Real-world Knowledge Research Articles

The role of alternative education to Students’ holistic learning: A case of Tanzanian schools in Morogoro

This research investigates how alternative education contributes to students' overall education. Data was gathered utilising a mixed-tech approach, which included questionnaires and interviews. The results demonstrated the need for alternative education in enhancing academic content because academic content alone is insufficient to help students build transferable skills, entrepreneurial abilities, and real-world situational knowledge. If a student fails the standardised national examination at the exit stages of school, they are still eligible for alternative education, which helps them deal with situations in the real world.

SSQ[formula omitted]: A Subgraph-based Semantic Query Approach for Temporal Knowledge Graph

Real-world knowledge graphs are growing in size with the explosion of data and rapid expansion of knowledge. There are some studies on knowledge graph query, but temporal knowledge graph (TKG) query is still a relatively unexplored field. A temporal knowledge graph is a knowledge graph that contains temporal information and contains knowledge that is likely to change over time. It introduces a temporal dimension that can characterize the changes and evolution of entities and relationships at different points in time. However, in the existing temporal knowledge graph query, the entity labels are one-sided, which cannot accurately reflect the semantic relationships of temporal knowledge graphs, resulting in incomplete query results. For the processing of temporal information in temporal knowledge graphs, we propose a temporal frame filtering approach and measure the acceptability of temporal frames by the new definition simtime based on the proposed three temporal frames and nine rules. For measuring the semantic relationship of predicates between entities, we vectorize the semantic similarity between predicates, i.e., edges, using the knowledge embedding model, and propose the new definition simpre to measure the semantic similarity of predicates. Based on these, we propose a new semantic temporal knowledge graph query method SSQTKG, and perform pruning operations to optimize the query efficiency of the algorithm based on connectivity. Extensive experiments show that SSQTKG can return more accurate and complete results that meet the query conditions in the semantic query and can improve the performance of the querying on the temporal knowledge graph.

Job Opportunities in Various Field for Criminology Graduates: Indian Perspective

ABSTRACT The study explores the wide range of career options available to criminology graduates in India, focusing on both traditional fields and emerging ones that have developed due to technological, sociopolitical, and economic changes. Employers in areas such as public policy, corporate security, intelligence, and the criminal justice system are increasingly seeking criminology graduates. In this evolving job market, these graduates face both opportunities and challenges, as employers demand specific skills, practical experience, and real-world knowledge from new candidates. This research provides a thorough analysis of how criminology graduates can adapt to the Indian job market and offers targeted recommendations for curriculum reform, industry collaborations, and government initiatives to enhance employability. Drawing from both international and Indian studies, the paper highlights how technological advancements and contemporary security needs have shaped employment patterns in India’s private sector and its criminal justice system.

Machine Learning-Based Prediction for High Health Care Utilizers by Using a Multi-Institutional Diabetes Registry: Model Training and Evaluation.

The cost of health care in many countries is increasing rapidly. There is a growing interest in using machine learning for predicting high health care utilizers for population health initiatives. Previous studies have focused on individuals who contribute to the highest financial burden. However, this group is small and represents a limited opportunity for long-term cost reduction. We developed a collection of models that predict future health care utilization at various thresholds. We utilized data from a multi-institutional diabetes database from the year 2019 to develop binary classification models. These models predict health care utilization in the subsequent year across 6 different outcomes: patients having a length of stay of ≥7, ≥14, and ≥30 days and emergency department attendance of ≥3, ≥5, and ≥10 visits. To address class imbalance, random and synthetic minority oversampling techniques were employed. The models were then applied to unseen data from 2020 and 2021 to predict health care utilization in the following year. A portfolio of performance metrics, with priority on area under the receiver operating characteristic curve, sensitivity, and positive predictive value, was used for comparison. Explainability analyses were conducted on the best performing models. When trained with random oversampling, 4 models, that is, logistic regression, multivariate adaptive regression splines, boosted trees, and multilayer perceptron consistently achieved high area under the receiver operating characteristic curve (>0.80) and sensitivity (>0.60) across training-validation and test data sets. Correcting for class imbalance proved critical for model performance. Important predictors for all outcomes included age, number of emergency department visits in the present year, chronic kidney disease stage, inpatient bed days in the present year, and mean hemoglobin A1c levels. Explainability analyses using partial dependence plots demonstrated that for the best performing models, the learned patterns were consistent with real-world knowledge, thereby supporting the validity of the models. We successfully developed machine learning models capable of predicting high service level utilization with strong performance and valid explainability. These models can be integrated into wider diabetes-related population health initiatives.

