Reviewerly/exHarmony huggingface dataset:
Design and Optimization of Image Recognition and Classification Algorithm Based on Machine Learning

In recent years, traditional keyword based image retrieval and classification have been unable to meet people’s daily needs, and image recognition methods based on image content have emerged. This article uses a computer to achieve visual understanding of human body images. The improvement effect of adding different numbers of virtual samples on recognition, as well as the impact of adding different proportions of virtual images. At the same time, the virtual sample algorithm is applied to SAR image recognition. The experimental results show that adding virtual samples constructed by this method to expand the training set can improve the target recognition rate of SAR images, especially in the case of small samples, and significantly improve the recognition rate. This algorithm effectively characterizes the features of the image, improves the accuracy of classification, and has good classification performance.
Research on UHV Transmission Line Selection Strategy Aided by Satellite Remote Sensing Image

The core of power transmission line planning and design is the selection of transmission line path scheme, while ultra-high voltage overhead transmission lines span a long distance, and the geographical environment is diverse, which involves many influencing factors of optimization. In this paper, taking UHV transmission line selection as an example, the uncontrolled positioning accuracy and controlled positioning accuracy of satellite RS (remote sensing) images are analyzed and compared, and its adjustment accuracy is statistically analyzed in the case of no control points and control points. The research shows that after adding control points, the adjustment accuracy of DSM’s regional network reaches 2.71 m in plane and 2.91 m in elevation. It can meet the requirements of 1:10,000 scale elevation accuracy in mountainous and alpine areas. GA (genetic algorithm) constructed in this study can effectively bypass the avoidance areas such as residential areas, disaster points, nature reserves, scenic spots and ecologically sensitive areas when optimizing UHV transmission line selection, which greatly improves the accuracy and level of initial design of UHV transmission line selection.
Preserving Privacy in Arabic Judgments: AI-Powered Anonymization for Enhanced Legal Data Privacy

Jurisprudence involves studying, interpreting, and applying the law to comprehend its societal impact. Judges annually review cases to ensure accurate law application, which raises privacy concerns when accessing files from other courts. While the legal field has garnered interest from the research community, the challenge of masking personal data, particularly in the Arabic language with limited resources, remains in its early stages. To address this research gap, we develop a two-component system for generating anonymous Arabic judgments. The first component, a personal data extractor model, utilizes Named Entity Recognition (NER) to identify key individual entities like names, addresses, birthdays, case numbers, and national identity codes. We train this model on a purpose-built Arabic legal corpus. The second component involves a Python module designed to mask the personal entities extracted by the first component. Together, these components enable the generation of anonymous judgments. Our model achieves an F1-score of 96.14% when detecting entities in the created Arabic Legal corpus. Additionally, experiments on the ANERCorp corpus, with training and testing splits of 70%-30% and 90%-10%, yield F1-scores of 93.78% and 95.77%, respectively. With these results, our proposed system demonstrates the promising potential for generating anonymous Arabic judgments. Furthermore, the built Arabic legal corpus provides a valuable resource for researchers aiming to enhance domain-specific NER models in Arabic text.
Lung Cancer Prediction Using Electronic Claims Records: A Transformer-Based Approach

Electronic claims records (ECRs) are large scale and longitudinal collections of individual's medical service seeking actions. Compared to in-hospital medical records (EMRs), ECRs are more standardized and cross-sites. Recently, there has been studies showing promising results on modeling claims data for a wide range of medical applications. However, few of them address the exclusion criteria on cohort selection to extract new incidence without prior signs and also often lack of emphasis on predicting cancer in early stages. In this work, we aim to design a lung cancer prediction framework using ECRs with rigorous exclusion design using state-of-the-art sequence-based transformer. Furthermore, this work presents one of the first results by applying disease prediction model to the entire population in Taiwan. The result shows over 2.1 predictive power, 5 average positive predictive value (PPV), and 0.668 area under curve (AUC) in all-stage lung cancer and around 2.0 predictive power, 1 average PPV and 0.645 AUC in early-stage in our dataset. Sub-cohort analysis could funnel high precision selective group into prioritized clinical examination. Onset analysis validates the effect of our exclusion criteria. This work presents comprehensive analyses on lung cancer prediction, and the proposed approach can serve as a state-of-the-art disease risk prediction framework on claims data.
NLP-Crowdsourcing Hybrid Framework for Inter-Researcher Similarity Detection

