Reviewerly/exHarmony huggingface dataset:
Video Event Extraction via Tracking Visual States of Arguments

Video event extraction aims to detect salient events from a video and identify the arguments for each event as well as their semantic roles. Existing methods focus on capturing the overall visual scene of each frame, ignoring fine-grained argument-level information. Inspired by the definition of events as changes of states, we propose a novel framework to detect video events by tracking the changes in the visual states of all involved arguments, which are expected to provide the most informative evidence for the extraction of video events. In order to capture the visual state changes of arguments, we decompose them into changes in pixels within objects, displacements of objects, and interactions among multiple arguments. We further propose Object State Embedding, Object Motion-aware Embedding and Argument Interaction Embedding to encode and track these changes respectively. Experiments on various video event extraction tasks demonstrate significant improvements compared to state-of-the-art models. In particular, on verb classification, we achieve 3.49% absolute gains (19.53% relative gains) in F1@5 on Video Situation Recognition. Our Code is publicly available at https://github.com/Shinetism/VStates for research purposes.
Construction of Career Planning Guidance Model Based on Vocational College Students

As China has paid more and more attention to culture in recent years, the overall education level of the people has gradually improved, and the general higher education has become more and more popular, the enrollment expansion has also better solved the problem of students' going to school, and the corresponding employment environment for college students has become increasingly severe.The ultimate goal of education is to move towards society and make better contributions to society.Therefore, in terms of career planning and employment guidance for vocational college students, colleges and universities should pay more attention to effectiveness, scientifically analyze the current employment situation for students, and actively guide students to establish a correct concept of employment.Helping college students find jobs is the top priority of all colleges and universities, and colleges and universities must pay attention to it.
Pengembangan LKPD Berbasis Question Prompt Scaffolding untuk Meningkatkan Scientific Writing Skill Peserta Didik pada Pembelajaran IPA SMP

In science learning, scientific writing skills are an important aspect that can be an indicator of learning success and are of great benefit for students. In fact, scientific writing skills still need to be improved. So teaching materials are needed that can provide assistance to students to practice their scientific writing skills, one of them is student worksheet based on question prompt scaffolding. The purpose of this study is to describe the validity, practicality, and effectiveness of student worksheet based on the question prompt scaffolding. The type of research used is research and development. The research design used is the ADDIE model which consists of five phase, namely analysis, design, develop, implement, and evaluate. The location where this research was canducted is SMP Negeri 1 Jember. Validity reached a value of 97% which is included in the valid category. Practicality reached a value of 94% which is included in the practical category. Analysis of effectiveness using the N-gain test which obtained an overall average N-gain in laboratory reports 1 and 2 of 0,123 in the low category, and an overall average N-gain in laboratory reports 2 and 3 of 0,301 in the medium category. These results indicate a fairly good increase, from the low to medium category. The students’ response result received a rating of 82.67% in the very good category. Thus, it can be concluded that this student worksheet is valid, practical, and effective in improving students' scientific writing skills in science learning.
VoiceEx: Voice Submission System for Interventions in Education

Generating self-explanations has been identified as a successful strategy in helping learners engage with course content and organize what they learn in a structured format. While typing an explanation may allow more structure and formality, explaining by voice can be more natural and help free cognitive resources to focus on learning goals and understanding concepts. As we investigated the effects and students' perceptions of using voice or text to self-explain new course concepts, we failed to find a tool that would meet our needs. We present our work in designing and developing VoiceEx, a submission courseware that allows text and voice input to collect data in both mediums. VoiceEx was created to support a self-explanations intervention for computer science students; however, given its features and the advantages of being able to collect spoken responses, it can be used in a variety of environments. Future refinement of this tool includes artificial intelligence features to better guide students' submissions.
形式名詞的「no」和「koto」名詞化用法之差異分析—以「V1 no wo V2」「V1 koto wo V2」構造為主—

&lt;p&gt;本研究主要以「BCCWJ」之語料庫資料為主，考察分析「V1 no wo V2」之構句與「V1 koto wo V2」構句中，形式名詞的「no」與「koto」用法之差異性。研究結果得到了以下的兩點結論。&lt;/p&gt; &lt;p&gt;①「V1 no wo V2」構句中「V1 no」之典型意思特徴可以規定為「僅有某一特定之具體動作・事態」。&lt;/p&gt; &lt;p&gt;②「V1 koto wo V2」構句中「V1 koto」之典型意思特徴則可定義為「匯聚數個被細分化事項・事態之集合，所形成的一項抽象化之概念。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;This study aims to investigate the differences between nominalized formal nouns &amp;quot;no&amp;quot; and &amp;quot;koto&amp;quot; within [V1 no wo V2] and [V1 koto wo V2] by adopting BCCWJ corpus database. The results are concluded in two aspects.&lt;/p&gt; &lt;p&gt;(1) In [V1 no wo V2], the typical semantic feature of [V1 no] can refer to one certain action and situation.&lt;/p&gt; &lt;p&gt;(2) In [V1 koto wo V2], the typical semantic feature of [V1 koto wo] can refer to a composition of several segments and an abstract concept of such composition.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Study of Financial Fraud Based on Crime Theory