A Statistical Analysis of Knowledge Graph and its Applications

In today's fast-paced world driven by big data and artificial intelligence, organizing and representing vast amounts of knowledge is crucial. Knowledge graphs are dynamic structures serve as repositories for real-world knowledge, presented in the form of interconnected graph data. With their ability to encapsulate complex information, knowledge graphs have swiftly captured the attention of both academia and industry alike. In this paper, we dive deep into understanding knowledge graphs. In first Section, we start by looking at their origins and evolution. In second section describes knowledge graphs, compares them with other graph forms, and explores how academic societies have incorporated them. In conclusion, we discuss the various applications of knowledge graphs in various domains and highlight the software tools that are facilitating their advancement. By providing a systematic overview of knowledge graphs, we aim to illuminate new avenues for research and foster advancements in this burgeoning field.

Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets

Abstract Recent machine translation (MT) metrics calibrate their effectiveness by correlating with human judgement. However, these results are often obtained by averaging predictions across large test sets without any insights into the strengths and weaknesses of these metrics across different error types. Challenge sets are used to probe specific dimensions of metric behaviour but there are very few such datasets and they either focus on a limited number of phenomena or a limited number of language pairs. We introduce ACES, a contrastive challenge set spanning 146 language pairs, aimed at discovering whether metrics can identify 68 translation accuracy errors. These phenomena range from basic alterations at the word/character level to more intricate errors based on discourse and real-world knowledge. We conducted a large-scale study by benchmarking ACES on 47 metrics submitted to the WMT 2022 and WMT 2023 metrics shared tasks. We also measure their sensitivity to a range of linguistic phenomena. We further investigate claims that Large Language Models (LLMs) are effective as MT evaluators, addressing the limitations of previous studies by using a dataset that covers a range of linguistic phenomena and language pairs and includes both low- and medium-resource languages. Our results demonstrate that different metric families struggle with different phenomena and that LLM-based methods are unreliable. We expose a number of major flaws with existing methods: most metrics ignore the source sentence; metrics tend to prefer surface level overlap; and over-reliance on language-agnostic representations leads to confusion when the target language is similar to the source language. To further encourage detailed evaluation beyond singular scores, we expand ACES to include error span annotations, denoted as SPAN-ACES and we use this dataset to evaluate span-based error metrics, showing that these metrics also need considerable improvement. Based on our observations, we provide a set of recommendations for building better MT metrics, including focusing on error labels instead of scores, ensembling, designing metrics to explicitly focus on the source sentence, focusing on semantic content rather than relying on the lexical overlap, and choosing the right pre-trained model for obtaining representations.

Defining College Student Financial Literacy Utilizing the Delphi Method

Utilizing the Delphi method, the purpose of this study was to determine the most appropriate financial planning topics for the financial literacy of traditionally aged undergraduate college students. The Delphi method is a qualitative approach used to achieve convergence of opinion concerning real-world knowledge. At the end of the Delphi process, 38 specific topics reached consensus among experts that they were important for all college students to know. These 38 topics can also be grouped into nine larger themes: (a) budgeting, (b) credit, (c) insurance, (d) retirement/benefits, (e) investing, (f) debt management, (g) taxes, (h) banking, and (i) others. Reaching consensus on important topics provides colleges and universities with a road map of what topics they need to ensure that their students are learning. It also lays the foundation for the future development of a valid and reliable instrument to measure college student financial literacy.

Template-Based Contrastive Distillation Pretraining for Math Word Problem Solving.

