Reviewerly/exHarmony huggingface dataset:
RDF-star2Vec: RDF-star Graph Embeddings for Data Mining

Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (< <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subject</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">predicate</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">object</i> >). Knowledge graph embedding (KGE) is crucial in machine learning applications, specifically in node classification and link prediction tasks. KGE remains a vital research topic within the semantic web community. RDF-star introduces the concept of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">quoted triple</i> (QT), a specific form of triple employed either as the subject or object within another triple. Moreover, RDF-star permits a QT to act as compositional entities within another QT, thereby enabling the representation of recursive, hyper-relational KGs with nested structures. However, existing KGE models fail to adequately learn the semantics of QTs and entities, primarily because they do not account for RDF-star graphs containing multi-leveled nested QTs and QT–QT relationships. This study introduces RDF-star2Vec, a novel KGE model specifically designed for RDF-star graphs. RDF-star2Vec introduces graph walk techniques that enable probabilistic transitions between a QT and its compositional entities. Feature vectors for QTs, entities, and relations are derived from generated sequences through the structured skip-gram model. Additionally, we provide a dataset and a benchmarking framework for data mining tasks focused on complex RDF-star graphs. Evaluative experiments demonstrated that RDF-star2Vec yielded superior performance compared to recent extensions of RDF2Vec in various tasks including classification, clustering, entity relatedness, and QT similarity.
A Study on Children's Experience Exhibition Programs in Children's Museums: Focusing on the Development of Creativity

The purpose of this paper is to discuss the development of a creativity development exhibition program in connection with the Nuri curriculum through a case analysis of an infant experience exhibition program at a children's museum that performs a complementary function to school education. For this purpose, independent children's museums in Korea were selected and the characteristics of children's experiential exhibitions were analyzed. Based on the results, we sought an exhibition program plan to support the development of children's creativity in connection with the Nuri curriculum. The direction for developing exhibition programs to improve creativity is as follows. First, lifestyle topics based on the Nuri course are organized as exhibition topics, and ‘Spring, Summer, Fall, Winter’ and ‘Life Tools’, which are reported to contain many creative elements, are mainly considered. Second, the cognitive and affective aspects of creativity are improved by applying experiential exhibition techniques that provide complex interactions that combine emotional thinking and physical activity. Third, we actively develop exhibition content with sustainable themes that can produce diverse experiences.
Tumor Markers

Tumor markers are substances that reflect the existence of tumors. They do not exist in normal adult tissues but only in embryonic tissues; their content in tumor tissues is much higher than in normal tissues. Their existence and quantitative change can indicate the nature of tumors, and help understand the histogenesis, cell differentiation, and cell function of tumors, thus aiding the diagnosis, classification, prognosis judgment, and treatment monitoring.
Multi-modal Deep Learning Approach to Improve Sentence level Sinhala Sign Language Recognition

Sign language is used across the world for communication purposes within hearing-impaired communities. Hearing people are not well versed in sign language and most hearing-impaired are not good in general text, creating a communication barrier. Research on Sign Language Recognition (SLR) systems have shown admirable solutions for this issue. In Sri Lanka, machine learning along with neural networks has been the prominent domain of research in Sinhala SLR. All previous research is mainly focused on word-level SLR using hand gestures for translation. While this works for a certain vocabulary, there are many signs interpreted through other spatial cues like lip movements and facial expressions. Therefore, translation is limited and sometimes the interpretations can be misleading. In this research, we propose a multi-modal Deep Learning approach that can effectively recognize sentence-level sign gestures using hand and lip movements and translate to Sinhala text. The model consists of modules for visual feature extraction (ResNet), contextual relationship modeling (transformer encoder with multi-head attention), alignment (CTC) and decoding (Prefix beam search). A dataset consisting 22 of sentences used for evaluations was collected under controlled conditions for a specific day-to-day scenario (a conversation between a vendor and a customer in a shop). The proposed model achieves a best Word Error Rate (WER) of 12.70 on the testing split, improving over the single-stream model which shows a best WER of 17.41, suggesting a multi-modal approach improves overall SLR.
Importance and Various Types of Task Scheduling in Cloud Computing System