Visualizing and examining the intellectual landscape and evolution of scientific communities to support collaboration is crucial for multiple research purposes. In some cases, measuring similarities and matching patterns between research publication document sets can help to identify people with similar interests for building research collaboration networks and university–industry linkages. The premise of this work is assessing feasibility for resolving ambiguous cases in similarity detection to determine authorship with natural language processing (NLP) techniques so that crowdsourcing is applied only in instances that require human judgment. Using an NLP-crowdsourcing convergence strategy, we can reduce the costs of microtask crowdsourcing while saving time and maintaining disambiguation accuracy over large datasets. This article contributes a next-gen crowd-artificial intelligence framework that used an ensemble of term frequency-inverse document frequency and bidirectional encoder representation from transformers to obtain similarity rankings for pairs of scientific documents. A sequence of content-based similarity tasks was created using a crowd-powered interface for solving disambiguation problems. Our experimental results suggest that an adaptive NLP-crowdsourcing hybrid framework has advantages for inter-researcher similarity detection tasks where fully automatic algorithms provide unsatisfactory results, with the goal of helping researchers discover potential collaborators using data-driven approaches.
On the Decomposition of Covariate Shift Assumption for the Set-to-Set Matching

The task of set matching, which models the quality of matching between pairs of sets, is expected to have a wide range of practical applications. However, many existing methods that address this task assume that the training and testing distributions are identical, which is frequently violated in real-world scenarios. To address this issue, the covariate shift assumption focuses on the shift in the distribution of covariates between the training and testing datasets. While several studies have analyzed this assumption for vector inputs, there is a lack of research on similar assumptions when the input is a pair of sets. In this study, we refine and redefine the covariate shift assumption in set matching and analyze how models perform under these conditions.
Deformable Object Manipulation With Constraints Using Path Set Planning and Tracking

In robotic deformable object manipulation (DOM) applications, constraints arise commonly from environments and task-specific requirements. Enabling DOM with constraints is therefore crucial for its deployment in practice. However, dealing with constraints turns out to be challenging due to many inherent factors such as inaccessible deformation models of deformable objects (DOs) and varying environmental setups. This article presents a systematic manipulation framework for DOM subject to constraints by proposing a novel path set planning and tracking scheme. First, constrained DOM tasks are formulated into a versatile optimization formalism which enables dynamic constraint imposition. Because of the lack of the local optimization objective and high state dimensionality, the formulated problem is not analytically solvable. To address this, planning of the path set, which collects paths of DO feedback points, is proposed subsequently to offer feasible path and motion references for DO in constrained setups. Both theoretical analyses and computationally efficient algorithmic implementation of path set planning are discussed. Lastly, a control architecture combining path set tracking and constraint handling is designed for task execution. The effectiveness of our methods is validated in a variety of DOM tasks with constrained experimental settings.
A Hierarchical Robot Learning Framework for Manipulator Reactive Motion Generation via Multi-Agent Reinforcement Learning and Riemannian Motion Policies

Manipulators motion planning faces new challenges as robots are increasingly used in dense, cluttered and dynamic environments. The recently proposed technique called Riemannian motion policies(RMPs) provides an elegant solution with clear mathematical interpretations to such challenging scenarios. It is based on differential geometry policies that generate reactive motions in dynamic environments with real-time performance. However, designing and combining RMPs is still a difficult task involving extensive parameter tuning, and typically seven or more RMPs need to be combined by using RMPflow to realize motions of a robot manipulator with more than 6 degrees-of-freedoms, where the RMPs parameters have to be empirically set each time. In this paper, we take a policy to decompose such complex policies into multiple learning modules based on reinforcement learning. Specifically, we propose a three-layer robot learning framework that consists of the basic-level, middle-level and top-level layers. At the basic layer, only two base RMPs i.e. target and collision avoidance are used to output reactive actions. At the middle-level layer, a hierarchical reinforcement learning approach is used to train an agent that automatically selects those RMPs and their parameters based on environmental changes and will be deployed at each joint. At the top-level layer, a multi-agent reinforcement learning approach trains all the joints with high-level collaborative policies to accomplish actions such as tracking a target and avoiding obstacles. With simulation experiments, we compare the proposed method with the baseline method and find that our method effectively produces superior actions and is better at avoiding obstacles, handling self-collisions, and avoiding singularities in dynamic environments. In addition, the proposed framework possesses higher training efficiency while leveraging the generalization ability of reinforcement learning to dynamic environments and improving safety and interpretability.
Securing Data in the Metaverse: What We Need to Know