Since math word problem (MWP) solving aims to transform natural language problem description into executable solution equations, an MWP solver needs to not only comprehend the real-world narrative described in the problem text but also identify the relationships among the quantifiers and variables implied in the problem and maps them into a reasonable solution equation logic. Recently, although deep learning models have made great progress in MWPs, they ignore the grounding equation logic implied by the problem text. Besides, as we all know, pretrained language models (PLM) have a wealth of knowledge and high-quality semantic representations, which may help solve MWPs, but they have not been explored in the MWP-solving task. To harvest the equation logic and real-world knowledge, we propose a template-based contrastive distillation pretraining (TCDP) approach based on a PLM-based encoder to incorporate mathematical logic knowledge by multiview contrastive learning while retaining rich real-world knowledge and high-quality semantic representation via knowledge distillation. We named the pretrained PLM-based encoder by our approach as MathEncoder. Specifically, the mathematical logic is first summarized by clustering the symbolic solution templates among MWPs and then injected into the deployed PLM-based encoder by conducting supervised contrastive learning based on the symbolic solution templates, which can represent the underlying solving logic in the problems. Meanwhile, the rich knowledge and high-quality semantic representation are retained by distilling them from a well-trained PLM-based teacher encoder into our MathEncoder. To validate the effectiveness of our pretrained MathEncoder, we construct a new solver named MathSolver by replacing the GRU-based encoder with our pretrained MathEncoder in GTS, which is a state-of-the-art MWP solver. The experimental results demonstrate that our method can carry a solver's understanding ability of MWPs to a new stage by outperforming existing state-of-the-art methods on two widely adopted benchmarks Math23K and CM17K. Code will be available at https://github.com/QinJinghui/tcdp.

Manufacturing resilience through disruption mitigation using attention-based consistently-attributed graph embedded decision support system

With global supply chains experiencing significant disruptions, there is increasing emphasis on enhancing manufacturing resilience by mitigating supply chain-propagated impacts on production entities in a holistic manner. However, existing mitigative strategies typically rely on implicit experience, which may be biased and suboptimal. Furthermore, the lack of consideration towards the chain of custody, multi-disruption management, and transportation networks results in unintuitive systems. To overcome these challenges, this study proposes a graph embedding-based mitigation decision support system (GEM-DSS) using a heterogeneous industrial knowledge graph to facilitate disruption management. Addressing the prevailing industrial challenge of knowledge representations and graph incompleteness, an attention-based consistently-attributed graph embedding (ACAGE) model is proposed, with the capability to learn and factor in multi-attributes and multi-structures within the knowledge graph. Through supplier selection and material substitution mitigation strategies, two computational pipelines are designed as part of a dual-stage search mechanism to generate feasible solutions. The experimental results of the ACAGE model for knowledge graph completion outperform several attributed graph embedding baselines, achieving 0.999 AUC-ROC and 0.990 AUC-PR on the constructed real-world industrial knowledge graph. The functionality and applicability of the GEM-DSS are demonstrated in an automotive case study with dynamic considerations via a cognitive computing methodology. This study aims to provide valuable insights for both academia and industry towards enabling manufacturing continuity in the face of global supply chain challenges.

Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study.

The integrity and reliability of clinical research outcomes rely heavily on access to vast amounts of data. However, the fragmented distribution of these data across multiple institutions, along with ethical and regulatory barriers, presents significant challenges to accessing relevant data. While federated learning offers a promising solution to leverage insights from fragmented data sets, its adoption faces hurdles due to implementation complexities, scalability issues, and inclusivity challenges. This paper introduces Federated Learning for Everyone (FL4E), an accessible framework facilitating multistakeholder collaboration in clinical research. It focuses on simplifying federated learning through an innovative ecosystem-based approach. The "degree of federation" is a fundamental concept of FL4E, allowing for flexible integration of federated and centralized learning models. This feature provides a customizable solution by enabling users to choose the level of data decentralization based on specific health care settings or project needs, making federated learning more adaptable and efficient. By using an ecosystem-based collaborative learning strategy, FL4E encourages a comprehensive platform for managing real-world data, enhancing collaboration and knowledge sharing among its stakeholders. Evaluating FL4E's effectiveness using real-world health care data sets has highlighted its ecosystem-oriented and inclusive design. By applying hybrid models to 2 distinct analytical tasks-classification and survival analysis-within real-world settings, we have effectively measured the "degree of federation" across various contexts. These evaluations show that FL4E's hybrid models not only match the performance of fully federated models but also avoid the substantial overhead usually linked with these models. Achieving this balance greatly enhances collaborative initiatives and broadens the scope of analytical possibilities within the ecosystem. FL4E represents a significant step forward in collaborative clinical research by merging the benefits of centralized and federated learning. Its modular ecosystem-based design and the "degree of federation" feature make it an inclusive, customizable framework suitable for a wide array of clinical research scenarios, promising to revolutionize the field through improved collaboration and data use. Detailed implementation and analyses are available on the associated GitHub repository.

