Reviewerly/exHarmony huggingface dataset:
Language-Grounded Control for Coordinated Robot Motion and Speech

Recent advancements have enabled human-robot collaboration through physical assistance and verbal guidance. However, limitations persist in coordinating robots' physical motions and speech in response to real-time changes in human behavior during collaborative contact tasks. We first derive principles from analyzing physical therapists' movements and speech during patient exercises. These principles are translated into control objectives to: 1) guide users through trajectories, 2) control motion and speech pace to align completion times with varying user cooperation, and 3) dynamically paraphrase speech along the trajectory. We then propose a Language Controller that synchronizes motion and speech, modulating both based on user cooperation. Experiments with 12 users show the Language Controller successfully aligns motion and speech compared to baselines. This provides a framework for fluent human-robot collaboration.
The emergence of clusters in self-attention dynamics

Viewing Transformers as interacting particle systems, we describe the geometry of learned representations when the weights are not time dependent. We show that particles, representing tokens, tend to cluster toward particular limiting objects as time tends to infinity. Cluster locations are determined by the initial tokens, confirming context-awareness of representations learned by Transformers. Using techniques from dynamical systems and partial differential equations, we show that the type of limiting object that emerges depends on the spectrum of the value matrix. Additionally, in the one-dimensional case we prove that the self-attention matrix converges to a low-rank Boolean matrix. The combination of these results mathematically confirms the empirical observation made by Vaswani et al. [VSP'17] that leaders appear in a sequence of tokens when processed by Transformers.
ColonMapper: topological mapping and localization for colonoscopy

We propose a topological mapping and localization system able to operate on real human colonoscopies, despite significant shape and illumination changes. The map is a graph where each node codes a colon location by a set of real images, while edges represent traversability between nodes. For close-in-time images, where scene changes are minor, place recognition can be successfully managed with the recent transformers-based local feature matching algorithms. However, under long-term changes -- such as different colonoscopies of the same patient -- feature-based matching fails. To address this, we train on real colonoscopies a deep global descriptor achieving high recall with significant changes in the scene. The addition of a Bayesian filter boosts the accuracy of long-term place recognition, enabling relocalization in a previously built map. Our experiments show that ColonMapper is able to autonomously build a map and localize against it in two important use cases: localization within the same colonoscopy or within different colonoscopies of the same patient. Code will be available upon acceptance.
Consistent Text Categorization using Data Augmentation in e-Commerce

The categorization of massive e-Commerce data is a crucial, well-studied task, which is prevalent in industrial settings. In this work, we aim to improve an existing product categorization model that is already in use by a major web company, serving multiple applications. At its core, the product categorization model is a text classification model that takes a product title as an input and outputs the most suitable category out of thousands of available candidates. Upon a closer inspection, we found inconsistencies in the labeling of similar items. For example, minor modifications of the product title pertaining to colors or measurements majorly impacted the model's output. This phenomenon can negatively affect downstream recommendation or search applications, leading to a sub-optimal user experience. To address this issue, we propose a new framework for consistent text categorization. Our goal is to improve the model's consistency while maintaining its production-level performance. We use a semi-supervised approach for data augmentation and presents two different methods for utilizing unlabeled samples. One method relies directly on existing catalogs, while the other uses a generative model. We compare the pros and cons of each approach and present our experimental results.
Efficient pattern-based anomaly detection in a network of multivariate devices

