Reviewerly/exHarmony huggingface dataset:
Binding Data Narrations - Corroborating the Plausibility of Scientific Narratives by Open Research Data

Narratives determine our worldviews, but need evidence to be believable. For scientific narratives, hard evidence usually is provided by specially curated research data and experiments within each publication. The aim is to strengthen the narratives' overall plausibility: the less plausible a narrative is, the more it is bound to be challenged. As recent advances in NLP enabled the scientific community to tackle important problems in fact-checking and more profound semantic interpretations of structured data, there is now also hope to unlock the rich narrative knowledge that such data sets can offer. Yet, current strategies to extract such narrative knowledge still heavily rely on exhaustive bottom-up analysis to cast insights from data into a human-understandable form. In this paper, we take a novel integration-based approach to design a system that reduces the task of finding narrative evidence to applying a sequence of simpler top-down matching tasks. Our BiND system builds upon an expressive definition of structured narratives. It uses them as templates for schema and instance matching against web tables, thereby computing flexible bindings between narratives and data. By combining structured narratives with a carefully chosen selection of statistical metrics to assess the inherent relationships between different attributes of the matched data, our system allows us to reliably identify the most plausible witnesses for a given narrative. We demonstrate the applicability of our system in the real world on the vast open data repository of the World Health Organization (WHO).
2nd International Workshop on Digital Language Archives*

LangArc 2023 virtual workshop on digital language archives - digital libraries that preserve, curate, and provide online access to language data - continues (after the initial LangArc 2021 workshop) addressing the growing need. LangArc 2023 explores a broad scope of issues related to digital language archives. This includes challenges and opportunities, strategies and solutions for: facilitating depositing and improving access; information organization, architecture, and retrieval; quality assurance; usability; ethical issues; ways of encouraging reuse of deposited data in research and education; and coursework and other training for information professionals that will develop and maintain digital language archives. This workshop is expected to support interdisciplinary collaboration among information professionals, linguists, educators, representatives of language communities (including indigenous and other underrepresented), students, and other interested audiences.
What's in the News? Towards Identification of Bias by Commission, Omission, and Source Selection (COSS)

In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) as a joint three-fold objective, as opposed to the previous work separately addressing these types of bias. In a pipeline concept, we describe the goals and tasks of its steps toward bias identification and provide an example of a visualization that leverages the extracted features and patterns of text reuse.
CoCon: A Data Set on Combined Contextualized Research Artifact Use

In the wake of information overload in academia, methodologies and systems for search, recommendation, and prediction to aid researchers in identifying relevant research are actively studied and developed. Existing work, however, is limited in terms of granularity, focusing only on the level of papers or a single type of artifact, such as data sets. To enable more holistic analyses and systems dealing with academic publications and their content, we propose CoCon, a large scholarly data set reflecting the combined use of research artifacts, contextualized in academic publications' full-text. Our data set comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. We additionally formalize a link prediction task for “combined research artifact use prediction” and provide code to utilize analyses of and the development of ML applications on our data. All data and code is publicly available at https://github.com/IllDepence/contextgraph.
A New Approach to Quality Assessment of Chinese-English Neural Machine Translation

In recent years, with the increasing correlation between the machine assessment and the human assessment, translation quality assessment has drawn great attention from a lot of machine translation researchers. Therefore, it is of great significance to conduct researches on approaches to sentence-level translation quality assessment. A present, such approaches are mainly from three different angles: traditional machine learning, neural networks, and pretrained language models. The neural networks translation is able to, although to some extent, identify the underlying syntactic structures of the source language, it cannot appropriately translate the syntax within the sentences from a linguistic perspective. Therefore, this paper proposes a simple but effective model to solve it by making use of Transformer. This model consists of Transformer bottleneck layer networks and two-way long short-term memory networks. Parameters of Transformer bottleneck layer are preliminarily optimized through bilingual parallel corpus, and all parameters of the model are jointly optimized and slightly adjusted through translation quality assessment corpora. The experimental results show that the proposed method can improve the model accuracy of the translation quality assessment.
Measuring the Growth of Ideas in a Title Corpus

