Reviewerly/exHarmony huggingface dataset:
MV-Datalog+/-: Effective Rule-based Reasoning with Uncertain Observations (Extended Abstract)

Modern data processing applications often combine information from a variety of complex sources. Oftentimes, some of these sources, like Machine-Learning systems or crowd-sourced data, are not strictly binary but associated with some degree of confidence in the observation. Ideally, reasoning over such data should take this additional information into account as much as possible. To this end, we propose extensions of Datalog and Datalog+/- to the semantics of Lukasiewicz logic Ł, one of the most common fuzzy logics. We show that such an extension preserves important properties from the classical case and how these properties can lead to efficient reasoning procedures for these new languages.
CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation

The hybrid architecture of convolutional neural networks (CNNs) and Transformer are very popular for medical image segmentation. However, it suffers from two challenges. First, although a CNNs branch can capture the local image features using vanilla convolution, it cannot achieve adaptive feature learning. Second, although a Transformer branch can capture the global features, it ignores the channel and cross-dimensional self-attention, resulting in a low segmentation accuracy on complex-content images. To address these challenges, we propose a novel hybrid architecture of convolutional neural networks hand in hand with vision Transformers (CiT-Net) for medical image segmentation. Our network has two advantages. First, we design a dynamic deformable convolution and apply it to the CNNs branch, which overcomes the weak feature extraction ability due to fixed-size convolution kernels and the stiff design of sharing kernel parameters among different inputs. Second, we design a shifted-window adaptive complementary attention module and a compact convolutional projection. We apply them to the Transformer branch to learn the cross-dimensional long-term dependency for medical images. Experimental results show that our CiT-Net provides better medical image segmentation results than popular SOTA methods. Besides, our CiT-Net requires lower parameters and less computational costs and does not rely on pre-training. The code is publicly available at https://github.com/SR0920/CiT-Net.
Mixed Realities

Not only real and virtual worlds can coexist, but they can be mixed. In this chapter, we emphasize the techniques of augmented reality and mixed reality. Augmented reality generally requires tracking methods to make strict correspondence between virtual and real worlds. Such methods need a collaboration between computer graphics and computer vision. The role of deep learning methods and AI methods is today essential. This chapter will discuss the role of deep learning in 3D hand pose.
Combining theory of mind and abductive reasoning in agent-oriented programming

Abstract This paper presents a novel model, called T om A bd , that endows autonomous agents with Theory of Mind capabilities. T om A bd agents are able to simulate the perspective of the world that their peers have and reason from their perspective. Furthermore, T om A bd agents can reason from the perspective of others down to an arbitrary level of recursion , using Theory of Mind of $$n^{\text {th}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>n</mml:mi> <mml:mtext>th</mml:mtext> </mml:msup> </mml:math> order. By combining the previous capability with abductive reasoning, T om A bd agents can infer the beliefs that others were relying upon to select their actions, hence putting them in a more informed position when it comes to their own decision-making. We have tested the T om A bd model in the challenging domain of Hanabi, a game characterised by cooperation and imperfect information. Our results show that the abilities granted by the T om A bd model boost the performance of the team along a variety of metrics, including final score, efficiency of communication, and uncertainty reduction.
A comprehensive survey of personal knowledge graphs

Abstract Information that can encapsulate a person's daily life and its different aspects provides insightful knowledge. This knowledge can prove to be more useful than general knowledge for improving personalized tasks. When it comes to storing such knowledge, personal knowledge graphs (PKGs) come in as handy saviors. PKGs are knowledge graphs which store details that are pertinent to a user but not, in general, useful to the rest of humanity. Conversational agents can access these PKGs to answer queries related to the user's day‐to‐day life, whereas recommender systems can harness the knowledge stored in PKGs to make personalized suggestions. Despite the immense applicability of PKGs, there has not been significant research in this area. We present an extensive review of PKGs. We categorize them according to the domains in which they are most relevant; in particular, we highlight the use of PKGs in medicine, finance, and education and research. We also categorize the different ways of constructing a PKG based on the source of data required for such constructions. Furthermore, we discuss the limitations of PKGs and suggest directions for future work. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Human Centricity and User Interaction Fundamental Concepts of Data and Knowledge &gt; Knowledge Representation Technologies &gt; Artificial Intelligence
Cyber hygiene concepts for nursing education