Many organisations manage service quality and monitor a large set devices and servers where each entity is associated with telemetry or physical sensor data series. Recently, various methods have been proposed to detect behavioural anomalies, however existing approaches focus on multivariate time series and ignore communication between entities. Moreover, we aim to support end-users in not only in locating entities and sensors causing an anomaly at a certain period, but also explain this decision. We propose a scalable approach to detect anomalies using a two-step approach. First, we recover relations between entities in the network, since relations are often dynamic in nature and caused by an unknown underlying process. Next, we report anomalies based on an embedding of sequential patterns. Pattern mining is efficient and supports interpretation, i.e. patterns represent frequent occurring behaviour in time series. We extend pattern mining to filter sequential patterns based on frequency, temporal constraints and minimum description length. We collect and release two public datasets for international broadcasting and X from an Internet company. \textit{BAD} achieves an overall F1-Score of 0.78 on 9 benchmark datasets, significantly outperforming the best baseline by 3\%. Additionally, \textit{BAD} is also an order-of-magnitude faster than state-of-the-art anomaly detection methods.
Pooling techniques in hybrid quantum-classical convolutional neural networks

Quantum machine learning has received significant interest in recent years, with theoretical studies showing that quantum variants of classical machine learning algorithms can provide good generalization from small training data sizes. However, there are notably no strong theoretical insights about what makes a quantum circuit design better than another, and comparative studies between quantum equivalents have not been done for every type of classical layers or techniques crucial for classical machine learning. Particularly, the pooling layer within convolutional neural networks is a fundamental operation left to explore. Pooling mechanisms significantly improve the performance of classical machine learning algorithms by playing a key role in reducing input dimensionality and extracting clean features from the input data. In this work, an in-depth study of pooling techniques in hybrid quantum-classical convolutional neural networks (QCCNNs) for classifying 2D medical images is performed. The performance of four different quantum and hybrid pooling techniques is studied: mid-circuit measurements, ancilla qubits with controlled gates, modular quantum pooling blocks and qubit selection with classical postprocessing. We find similar or better performance in comparison to an equivalent classical model and QCCNN without pooling and conclude that it is promising to study architectural choices in QCCNNs in more depth for future applications.
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning (MARL) algorithms. The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, but is built on top of a heavy, closed-source computer game, StarCraft II. Thus, SMAC is computationally expensive and requires knowledge and the use of proprietary tools specific to the game for any meaningful alteration or contribution to the environment. We introduce SMAClite -- a challenge based on SMAC that is both decoupled from Starcraft II and open-source, along with a framework which makes it possible to create new content for SMAClite without any special knowledge. We conduct experiments to show that SMAClite is equivalent to SMAC, by training MARL algorithms on SMAClite and reproducing SMAC results. We then show that SMAClite outperforms SMAC in both runtime speed and memory.
On the Structure of Higher Order MDS Codes

A code of length $n$ is said to be (combinatorially) $(\rho,L)$-list decodable if the Hamming ball of radius $\rho n$ around any vector in the ambient space does not contain more than $L$ codewords. We study a recently introduced class of higher order MDS codes, which are closely related (via duality) to codes that achieve a generalized Singleton bound for list decodability. For some $\ell\geq 1$, higher order MDS codes of length $n$, dimension $k$, and order $\ell$ are denoted as $(n,k)$-MDS($\ell$) codes. We present a number of results on the structure of these codes, identifying the `extend-ability' of their parameters in various scenarios. Specifically, for some parameter regimes, we identify conditions under which $(n_1,k_1)$-MDS($\ell_1$) codes can be obtained from $(n_2,k_2)$-MDS($\ell_2$) codes, via various techniques. We believe that these results will aid in efficient constructions of higher order MDS codes. We also obtain a new field size upper bound for the existence of such codes, which arguably improves over the best known existing bound, in some parameter regimes.
Does Code Smell Frequency Have a Relationship with Fault-proneness?

Fault-proneness is an indication of programming errors that decreases software quality and maintainability. On the contrary, code smell is a symptom of potential design problems which has impact on fault-proneness. In the literature, negative impact of code smells on fault-proneness has been investigated. However, it is still unclear that how frequency of each code smell type impacts on the fault-proneness. To mitigate this research gap, we present an empirical study to identify whether frequency of individual code smell types has a relationship with fault-proneness. More specifically, we identify 13 code smell types and fault-proneness of the corresponding smelly classes in the well-known open source systems from Apache and Eclipse ecosystems. Then we analyse the relationship between their frequency of occurrences based on the correlation. The results show that Anti Singleton, Blob and Class Data Should Be Private smell types have strong relationship with fault-proneness though their frequencies are not very high. On the other hand, comparatively high frequent code smell types such as Complex Class, Large Class and Long Parameter List have moderate relationship with fault-proneness. These findings will assist developers to prioritize code smells while performing refactoring activities in order to improve software quality.
Collaborative Chinese Text Recognition with Personalized Federated Learning