Beyond bibliometrics, there is interest in characterizing the evolution of the number of ideas in scientific papers, or more generally, exploring the progress of science. We use various tokenization and phrase extraction strategies combined with lexical diversity metrics to analyze titles in our corpus. We compared four lexical diversity metrics for each corpora variants, to look for indications that new concepts might be emerging over time.
Persistent Identifiers &amp; US Federal Agency Policies An Overview of the Recent Policies, PID Requirements and Timelines

In recent years the US Government has issued two policy guidance memos that will likely have a large impact on academic research. This workshop seeks to walk through the 2021 National Security Presidential Memo 33 (NSPM-33) [1] and the 2022 OSTP Public Access Memo [2] and share how use of persistent identifiers (PIDs) are key to ensuring compliance. We'll share PID aspects of the implementation guidance provided for NSPM-33, timelines and next steps for the Public Access Memo, and implementation options for both memos. Moving to the technical side, we'll discuss how the various persistent identifiers function in ensuring trustworthy metadata by from other systems via the API, specifically affiliation metadata (ROR), publication and grant metadata (Crossref) and author and contributing author metadata (ORCID). Finally, we'll ensure time for interaction and discussion to ensure that research institutions, research administrators, repository managers, are familiar with how their landscape as well as that of funders and publishers will change and how to ensure their community is well-prepared.
Reducing Overfitting In Deep Neural Networks By Intra-class Decorrelation

Although deep learning models have achieved remarkable progress in various domains, the issue of overfitting remains a significant challenge. Our research proposes a novel regularization technique called ICD, that addresses this problem by enhancing generalization performance. We consider each hidden unit of a specific layer as a base-learner, and ICD regularization ensures the creation of diverse and non-redundant representations in deep learning models by minimizing intra-class cross-covariance among these base learners. Our experiments on diverse datasets and network architectures illustrate that ICD reduces overfitting and consistently enhances generalization performance.
Design and Implementation of Instant Messaging Service System for Internet Enterprises Based on Hierarchical Protection

Aiming at the problems of serious information leakage and high control difficulty in the instant messaging service system of Internet enterprises, this paper designs a hierarchical protection instant messaging service system, analyzes the software architecture around identity authentication, notification service, message service and file service, and constructs a hierarchical protection system of instant messaging service system. Finally, the system implementation is analyzed for important modules. The overall performance of the communication system is good, and the information security, protection and controllability are remarkable, which can meet the development needs of Internet enterprises, and make sure they can provide excellent service.
Internet of Things (IoT) and Smart Home Automation: Enhancing Living Spaces

The Internet of Things (IoT) has emerged as a transformative technology with the potential to revolutionize various aspects of modern living, including the way we interact with our living spaces. This paper explores the integration of IoT into smart home automation systems and its implications for enhancing the functionality, efficiency, and convenience of living spaces. We discuss the challenges and opportunities associated with this technology, and present a comprehensive analysis of existing and proposed systems. Through an extensive review of the literature, we highlight the benefits and potential drawbacks of IoT-enabled smart homes. The findings of this research emphasize the need for robust security measures, interoperability standards, and user-centered design to fully realize the potential of IoT in shaping the future of living spaces
Machine failure prediction using joint reserve intelligence with feature selection technique

A model with high accuracy of machine failure prediction is important for any machine life cycle.In this paper, a prediction model based on machine learning methods is proposed.The used method is a combination of machine learning algorithms and techniques.The machine learning algorithm is a data mining technique that has been widely used as a prediction model for classifying problems.Five algorithms have been tested including JRIP, logistic, KStar, Bayes network and decision table machine learning.The evaluation process is done by applying the algorithms on a predictive dataset using different performance measures.In the proposed model, the feature selection and voting techniques are used and applied in the classification process for each classifier.From the comparison of the result, the feature selection shows the best performance result.Paired ttest evaluation measures were considered to confirm our conclusion.The best accuracy result among the five classifiers shows that joint reserve intelligence classifier can be used to predict the failure with an accuracy high as 0.983.Applying classifier subset evaluation using the JRIP classifier can enhance the accuracy result to be 0.985.The finding shows that the proposed model improves the results of the classifiers.
Portable and integrated entanglement sources for quantum communication