The Metaverse is the next frontier for digital engagement, but with this new digital landscape comes new security challenges. As more people and businesses flock to the Metaverse to engage in virtual experiences and transactions, the need for secure data management has become increasingly critical. This chapter will examine the current state of data security in the Metaverse and explore the key challenges and risks associated with securing data in this new digital environment. The chapter will provide an overview of the various types of data that are vulnerable in the Metaverse, and the methods used to secure them. Additionally, the chapter will explore the potential use of emerging technologies such as Blockchain and Artificial Intelligence/Machine Learning to enhance data security in the Metaverse. The chapter will conclude with recommendations for organizations and individuals who are looking to operate securely in the Metaverse and will highlight the importance of considering data security in the Metaverse as a critical component of any digital strategy.
Training a Radial Basis Function Network Under Transformed Probability Measure

This letter deals with robust estimation of the output layer weights in a radial basis function network (RBFN) with predetermined hidden layer parameters. Specifically, we presume a RBFN regression model interfered by non-Gaussian impulsive noise. Under this framework, a new robust extension of the least-squares-estimator (LSE) is introduced. This estimator, called measure-transformed LSE (MT-LSE), operates by applying a transform to the joint probability measure associated with reshaped versions of the input-target training data pairs. The considered transform is generated by a non-negative function, called MT-function, that weights the data points. We show that proper selection of the MT-function substantially improves the estimation accuracy in the presence of impulsive noise, while maintaining the implementation simplicity of the standard LSE. The performance advantage of the MT-LSE, comparing to the LSE and other robust alternatives, is illustrated in a simulation study focusing on time-series prediction.
Companion Animal Disease Diagnostics Based on Literal-Aware Medical Knowledge Graph Representation Learning

Knowledge graph (KG) embedding has been used to benefit the diagnosis of animal diseases by analyzing electronic medical records (EMRs), such as notes and veterinary records. However, learning representations to capture entities and relations with literal information in KGs is challenging as the KGs show heterogeneous properties and various types of literal information. Meanwhile, the existing methods mostly aim to preserve graph structures surrounding target nodes without considering different types of literals, which could also carry significant information. In this paper, we propose a knowledge graph embedding model for the efficient diagnosis of animal diseases, which could learn various types of literal information and graph structure and fuse them into unified representations, namely LiteralKG. Specifically, we construct a knowledge graph that is built from EMRs along with literal information collected from various animal hospitals. We then fuse different types of entities and node feature information into unified vector representations through gate networks. Finally, we propose a self-supervised learning task to learn graph structure in pretext tasks and then towards various downstream tasks. Experimental results on link prediction tasks demonstrate that our model outperforms the baselines that consist of state-of-the-art models.
A Software Platform for Programmable Linear Optical Quantum Computer

Optical quantum computers represent qubits as photons created by laser and implement arbitrary quantum operations through Mach-Zehnder interferometers (MZIs) which is called linear optical quantum computing (LOQC). Since the number of qubits and quantum system is getting bigger, we need a scalable and universal quantum computing platform based on LOQC. In this work, we propose a software framework which can support a universal programming with flexibility of LOQC hardware configuration. The framework consists of four parts such as decomposer, mapper, controller, and user-interface controller. As an example, we show a use case of this framework for Bell state generation on Reck <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> .’s MZI network.
A Symmetrical Siamese Network Framework With Contrastive Learning for Pose-Robust Face Recognition

Face recognition has achieved remarkable success owing to the development of deep learning. However, most of existing face recognition models perform poorly against pose variations. We argue that, it is primarily caused by pose-based long-tailed data - imbalanced distribution of training samples between profile faces and near-frontal faces. Additionally, self-occlusion and nonlinear warping of facial textures caused by large pose variations also increase the difficulty in learning discriminative features of profile faces. In this study, we propose a novel framework called Symmetrical Siamese Network (SSN), which can simultaneously overcome the limitation of pose-based long-tailed data and pose-invariant features learning. Specifically, two sub-modules are proposed in the SSN, i.e., Feature-Consistence Learning sub-Net (FCLN) and Identity-Consistence Learning sub-Net (ICLN). For FCLN, the inputs are all face images on training dataset. Inspired by the contrastive learning, we simulate pose variations of faces and constrain the model to focus on the consistent areas between the original face image and its corresponding virtual pose face images. For ICLN, only profile images are used as inputs, and we propose to adopt Identity Consistence Loss to minimize the intra-class feature variation across different poses. The collaborative learning of two sub-modules guarantees that the parameters of network are updated in a relatively equal probability between near-frontal face images and profile images, so that the pose-based long-tailed problem can be effectively addressed. The proposed SSN shows comparable results over the state-of-the-art methods on several public datasets. In this study, LightCNN is selected as the backbone of SSN, and existing popular networks also can be used into our framework for pose-robust face recognition.
The New Economic Models of Metaverse and Its Implications in International Financial Markets

