Reviewerly/exHarmony huggingface dataset:
Practical Exercise on Financial Statements Analysis in Liberal Arts Class

본 논문은 C대학의 교양과목에서 진행한 재무제표 분석 실습 사례를 제시하고 교육적 시사점을 제안한다. 해당 과목에서 담당교수는 매 학기마다 총 8주에 걸쳐 비대면 강의로 재무제표 분석에 대한 이론 및 실습 교육을 진행하고, 기말 과제를 통해 학생 스스로 관심 기업의 재무제표를 종합적으로 분석하도록 하였다. 재무제표 분석 실습 관련 수업은 다음과 같이 진행되었다. 첫째, 데이터베이스에서 다운로드한 실제 재무제표 공시자료를 실습에 사용하였다. 둘째, 수업 교안을 상세하게 구성하여 학생들에게 사전 배포하였다. 셋째, 학생 스스로 엑셀 프로그램을 통해 다양한 재무비율을 직접 계산하도록 하였다. 넷째, 학생 스스로 관심 기업의 재무제표를 분석할 수 있도록 기말과제를 부여하였으며, 재무비율뿐만 아니라 사업 환경에 대한 분석도 함께 수행하도록 하였다. 강의평가 결과, 수강생은 재무제표분석 교육에서 실습이 매우 효과적이고 유용하다는 의견을 제시하였다. 본 수업 사례는 다음과 같은 교육적 시사점을 제공할 수 있다. 첫째, 재무제표 분석 교육에서 실습이 매우 중요하며, 특히 가상 데이터보다는 실제 데이터를 활용하는 것이 효과적이다. 둘째, 과제를 통해 학습동기를 유발할 필요가 있으며, 온라인 강의를 적극적으로 활용할 필요가 있다. 앞으로 교양과목에서 재무제표 분석 교육이 더욱 효과적이고 활발하게 진행되기를 기대한다. This paper presents educational case of practical exercise on financial statements analysis practices conducted in the liberal arts course of C University and suggests educational implications. In the course, the professor conducted theoretical lecture and practical exercise on financial statements analysis through online classes for eight weeks of each semester. Students of the course were required to comprehensively analyze the financial statements of firms they were interested in through the final assignment. Practical exercise on financial statement analysis were conducted as follows. First of all, actual financial statements data downloaded from a database were used for practice. And detailed instructional plan as well as lecture materials were distributed to students in advance. Students were asked to calculate various financial ratios by themselves using the Excel program. Moreover, students had to submit final assignment so that they could analyze the financial statements of the firms they were interested in. The analysis included the business environments as well as calculation of financial ratios. At the end of each semester, the students suggested that practical exercise was very effective and useful in financial statement analysis education. This case can provide some educational implications. Firstly, practical exercise is very important in education of financial statement analysis, and it is effective to use actual data rather than virtual data. Furthermore, it is necessary to induce learning motivation through assignments, and it is necessary to actively utilize online lectures. Hopefully, financial statement analysis education would be conducted more effectively and actively in liberal arts class referencing the case of this paper.
External-attention-based deep neural network model for reliable detection of oral cancer from histopathological images

Oral cancer is one of the deadliest diseases around the world with varied morphological traits, hence making it difficult to manually achieve accurate classification. Further, the traditional methods of diagnosis used by clinicians can be time-consuming and prone to error. Therefore, computer-assisted histopathological image classification is of extreme importance for the detection of oral cancer. We propose an image classification model known as External Attention Transformer model based on external attention mechanism, aiming to extract discriminating fine features from oral cancer tissue sections and their normal counterparts. We have used 4946 oral histopathological images classified into two categories: normal and oral squamous cell carcinoma (OSCC). Of the total images, 2435 of them are categorized as normal and 2511 as OSCC. External attention based deep neural network model attained 96.97% classification accuracy. Sensitivity and specificity were recorded as 97.61% and 96.41% respectively. It is found that the effectiveness of artificial intelligence methods for classifying oral cancer has significantly improved in comparison to leading edge methods, and this has a potential for early oral cancer detection.
Diffusion of Aviation and the Activities of Korean Women under Imperial Japanese Colonial Rule