The healthcare industry has increasingly been targeted by cybercrime putting patients, organizations, and employees at risk for financial loss and breach of privacy. Malware events compromise system integrity and patient privacy which could lead to delays in treatment, loss of patient data, inability to provide care, and increase in patient harm. In addition, these attacks may also compromise private and personal information for those targeted.Nurses represent a large portion of frontline healthcare workers and are uniquely positioned to help prevent cyber-attacks. Nursing curriculum should include education about the risks to patient safety from cybercrime and the nurse's role in preventing cybercrime. Nursing education has focused on hygiene for patient safety. Adding cyber hygiene to the essential practices of pre-licensure and advanced practice nurses is a first step to protecting patients, organizations, and employees from the repercussions of a healthcare cyber-attack.
Remittances and emerging African economies' growth nexus in a post <scp>COVID</scp>‐19 era

Abstract This article aims to study the relationship between remittances and economic growth in African emerging economies from 1990 to 2021. The study centers on the optimistic, pessimistic, and the dual‐gap theories. The autoregressive distributed lag and cointegration techniques are applied. The findings indicate that remittances have a nonsignificantly negative effect on the gross domestic product growth rate. Domestic credit to the private sector and the inflation rate negatively influence economic growth. Policymakers should prioritize targeted response to the COVID‐19 crisis by reducing the costs of access to vaccines and healthcare for the populace so that the received remittances should significantly improve the population's welfare positively. Governments should recognize remittance services and reduce remittance costs by reducing taxes on remittances. Governments should increase the formalization of remittance transfers to improve accessibility to financial services to decrease the unbanked population. The monetary authorities should establish remittance‐based policies that promote the use of formal channels, ease the pressure on emerging countries' credit services, and improve foreign exchange rates. Banks should create financial products and services that attract investments and growth for the remittances of diasporas in developing economies.
Synergies and scaling of use case combinations in the field of asset logging and labeling

Abstract The digitalization of the energy sector enables a broad range of new digital use cases and business models. For instance, blockchain-technology can be used for the verification of tamper-resistant storage of asset data (asset logging) or manipulation-resistant guarantees of origin for electricity (labeling). Yet, it is associated with high implementation and operating effort. But many of these use cases require similar players, interfaces, data sets and data processing, so that synergies can result from a joint implementation. We thus evaluate these synergies in implementation and operating effort for use cases in the field of asset logging and labeling using a bottom-up evaluation of the components based on a methodology of Dossow (Energies 16:2424, 2022). Additionally, we extend this methodology to analyze the scalability of the use cases by assessing the relative effort reduction for an increasing number of players involved. The analysis already shows substantial synergies for combinations of two use cases. Yet, especially for combinations of three or more use cases a high effort reduction potential is derived. The highest synergies are obtained among the asset logging use cases, while a combination of asset logging and labeling use cases shows lower synergies in comparison. The analysis of the scaling of the use cases demonstrates that for labeling use cases the main effort driver is the number of consumers, while for asset logging use cases the number of asset operators shows to be more relevant. Thus, scaling effects outweigh the effort reduction potential of use case combinations especially for combinations of asset logging and labeling cases.
Document-Level Chemical-Induced Disease Semantic Relation Extraction Using Bidirectional Long Short-Term Memory on Dependency Graph

Abstract Identifying chemical-induced disease (CID) semantic relations in the biomedical literature, including both intra- and inter-sentence interactions, has significant implications for various downstream applications. Although various advanced methods have been proposed, they often overlook the cross-sentence dependency information, which is crucial for accurately predicting inter-sentence relations. In this study, we propose DEGREx, a novel graph-based neural model that presents a biomedical document as a dependency graph. DEGREx improves the long-distance relation extraction by allowing direct information exchange among document graph nodes through dependency connections. The information transition process is based on the idea of controller gates in long short-term memory networks. Our model, DEGREx, exerts a multi-task learning framework to jointly train relation extraction with named entity recognition, improving the performance of the CID extraction task. Experimental results on the benchmark dataset demonstrate that our model DEGREx outperforms all nine compared recent state-of-the-art models.
Dichotomous Image Segmentation with Frequency Priors