Cloud Computing has arisen as quite possibly the main new computing procedures in the undertaking. A blend of advances and cycles has prompted an insurgency in the manner that computing is created and conveyed to end client. Cloud Computing traces all the way back to 1950 for example the enormous scope centralized servers were made accessible to huge undertakings. Basically, the cloud architecture is built with the combined perspective of cloud provider, user, and the broker. Scheduling is the most important task in any working framework, and it is controlled by the CPU. This is due to the fact that resources are not stored in a particularly designated manner, but rather in a strictly handy manner in order to ensure maximum skills. Cloud computing is a relatively new technology that allows users to access computer infrastructure and other services on a demand basis. Various types of task schedulers have been developed to make use of the vast amount of cloud-based resources users can access while also improving the management of data centers. We can say that cloud computing has transformed IT by allowing users and consumers to access services via the Internet. These services range from hardware to software, saving money on both the expense of setting up physical resources and the cost of obtaining the appropriate software licenses. The task scheduling problem is one of the most important and prominent challenges facing the cloud computing system.
Designing intervention prototypes to improve infant and young child nutrition in Peru: a participatory design study protocol

Introduction Nutrition during the complementary feeding period (6–23 months) is critical to ensure optimal growth and reduce the risk of diet-related disease across the life course. Strategies to reduce multiple forms of malnutrition (stunting, overweight/obesity and anaemia) in infants and young children (IYC) are a key priority in low-income and middle-income countries, including Peru. This study aims to co-design and develop prototypes for interventions to address the multiple forms of malnutrition in IYC in urban Peru, using a participatory design approach. Methods and analysis The study will be based within peri-urban communities in two areas of Peru (Lima and Huánuco city). Following the identification of key nutritional challenges for IYC aged 6–23 months through formative research (phase I), we will conduct a series of workshops bringing together healthcare professionals from government health centres and caregivers of IYC aged 6–23 months. Workshops (on idea generation; creating future scenarios; storyboarding and early implementation and feedback) will take place in parallel in the two study areas. Through these workshops, we will engage with community participants to explore, experiment, co-design and iteratively validate new design ideas to address the challenges around IYC complementary feeding from phase I. Workshop outputs and transcripts will be analysed qualitatively using affinity diagramming and thematic analyses. The intervention prototypes will be evaluated qualitatively and piloted with the participating communities. Ethics and dissemination Ethical approval for this study was obtained from the Ethical Review Committee of the Instituto de Investigación Nutricional (IIN) Peru (388-2019/CIEI-IIN), Loughborough University (C19-87) and confirmed by Cardiff University. Findings of the participatory design process will be disseminated through a deliberative workshop in Lima, Peru with national and regional government stakeholders, as well as participants and researchers involved in the design process. Further dissemination will take place through policy briefs, conferences and academic publications.
Secure Cloud Storage using End-to-End Encryption

Abstract: Cloud Storage is revolutionizing how the world man- ages data by providing an incredible level of flexibility, increased capacity, and easy accessibility. Nevertheless, the technology development is associated with a number of fears related to data protection and privacy. E2EE is an end-to-end encryption concept for ensuring secure storage, transmission as well as integration of data in a cloud environment. With E2EE in place, data is encrypted right at the source and decrypted only when it reaches its intended end-points, ensuring that it remains securely hidden from eavesdroppers or other unauthorized party. As the world becomes more connected, with data housed on distant servers and traversed by broad networks, it becomes essential that one comprehends the significance of E2EE in securing cloud storage. This encryption technique does not only act as a safeguard; it is an emerging and ever-changing field that has broad implication on data protection, users’ privacy and the future of Cloudbased information management. The primary goal of this paper is to provide a thorough understanding of the evolving trends and implications of End- to-End Encryption (E2EE) in the realm of secure cloud storage. By synthesizing and analysing the latest research findings and practical implementations, we aim to shed light on how E2EE fundamentally reshapes the landscape of cloud data security.
RANCANG BANGUN SISTEM INFORMASI PENCATATAN KEUANGAN PADA UMKM STUDI KASUS TOKO LILIS

UMKM or Micro, Small and Medium Enterprises is a term for small businesses in Indonesia. MSMEs are one of the important pillars of the Indonesian economy, because they help absorb labor and strengthen the economic structure in various regions. However, even so, most MSME business actors, one of which is Mrs. Llilis' shop, have difficulty keeping financial records on their business. Meanwhile, when a business can carry out financial records quickly and well, at least the business will get an overview of strategic planning in the future. Because of this, analysis and design research was carried out for SMEs in the case study of this research, namely the Lilis shop. This research begins with analyzing the business processes at Lilis shop, after that it designs future applications and ends with implementation and testing.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI MANAJEMEN EKSTRAKURIKULER DI SD KARTKA X-3