In Chinese text recognition, to compensate for the insufficient local data and improve the performance of local few-shot character recognition, it is often necessary for one organization to collect a large amount of data from similar organizations. However, due to the natural presence of private information in text data, such as addresses and phone numbers, different organizations are unwilling to share private data. Therefore, it becomes increasingly important to design a privacy-preserving collaborative training framework for the Chinese text recognition task. In this paper, we introduce personalized federated learning (pFL) into the Chinese text recognition task and propose the pFedCR algorithm, which significantly improves the model performance of each client (organization) without sharing private data. Specifically, pFedCR comprises two stages: multiple rounds of global model training stage and the the local personalization stage. During stage 1, an attention mechanism is incorporated into the CRNN model to adapt to various client data distributions. Leveraging inherent character data characteristics, a balanced dataset is created on the server to mitigate character imbalance. In the personalization phase, the global model is fine-tuned for one epoch to create a local model. Parameter averaging between local and global models combines personalized and global feature extraction capabilities. Finally, we fine-tune only the attention layers to enhance its focus on local personalized features. The experimental results on three real-world industrial scenario datasets show that the pFedCR algorithm can improve the performance of local personalized models by about 20\% while also improving their generalization performance on other client data domains. Compared to other state-of-the-art personalized federated learning methods, pFedCR improves performance by 6\% $\sim$ 8\%.
Comparing Five Machine Learning-Based Regression Models for Predicting the Study Period of Mathematics Students at IPB University

Grade point average (GPA) is initial information for supervisors to characterize their supervised students. One model that can be used to predict a student's study period based on GPA is a machine learning-based regression model so that supervisors can apply the right strategy for their students. Therefore, this study aims to implement and select a machine learning-based regression model to predict a student's study period based on GPA in semesters 1-6. Several regression models used are least-square regression, ridge regression, Huber regression, quantile regression, and quantile regression with l_2-regularization provided by Machine Learning in Julia (MLJ). The model is evaluated and selected based on several criteria such as maximum error, RMSE, and MAPE. The results showed that the least-square regression model gave the worst evaluation results, although the calculation method was easy and fast. Meanwhile, the quantile regression model provided the best evaluation results. The quantile regression model without regularization gives the smallest RMSE (2.31 months) and MAPE (3.56%), while the quantile regression model with l_2-regularization has a better maximum error (4.9 months). The resulting model can be used by supervisors to predict the study period of their supervised students so that supervisors can characterize their students and can design appropriate strategies. Thus, the student's study period is expected to be accelerated with a high-quality final project.
Neural 3D Rendering with Periodic Activation Functions

Implicit neural representations (INRs) have demonstrated strong representation performance in various computer vision applications, such as image and video representations. Recently, inspired by INRs, many studies have been conducted on neural 3D rendering. However, representing fine details (high-frequency signals) in terms of geometry and appearance remains challenging because the neural network generally starts to fit low-frequency signals during training. This also leads to an extremely long training time for convergence. To solve this issue, we present a novel neural 3D rendering method based on neural radiance fields (NeRFs) that leverage the characteristics of periodic activation functions. Experimental results demonstrate that the proposed model with a periodic activation function could achieve high-fidelity 3D rendering results with fewer iterations compared to the existing 3D rendering work, NeRF.
An Empirical Case Study on the Growth Process of Low-Career Elementary Teachers: Using Causal Process Tracing