Capital markets have flourished in recent years, and while regulatory forces have continued to advance, there is no shortage of leakages that have caused major shocks to capital markets. It has seriously threatened the confidence of market participants in financial information and deteriorated the quality of the audit function. In order to safeguard the interests of all parties in the capital markets and to promote a thriving industry and business, the fraud problem needs to be curbed more effectively. In this paper, we will focus on five aspects of the case, namely the characteristics of the fraudster, the secret of fraud, the mechanism of incentivising fraud, internal and external monitoring and the results of fraud, from the Crime theory, taking YiHua Lifestyle Technology Co.Ltd. as an example, and offer fraud prevention advice on internal corporate governance, business strategies and external auditing.
A BERT-ABiLSTM Hybrid Model-Based Sentiment Analysis Method for Book Review

Aiming at the problem of low accuracy rate of current sentiment analysis methods for book review texts, a book review sentiment analysis method based on BERT-ABiLSTM hybrid model is proposed. First, the overall framework of sentiment analysis is constructed by integrating sentiment vocabulary and deep learning methods, and the fine-grained sentiment analysis is divided into three stages: topic identification, sentiment identification and thematic sentiment identification. Then, a dynamic character-level word vector containing contextual information is generated using a bidirectional encoder representation from transformers (BERT) pre-trained language model. Then, the contextual information in the text data is fully learned by introducing the bidirectional long short-term memory (BiLSTM) model. Finally, the accurate analysis of book review sentiment is achieved by using Attention mechanism to highlight important features and improve the efficiency of resource utilization. Through an experimental comparison with existing advanced algorithms, the proposed method in this study has improved at least 4.2%, 3.9% and 3.79% in precision, recall and F1 values, respectively. The experimental results show that the proposed BERT-ABiLSTM is higher than the existing models under different metrics, indicating that the proposed model has a good application prospect in the fields of book review analysis and book recommendation.
An Approach Based on Machine Learning for the Cybersecurity of Blockchain-Based Smart Internet of Medical Things (IoMT) Networks

This paper presents a hybrid blockchain architecture for Internet of Medical Things (IoMT) systems, aiming to enhance their security and performance. The proposed approach combines artificial intelligence (AI) models with blockchain technology to create a safe and efficient healthcare system. The study focuses on addressing the challenges related to data storage, data management, real-time medical applications, and system precision in IoMT. Through experimental evaluations, the effectiveness of the proposed techniques in terms of communication overhead, transaction performance, and privacy preservation is demonstrated. The results highlight the potential of leveraging AI and blockchain to improve the overall functionality of IoMT systems.
An Efficient and Reliable Blockchain-based Trust Management Model for Electricity Trading Terminal

&lt;p&gt;A safe and reliable terminal environment is crucial to ensure the security of the electricity trading system. The existing terminal security system based on identity authentication and access control has internal threats that are difficult to solve. For example, multiple internal malicious nodes cause broadcast message tampering attacks and malicious packet loss, resulting in message dissemination failure. Existing blockchain-based trust management systems are good for addressing insider threats, but suffer from low efficiency. In order to solve the internal threat problem of the electric electricity trading system, from the perspective of trust evaluation and trust management of the terminal environment, an efficient and reliable blockchain-based electric trading terminal (ERBTM) trust management model is proposed. First of all, we collect a variety of trust factors to evaluate the credibility of the terminal, which solves the problem of accuracy in the process of trust assessment; secondly, we improve the speed of storing trust values on the chain and ensure the robustness of the system by improving the consensus algorithm; Finally, we designed the structure of the block that stores the trust value to ensure that the trust value is not tampered with. The experimental results show that, compared with similar methods, the ERBTM model can effectively deal with the endogenous security threats of terminals in the electricity trading environment, and has significant advantages in terms of efficiency and reliability. &lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Comparative Analysis of Metaverse Platforms for English Learning Using Metaverse