Dichotomous image segmentation (DIS) has a wide range of real-world applications and gained increasing research attention in recent years. In this paper, we propose to tackle DIS with informative frequency priors. Our model, called FP-DIS, stems from the fact that prior knowledge in the frequency domain can provide valuable cues to identify fine-grained object boundaries. Specifically, we propose a frequency prior generator to jointly utilize a fixed filter and learnable filters to extract informative frequency priors. Before embedding the frequency priors into the network, we first harmonize the multi-scale side-out features to reduce their heterogeneity. This is achieved by our feature harmonization module, which is based on a gating mechanism to harmonize the grouped features. Finally, we propose a frequency prior embedding module to embed the frequency priors into multi-scale features through an adaptive modulation strategy. Extensive experiments on the benchmark dataset, DIS5K, demonstrate that our FP-DIS outperforms state-of-the-art methods by a large margin in terms of key evaluation metrics.
Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding

Transformers achieve promising performance in document understanding because of their high effectiveness and still suffer from quadratic computational complexity dependency on the sequence length. General efficient transformers are challenging to be directly adapted to model document. They are unable to handle the layout representation in documents, e.g. word, line and paragraph, on different granularity levels and seem hard to achieve a good trade-off between efficiency and performance. To tackle the concerns, we propose Fast-StrucTexT, an efficient multi-modal framework based on the StrucTexT algorithm with an hourglass transformer architecture, for visual document understanding. Specifically, we design a modality-guided dynamic token merging block to make the model learn multi-granularity representation and prunes redundant tokens. Additionally, we present a multi-modal interaction module called Symmetry Cross-Attention (SCA) to consider multi-modal fusion and efficiently guide the token mergence. The SCA allows one modality input as query to calculate cross attention with another modality in a dual phase. Extensive experiments on FUNSD, SROIE, and CORD datasets demonstrate that our model achieves the state-of-the-art performance and almost 1.9x faster inference time than the state-of-the-art methods.
Knowledge Graphs Querying

Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.
Adapting Knowledge Inference Algorithms to Measure Geometry Competencies through a Puzzle Game

The rapid technological evolution of the last years has motivated students to develop capabilities that will prepare them for an unknown future in the 21st century. In this context, many teachers intend to optimise the learning process, making it more dynamic and exciting through the introduction of gamification. Thus, this article focuses on a data-driven assessment of geometry competencies, which are essential for developing problem-solving and higher-order thinking skills. Our main goal is to adapt, evaluate and compare Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), Elo, and Deep Knowledge Tracing (DKT) algorithms applied to the data of a geometry game named Shadowspect, in order to predict students’ performance by means of several classifier metrics. We analysed two algorithmic configurations, with and without prioritisation of Knowledge Components (KCs) – the skills needed to complete a puzzle successfully, and we found Elo to be the algorithm with the best prediction power with the ability to model the real knowledge of students. However, the best results are achieved without KCs because it is a challenging task to differentiate between KCs effectively in game environments. Our results prove that the above-mentioned algorithms can be applied in formal education to improve teaching, learning, and organisational efficiency.
Symmetric Enhancement of Visual Clarity through a Multi-Scale Dilated Residual Recurrent Network Approach for Image Deraining

Images captured during rainy days present the challenge of maintaining a symmetrical balance between foreground elements (like rain streaks) and the background scenery. The interplay between these rain-obscured images is reminiscent of the principle of symmetry, where one element, the rain streak, overshadows or disrupts the visual quality of the entire image. The challenge lies not just in eradicating the rain streaks but in ensuring the background is symmetrically restored to its original clarity. Recently, numerous deraining algorithms that employ deep learning techniques have been proposed, demonstrating promising results. Yet, achieving a perfect symmetrical balance by effectively removing rain streaks from a diverse set of images, while also symmetrically restoring the background details, is a monumental task. To address this issue, we introduce an image-deraining algorithm that leverages multi-scale dilated residual recurrent networks. The algorithm begins by utilizing convolutional activation layers to symmetrically process both the foreground and background features. Then, to ensure the symmetrical dissemination of the characteristics of rain streaks and the background, it employs long short-term memory networks in conjunction with gated recurrent units across various stages. The algorithm then incorporates dilated residual blocks (DRB), composed of dilated convolutions with three distinct dilation factors. This integration expands the receptive field, facilitating the extraction of deep, multi-scale features of both the rain streaks and background information. Furthermore, considering the complex and diverse nature of rain streaks, a channel attention (CA) mechanism is incorporated to capture richer image features and enhance the model’s performance. Ultimately, convolutional layers are employed to fuse the image features, resulting in a derained image. An evaluation encompassing seven benchmark datasets, assessed using five quality metrics against various conventional and modern algorithms, confirms the robustness and flexibility of our approach.
General Theory of Information, Digital Genome, Large Language Models, and Medical Knowledge-Driven Digital Assistant