We present integrated sources of polarization entanglement built on portable platforms for quantum communication. We employ type-II degenerate spontaneous parametric down conversion processes in periodically poled potassium titanyl phosphate (PPKTP) waveguides to generate pairs of orthogonally polarized photons at near-infrared wavelengths. We use balanced non-polarizing beam splitter (NPBS) to generate post-selected polarization-entangled states via coincidence measurements and investigate the power and spectral tunability of the system. The small size of the waveguides allows the installation of several units on the same platform, and thereby the generation of multiple entangled states simultaneously. We built a separate platform containing a computer-controlled tomography system to characterize the produced states and another NPBS is installed to combine photons from adjacent waveguides and assess their interferability, a prerequisite to entanglement swapping. The overall experiment is compact and requires little alignment when set, making it an attractive option for local quantum networking using mobile platforms, e.g., drones or satellites.
Expanding the scope of quantum annealing applicability

With the emergence of D-wave’s quantum annealing machine, the power of quantum computing has become tangible, and the development of quantum computers is accelerating rapidly. Unfortunately, at present, the minimum value search that D-wave’s quantum annealing machine can handle is limited to quadratic functions of binary integer variables. This constraint is due to both hardware and software limitations. If this constraint was to be lifted, it would undoubtedly bring about a revolution in the world. Therefore, in this study, we obtained the basic mathematics of using quantum annealing, which enables hyper-parallel computing using qubits, to find the global minimum of any minimum value problem. Furthermore, we conducted simple simulations using classical computers to demonstrate the effectiveness of the ideas and algorithms. Furthermore, we conducted simple simulations using classical computers to demonstrate the effectiveness of the ideas and algorithms. We obtained also the mathematical method of using quantum annealing in learning neural networks and solving linear equations.
A mobile robot path planning using improved beetle swarm optimization algorithm in static environment

Optimization problems in the field of industrial engineering usually involve massive amounts of information and complex scheduling process with the characteristics of high-dimension and non-convexity, which bring many challenges to finding an optimal solution. We proposed an improved beetle swarm optimization (IBSO) algorithm demonstrating the potential to solve different problems of path planning in static environment with good performance. Firstly, the algorithm is an upgrade of the original beetle antennae search (BAS) algorithm and the search strategy is improved by replacing a single beetle by multiple beetles. Secondly, the global search ability gets enhanced, and the diversity of optimization is improved through introducing nonlinear sinusoidal disturbance with Levy flight mechanism in beetles’ position. Finally, the search performance of beetle swarm is improved by simulating the characteristics of employment bees to search for a better solution near the honey source field in the Artificial Bee Colony (ABC) algorithm. Our experiment results show that IBSO algorithm can achieve higher search efficiency and wider search ranges through well balancing the advantages of local search and fast optimization of the BAS algorithm with the global search of the improved mechanism. The IBSO algorithm has shown the potential to provide a new solution for several optimization problems in path planning in static environment.
EDUKASI BELAJAR MEMBACA MENGGUNAKAN TEKNOLOGI SPEECH RECOGNITION

One of the problem with learning facilities, teachers in teaching are still using conventional methods where teachers provide material to students according to the 2013 curriculum standards, namely using thematic books by inviting students to directly participate directly in learning while students who cannot read and pronounce letters properly The teacher must provide separate time for students. In this research the development method used is Waterfall planning, analysis, design, implementation, and the final stage, namely. making applications with the Speech Recognition Method. The results of the research are in the form of media applications for learning to read using speech recognition technology on smartphones based on learning media that presents letters of the alphabet, recognizes consonants, recognizes vowels and games in the form of guessing words and finding pairs of pictures&#x0D;
The Impact of Puzzle-Based Game With Scaffolding-Aid on Cultural Heritage Learning: Evidence From Eye Movements

