Reviewerly/exHarmony huggingface dataset:
Comparison Analysis of Nearest Road Calculations on Dijkstra Algorithm and A*(A-Star) Algorithm for Mapping BTS Tower Area

BTS (Base Transceiver Station) is a telecommunications infrastructure in the form of a tower with a transmitting antenna that facilitates wireless communication between communication devices and operator networks. BTS as a signal receiver and transmitter, its existence must be known to a user, such as maintenance staff and BTS tower operation staff to deal with existing problems. So a special system is needed for users to use in determining the closest path between the user and the location of the BTS tower. The purpose of this study is to make it easier for the user to determine the shortest path to the intended BTS tower location and to analyze the Dijkstra algorithm and the A* algorithm in determining the shortest route between the user's location and the location of the Telkomsel BTS tower in Semarang, Central Java. The results obtained from the 82 test data tested with the information system created show that Dijkstra's algorithm is more efficient than the A* algorithm. Validation is carried out by calculating with the system between the user's location and 82 test data or the location of BTS towers in the city of Semarang. The results of the validation carried out explained that the system was running according to the functions made and the results of the calculations carried out by the system were appropriate.
Large deviations for the longest alternating and the longest increasing subsequence in a random permutation avoiding a pattern of length three

We calculate the large deviations for the length of the longest alternating subsequence and for the length of the longest increasing subsequence in a uniformly random permutation that avoids a pattern of length three. We treat all six patterns in the case of alternating subsequences. In the case of increasing subsequences, we treat two of the three patterns for which a classical large deviations result is possible. The same rate function appears in all six cases for alternating subsequences. This rate function is in fact the rate function for the large deviations of the sum of IID symmetric Bernoulli random variables. The same rate function appears in the two cases we treat for increasing subsequences. This rate function is twice the rate function for alternating subsequences.
Optimal Scaling transformations to model non-linear relations in GLMs with ordered and unordered predictors

In Generalized Linear Models (GLMs) it is assumed that there is a linear effect of the predictor variables on the outcome. However, this assumption is often too strict, because in many applications predictors have a nonlinear relation with the outcome. Optimal Scaling (OS) transformations combined with GLMs can deal with this type of relations. Transformations of the predictors have been integrated in GLMs before, e.g. in Generalized Additive Models. However, the OS methodology has several benefits. For example, the levels of categorical predictors are quantified directly, such that they can be included in the model without defining dummy variables. This approach enhances the interpretation and visualization of the effect of different levels on the outcome. Furthermore, monotonicity restrictions can be applied to the OS transformations such that the original ordering of the category values is preserved. This improves the interpretation of the effect and may prevent overfitting. The scaling level can be chosen for each individual predictor such that models can include mixed scaling levels. In this way, a suitable transformation can be found for each predictor in the model. The implementation of OS in logistic regression is demonstrated using three datasets that contain a binary outcome variable and a set of categorical and/or continuous predictor variables.
New metrics for analyzing continual learners

Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, it can be desirable to train models on a continuous stream of data where multiple classification tasks are presented sequentially. This scenario, known as Continual Learning (CL) poses challenges to standard learning algorithms which struggle to maintain knowledge of old tasks while learning new ones. This stability-plasticity dilemma remains central to CL and multiple metrics have been proposed to adequately measure stability and plasticity separately. However, none considers the increasing difficulty of the classification task, which inherently results in performance loss for any model. In that sense, we analyze some limitations of current metrics and identify the presence of setup-induced forgetting. Therefore, we propose new metrics that account for the task's increasing difficulty. Through experiments on benchmark datasets, we demonstrate that our proposed metrics can provide new insights into the stability-plasticity trade-off achieved by models in the continual learning environment.
Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information

In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by aligning the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework's efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms.
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering

Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g. due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets.
Iterative Multi-granular Image Editing using Diffusion Models

Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such creative endeavors, the process should possess the ability to: 1) iteratively edit the generations and 2) control the spatial reach of desired changes (global, local or anything in between). We formalize this pragmatic problem setting as Iterative Multi-granular Editing. While there has been substantial progress with diffusion-based models for image synthesis and editing, they are all one shot (i.e., no iterative editing capabilities) and do not naturally yield multi-granular control (i.e., covering the full spectrum of local-to-global edits). To overcome these drawbacks, we propose EMILIE: Iterative Multi-granular Image Editor. EMILIE introduces a novel latent iteration strategy, which re-purposes a pre-trained diffusion model to facilitate iterative editing. This is complemented by a gradient control operation for multi-granular control. We introduce a new benchmark dataset to evaluate our newly proposed setting. We conduct exhaustive quantitatively and qualitatively evaluation against recent state-of-the-art approaches adapted to our task, to being out the mettle of EMILIE. We hope our work would attract attention to this newly identified, pragmatic problem setting.
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes

The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations. To address these challenges, we first create synthetic large-scale clinical notes using publicly available case reports extracted from biomedical literature. We then use these synthetic notes to train our specialized clinical large language model, Asclepius. While Asclepius is trained on synthetic data, we assess its potential performance in real-world applications by evaluating it using real clinical notes. We benchmark Asclepius against several other large language models, including GPT-3.5-turbo and other open-source alternatives. To further validate our approach using synthetic notes, we also compare Asclepius with its variants trained on real clinical notes. Our findings convincingly demonstrate that synthetic clinical notes can serve as viable substitutes for real ones when constructing high-performing clinical language models. This conclusion is supported by detailed evaluations conducted by both GPT-4 and medical professionals. All resources including weights, codes, and data used in the development of Asclepius are made publicly accessible for future research.
Leveraging Learning Metrics for Improved Federated Learning

Currently in the federated setting, no learning schemes leverage the emerging research of explainable artificial intelligence (XAI) in particular the novel learning metrics that help determine how well a model is learning. One of these novel learning metrics is termed `Effective Rank' (ER) which measures the Shannon Entropy of the singular values of a matrix, thus enabling a metric determining how well a layer is mapping. By joining federated learning and the learning metric, effective rank, this work will \textbf{(1)} give the first federated learning metric aggregation method \textbf{(2)} show that effective rank is well-suited to federated problems by out-performing baseline Federated Averaging \cite{konevcny2016federated} and \textbf{(3)} develop a novel weight-aggregation scheme relying on effective rank.
Information Fusion for Assistance Systems in Production Assessment

We propose a novel methodology to define assistance systems that rely on information fusion to combine different sources of information while providing an assessment. The main contribution of this paper is providing a general framework for the fusion of n number of information sources using the evidence theory. The fusion provides a more robust prediction and an associated uncertainty that can be used to assess the prediction likeliness. Moreover, we provide a methodology for the information fusion of two primary sources: an ensemble classifier based on machine data and an expert-centered model. We demonstrate the information fusion approach using data from an industrial setup, which rounds up the application part of this research. Furthermore, we address the problem of data drift by proposing a methodology to update the data-based models using an evidence theory approach. We validate the approach using the Benchmark Tennessee Eastman while doing an ablation study of the model update parameters.
SortedNet, a Place for Every Network and Every Network in its Place: Towards a Generalized Solution for Training Many-in-One Neural Networks

Deep neural networks (DNNs) must cater to a variety of users with different performance needs and budgets, leading to the costly practice of training, storing, and maintaining numerous specific models. There are solutions in the literature to deal with single dynamic or many-in-one models instead of many individual networks; however, they usually suffer from heavy model search requirements, being architecture-specific, working only on a limited number of dimensions (e.g. depth only or width only) or sub-models. To address these problems, we propose SortedNet, a generalized and scalable training solution to harness the inherent modularity of DNNs. Thanks to a generalized nested architecture (which we refer to as \textit{sorted} architecture in this paper) with shared parameters and its novel update scheme combining random sub-model sampling and gradient accumulation, SortedNet enables the training of numerous sub-models simultaneously, simplifies dynamic model selection and deployment during inference, and reduces the model storage requirement significantly. The versatility and scalability of SortedNet are validated through various architectures and tasks including LLaMA, BERT, RoBERTa (NLP tasks), ResNet and MobileNet (image classification) demonstrating its superiority over existing dynamic training methods. SortedNet is able to train up to 160 sub-models at once, achieving at least 96\% of the original model's performance.
Subjectivity in Unsupervised Machine Learning Model Selection