Language models, like humans, show content effects on reasoning tasks.

reasoning is a key ability for an intelligent system. Large language models (LMs) achieve above-chance performance on abstract reasoning tasks but exhibit many imperfections. However, human abstract reasoning is also imperfect. Human reasoning is affected by our real-world knowledge and beliefs, and shows notable "content effects"; humans reason more reliably when the semantic content of a problem supports the correct logical inferences. These content-entangled reasoning patterns are central to debates about the fundamental nature of human intelligence. Here, we investigate whether language models-whose prior expectations capture some aspects of human knowledge-similarly mix content into their answers to logic problems. We explored this question across three logical reasoning tasks: natural language inference, judging the logical validity of syllogisms, and the Wason selection task. We evaluate state of the art LMs, as well as humans, and find that the LMs reflect many of the same qualitative human patterns on these tasks-like humans, models answer more accurately when the semantic content of a task supports the logical inferences. These parallels are reflected in accuracy patterns, and in some lower-level features like the relationship between LM confidence over possible answers and human response times. However, in some cases the humans and models behave differently-particularly on the Wason task, where humans perform much worse than large models, and exhibit a distinct error pattern. Our findings have implications for understanding possible contributors to these human cognitive effects, as well as the factors that influence language model performance.

GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding

Integrating large language models with knowledge graphs derived from domain-specific data represents an important advancement towards more powerful and factual reasoning. As these models grow more capable, it is crucial to enable them to perform multi-step inferences over real-world knowledge graphs while minimizing hallucination. While large language models excel at conversation and text generation, their ability to reason over domain-specialized graphs of interconnected entities remains limited. For example, can we query a model to identify the optimal contact in a professional network for a specific goal, based on relationships and attributes in a private database? The answer is no – such capabilities lie beyond current methods. However, this question underscores a critical technical gap that must be addressed. Many high-value applications in areas such as science, security, and e-commerce rely on proprietary knowledge graphs encoding unique structures, relationships, and logical constraints. We introduce a fine-tuning framework for developing Graph-aligned Language Models (GaLM) that transforms a knowledge graph into an alternate text representation with labeled question-answer pairs. We demonstrate that grounding the models in specific graph-based knowledge expands the models’ capacity for structure-based reasoning. Our methodology leverages the large-language model's generative capabilities to create the dataset and proposes an efficient alternate to retrieval-augmented generation styled methods.

Fuser: An enhanced multimodal fusion framework with congruent reinforced perceptron for hateful memes detection

As a multimodal form of hate speech on social media, hateful memes are more aggressive and cryptic threats to the real life of humans. Automatic detection of hateful memes is crucial, but the images and texts in most memes are only weakly consistent or even irrelevant. Although existing works have achieved the initial goal of detecting hateful memes with pre-trained models, they are limited to monolithic inference methods while ignoring the semantic differences between multimodal representations. To strengthen the comprehension and reasoning of the hidden meaning behind the memes by combining real-world knowledge, we propose an enhanced multimodal fusion framework with congruent reinforced perceptron for hateful memes detection. Inspired by the human cognitive mechanism, we first divide the extracted multisource representations into main semantics and auxiliary contexts based on their strength and relevance, and then precode them into lightly correlated embeddings with unified spatial dimensions via a novel prefix uniform layer, respectively. To jointly learn the intrinsic correlation between primary and secondary semantics, a congruent reinforced perceptron with brain-like perceptual integration is designed to seamlessly fuse multimodal representations in a shared latent space while maintaining the feature integrity in the sub-fusion space, thereby implicitly reasoning about the subtle metaphors behind the memes. Extensive experiments on four benchmark datasets fully demonstrate the effectiveness and superiority of our architecture compared with previous state-of-the-art methods.