The pedagogical value and global relevance of heritage education regarding the safeguarding of global tradition and practice for future generations is indisputable. This research aims to investigate the efficacy of integrating a puzzle-based game, with scaffolding strategies, to enhance students’ understanding and appreciation of cultural heritage content. In the activity, students are tasked with reconstructing art patterns from cultural heritage artifacts, a process which is designed to bolster their performance in recognizing and appreciating such patterns. A control group, utilizing a non-interactive instructional video with the same content, was also established to contrast and evaluate the benefits of the game-based approach. The evaluation of knowledge acquisition and transfer was conducted through pattern tests, with the participants’ eye movements recorded during the testing process. The results show that the participants in the game group exhibited significant advantages in terms of efficiently recognizing fundamental content and effectively transferring pattern recognition. Furthermore, the examination of the visual behavior within the game reveals that the integration of the jigsaw puzzle (game mechanic) and scaffolding (instructional strategy) can enhance the efficacy and cognitive strategies employed by participants in comprehending heritage content. This study develops design methodologies for cultural heritage games that enhances learning and appreciation.
Semantic models and tools for designing adaptive user interfaces of intelligent systems

Objectives . Models and tools for designing adaptive user interfaces for intelligent systems are being developed. The relevance is determined by the need to reduce overhead costs and development time for user interfaces and to provide their adaptation to the specific characteristics of the user of the intelligent system. Methods . Existing approaches to designing user interfaces are being analyzed. A semantic model of an adaptive user interface for intelligent systems is proposed, implemented using a basic universal language for representing knowledge based on set theory and graph theory. Results . An adaptive user interface model for intelligent systems has been developed, which includes a knowledge base model of the user interface, an agent-oriented model of the user interface, and a library of reusable components that provide integration of the user interface into both individual intelligent systems and intelligent systems groups. A method of transferring user interface components within an intelligent systems group during the operation of an intelligent system has also been developed. Conclusion . Developed models and tools allow to simplify the reuse of user interface components together with knowledge base and problem-solving components in the design and development of individual intelligent systems, as well as in the design and development of a group of semantically compatible intelligent systems, ensuring automation of integration of user interfaces and their adaptation for each user. The developed set of user interface components has been included in a library of reusable user interface components. Further expansion of the set of components in the library and their integration into a group of semantically compatible intelligent systems is planned.
Integrating AI in Education: Opportunities, Challenges and Responsible Use of ChatGPT

The rapid evolution of artificial intelligence (AI) and associated technologies like machine learning, neural networks and natural language processing has led to innovative applications across diverse fields including education. One of the most prominent examples is ChatGPT, launched in November 2022, which is an AI system designed by OpenAI to engage in natural conversations and accomplish tasks based on prompts provided in plain language (Abdullah et al., 2022). ChatGPT has quickly gained immense popularity due to its ability to produce remarkably human-like text. It has over 100 million users interacting with it to generate essays, poems, dialogues, explanations and various other texts within seconds in a conversational format.The implications of AI systems like ChatGPT for the future of education have become a topic of animated debate and speculation. While some believe that AI will revolutionize education, others urge caution about overestimating its capabilities and minimizing its risks. This essay aims to provide a comprehensive overview of the opportunities and challenges associated with the application of ChatGPT and other AI technologies in education. It will discuss the key strengths and weaknesses of ChatGPT, analyze the main external factors that present potential benefits and risks, and propose strategies to integrate AI responsibly and ethically into education to enhance teaching and learning.
Class-Imbalanced and Fine-Grained Object Detection in Video Surveillance for Production Safety

Intelligent video surveillance has become increasingly popular due to its ability to perform real-time and highly precise scene analysis with little labor. However, real-world datasets for video surveillance in production safety face two major challenges that significantly impact the performance of recognition models. Our research aims to address them of imbalanced class distribution and fine-grained visual categorization while keeping computational costs low and minimizing model structure modifications. We incorporated plug-and-play attention modules into the YOLOX advanced object detection model and achieved significant performance improvements by combining a balanced cross-entropy loss, which uses a re-weighting strategy, and an entropy loss that enhances the ability to distinguish between similar categories. Our experiments demonstrate the effectiveness of this approach, with a total mAP increase of 3.7 and multiple visual contrast examples.