Model selection is a necessary step in unsupervised machine learning. Despite numerous criteria and metrics, model selection remains subjective. A high degree of subjectivity may lead to questions about repeatability and reproducibility of various machine learning studies and doubts about the robustness of models deployed in the real world. Yet, the impact of modelers' preferences on model selection outcomes remains largely unexplored. This study uses the Hidden Markov Model as an example to investigate the subjectivity involved in model selection. We asked 33 participants and three Large Language Models (LLMs) to make model selections in three scenarios. Results revealed variability and inconsistencies in both the participants' and the LLMs' choices, especially when different criteria and metrics disagree. Sources of subjectivity include varying opinions on the importance of different criteria and metrics, differing views on how parsimonious a model should be, and how the size of a dataset should influence model selection. The results underscore the importance of developing a more standardized way to document subjective choices made in model selection processes.
Typing on Any Surface: A Deep Learning-based Method for Real-Time Keystroke Detection in Augmented Reality

Frustrating text entry interface has been a major obstacle in participating in social activities in augmented reality (AR). Popular options, such as mid-air keyboard interface, wireless keyboards or voice input, either suffer from poor ergonomic design, limited accuracy, or are simply embarrassing to use in public. This paper proposes and validates a deep-learning based approach, that enables AR applications to accurately predict keystrokes from the user perspective RGB video stream that can be captured by any AR headset. This enables a user to perform typing activities on any flat surface and eliminates the need of a physical or virtual keyboard. A two-stage model, combing an off-the-shelf hand landmark extractor and a novel adaptive Convolutional Recurrent Neural Network (C-RNN), was trained using our newly built dataset. The final model was capable of adaptive processing user-perspective video streams at ~32 FPS. This base model achieved an overall accuracy of $91.05\%$ when typing 40 Words per Minute (wpm), which is how fast an average person types with two hands on a physical keyboard. The Normalised Levenshtein Distance also further confirmed the real-world applicability of that our approach. The promising results highlight the viability of our approach and the potential for our method to be integrated into various applications. We also discussed the limitations and future research required to bring such technique into a production system.
TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes

&lt;p&gt;Video data and algorithms have been driving advances in multi-object tracking (MOT). While existing MOT datasets focus on occlusion and appearance similarity, complex motion patterns are widespread yet overlooked. To address this issue, we introduce a new dataset called BEE23 to highlight complex motions. Identity association algorithms have long been the focus of MOT research. Existing trackers can be categorized into two association paradigms: single-feature paradigm (based on either motion or appearance feature) and serial paradigm (one feature serves as secondary while the other is primary). However, these paradigms are incapable of fully utilizing different features. In this paper, we propose a parallel paradigm and present the Two rOund Parallel matchIng meChanism (TOPIC) to implement it. The TOPIC leverages both motion and appearance features and can adaptively select the preferable one as the assignment metric based on motion level. Moreover, we provide an Attention-based Appearance Reconstruct Module (AARM) to reconstruct appearance feature embeddings, thus enhancing the representation of appearance features. Comprehensive experiments show that our approach achieves state-of-the-art performance on four public datasets and BEE23. Notably, our proposed parallel paradigm surpasses the performance of existing association paradigms by a large margin, e.g., reducing false negatives by 12% to 51% compared to the single-feature association paradigm. The introduced dataset and association paradigm in this work offer a fresh perspective for advancing the MOT field. The source code and dataset are available at https://github.com/holmescao/TOPICTrack.&lt;/p&gt;
Improvements in School Autonomous Time Based on Critical Analysis