이 글은 일제하 항공 분야에서 조선인 여성의 존재를 살펴보았다. 1910년대 일제는 군(軍)을 중심으로 항공 분야를 도입하였고, 여기서 입대가 제한된 여성과 식민지인은 참여할 수 없었다. 1920년대에 들어서 민간항공이 본격적으로 개시되자 그동안 배제되었던 계층에서도 비행사들이 배출되기 시작했다. 다만 식민지 여성은 항공 기술의 습득에 요구되는 도일(渡日) 유학의 부담을 더 크게 받았기 때문에 조선인 여성 비행사의 등장은 다소 늦었다. 1920년대 후반 박경원과 이정희가 비행사 자격을 획득했을 때, 민간항공은 교통의 한 분야로 도약하고 있었다. 일제는 항공교통에서 여성 비행사들의 참여를 제도적으로 제한했다. 항공교통은 ‘에어걸’이란 객실승무원을 도입하면서 항공에서 성역할의 분화를 촉발했다. 일본 회사 위주의 항공교통에서 조선인 여성은 객실승무원의 기회에서도 불리했다. 조선인 여성들은 비행사로서의 활로를 찾고자했고, 그 일환으로 통치권력의 선전을 위한 비행에 동참하기도 했다. 그렇지만 전시체제하 민간항공이 군사항공으로 전환되면서 여성 비행사의 활로는 완전 차단되었다. 전시 항공에서 여성은 미래의 항공군을 키우는 어머니와 같은 전통적인 성역할의 방식으로 참여를 요구받았다. 일제하 항공 분야에서 여성의 역할은 갈수록 한정되는 경향을 띠었다. 그럼에도 불구하고 비행의 열망을 최대한 실현하려는 조선인 여성들은 여전히 등장했다. 이러한 노력은 해방 후 ‘항공 건설’에서 여성의 적극적 가담이 나오는 배경이 되었다. This article examines the presence of Korean women in aviation under Imperial Japanese colonial rule. The military of Imperial Japan introduced aviation in the 1910s, but colonial people, including women, could not participate as they were limited from enlisting in the army. In the 1920s, civil aviation was initiated, and pilots started to be trained from marginalized classes. However, as colonial women were burdened more with studying on the Japanese mainland to acquire the necessary aviation skills, the emergence of Korean female pilots was delayed. In the late 1920s, when Park Gyeongwon and Yi Jeongheui obtained pilot licenses, civil aviation was becoming into a part of transportation. Imperial Japan systemically restricted the participation of female pilots in aviation transportation. However, the aviation industry triggered the differentiation of gender roles in aviation by imposing a cabin crew called “Air Girl.” Since the aviation industry was dominated by Japanese companies, Korean women were at an unfavorable position in opportunities to become cabin crew. Korean women participated in propaganda flights for the ruling power in an attempt to overcome the barriers they faced. Still, the transition of civil aviation during wartime completely obstructed female entry. In wartime aviation, females were called upon to fulfill traditional gender roles as mothers raising the future air force. At least under Japanese colonial rule, women’s roles in the aviation tended to become more specific. Nevertheless, Korean women persisted in their attempts to realize the aspirations of flight and still appeared to show aggression. The Korean liberation from colonial rule led to the active participation of women in air power buildup.
A Comparative Study on Character Evaluations of Female Literati in Korea and Vietnam - Focusing on the Yunji-dang Yugo (允摯堂遺稿) and the Hong ha Phu nhan Di van (紅霞夫人遺文)