Recently, the word Metaverse is becoming more important to the extent that it is not an exaggeration to say that services using the Internet or IT are developing around the word Metaverse. In the future, the time people spend in the metaverse will increase, and it is expected that economic and social activities in reality will be connected and converted into the metaverse. This study analyzes the currently serviced metaverse platforms, identified the characteristics of each platform, and proposes how to use these characteristics for English teaching/learning. To this end, the metaverse platforms currently in service were selected, and through the first analysis, the metaverse platforms for the second analysis were selected, and the characteristics of each metaverse platform were analyzed through the second in-depth analysis. The metaverse platforms selected for the first analysis are 1) Pokemon Go, 2) Animal Crossing New Horizons, 3) Minecraft, 4) Gathertown, 5) Zeppetto, 6) Roblox. In the result part of this paper, the characteristics of each metaverse platform identified through analysis are presented, and in the conclusion and suggestion part, suggestions for how to use the characteristics of each metaverse when using metaverse for English learning are presented.
Bridging the Divide: Creating an accessible transparent AR display

Asynchronous Signal Transmutation (AST) is the latest iteration of the Post-Bits Human Universe (PBHU), a continually updated interdisciplinary project.This iteration focuses on bridging human artists, audiences, hackers, and developers with artificial intelligence and beings (Bits), exploring the complexity and poetry of relations in diverse spatial-temporal worlds, and granting the term "world" in the Anthropocene and post-Anthropocene a new implication and value in revolution to the current consensus of "world".Building upon existing philosophical frameworks of 'worldlessness' the authors propose an artistic and experimental approach to examining the positive worldlessness in both the Anthropocene and post-Anthropocene through a 24/7 structured space system where the artwork is living in-real-time as a 'worldless' universe that parallels, overlaps, and transmutes between physical and electronic worlds on different space-time dimensions.Responding to those well-encapsulated art projects that exclusively present their work during a specific time and location, AST thus presents a positive instance of the nature and potential of worldlessness from an ontological, phenomenological, and political standpoint.
Padam Persona—Personalizing Text to Image Using Artificial Intelligence

Personalized Text-to-image models give extra creative freedom to people/artists by converting natural language concepts into images that depict an idea. However, it is unambiguous how this freedom can be used to create personalized and unique concept images, change their appearance, incorporate them into new characters and scenes. Input from the user is used to generate a painting or portrait based on the user’s likeness, or to generate a new product concept using language-based models. We suggest a straightforward approach that facilitates this kind of imaginative independence. Using only few training images of user-supplied concepts/prompts (e.g.: objects or patterns of text), this Al model represents them with new “images” by converting the text input. The input can also be provided in regional languages such as Hindi, Tamil, Telugu, etc. These Al generated images can be used for several artistic purposes, which intuitively guide individual creativity. In instance, we discover that only one word (text input) is enough to accurately express a wide range of thoughts. We evaluate our approach/system against a broad range of foundational ideas and demonstrate that it provides more precise descriptions of concepts/ideas in a wide range of contexts and activities via the use of unique aesthetic imagery.
Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms

On-demand service platforms face a challenging problem of forecasting a large collection of high-frequency regional demand data streams that exhibit instabilities. This paper develops a novel forecast framework that is fast and scalable, and automatically assesses changing environments without human intervention. We empirically test our framework on a large-scale demand data set from a leading on-demand delivery platform in Europe, and find strong performance gains from using our framework against several industry benchmarks, across all geographical regions, loss functions, and both pre- and post-Covid periods. We translate forecast gains to economic impacts for this on-demand service platform by computing financial gains and reductions in computing costs.
Impact of Experiencing Misrecognition by Teachable Agents on Learning and Rapport

While speech-enabled teachable agents have some advantages over typing-based ones, they are vulnerable to errors stemming from misrecognition by automatic speech recognition (ASR). These errors may propagate, resulting in unexpected changes in the flow of conversation. We analyzed how such changes are linked with learning gains and learners’ rapport with the agents. Our results show they are not related to learning gains or rapport, regardless of the types of responses the agents should have returned given the correct input from learners without ASR errors. We also discuss the implications for optimal error-recovery policies for teachable agents that can be drawn from these findings.
Towards Automatic Tutoring of Custom Student-Stated Math Word Problems

Math Word Problem (MWP) solving for teaching math with Intelligent Tutoring Systems (ITSs) faces a major limitation: ITSs only supervise pre-registered problems, requiring substantial manual effort to add new ones. ITSs cannot assist with student-generated problems. To address this, we propose an automated approach to translate MWPs to an ITS’s internal representation using pre-trained language models to convert MWP to Python code, which can then be imported easily. Experimental evaluation using various code models demonstrates our approach’s accuracy and potential for improvement.
Annotating Educational Dialog Act with Data Augmentation in Online One-on-One Tutoring