본 연구는 저경력 초등교사의 성장 과정을 인과적 과정추적법(Causal Process Tracing)을 접목한 실증주의적 사례연구 방식을 통해 분석하였다. 분석을 통해 도출된 인과기제를 바탕으로 교사 성장의 메커니즘 이론인 엄문영(2021)의 Y-성장이론을 Van Evera (1997)가 제시한 확실성(certainty)과 고유성(uniqueness) 중심의 가설 검증 방식을 통해 검증함으로써 초등교사의 성장 과정을 심층적으로 살펴보았다. 연구 목적은 크게 두 가지이다. 하나는 저경력 초등교사의 성장에 대한 인과기제 검증이라는 본연의 목적이며, 다른 하나는 교육행정학에서의 인과적 과정추적법의 적용 가능성을 탐색하는 것이다. 주요 연구 결과는 다음과 같다. 첫째, 교사로서의 성장에 미치는 인과기제로써 참여, 자율성, 영향력, 성찰, 모델링, 배움의 갈급함, 낮은 영향력과 성찰, 수업 지도, 선진교육경험, 이상 총 9개의 요소가 도출되었다. 둘째, Y-성장이론의 요소 중 참여, 자율성, 영향력, 성찰은 과정 추적 속에서 그대로 드러났으며, 외적조건인 환경적 통제는 관리자의 관심과 심리적 지지를 통해 확인되었다. 이러한 연구 결과를 바탕으로 다음과 같이 제언하였다. 첫째, 교사의 성장관점에서 교육활동 사업 및 프로그램 관련 참여 기회를 적극적으로 제공해야 한다. 둘째, 교사의 성장 관점에서 성찰과 반추의 기회를 제공해야 한다. 셋째, 관리자의 제도적, 물적, 심리적 지원이 적극적으로 수반되어야 한다. 넷째, 교원양성기관의 교육과정이 예비교사의 실질적 성장을 이끌어내도록 개선될 필요가 있다. 본 연구를 통해 교육행정분야 내 인과추론이 필요한 연구에서 과정추적 기반의 실증적 사례연구가 적용력이 있음을 확인하였다. 아울러 본 연구는 인과적 과정추적법을 국내 교육학 연구에 접목한 첫 시도로써 교육학 연구 내에서의 인과추론 방법에 새로운 대안을 제시하였다는 점에서 의의를 갖는다. This study analyzed the growth process of low-career elementary school teachers through an empirical case study method that combines Causal Process Tracing. Based on the causal mechanism derived through the analysis, Eom’s Y-growth theory, which is the mechanism theory of teacher growth, was verified through the certainty and uniqueness-centered hypothesis verification method proposed by Van Evera (1997), and the growth process of elementary school teachers was examined in depth. The purpose of this study is twofold. One is to test the causal mechanisms behind the growth of low-experience elementary school teachers, and the other is to explore the applicability of causal process tracing in educational administration. The main findings are as follows. First, a total of nine factors were derived as a causal mechanism for growth as a teacher. Second, the elements of the Y-growth theory were identified in process tracing. Implications and policy suggestions are as follows. First, it is necessary to provide teachers with opportunities to participate in educational activity projects and programs. Second, it is necessary to provide opportunities for reflection and reflection from the perspective of teacher growth. Third, various support from managers must be actively accompanied. Fourth, the curriculum of teacher training institutions should be improved so that the actual growth of pre-service teachers is achieved. This study confirms the applicability of empirical case studies based on process tracing in the field of educational administration in terms of causal reasoning. And this study is significant in that it presented a new alternative to causal inference methods within pedagogy research as the first attempt to incorporate causal process tracing methods into domestic pedagogy research.
Exploring the possibilities and difficulties of operating non-face-to-face classes at local community colleges : Focusing on the case of G-College

