Reviewerly/exHarmony huggingface dataset:
Detection of Fraudulent Health Insurance Claims Based on Decision Tree with Principal Component Analysis

Fraudulent health insurance claims pose a significant challenge to insurance companies and healthcare providers, leading to substantial financial losses and compromised service quality. In this study, we focused on detecting fraudulent health insurance claims using the decision tree algorithm and principal component analysis (PCA). The objective was to gain valuable insights and extract meaningful patterns from the dataset to enhance fraud detection capabilities. We developed a comprehensive method that employed the decision tree algorithm to build a decision tree-based model and the PCA for dimensionality reduction. By analyzing the data using these algorithms, we were able to capture important patterns and relationships within the dataset. The decision tree algorithm demonstrated reasonable performance, while the PCA exhibited even better results, leveraging the advantage of dimensionality reduction. The findings of this study have significant implications for fraud detection in the healthcare industry. The insights gained from applying the decision tree algorithms and PCA can aid in making informed decisions, identifying trends, and uncovering hidden patterns within the data. Our study recommends implementing advanced fraud detection systems that incorporate these algorithms, continuous monitoring and evaluation, collaboration and data sharing, further research and development, and adherence to regulatory compliance. By following these recommendations, stakeholders in the insurance and healthcare industries can strengthen their fraud detection capabilities, protect their organizations from financial losses, and maintain the integrity of their services. The use of decision tree algorithms and PCA, combined with effective strategies, can significantly contribute to the detection of fraudulent health insurance claims and the overall security and sustainability of the healthcare system.
Path planning method of unmanned ship based on PPO and DWA

In order to solve the path planning problem of USV(unmanned surface vehicle), a global path planning method integrating global path and local path is proposed in this paper. First, the global path planning of grid electronic chart is carried out using two different reward modes of PPO(Proximal Policy Optimization Algorithms) algorithm, and the global optimal path is selected after comparison. Secondly, The inflection point on the global optimal path is selected as the key point of the DWA(dynamic window algorithm) to optimize the local path. The simulation results show that the method can take into account the requirements of high smoothness and short navigation distance, and plan a safe and reasonable route in the static environment.
Kinodynamic RRT* Based UAV Optimal State Motion Planning with Collision Risk Awareness

In this paper, an autonomous navigation strategy is proposed for unmanned aerial vehicles (UAVs) based on consideration of dynamic sampling and field of view (FOV). Compare to search-based motion planning, sampling-based kinodynamic planning schemes can often find feasible trajectories in complex environments. Specifically, a global trajectory is first generated with physical information, and an expansion algorithm is constructed regarding to kinodynamic rapidly-exploring random tree* (KRRT*). Then, a KRRT* expansion strategy is designed to find local collision-free trajectories. In trajectory optimization, bending radius, collision risk function, and yaw angle penalty term are defined by taking into account onboard sensor FOV and potentialrisk. Then, smooth and dynamic feasible terms are penalized based on initial trajectory generation. Trajectories are refined by time reallocation, and weights are solved by optimization. Effectiveness of the proposed strategy is demonstrated by both simulation and experiment.
NOC: High-Quality Neural Object Cloning with 3D Lifting of Segment Anything

With the development of the neural field, reconstructing the 3D model of a target object from multi-view inputs has recently attracted increasing attention from the community. Existing methods normally learn a neural field for the whole scene, while it is still under-explored how to reconstruct a certain object indicated by users on-the-fly. Considering the Segment Anything Model (SAM) has shown effectiveness in segmenting any 2D images, in this paper, we propose Neural Object Cloning (NOC), a novel high-quality 3D object reconstruction method, which leverages the benefits of both neural field and SAM from two aspects. Firstly, to separate the target object from the scene, we propose a novel strategy to lift the multi-view 2D segmentation masks of SAM into a unified 3D variation field. The 3D variation field is then projected into 2D space and generates the new prompts for SAM. This process is iterative until convergence to separate the target object from the scene. Then, apart from 2D masks, we further lift the 2D features of the SAM encoder into a 3D SAM field in order to improve the reconstruction quality of the target object. NOC lifts the 2D masks and features of SAM into the 3D neural field for high-quality target object reconstruction. We conduct detailed experiments on several benchmark datasets to demonstrate the advantages of our method. The code will be released.
Background and Preliminaries

The aim of this chapter is to familiarize the reader with the mathematical concepts and notations used in this book and cryptography terminology and notations. Also, the chapter presents which are the challenges for the security of cloudCloud computing computing and big data and why searchable and homomorphic encryption may resolve (some of) them.
IMPLEMENTASI METODE K-MEANS CLUSTERING DALAM MENGELOMPOKKAN KOMPETENSI MAHASISWA