A comparison of chain-of-thought reasoning strategies across datasets and models

Emergent chain-of-thought (CoT) reasoning capabilities promise to improve the performance and explainability of large language models (LLMs). However, uncertainties remain about how reasoning strategies formulated for previous model generations generalize to new model generations and different datasets. In this small-scale study, we compare different reasoning strategies induced by zero-shot prompting across six recently released LLMs (davinci-002, davinci-003, GPT-3.5-turbo, GPT-4, Flan-T5-xxl and Cohere command-xlarge). We test them on six question-answering datasets that require real-world knowledge application and logical verbal reasoning, including datasets from scientific and medical domains. Our findings demonstrate that while some variations in effectiveness occur, gains from CoT reasoning strategies remain robust across different models and datasets. GPT-4 benefits the most from current state-of-the-art reasoning strategies and performs best by applying a prompt previously discovered through automated discovery.

Zero-Shot Construction of Chinese Medical Knowledge Graph with GPT-3.5-turbo and GPT-4

Knowledge graphs have revolutionized the organization and retrieval of real-world knowledge, prompting interest in automatic NLP-based approaches for extracting medical knowledge from texts. However, the availability of high-quality Chinese medical knowledge remains limited, posing challenges for constructing Chinese medical knowledge graphs. As LLMs like ChatGPT show promise in zero-shot learning for many NLP downstream tasks, their potential on constructing Chinese medical knowledge graphs is still uncertain. In this study, we create a Chinese medical knowledge graph by manually annotating textual data and using ChatGPT to automatically generate the graph. We refine the results using filtering and mapping rules to align with our schema. The manually generated graph serves as the ground truth for evaluation, and we explore different methods to enhance its accuracy through knowledge graph completion techniques. As a result, we emphasize the potential of employing ChatGPT for automated knowledge graph construction within the Chinese medical domain. While ChatGPT successfully identifies a larger number of entities, further enhancements are required to improve its performance in extracting more qualified relations.

Bridging Gaps: Pre-Service Mathematics Teachers’ Handling the Difficulties in Posing Real-World Mathematical Problems

Real-world mathematical problem (RWMP) solving and posing are important aspects of teaching and learning mathematical modelling, as well as developing a mathematization disposition for both teachers and students. Several researchers have explored blockages or difficulties in such modelling processes and in problem posing. However, prior research has identified difficulties that the pre-service mathematics teachers (PSMTs) encountered when they tried to pose a modelling problem by choosing the general topic themselves, as there is little known about possible obstacles that PSMTs can encounter when trying to pose a real-world problem relevant to a given mathematical topic. The current study explored the difficulties encountered by PSMTs in a RWMP-posing activity. The target group was 23 PSMTs with prior experience in mathematical modelling and mathematical problem posing. The findings showed that the PSMTs struggled with: (a) task organization, which involved selecting and understanding mathematical knowledge; (b) specialized content knowledge, which included a lack of real-world knowledge and difficulty in connecting mathematical concepts to real-world contexts; and (c) individual considerations of aptness, which encompassed authenticity, interest, complexity, language, and relevance to task organization. The PSMTs applied various strategies to complete the posing task, such as using problem-posing and solving heuristics, adapting existing problems, sharing and discussing with friends, and considering the perspective of a typical student. The implications of these findings should help in developing preparatory instructional practices for mathematics teachers.

Multi-Filter soft shrinkage network for knowledge graph embedding

Incompleteness is a prominent issue pervasive in real-world knowledge graphs, and link prediction techniques, which utilize known facts to forecast missing or unknown links, have emerged as a critical technology for knowledge graph completion. Recently, convolutional neural network-based knowledge graph embedding (KGE) models have achieved breakthroughs in the field of link prediction by increasing the number of interactions between entities and relations. However, current models mainly emphasize stacking complex architectures to capture more interaction features, ignoring the fact that, as the number of features increases significantly, irrelevant features might overshadow important ones, thus impacting the performance of link prediction. To tackle this problem, this paper introduces a novel KGE model that is based on a multi-filter soft shrinkage network (MFSSN). First, this model adaptively constructs filters based on entity embedding and relationship embedding, thereby increasing the interactions between entities and relationships. Secondly, the soft shrinkage function is innovatively introduced into the model, and a specialized soft shrinkage sub-network is innovatively designed to effectively eliminate noise features and improve attention to important features. Finally, the attention mechanism is added to the network to enhance important features and suppress useless features, improving the ability to utilize features, and thereby enhancing model performance. Through extensive experiments on five benchmark datasets of different sizes, the strong performance and generalization ability of the proposed model are proven. Compared with contrasting methods, it achieves performance leadership in almost all indicators.