이 연구는 학령인구감소 등으로 인해 위기를 맞고 있는 지방소재 전문대학에서 비대면 수업을 활용할 수 있는 가능성을 살펴보고, 이를 어렵게 만드는 요인이 무엇인지를 탐색함으로써 적절한 행정적·제도적 방안을 제안하기 위해 수행되었다. 이를 위해 지방 소재 G전문대학교수자 및 학생들을 대상으로 단일사례연구를 실시하였고, 기노시타의 수정근거이론방법 절차에 따라 자료를 수집하고 분석하였다. 총 11회 면담을 통해 이 연구는 비대면 수업이 짧은 수업연한으로 인해 학사일정이 빠듯한 전문대학에서 시간에 구애받지 않는 탄력적 수업방식으로 가능하고, 실험 · 실습이 중요한 전문대학에서 플립드러닝을 통해 실습시간을 추가 확보할 수 있으며, 기초학습능력이 부족한 전문대학생에게 반복 학습을 제공할 수 있는 가능성을 발견하였다. 하지만, 비대면 수업에서 전문대 학습자의 수업 집중 저하, 학습동기 유발의 어려움, 교수자의 디지털 리터러시 역량 부족, 실체감 구현이 부족한 실험실습 기자재로 인해 비대면 수업 운영에 어려움이 있었다. 또한 일과 수업을 병행하고 싶어하는 전문대학 학습자들의 요구에 따라 대학본부가 실시간 비대면 수업보다 동영상 수업영상을 유도하였고, 비대면 수업에 대한 전문적 지원 인력 등을 갖추지 못한 대학 본부가 정부의 감사나 평가를 대비하여 전면 대면수업으로 전환하면서 G전문대학에서 비대면 수업 활성화를 더욱 어렵게 만들었다. 이 연구는 양질의 비대면 수업 관련 콘텐츠들이 재정적으로 열악한 지방 전문대학에 도움이 될 수 있는 가능성을 발견하였고, 이와 같은 분석결과에 기반하여 전문대학에서 비대면 수업 활성화를 위해 대학 수준의 노력을 넘어서서 정부의 강력한 정책 메시지와 구체적인 지침이 필요함을 제안하였다. This study looked at the possibility of using non-face-to-face classes at community colleges located in provinces that are facing a crisis due to the recent decrease in school age population. In addition, it was conducted to suggest appropriate administrative and institutional measures by exploring what factors make this difficult. To this end, a single case study was conducted targeting professors and students at G College located in the metropolitan city, and data were collected and analyzed according to Kinoshita’s modified ground theory method procedure. As a result of this study, it was found that non-face-to-face classes are possible as a flexible class method regardless of time in colleges with tight academic schedules due to short class periods. In addition, this study found that in colleges where experiments and practice are important, additional practice time can be secured through flipped learning, and it is possible to provide repetitive learning to junior college students who lack basic learning ability. However, in non-face-to-face classes, there were difficulties in conducting monotonous non-face-to-face classes for junior college learners due to poor class concentration, difficulty in inducing learning motivation, and insufficient digital literacy capabilities. This study found the possibility that high-quality non-face-to-face class contents could help financially underserved local colleges. It was suggested that strong policy messages and specific guidelines from the government are needed.
The practical approach of social identity in Chinese vocational undergraduate education

Vocational undergraduate education is an important strategic measure for the country to develop a modern vocational education system, and it is an inherent requirement for the high-quality development of China's social economy. General Office of CCCPC have released a set of “guidelines on promoting the high-quality development of modern vocational education” (hereinafter referred to as the “Guidelines”), which clarified the development process of vocational undergraduate education. However, there are still many problems in the current development of vocational undergraduate education. Vocational undergraduate education has a certain degree of social identity barriers. Starting from the historical attribution of the development of vocational undergraduate education and the dictates of the times, this paper sorts out the internal logic, external logic and coupling logic of vocational undergraduate social identity. Finally, through the positioning of characteristic vocational undergraduate education, the construction of standardized systems, and the “community of destiny” Implement and improve the integration of vocational and general education to explore the practical way of social recognition of vocational undergraduate education, and provide basic theory and practice for the realization of the development goals of vocational undergraduate education in the “Guidelines”.