Academic ability is the ability of a person who is included in the academic field with a measure can be in the form of value or so-called academic achievement. With this system helps in grouping students in their fields, and students can find out the competencies obtained based on the achievement index of the courses that have been determined. In making this system, researchers used Visual Studio 2012 and applied the K-Means Clustering algorithm method to get more accurate results in classifying student competencies. This system can be used for all departments, the testing is done at the Informatics Engineering Department of Malang Widyagama University. in this system uses the mysql database for storage. Based on the user test results that have been done, Based on the results of the user trials that have been conducted, of the 5 weighted values that have been determined, there is an average of the results of the trial which is 85.6% strongly agree with the existence of this system to find out the student competency group.It is expected that with this system. Can find out competency groups that are suitable for their fields, so that later they will produce graduates who are competent in accordance with their fields.Keywords : Competence, K-means Clustering, software visual
Coda — Managing the Commonplace Book Assignment: Putting this Volume to Practice

many of the contributors to this volume first began discussing how to use commonplace books in the classroom at the annual International Congress on Medi eval Studies in Kalamazoo, Michigan.In a roundtable format, we shared a variety of strategies for using commonplace books as a pedagogical tool, primarily in the British Literature 1 survey classroom.In the course of that discussion, one of our panel participants commented that pedagogical scholarship sometimes has a tendency to present a given strategy or classroom technique as a panacea.We read pedagogical scholarship hoping for the approach to teaching, the assignment idea, the insight that will jazz up our classroom, engage our students, improve retention, and make our lives as teachers easier with lightning-fast assessment.Many scholars of pedagogy write with an enviable optimism about how their ideas will accomplish all of these things. 1 Of course, no teaching strategy is perfect.There are a number of issues that may leave readers hesitant to integrate commonplace books into their teaching.In this coda to the volume, I hope to address some of the inherent challenges of using commonplace books that tend to surface when using this assignment.I will synthesize and 1 That said, there are important exceptions to this tendency.See
The Effect of Self-directed Learning Attitude on Class Satisfaction through Resilience

This study aims to analyze the effect of high school students' self-directed learning attitude on class satisfaction through resilience. To achieve this, a structural equation model was established to examine the relationship between the three variables, and data from 2,793 general high school students within the 9th year of SELS2010 were analyzed. The results showed the fit of the structural equation model was very good (CFI=.983, TLI=.976, RMSEA=.059, SRMR=.033). Moreover, self-directed learning attitude had a statistically significant positive effect on resilience and class satisfaction, and resilience also had a statistically significant positive effect on class satisfaction. Additionally, the mediating effect of resilience was found to be statistically significant. These findings suggest that educational efforts to enhance not only self-directed learning attitude but also resilience is needed to improve high school students' class satisfaction.
Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets

To achieve a high learning accuracy, generative adversarial networks (GANs) must be fed by large datasets that adequately represent the data space. However, in many scenarios, the available datasets may be limited and distributed across multiple agents, each of which is seeking to learn the distribution of the data on its own. In such scenarios, the agents often do not wish to share their local data as it can cause communication overhead for large datasets. In this paper, to address this multi-agent GAN problem, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner. BGAN allows the agents to gain information from other agents without sharing their real datasets but by ``brainstorming'' via the sharing of their generated data samples. In contrast to existing distributed GAN solutions, the proposed BGAN architecture is designed to be fully distributed, and it does not need any centralized controller. Moreover, BGANs are shown to be scalable and not dependent on the hyperparameters of the agents' deep neural networks (DNNs) thus enabling the agents to have different DNN architectures. Theoretically, the interactions between BGAN agents are analyzed as a game whose unique Nash equilibrium is derived. Experimental results show that BGAN can generate real-like data samples with higher quality and lower Jensen-Shannon divergence (JSD) and Fr\`echet Inception distance (FID) compared to other distributed GAN architectures.
SCREWS: A Modular Framework for Reasoning with Revisions

Large language models (LLMs) can improve their accuracy on various tasks through iteratively refining and revising their output based on feedback. We observe that these revisions can introduce errors, in which case it is better to roll back to a previous result. Further, revisions are typically homogeneous: they use the same reasoning method that produced the initial answer, which may not correct errors. To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions. It is comprised of three main modules: Sampling, Conditional Resampling, and Selection, each consisting of sub-modules that can be hand-selected per task. We show that SCREWS not only unifies several previous approaches under a common framework, but also reveals several novel strategies for identifying improved reasoning chains. We evaluate our framework with state-of-the-art LLMs (ChatGPT and GPT-4) on a diverse set of reasoning tasks and uncover useful new reasoning strategies for each: arithmetic word problems, multi-hop question answering, and code debugging. Heterogeneous revision strategies prove to be important, as does selection between original and revised candidates.
Computational Natural Philosophy: A Thread from Presocratics through Turing to ChatGPT

Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this approach's capabilities, utilizing reinforcement learning with human feedback (RLHF). Current research initiatives aim to integrate neural networks with symbolic computing, introducing a new generation of hybrid computational models.
Insights from an OTTR-centric Ontology Engineering Methodology

OTTR is a language for representing ontology modeling patterns, which enables to build ontologies or knowledge bases by instantiating templates. Thereby, particularities of the ontological representation language are hidden from the domain experts, and it enables ontology engineers to, to some extent, separate the processes of deciding about what information to model from deciding about how to model the information, e.g., which design patterns to use. Certain decisions can thus be postponed for the benefit of focusing on one of these processes. To date, only few works on ontology engineering where ontology templates are applied are described in the literature. In this paper, we outline our methodology and report findings from our ontology engineering activities in the domain of Material Science. In these activities, OTTR templates play a key role. Our ontology engineering process is bottom-up, as we begin modeling activities from existing data that is then, via templates, fed into a knowledge graph, and it is top-down, as we first focus on which data to model and postpone the decision of how to model the data. We find, among other things, that OTTR templates are especially useful as a means of communication with domain experts. Furthermore, we find that because OTTR templates encapsulate modeling decisions, the engineering process becomes flexible, meaning that design decisions can be changed at little cost.
ClusterFormer: Clustering As A Universal Visual Learner

This paper presents CLUSTERFORMER, a universal vision model that is based on the CLUSTERing paradigm with TransFORMER. It comprises two novel designs: 1. recurrent cross-attention clustering, which reformulates the cross-attention mechanism in Transformer and enables recursive updates of cluster centers to facilitate strong representation learning; and 2. feature dispatching, which uses the updated cluster centers to redistribute image features through similarity-based metrics, resulting in a transparent pipeline. This elegant design streamlines an explainable and transferable workflow, capable of tackling heterogeneous vision tasks (i.e., image classification, object detection, and image segmentation) with varying levels of clustering granularity (i.e., image-, box-, and pixel-level). Empirical results demonstrate that CLUSTERFORMER outperforms various well-known specialized architectures, achieving 83.41% top-1 acc. over ImageNet-1K for image classification, 54.2% and 47.0% mAP over MS COCO for object detection and instance segmentation, 52.4% mIoU over ADE20K for semantic segmentation, and 55.8% PQ over COCO Panoptic for panoptic segmentation. For its efficacy, we hope our work can catalyze a paradigm shift in universal models in computer vision.
ChEDDAR: Student-ChatGPT Dialogue in EFL Writing Education

The integration of generative AI in education is expanding, yet empirical analyses of large-scale, real-world interactions between students and AI systems still remain limited. In this study, we present ChEDDAR, ChatGPT & EFL Learner's Dialogue Dataset As Revising an essay, which is collected from a semester-long longitudinal experiment involving 212 college students enrolled in English as Foreign Langauge (EFL) writing courses. The students were asked to revise their essays through dialogues with ChatGPT. ChEDDAR includes a conversation log, utterance-level essay edit history, self-rated satisfaction, and students' intent, in addition to session-level pre-and-post surveys documenting their objectives and overall experiences. We analyze students' usage patterns and perceptions regarding generative AI with respect to their intent and satisfaction. As a foundational step, we establish baseline results for two pivotal tasks in task-oriented dialogue systems within educational contexts: intent detection and satisfaction estimation. We finally suggest further research to refine the integration of generative AI into education settings, outlining potential scenarios utilizing ChEDDAR. ChEDDAR is publicly available at https://github.com/zeunie/ChEDDAR.
Secure Inter-domain Routing and Forwarding via Verifiable Forwarding Commitments