Recent advances in large language models, our understanding of the general theory of information, and the availability of new approaches to building self-regulating domain-specific software are driving the creation of next-generation knowledge-driven digital assistants to improve the efficiency, resiliency, and scalability of various business processes while fulfilling the functional requirements addressing a specific business problem. Here, we describe the implementation of a medical-knowledge-based digital assistant that uses medical knowledge derived from various sources including the large language models and assists the early medical diagnosis process by reducing the knowledge gap between the patient and medical professionals involved in the process.
Introduction to mineral exploration

Given the ever-rising demand for raw earth materials, mineral reserves are being increasingly depleted, and there is a need to pinpoint the location of additional mineral resources. However, with the absence of grassroots exploration opportunities, one should employ a suite of tools for discovering covered, buried, or blind deposits. This book endeavors to delve into the recent advances in spatial toolboxes employed for mineral exploration. These techniques are remote sensing, geochemical, geophysical, machine-learning, and deep-learning tools, all processed in geographic information systems. This chapter covers the basic ideas applied to mineral exploration, including several stages leading to discovering an ore deposit. These include, but are not limited to, the reconnaissance stage, regional exploration, and local-scale surveys. This chapter will then introduce the spatial toolbox applied to mineral exploration surveys in the stages mentioned previously. Then, different aspects covered in the upcoming chapters will be comprehensively explained.
Type-1 and Type-2 Fuzzy Techniques: Application to Robotic Systems

Autonomous mobile robot navigation is a very important task in mobile robotic field. The navigation process is defined by the task to define the values of motion permitting the robot to displace from the start situation to the final destination without human actions and safely. If the mobile robot environment is free of obstacles, the problem is easy to handle. But as the environment is more complex, the robot control system needs more treatments, intelligence and capacities to handle the interactive uncertainties, imprecision that have affect each application of such robotic system. Behaviour-based navigation approaches are successful tools of subdivides the global navigation process into small systems in order to simplify the global task. These basic behaviours are: goal seeking, obstacle avoidance, wall-following, moving target pursuing, avoiding dynamic objects... Mobile robot navigation is one of the popular tasks encountered by imprecision and uncertainties and it has been studied using fuzzy logic systems. Controllers based on fuzzy theory have been applied and implemented in various types of engineering problems, especially in control and robotics. Type-1 fuzzy system is based on two-dimensional fuzzy membership functions. In some applications, this function cannot deal with the resulted uncertainty. Type-2 fuzzy membership function with their imprecise boundaries can give fuzzy sets with membership values instead of a crisp number. Type-1 fuzzy logic systems (T1FLSs) use precise and crisp type-1 fuzzy sets. The modelling process is based on the behaviour of the operator under specific conditions. However, this type of control system presents some limitations in treatment of uncertainties. Type-2 fuzzy logic systems (T2FLSs) introduce important tools to improve the robot performances. Type-2 fuzzy sets and their theoretical concepts create a new version of fuzzy logic controllers in robotic field. Type-2 fuzzy logic systems offer a good tool to model some levels of uncertainties which type-1 fuzzy logic system cannot do. The additional dimension of T2MF may give a better representation of uncertainty than T1MF. The T1FLS produces a T1FM and the result is a real value, whereas a Type-2 FLS produces a T2FM, and using the type-reducer, this fuzzy set will be converted to a T1FM. The type-reduced gives decision-making flexibilities. The objective of this chapter is to control a wheeled mobile robot using type-1 and type-2 Takagi-Sugeno (TS) fuzzy logic controllers. The proposed TS fuzzy controllers are used to infer actions for autonomous robot motion based-behaviors: goal seeking, obstacle avoidance, and wall-following. The simulation results are discussed and compared to show the effectiveness of the designed fuzzy logic controllers for automatic robot motion.