SD Kartika X-3 stands out among elementary schools in West Bandung due to its exceptional non-academic achievements, which is why the school mandates student participation in extracurricular activities. However, managing a large number of extracurricular members poses challenges for the school. These issues encompass the registration process, reporting of values, data retrieval, and communication regarding extracurricular activities. To address these challenges, the authors have developed an Extracurricular Management Information System. Data collection methods employed include observation, interviews, and documentation, with the system development based on the prototype methodology. System analysis involves tools like Flowmap and evaluation tables, while system design employs UML diagrams, such as Use Case Diagrams, Activity Diagrams, Sequence Diagrams, and Class Diagrams, in addition to utilizing Balsamiq for interface design, and MySQL for database design. System testing is conducted using the BlackBox approach. The Extracurricular Management Information System streamlines extracurricular data management, prevents paper accumulation, and facilitates the generation of attendance reports.
ANALISIS DAN PENERAPAN SISTEM INFORMASI PRESENSI GURU BERBASIS WEB MENGGUNAKAN QR CODE PADA SMP NEGERI 2 PARONGPONG

The teacher presence information system is a strategic step to increase the effectiveness and efficiency of education in schools. With advances in technology, currently, especially in the field of website development, a website attendance system is needed to facilitate the academic community in carrying out administrative processes. By using the website, the presence system becomes more effective because the academic community can access it from any device. The existence of a web-based presence system also reduces the risk of data loss and damage because the data has been stored in computerized Big Data. The method used in this study uses the Waterfall method or what is often called the Waterfall model. The steps taken are needs analysis, system design using UML (Unifeld Modeling Language), Use Case Diagrams, Activity Diagrams, Class Diagrams, Sequence Diagrams, the programming language used is HTML (Hyper Text Markup Language), PHP (Hypertext Preprocessor), CSS (Cascading Style Sheets) in addition to Jquery, MySQL as a Database and testing with black box testing. Designing, creating and building an information system is a complex process, involving various aspects such as User requirements, Interface design, the ability to record attendance history, data management processes, and the use of software development methods. the main goal is to create a system that can provide maximum benefits in managing teacher attendance in the school environment. The use of QR Code technology in designing a website teacher attendance information system is to take advantage of modern innovations to optimize efficiency and accuracy in the attendance process.
Scene Graph Enhanced Pseudo-Labeling for Referring Expression Comprehension

Referring Expression Comprehension (ReC) is a task that involves localizing objects in images based on natural language expressions. Most ReC methods typically approach the task as a supervised learning problem. However, the need for costly annotations, such as clear image-text pairs or region-text pairs, hinders the scalability of existing approaches. In this work, we propose a novel scene graph-based framework that automatically generates high-quality pseudo region-query pairs. Our method harnesses scene graphs to capture the relationships between objects in images and generate expressions enriched with relation information. To ensure accurate mapping between visual regions and text, we introduce an external module that employs a calibration algorithm to filter out ambiguous queries. Additionally, we employ a rewriter module to enhance the diversity of our generated pseudo queries through rewriting. Extensive experiments demonstrate that our method outperforms previous pseudo-labeling methods by about 10%, 12%, and 11% on RefCOCO, RefCOCO+, and RefCOCOg, respectively. Furthermore, it surpasses the state-of-the-art unsupervised approach by more than 15% on the RefCOCO dataset.
ReasoningLM: Enabling Structural Subgraph Reasoning in Pre-trained Language Models for Question Answering over Knowledge Graph

Question Answering over Knowledge Graph (KGQA) aims to seek answer entities for the natural language question from a large-scale Knowledge Graph (KG). To better perform reasoning on KG, recent work typically adopts a pre-trained language model (PLM) to model the question, and a graph neural network (GNN) based module to perform multi-hop reasoning on the KG. Despite the effectiveness, due to the divergence in model architecture, the PLM and GNN are not closely integrated, limiting the knowledge sharing and fine-grained feature interactions. To solve it, we aim to simplify the above two-module approach, and develop a more capable PLM that can directly support subgraph reasoning for KGQA, namely ReasoningLM. In our approach, we propose a subgraph-aware self-attention mechanism to imitate the GNN for performing structured reasoning, and also adopt an adaptation tuning strategy to adapt the model parameters with 20,000 subgraphs with synthesized questions. After adaptation, the PLM can be parameter-efficient fine-tuned on downstream tasks. Experiments show that ReasoningLM surpasses state-of-the-art models by a large margin, even with fewer updated parameters and less training data. Our codes and data are publicly available at https://github.com/RUCAIBox/ReasoningLM.
Generating Irish Text with a Flexible Plug-and-Play Architecture