The Internet inter-domain routing system is vulnerable. On the control plane, the de facto Border Gateway Protocol (BGP) does not have built-in mechanisms to authenticate routing announcements, so an adversary can announce virtually arbitrary paths to hijack network traffic; on the data plane, it is difficult to ensure that actual forwarding path complies with the control plane decisions. The community has proposed significant research to secure the routing system. Yet, existing secure BGP protocols (e.g., BGPsec) are not incrementally deployable, and existing path authorization protocols are not compatible with the current Internet routing infrastructure. In this paper, we propose FC-BGP, the first secure Internet inter-domain routing system that can simultaneously authenticate BGP announcements and validate data plane forwarding in an efficient and incrementally-deployable manner. FC-BGP is built upon a novel primitive, name Forwarding Commitment, to certify an AS's routing intent on its directly connected hops. We analyze the security benefits of FC-BGP in the Internet at different deployment rates. Further, we implement a prototype of FC-BGP and extensively evaluate it over a large-scale overlay network with 100 virtual machines deployed globally. The results demonstrate that FC-BGP saves roughly 55% of the overhead required to validate BGP announcements compared with BGPsec, and meanwhile FC-BGP introduces a small overhead for building a globally-consistent view on the desirable forwarding paths.
Discwise Active Learning for LiDAR Semantic Segmentation

While LiDAR data acquisition is easy, labeling for semantic segmentation remains highly time consuming and must therefore be done selectively. Active learning (AL) provides a solution that can iteratively and intelligently label a dataset while retaining high performance and a low budget. In this work we explore AL for LiDAR semantic segmentation. As a human expert is a component of the pipeline, a practical framework must consider common labeling techniques such as sequential labeling that drastically improve annotation times. We therefore propose a discwise approach (DiAL), where in each iteration, we query the region a single frame covers on global coordinates, labeling all frames simultaneously. We then tackle the two major challenges that emerge with discwise AL. Firstly we devise a new acquisition function that takes 3D point density changes into consideration which arise due to location changes or ego-vehicle motion. Next we solve a mixed-integer linear program that provides a general solution to the selection of multiple frames while taking into consideration the possibilities of disc intersections. Finally we propose a semi-supervised learning approach to utilize all frames within our dataset and improve performance.
Contrastive Speaker Embedding With Sequential Disentanglement

Contrastive speaker embedding assumes that the contrast between the positive and negative pairs of speech segments is attributed to speaker identity only. However, this assumption is incorrect because speech signals contain not only speaker identity but also linguistic content. In this paper, we propose a contrastive learning framework with sequential disentanglement to remove linguistic content by incorporating a disentangled sequential variational autoencoder (DSVAE) into the conventional SimCLR framework. The DSVAE aims to disentangle speaker factors from content factors in an embedding space so that only the speaker factors are used for constructing a contrastive loss objective. Because content factors have been removed from the contrastive learning, the resulting speaker embeddings will be content-invariant. Experimental results on VoxCeleb1-test show that the proposed method consistently outperforms SimCLR. This suggests that applying sequential disentanglement is beneficial to learning speaker-discriminative embeddings.
OATS: Opinion Aspect Target Sentiment Quadruple Extraction Dataset for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) delves into understanding sentiments specific to distinct elements within a user-generated review. It aims to analyze user-generated reviews to determine a) the target entity being reviewed, b) the high-level aspect to which it belongs, c) the sentiment words used to express the opinion, and d) the sentiment expressed toward the targets and the aspects. While various benchmark datasets have fostered advancements in ABSA, they often come with domain limitations and data granularity challenges. Addressing these, we introduce the OATS dataset, which encompasses three fresh domains and consists of 27,470 sentence-level quadruples and 17,092 review-level tuples. Our initiative seeks to bridge specific observed gaps: the recurrent focus on familiar domains like restaurants and laptops, limited data for intricate quadruple extraction tasks, and an occasional oversight of the synergy between sentence and review-level sentiments. Moreover, to elucidate OATS's potential and shed light on various ABSA subtasks that OATS can solve, we conducted experiments, establishing initial baselines. We hope the OATS dataset augments current resources, paving the way for an encompassing exploration of ABSA (https://github.com/RiTUAL-UH/OATS-ABSA).
Spanish Resource Grammar version 2023

We present the latest version of the Spanish Resource Grammar (SRG), a grammar of Spanish implemented in the HPSG formalism. Such grammars encode a complex set of hypotheses about syntax making them a resource for empirical testing of linguistic theory. They also encode a strict notion of grammaticality which makes them a resource for natural language processing applications in computer-assisted language learning. This version of the SRG uses the recent version of the Freeling morphological analyzer and is released along with an automatically created, manually verified treebank of 2,291 sentences. We explain the treebanking process, emphasizing how it is different from treebanking with manual annotation and how it contributes to empirically-driven development of syntactic theory. The treebanks' high level of consistency and detail makes them a resource for training high-quality semantic parsers and generally systems that benefit from precise and detailed semantics. Finally, we present the grammar's coverage and overgeneration on 100 sentences from a learner corpus, a new research line related to developing methodologies for robust empirical evaluation of hypotheses in second language acquisition.