What drives the automatic retrieval of real-world object size knowledge?

Real-world object size is a behaviorally relevant object property that is automatically retrieved when viewing object images: participants are faster to indicate the bigger of two object images when this object is also bigger in the real world. What drives this size Stroop effect? One possibility is that it reflects the automatic retrieval of real-world size after objects are recognized at the basic level (e.g., recognizing an object as a plane activates large real-world size). An alternative possibility is that the size Stroop effect is driven by automatic associations between low-/mid-level visual features (e.g., rectilinearity) and real-world size, bypassing object recognition. Here, we tested both accounts. In Experiment 1, objects were displayed upright and inverted, slowing down recognition while equating visual features. Inversion strongly reduced the Stroop effect, indicating that object recognition contributed to the Stroop effect. Independently of inversion, however, trial-wise differences in rectilinearity also contributed to the Stroop effect. In Experiment 2, the Stroop effect was compared between manmade objects (for which rectilinearity was associated with size) and animals (no association between rectilinearity and size). The Stroop effect was larger for animals than for manmade objects, indicating that rectilinear feature differences were not necessary for the Stroop effect. Finally, in Experiment 3, unrecognizable "texform" objects that maintained size-related visual feature differences were displayed upright and inverted. Results revealed a small Stroop effect for both upright and inverted conditions. Altogether, these results indicate that the size Stroop effect partly follows object recognition with an additional contribution from visual feature associations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Anchoring Path for Inductive Relation Prediction in Knowledge Graphs

Aiming to accurately predict missing edges representing relations between entities, which are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role in enhancing the comprehensiveness and utility of KGs. Recent research focuses on path-based methods due to their inductive and explainable properties. However, these methods face a great challenge when lots of reasoning paths do not form Closed Paths (CPs) in the KG. To address this challenge, we propose Anchoring Path Sentence Transformer (APST) by introducing Anchoring Paths (APs) to alleviate the reliance of CPs. Specifically, we develop a search-based description retrieval method to enrich entity descriptions and an assessment mechanism to evaluate the rationality of APs. APST takes both APs and CPs as the inputs of a unified Sentence Transformer architecture, enabling comprehensive predictions and high-quality explanations. We evaluate APST on three public datasets and achieve state-of-the-art (SOTA) performance in 30 of 36 transductive, inductive, and few-shot experimental settings.

Spatiotemporal knowledge graph completion via diachronic and transregional word embedding

Knowledge Graph Completion (KGC) is an essential application in the field of knowledge graphs (KGs) that attempts to fill in the missing information in the process of KG modelling. With the popularity of temporal knowledge graphs (TKGs), a wide range of techniques based on temporal knowledge graph completion (TKGC) have appeared, solving the issue of real-world knowledge with temporal properties. However, there is little study on KGC with spatiotemporal attributes, some real-world data include both spatial and temporal attributes. Effectively handling the completion of missing entities or predicates in spatiotemporal knowledge graphs (STKGs) is an important challenge. Our study fills the gap in knowledge completion techniques in the field of STKG. We present a model for completion based on the well-known tensor factorization canonical polyadic (CP) decomposition. It introduces temporal and spatial attributes into the decomposition vector to achieve entity and link predictions. We name it diachronic and transregional word embedding (DT-WE), which includes two different modules: the embedding framework and the scoring module. Firstly, send the vectors to the embedding framework to get the new vector representation, then, we send it to the scoring module to compute, and finally, the resulting values are added together to compute the prediction probability. We conducted extensive experiments on three real-world STKGs: YAGO10K, Wikidata40K and Opensky. The results indicate that the newly introduced spatiotemporal attributes not only improve accuracy in predicting entities and predicates compared to temporal models but also achieve state-of-the-art performance with lower spatial complexity.