During the COVID-19 pandemic, educational activities have shifted online, providing opportunities for researchers to analyze interaction data between teachers and students. In this study, we focus on automatically annotating dialog acts in one-on-one tutoring on online platforms. We address the challenge of limited training data, particularly for “rare codes”, by proposing a data augmentation pipeline that leverages GPT-3.5’s generative ability to create synthetic, multi-labeled dialog data. Experiments with real online tutoring platform data demonstrate the effectiveness of our approach in enhancing the machine annotator’s accuracy.
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization

Out-of-distribution (OOD) graph generalization are critical for many real-world applications. Existing methods neglect to discard spurious or noisy features of inputs, which are irrelevant to the label. Besides, they mainly conduct instance-level class-invariant graph learning and fail to utilize the structural class relationships between graph instances. In this work, we endeavor to address these issues in a unified framework, dubbed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> ndividual and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tructural <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</b> nformation <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b> ottlenecks ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IS-GIB</i> ). To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings. To leverage the structural intra- and inter-domain correlations, we propose Structural Graph Information Bottleneck (S-GIB). Specifically for a batch of graphs with multiple domains, S-GIB first computes the pair-wise input-input, embedding-embedding, and label-label correlations. Then it minimizes the mutual information between input graph and embedding pairs while maximizing the mutual information between embedding and label pairs. The critical insight of S-GIB is to simultaneously discard spurious features and learn invariant features from a high-order perspective by maintaining class relationships under multiple distributional shifts. Notably, we unify the proposed I-GIB and S-GIB to form our complementary framework IS-GIB. Extensive experiments conducted on both node- and graph-level tasks consistently demonstrate the superior generalization ability of IS-GIB. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/YangLing0818/GraphOOD</uri> .
Discourse Markers Used by Non-Native Speakers in Good Morning America Talk Show

Discourse markers have been studied extensively in linguistics and communication studies. Its use is very helpful in understanding the message conveyed, both in formal and informal situations. This study examines the discourse markers used by non-native speakers in Good Morning America Talk Show. Unveil the types and their functions, this study uses a qualitative descriptive method in identifying the use of discourse markers. The data were taken from conversations of non-native speakers using Biber et al (1999) theory to determine the types and functions of discourse markers. The results of this study showed that there are ten types of discourse markers interjection, greetings and farewells, linking adverbials, stance adverbials, vocatives, response elicitors, response forms, hesitators, various polite speech-act formulas and expletives. Those markers have variations functions as marker of information management, marker of response, marker of connectives, marker of cause and effect, marker of temporal adverb and marker of information participation. This study has shown that the use of discourse markers varies across languages and cultures. Moreover, the use of discourse markers can also vary depending on the context of the communication.
UAV for target placement in buildings for retrofitting purposes

UAV for target placement in buildings for retrofitting purposes Kepa Iturralde, Wenlan Shen, Tao Ma, Soroush Fazeli, Hükar Suci, Runfeng Lyu, Weihang Li, Jui Wen Yeh, Phillip Hübner, Nikhita Kurupakulu Venkat, Thomas Bock Pages 593-596 (2023 Proceedings of the 40th ISARC, Chennai, India, ISBN 978-0-6458322-0-4, ISSN 2413-5844) Abstract: The use of UAVs in the construction industry is a rather novel concept whose applications are not widespread yet. This research aims to develop a UAV with an end effector used to fix targets on a building façade. With the use of this application, the use of manual force and cranes can be eliminated. The UAV also can reach places with complicated geometry and greater heights which is difficult to be performed manually. The finite element analysis was performed before manufacturing and the flight simulation was performed using ROS. Results are still ongoing but promising. Keywords: UAV, end effector, simulation, ROS DOI: https://doi.org/10.22260/ISARC2023/0080 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley Presentation Video: https://youtu.be/LXJncuH2gds
Asymptote-based scientific animation

This article discusses a universal way to create animation using Asymptote the language for vector graphics. The Asymptote language itself has a built-in library for creating animations, but its practical use is complicated by an extremely brief description in the official documentation and unstable execution of existing examples. The purpose of this article is to eliminate this gap. The method we describe is based on creating a PDF-file with frames using Asymptote, with further converting it into a set of PNG images and merging them into a video using FFmpeg. All stages are described in detail, which allows the reader to use the described method without being familiar with the used utilities.