방안을 탐색하고자 하였다. 이를 위해 전문가 의견 조사를 실시하였으며, 연구자가 제시한 문항에 대한 CVR 값을 제시하여 전문가 의견 일치 여부를 판단하였고, 서술 응답과 추가 면담 내용을 전사, 분석하였다. 연구의 결과 전문가들은 실행 주체의 자율성 확대가 반드시 교육과정이 추구하는 목적 달성을 수월하게 해주진 않는다고 하였으며, 지역 여건 반영 및 개별 맞춤형 교육을 위해서도 현재보다 자율성이 더 필요하지는 않다고 하였다. 공통필수 교육과정기에서는 자율시간보다 기초⸱기본 학력 신장이 우선되어야 함에 유의미하게 동의하였고, 2022 개정 교육과정 학교자율시간의 실행에 대한 현장의 준비도는 아직 부족한 상태라고 응답하였다. 또한 자율성에는 책무성이 수반되므로 책무성을 강화하기 위한 방안을 마련해야 한다는 의견이었다. 연구를 통해 교육과정 자율성에 대한 교사의 요구 조사와 학교의 준비도를 점검하는 과정이 필요하고, 공통필수 교육과정기 교육 내용에 대한 우선순위를 확립하는 것이 필요하다고 제언하였다.This study aimed to critically analyze the newly introduced school autonomous time in the 2022 revised curriculum and explore implementation plans. To this end, a literature study and expert opinion survey were conducted, CVR values for the questions suggested by the researcher were presented to determine whether the expert opinion was consistent, and descriptive responses and additional interviews were transcribed and analyzed. As a result of the study, experts said that the expansion of the autonomy of the executing entity does not necessarily make it easier to achieve the goal pursued by the curriculum, and that autonomy is not more necessary than now for reflecting regional conditions and individually customized education. In the common mandatory curriculum period, it was significantly agreed that basic and basic academic ability development should take precedence over autonomous time, and the field readiness for the implementation of the 2022 revised curriculum school autonomous time was still insufficient. It was also suggested that since autonomy accompanies accountability, measures to strengthen accountability should be prepared. Through the research, it was suggested that it is necessary to investigate teachers' needs for curriculum autonomy and to check the school's readiness, and to establish priorities for the contents of the common compulsory curriculum.
Operation strategies of capital-constrained small and medium-sized enterprises based on blockchain technology

Introduction: In reality, due to the low credit rating of small and medium-sized enterprises (SMEs), it is difficult for them to obtain sufficient financing from a single financier. This paper considers a dual-channel supply chain consisting of a capital-constrained manufacturer, an e-commerce platform (ECP), a third-party logistics company (3PL) and consumers. There are two innovations in this paper: the manufacturer obtains sufficient production funds through hybrid financing of the ECP and 3PL, and consumers want to know product information and compare prices. The contributions of this paper are to investigate new applications of blockchain in both hybrid financing and meeting consumer information search needs. Methodology: We discuss the operation and pricing decisions of supply chain in two scenarios. These two scenarios are without adopting blockchain (N) and with adopting blockchain (B). Then, we compare the equilibrium decisions in two scenarios. Results: The results show that the supply chain will adopt blockchain when certain conditions are met. The initial adoption of blockchain is bad for the ECP and 3PL. Further, we find that with the increase of financing ratio, the optimal financing interest rate of the ECP decreases, while the optimal financing interest rate of the 3PL increases. Discussion: The numerical analysis shows that the adoption of blockchain can be more profitable when the cost of information search is high. Management insights: In order to achieve supply chain coordination, the manufacturer should give subsidies the ECP and 3PL.
Evaluating a Radius-based Pipeline for Question Answering over Cultural (CIDOC-CRM based) Knowledge Graphs

CIDOC-CRM is an event-based international standard for cultural documentation that has been widely used for offering semantic interoperability in the Cultural Heritage (CH) domain. Although there are several Knowledge Graphs (KGs) expressed by using CIDOC-CRM, the task of Question Answering (QA) has not been studied over such graphs. For this reason, in this paper we propose and evaluate a Radius-based QA pipeline over CIDOC-CRM KGs for single-entity factoid questions. In particular, we propose a generic QA pipeline that comprises several models and methods, including a keyword search model for recognizing the entity of the question (and linking it to the KG), methods that are based on path expansion for constructing subgraphs of different radius (i.e., path lengths) starting from the recognized entity, i.e., for being used as a context, and pre-trained neural models (based on BERT) for answering the question using the mentioned context. Moreover, since there are no available benchmarks over CIDOC-CRM KGs, we construct (by using a real KG) an evaluation benchmark having 10,000 questions, i.e., 5,000 single-entity factoid, 2,500 comparative and 2,500 confirmation questions. For evaluating the QA pipeline, we use the 5,000 single-entity factoid questions. Concerning the results, the QA pipeline achieves satisfactory results both in the entity recognition step (78% accuracy) and in the QA process (51% F1 score).