledge Graph and Its Vertical Applications in Industry

Knowledge graph (KG) are powerful tools for organizing and managing knowledge, and have been widely applied in many fields. KG describes the objective physical world intuitively using the semantic network, and it explicitly represents and stores related data, enabling intelligent applications like semantic search, intelligent question answering, and decision support. This paper begins with a brief history of the development of KG, as well as an explanation of its concept and technical advantages. Next, it introduces the key technologies of Information extraction, knowledge fusion and knowledge processing. Finally, it summarizes the status of research on knowledge graph and the progress made in their application in vertical areas.
Autonomous Mapless Navigation via Maximum Entropy Learning

Autonomous mapless navigation is a significant problem for autonomous robots when the entire environment is unknown. In recent years, an increasing number of methods based on deep reinforcement learning have been used for autonomous navigation. However, some of these methods are closely tied to the training environment, making it difficult for them to be generalized to mapless environments. Moreover, researchers are often confused by the problem of easily getting trapped in the local optimum zone. To address these issues, this paper proposes a method based on deep reinforcement learning that 1) takes the neighboring environment as input to the agent, ensuring that the policy learned by the agent can be generalized to mapless environments and 2) replaces maximum value learning with maximum entropy learning to improve the agent's exploration ability. The final results show that our method can autonomously navigate in mapless scenarios.
Fault Detection Filter Design for Positive Markov Jump Systems Based on T-S Fuzzy Model

The problem of fault detection filter (FDF) design for positive Markov jump systems (PMJSs) based on T-S fuzzy model is researched. With the help of a co-positive Lyapunov function, the solvability criterion of the related problem is given. By solving obtained linear programming problems, the parameters of the proposed FDF are further determined. Finally, the feasibility of the theory is verified by an example.
Visible RRT*: Asymptotically Optimal Random Search Tree for Visual Servo Tasks with the FOV Constraint

This paper presents a novel visibility-based asymptotically optimal random search tree trajectory planning algorithm (Visible RRT*), which is dedicated to addressing the visual servoing tasks under the FOV constraint. Firstly, a search tree reconstruction method under the FOV constraint is proposed to obtain an asymptotically optimal solution. Meanwhile, a novel greedy search strategy is designed to improve the searching efficiency by segmenting the greedy results based on the FOV constraint. Finally, comparative simulations are conducted in two challenging scenarios, and the results show that the planned trajectory is asymptotically optimal, thus confirming the effectiveness of the proposed algorithm.
Being friendly and competent: Service robots' proactive behavior facilitates customer value co-creation

The rapid expansion and integration of robots in the service industry have transformed the way services are delivered. With service robots increasingly replacing frontline human staff, the dynamics of service encounters have shifted from human-to-human to human-to-robot interactions, thereby significantly impacting the process of value co-creation. The active involvement of customers in co-creating value with these robots becomes crucial for the successful implementation of robotic services. Drawing upon social perception theory, we investigate the influence of service robots' proactivity on customers' willingness to engage in value co-creation and delve into the underlying mechanisms. Three studies were conducted across diverse service settings, each involving different service robots. Consistently, the findings demonstrate that customers display a greater inclination to co-create value with proactive service robots. The proactive behavior exhibited by these robots substantially enhances customers' perceptions of warmth and competence. Importantly, these effects are observed across various service scenarios and customer segments, including both premium and regular customers, thus affirming the robustness and generalizability of our findings. Our research provides practical guidance for practitioners involved in the implementation of service robots. Additionally, theoretical advancements and suggest potential directions for future research are discussed.
Constrained Reentry Trajectory Design using Whale Optimization Algorithm for Hypersonic Glide Vehicle

Reentry trajectory design for hypersonic glide vehicle with multiple constrains is a critical issue in the field of aircraft guidance and control. In this paper, we attempt to handle the constrained reentry trajectory design problem using the whale optimization algorithm. First, the constrained trajectory design problem is converted to unconstrained using the penalty function method. Then, the penalty factor coefficients of the resulting synthesized fitness function are calculated from pairwise comparison judgments of the analytic hierarchy process. Finally, combined with the suggested constraint handling method, the improved WOA is applied to the reentry trajectory design problem of HGV successfully. Numerical simulations show that the proposed optimization strategy is competitive in searching accuracy and convergence speed.
Fast-Moving Target Tracking Based on KCF with Motion Prediction

Tracking fast-moving targets with high accuracy is a challenging task in the field of target tracking. The speed of the algorithm should be taken into consideration when it is used in a real-time system. In this paper, we introduce motion prediction into the correlation filtering algorithms based on KCF and SAMF, which can achieve fast and accurate tracking of fast-moving targets. The target's motion characteristics are used to predict the position and scale information of the target in the detection frame, which greatly improves the performance of the correlation filtering algorithm. In addition, result evaluation and dynamic model update strategy are added to our algorithm to ensure that only the target's feature information is learned by the filter. Finally, the tracking result is refined using motion prediction and evaluation confidence for greater accuracy. The experiments demonstrate that our algorithm is more accurate and robust in tracking fast-moving targets and its speed is also greatly improved compared to SAMF.
Adaptive Incremental Learning Stochastic Configuration Network and Its NIR Modeling Application

Near-infrared (NIR) technologies have shown promising advantages in industrial processes. With the continuous increase of spectral dimension, establishing a prediction model with stability and generalization has been a significant research problem in NIR modeling. Compared with traditional methods, stochastic configuration network (SCN) based on randomized approach has demonstrated great advantages in developing nonlinear models due to its effectiveness and efficiency in model structure and parameters learning. However, none of the existing point or block increment learning strategies can obtain a compact network structure while converging fast. To further improve modeling efficiency for high-dimensional NIR analysis, the adaptive incremental learning SCN (SCN-A) algorithm is proposed in this work, in which the size of the increment learning block adaptively adjusts during the iteration process. Comparative results on two measured NIR datasets verify the effectiveness of the proposed method. Compared with the state-of-the-art SCNs, the SCN-A method can obtain similar prediction performance using fewer iteration times.
A Survey of Undergraduate Chinese Students’ Self-perceived Reading Ability in English

Reading is one of the indicators of how much a reader understands a language. Though reading is taught early in students’ education, some still struggle to comprehend texts, especially if a language is a stranger. Reading is a skill that enhances intellectual and affective processes, including reasoning, inferences, empathy, and critical thinking. Thus, a lack of language familiarity to interact with the text can hinder reading comprehension. This article focuses on Chinese students’ self-perceived reading ability that hinders reading comprehension. The aim is to answer the question; To what extent do Undergraduate Chinese students’ of University X perceive their reading ability in English? This survey is significant because letting the students understand their reading ability helps the teacher understand what to focus on in the English course and provides the correct reading strategies to achieve the reading goal. One hundred ninety-nine students from one of the private universities participated in this study. Using a questionnaire survey to assess students’ self-perceived reading ability supported by the semi-structured interview, this study found that although Chinese students employed good reading strategies, they still found their reading comprehension problematic because of the influence of their reading speed. Thus, this affects the reading goal and students’ interest in reading.