본고에서는 임윤지당(任允摯堂, 1721~1793)과 홍하부인(紅霞夫人, 1705~1749)의 글을 중심으로 한 · 월 여성 문인이 작성한 인물평의 특징을 비교하고자 하였다. 18세기 한·월 사회에서 여성이 담당한 역할은 외부 세계와 단절된 것일 수밖에 없었다. 그러나 임윤지당과 홍하부인은 문학 활동을 통해 외부/남성의 영역에 적극적으로 접촉하였다. 조선의 임윤지당은 부덕(婦德)을 몸소 실천한 인물이었으나 글에서만큼은 대상에 대해 과격하다 할 정도로 신랄한 논평을 가하여 부덕유순(婦德柔順)과 상반된 면모를 보인다. 임윤지당은 역대 의사(義士) · 충신(忠臣)으로 칭송받았던 인물들도 거세게 비판하는 한편 안회(顔回)와 조선 여인 3명만을 긍정적으로 평가하였다. 대월의 홍하부인은 오랜 기간, 당대 사회에서 용인이 어려웠던 미혼의 상태로 경제 활동과 문학활동을 병행하였던 인물이다. 임윤지당과 달리 홍하부인의 인물 평가는 중국 역사 인물이 아닌 오직 국내(대월)에 집중되어 현실/비현실 존재를 칭찬하고 있는 점이 주목할 만하다. 임윤지당과 홍하부인의 글에서 살펴본바, 여성의 관심 영역은 단순히 가정사(家庭事) 범위에만 국한되지 않았다. 교육의 수준, 활동의 영역이 남성에 비해 축소되었던 것은 사실이나 ‘외부’를 향한 관심과 문학 활동을 그치지 않았던 것이다. 이것을 여성 문학 활동의 일반적인 양상이라고 할 수 없겠으나 여성 문인의 활동 영역을 확장시킨 인물이 18세기 동시기에 한국과 월남에 존재하고 있었음은 분명하다. This article compares characterizations of women writers in Korea and Vietnam, focusing on the posthumous collections of Im Yunji-dang (任允摯堂, 1721~1793) and Hong Ha phu nhan (紅霞夫人, 1705~1749). In 18th-century Korean and Vietnamese society, women were typically isolated from the world outside their homes However, Im Yunji-dang and Hong Ha phu nhan actively engaged with the outside/male sphere through their literary activities. Yunji-dang harshly criticized even those praised as martyrs and loyalists; only Yan Hui(顔回) and the three Joseon women were evaluated positively. Hong Ha phu nhan was an unmarried woman who pursued both economic and literary activities for an extended period, which was not acceptable in the society of her time. It is noteworthy that, unlike Yunji-dang, Hong Ha phu nhan’s character evaluation is not focused on Chinese historical figures but only on domestic (Vietnamese) ones, praising their real and unreal existence. As seen in the texts of Im Yunji-dang and Hong Ha phu nhan, women’s interests were not simply limited to domestic affairs. Although their education level and sphere of activity were minor compared to men, their interests in the “outside” and literary activities made them persistent. This was not the usual pattern of women’s literary activities during this period. However, some figures clearly expanded women writers’ sphere of activity during the 18th century.
Re-examining the Concept and Basic Principles of Justice in Education

이 연구는 교육정의에 관한 그간의 논의와 연구가 제한된 정의론에 기초하여 이루어져 왔다는 문제인식 하에 다양한 정치사상에 기반한 정의론과 국내 교육정의 연구를 체계적으로 분석하여 교육정의의 개념과 원리를 새롭게 정립하는 데 목적이 있다. 이를 위해 문헌분석방법을 주요 연구방법으로 활용하였다. 연구 결과, 첫째, 정의를 이해하는 정치사상을 자유지상주의, 평등주의, 공리주의, 공동체주의로 구분하고 각 입장에서 정의의 개념과 특징을 분석하였다. 둘째, 정의를 판단하는 기준으로 필요, 응분, 평등, 계약자유 원칙의 주요 특징을 정리하였다. 셋째, Nozick, Rawls, Dworkin, Sen, Walzer, Young, Sandel의 정의론과 이돈희와 허병기, 김창환 외의 교육정의론의 주요 특징을 분석·종합하였다. 넷째, 이러한 분석을 토대로 교육정의의 개념, 이념과 원리를 제시하고, 교육제도 및 교육정책 운영에 주는 시사점을 제언하였다. 이 연구는 교육정의의 논의 기반을 확장하고 교육정의의 개념과 원리를 새롭게 정립하였다는 점에서 의의가 있다. The purpose of this study is to systematically analyze various theories of justice and to establish the concept and principles of justice in education. For this purpose, the literature analysis was used as the main research method. As a result of the study, first, the concept of educational justice was analyzed from the perspectives of libertarian, egalitarianism, utilitarianism, and community theory. Second, the criteria for judging justice were reviewed from the principles of necessity, desert, equality, and freed contract. Third, the main characteristics of Nozick, Rawls, Dworkin, Sen, Walzer, Young, and Sandel’s theories of justice and the educational justice theories of Lee Don-hee, Heo Byung-ki, and Kim Chang-hwan et al. were analyzed and synthesized. Fourth, based on this analysis, the ideology and principle of justice in education and the concept of educational justice were presented, and implications for the operation of educational system and educational policy were suggested. This study is significant in that it expanded the basis for discussion of educational justice and newly established the concept and principles of educational justice.
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization

Continuous-time reinforcement learning tasks commonly use discrete steps of fixed cycle times for actions. As practitioners need to choose the action-cycle time for a given task, a significant concern is whether the hyper-parameters of the learning algorithm need to be re-tuned for each choice of the cycle time, which is prohibitive for real-world robotics. In this work, we investigate the widely-used baseline hyper-parameter values of two policy gradient algorithms -- PPO and SAC -- across different cycle times. Using a benchmark task where the baseline hyper-parameters of both algorithms were shown to work well, we reveal that when a cycle time different than the task default is chosen, PPO with baseline hyper-parameters fails to learn. Moreover, both PPO and SAC with their baseline hyper-parameters perform substantially worse than their tuned values for each cycle time. We propose novel approaches for setting these hyper-parameters based on the cycle time. In our experiments on simulated and real-world robotic tasks, the proposed approaches performed at least as well as the baseline hyper-parameters, with significantly better performance for most choices of the cycle time, and did not result in learning failure for any cycle time. Hyper-parameter tuning still remains a significant barrier for real-world robotics, as our approaches require some initial tuning on a new task, even though it is negligible compared to an extensive tuning for each cycle time. Our approach requires no additional tuning after the cycle time is changed for a given task and is a step toward avoiding extensive and costly hyper-parameter tuning for real-world policy optimization.
Regular Splitting Graph Network for 3D Human Pose Estimation

In human pose estimation methods based on graph convolutional architectures, the human skeleton is usually modeled as an undirected graph whose nodes are body joints and edges are connections between neighboring joints. However, most of these methods tend to focus on learning relationships between body joints of the skeleton using first-order neighbors, ignoring higher-order neighbors and hence limiting their ability to exploit relationships between distant joints. In this paper, we introduce a higher-order regular splitting graph network (RS-Net) for 2D-to-3D human pose estimation using matrix splitting in conjunction with weight and adjacency modulation. The core idea is to capture long-range dependencies between body joints using multi-hop neighborhoods and also to learn different modulation vectors for different body joints as well as a modulation matrix added to the adjacency matrix associated to the skeleton. This learnable modulation matrix helps adjust the graph structure by adding extra graph edges in an effort to learn additional connections between body joints. Instead of using a shared weight matrix for all neighboring body joints, the proposed RS-Net model applies weight unsharing before aggregating the feature vectors associated to the joints in order to capture the different relations between them. Experiments and ablations studies performed on two benchmark datasets demonstrate the effectiveness of our model, achieving superior performance over recent state-of-the-art methods for 3D human pose estimation.
iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?

There are two competing approaches for modelling annotator disagreement: distributional soft-labelling approaches (which aim to capture the level of disagreement) or modelling perspectives of individual annotators or groups thereof. We adapt a multi-task architecture -- which has previously shown success in modelling perspectives -- to evaluate its performance on the SEMEVAL Task 11. We do so by combining both approaches, i.e. predicting individual annotator perspectives as an interim step towards predicting annotator disagreement. Despite its previous success, we found that a multi-task approach performed poorly on datasets which contained distinct annotator opinions, suggesting that this approach may not always be suitable when modelling perspectives. Furthermore, our results explain that while strongly perspectivist approaches might not achieve state-of-the-art performance according to evaluation metrics used by distributional approaches, our approach allows for a more nuanced understanding of individual perspectives present in the data. We argue that perspectivist approaches are preferable because they enable decision makers to amplify minority views, and that it is important to re-evaluate metrics to reflect this goal.
Patch-wise Mixed-Precision Quantization of Vision Transformer

As emerging hardware begins to support mixed bit-width arithmetic computation, mixed-precision quantization is widely used to reduce the complexity of neural networks. However, Vision Transformers (ViTs) require complex self-attention computation to guarantee the learning of powerful feature representations, which makes mixed-precision quantization of ViTs still challenging. In this paper, we propose a novel patch-wise mixed-precision quantization (PMQ) for efficient inference of ViTs. Specifically, we design a lightweight global metric, which is faster than existing methods, to measure the sensitivity of each component in ViTs to quantization errors. Moreover, we also introduce a pareto frontier approach to automatically allocate the optimal bit-precision according to the sensitivity. To further reduce the computational complexity of self-attention in inference stage, we propose a patch-wise module to reallocate bit-width of patches in each layer. Extensive experiments on the ImageNet dataset shows that our method greatly reduces the search cost and facilitates the application of mixed-precision quantization to ViTs.
A New Semantic Segmentation Diagram for Intelligent Transportation Based on Heterogeneous Knowledge Base

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.
DNN-Defender: An in-DRAM Deep Neural Network Defense Mechanism for Adversarial Weight Attack