In recent years, the term “Metaverse” has gained popularity and attracted researchers and professionals from all around the world due to its rapid adaptation to emerging technologies, such as Web 3.0, virtual reality, and non-fungible tokens (NFTs). Additionally, the Metaverse has not only been integrated with the Decentralized Finance (DeFi) systems to exchange money but also been used to make insurance policies and secure loans, as well as to invest in various financial instruments such as funds and bonds. According to the forecasts of Goldman Sachs, the Metaverse system is expected to reach a US $8 trillion volume in the coming years due to its growing integration with the core functions of the traditional banking system. However, the development of the Metaverse itself is still in the early phases, and the potential risk exposure for the global financial system remains unknown. Therefore, this study seeks to identify the impacts of the Metaverse on the international financial system by focusing on its innovations and opportunities, as well as providing a macro framework for its ecosystem. The present study further analyses the economic advantages and disadvantages of building a blockchain-based decentralized Metaverse. This work is one of the first studies about Metaverse finance and thus offers important implications for financial literature. The findings of the study include valuable information for academics, investors, and policymakers in terms of potential financial and economic risks that the Metaverse may present.
An Adaptive Binary rANS With Probability Estimation in Reverse Order

We propose an adaptive binary implementation of the range version of Asymmetric Numeral Systems (rANS). First, as rANS encoder processes symbols in reverse order, we estimate the probabilities in forward order, store them into the encoder memory, and use them during the reverse encoding. It guarantees that both the encoder and the decoder have exactly the same probability estimation for each symbol. Second, we show how this approach can be implemented using probability estimation via Virtual Sliding Window (VSW). Finally, we demonstrate that comparing to rANS with a static model, the proposed adaptive binary rANS provides better compression performance having similar decoding complexity.
On Measures of Uncertainty in Classification

Uncertainty is unavoidable in classification tasks and might originate from data (e.g., due to noise or wrong labeling), or the model (e.g., due to erroneous assumptions, etc). Providing an assessment of uncertainty associated with each outcome is of paramount importance in assessing the reliability of classification algorithms, especially on unseen data. In this work, we propose two measures of uncertainty in classification. One of the measures is developed from a geometrical perspective and quantifies a classifier’s distance from a random guess. In contrast, the second proposed uncertainty measure is homophily-based since it takes into account the similarity between the classes. Accordingly, it reflects the type of mistaken classes. The proposed measures are not aggregated, i.e., they provide an uncertainty assessment to each data point. Moreover, they do not require label information. Using several datasets, we demonstrate the proposed measures’ differences and merit in assessing uncertainty in classification. The source code is available at github.com/pioui/uncertainty.
K-Deep Simplex: Manifold Learning via Local Dictionaries

We propose K- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Simplex (KDS)</i> which, given a set of data points, learns a dictionary comprising synthetic landmarks, along with representation coefficients supported on a simplex. KDS employs a local weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> penalty that encourages each data point to represent itself as a convex combination of nearby landmarks. We solve the proposed optimization program using alternating minimization and design an efficient, interpretable autoencoder using algorithm unrolling. We theoretically analyse the proposed program by relating the weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> penalty in KDS to a weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> program. Assuming that the data are generated from a Delaunay triangulation, we prove the equivalence of the weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and weighted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ℓ</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> programs. We further show the stability of the representation coefficients under mild geometrical assumptions. If the representation coefficients are fixed, we prove that the sub-problem of minimizing over the dictionary yields a unique solution. Further, we show that low-dimensional representations can be efficiently obtained from the covariance of the coefficient matrix. Experiments show that the algorithm is highly efficient and performs competitively on synthetic and real data sets.