In this paper, we describe M-FleNS, a multilingual flexible plug-and-play architecture designed to accommodate neural and symbolic modules, and initially instantiated with rule-based modules. We focus on using M-FleNS for the specific purpose of building new resources for Irish, a language currently under-represented in the NLP landscape. We present the general M-FleNS framework and how we use it to build an Irish Natural Language Generation system for verbalising part of the DBpedia ontology and building a multilayered dataset with rich linguistic annotations. Via automatic and human assessments of the output texts we show that with very limited resources we are able to create a system that reaches high levels of fluency and semantic accuracy, while having very low energy and memory requirements.
Search Augmented Instruction Learning

Large language models (LLMs) have been significantly improved by instruction fine-tuning, but still lack transparency and the ability to utilize up-to-date knowledge and information. In this work, we propose search-augmented instruction learning (SAIL), which grounds the language generation and instruction following abilities on complex search results generated by in-house and external search engines. With an instruction tuning corpus, we collect search results for each training case from different search APIs and domains, and construct a new search-grounded training set containing (instruction, grounding information, response) triplets. We then fine-tune the LLaMA-7B model on the constructed training set. Since the collected results contain unrelated and disputing languages, the model needs to learn to ground on trustworthy search results, filter out distracting passages, and generate the target response. The search result-denoising process entails explicit trustworthy information selection and multi-hop reasoning, since the retrieved passages might be informative but not contain the instruction-following answer. Experiments show that the fine-tuned SAIL-7B model has a strong instruction-following ability, and it performs significantly better on transparency-sensitive tasks, including open-ended question answering and fact checking.
CASSI: Contextual and Semantic Structure-based Interpolation Augmentation for Low-Resource NER

While text augmentation methods have been successful in improving performance in the low-resource setting, they suffer from annotation corruption for a token-level task like NER. Moreover, existing methods cannot reliably add context diversity to the dataset, which has been shown to be crucial for low-resource NER. In this work, we propose Contextual and Semantic Structure-based Interpolation (CASSI), a novel augmentation scheme that generates high-quality contextually diverse augmentations while avoiding annotation corruption by structurally combining a pair of semantically similar sentences to generate a new sentence while maintaining semantic correctness and fluency. To accomplish this, we generate candidate augmentations by performing multiple dependency parsing-based exchanges in a pair of semantically similar sentences that are filtered via scoring with a pretrained Masked Language Model and a metric to promote specificity. Experiments show that CASSI consistently outperforms existing methods at multiple low resource levels, in multiple languages, and for noisy and clean text.
Aligning Language Models to User Opinions

An important aspect of developing LLMs that interact with humans is to align models’ behavior to their users. It is possible to prompt an LLM into behaving as a certain persona, especially a user group or ideological persona the model captured during its pertaining stage. But, how to best align an LLM with a specific user and not a demographic or ideological group remains an open question. Mining public opinion surveys (by PEW research), we find that the opinions of a user and their demographics and ideologies are not mutual predictors. We use this insight to align LLMs by modeling relevant past user opinions in addition to user demographics and ideology, achieving up to 7 points accuracy gains in predicting public opinions from survey questions across a broad set of topics. Our work opens up the research avenues to bring user opinions as an important ingredient in aligning language models.
Hierarchical Catalogue Generation for Literature Review: A Benchmark

Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy. We observe that a high-quality catalogue-guided generation process can effectively alleviate this problem. Therefore, we present an atomic and challenging task named Hierarchical Catalogue Generation for Literature Review as the first step for review generation, which aims to produce a hierarchical catalogue of a review paper given various references. We construct a novel English Hierarchical Catalogues of Literature Reviews Dataset with 7.6k literature review catalogues and 389k reference papers. To accurately assess the model performance, we design two evaluation metrics for informativeness and similarity to ground truth from semantics and structure. Our extensive analyses verify the high quality of our dataset and the effectiveness of our evaluation metrics. We further benchmark diverse experiments on state-of-the-art summarization models like BART and large language models like ChatGPT to evaluate their capabilities. We further discuss potential directions for this task to motivate future research.
SJTU-MTLAB’s Submission to the WMT23 Word-Level Auto Completion Task

Word-level auto-completion (WLAC) plays a crucial role in Computer-Assisted Translation. In this paper, we describe the SJTU-MTLAB’s submission to the WMT23 WLAC task. We propose a joint method to incorporate the machine translation task to the WLAC task. The proposed approach is general and can be applied to various encoder-based architectures. Through extensive experiments, we demonstrate that our approach can greatly improve performance, while maintaining significantly small model sizes.