With deep learning deployed in many security-sensitive areas, machine learning security is becoming progressively important. Recent studies demonstrate attackers can exploit system-level techniques exploiting the RowHammer vulnerability of DRAM to deterministically and precisely flip bits in Deep Neural Networks (DNN) model weights to affect inference accuracy. The existing defense mechanisms are software-based, such as weight reconstruction requiring expensive training overhead or performance degradation. On the other hand, generic hardware-based victim-/aggressor-focused mechanisms impose expensive hardware overheads and preserve the spatial connection between victim and aggressor rows. In this paper, we present the first DRAM-based victim-focused defense mechanism tailored for quantized DNNs, named DNN-Defender that leverages the potential of in-DRAM swapping to withstand the targeted bit-flip attacks. Our results indicate that DNN-Defender can deliver a high level of protection downgrading the performance of targeted RowHammer attacks to a random attack level. In addition, the proposed defense has no accuracy drop on CIFAR-10 and ImageNet datasets without requiring any software training or incurring additional hardware overhead.
GCNSLIM: Graph Convolutional Network with Sparse Linear Methods for E-government Service Recommendation

Graph Convolutional Networks have made significant strides in Collabora-tive Filtering recommendations. However, existing GCN-based CF methods are mainly based on matrix factorization and incorporate some optimization tech-niques to enhance performance, which are not enough to handle the complexities of diverse real-world recommendation scenarios. E-government service recommendation is a crucial area for recommendation re-search as it involves rigid aspects of people's lives. However, it has not received ad-equate attention in comparison to other recommendation scenarios like news and music recommendation. We empirically find that when existing GCN-based CF methods are directly applied to e-government service recommendation, they are limited by the MF framework and showing poor performance. This is because MF's equal treatment of users and items is not appropriate for scenarios where the number of users and items is unbalanced. In this work, we propose a new model, GCNSLIM, which combines GCN and sparse linear methods instead of combining GCN and MF to accommodate e-government service recommendation. In particular, GCNSLIM explicitly injects high-order collaborative signals obtained from multi-layer light graph convolutions into the item similarity matrix in the SLIM frame-work, effectively improving the recommendation accuracy. In addition, we propose two optimization measures, removing layer 0 embedding and adding nonlinear acti-vation, to further adapt to the characteristics of e-government service recommenda-tion scenarios. Furthermore, we propose a joint optimization mode to adapt to more diverse recommendation scenarios. We conduct extensive experiments on a real e-government service dataset and a common public dataset and demonstrate the ef-fectiveness of GCNSLIM in recommendation accuracy and operational performance.
LPMM: Intuitive Pose Control for Neural Talking-Head Model via Landmark-Parameter Morphable Model

While current talking head models are capable of generating photorealistic talking head videos, they provide limited pose controllability. Most methods require specific video sequences that should exactly contain the head pose desired, being far from user-friendly pose control. Three-dimensional morphable models (3DMM) offer semantic pose control, but they fail to capture certain expressions. We present a novel method that utilizes parametric control of head orientation and facial expression over a pre-trained neural-talking head model. To enable this, we introduce a landmark-parameter morphable model (LPMM), which offers control over the facial landmark domain through a set of semantic parameters. Using LPMM, it is possible to adjust specific head pose factors, without distorting other facial attributes. The results show our approach provides intuitive rig-like control over neural talking head models, allowing both parameter and image-based inputs.
CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning

Sampling-based path planning algorithms play an important role in autonomous robotics. However, a common problem among the RRT-based algorithms is that the initial path generated is not optimal and the convergence is too slow to be used in real-world applications. In this paper, we propose a novel image-based learning algorithm (CBAGAN-RRT) using a Convolutional Block Attention Generative Adversarial Network with a combination of spatial and channel attention and a novel loss function to design the heuristics, find a better optimal path, and improve the convergence of the algorithm both concerning time and speed. The probability distribution of the paths generated from our GAN model is used to guide the sampling process for the RRT algorithm. We train and test our network on the dataset generated by \cite{zhang2021generative} and demonstrate that our algorithm outperforms the previous state-of-the-art algorithms using both the image quality generation metrics like IOU Score, Dice Score, FID score, and path planning metrics like time cost and the number of nodes. We conduct detailed experiments and ablation studies to illustrate the feasibility of our study and show that our model performs well not only on the training dataset but also on the unseen test dataset. The advantage of our approach is that we can avoid the complicated preprocessing in the state space, our model can be generalized to complicated environments like those containing turns and narrow passages without loss of accuracy, and our model can be easily integrated with other sampling-based path planning algorithms.