Reviewerly/exHarmony huggingface dataset:
Comparison of Three Korean Sentiment/Emotion Word Dic tionaries

We compare three publicly available Korean sentiment/emotion word dictionaries with emphasis on a newly accessible multi-label Korean emotion word dictionary developed by Humanities Research Institute, Chung-Ang University. The three dictionaries are KOSAC sentiment lexicon, KNU Korean sentiment lexicon, and a new multi-label Korean emotion word dictionary with 24 emotions.
Conceptual Attention in StyleGAN

Attention is an important function in human cognition. Since the human brain can only process a limited amount of information at a time, attention selects and focuses on just the important information among all the perceived information. Recent years have seen the advent of methods that imitate the human attention mechanism by means of deep learning, one of which is the multi-head attention proposed for Transformer. Humans can consider a single object from multiple perspectives, so multi-head attention linearly maps the input to multiple latent spaces and computes the attention for each of them. However, when humans pay attention to a single object from various aspects, there is an explicit context in the brain (e.g., a concept), which they pay attention to and accordingly perceive in a top-down manner—and multi-head attention cannot do this. We have therefore developed Conceptual Attention (CA), which can more explicitly compute the attention for each concept. The aim of CA is to achieve the same concept-by-concept attentional mechanism as the human brain by using style vectors learned by StyleGAN as concepts. Our experiments demonstrate that introducing CA into the StyleGAN2 discriminator and then fine-tuning it improves the quality of the generated images for large, small, and noisy data. We also visualize the results of attention to show that the proposed method can capture the concepts and qualitatively clarify how the concepts are reflected in the generated images.
Nearest Neighbor Search using Metric-Preserving Function for Retrieval-based Dialogue System

The Nearest Neighbor Algorithm is commonly used in Retrieval-based Dialogue Systems to find the next response candidate for Contexts. In this paper, We experiment with two metric functions for the metric tree-based Nearest Neighbor Search Algorithm to utilize in the dialog system. (1) we experiment with leveraging the embeddings of a pretrained model using cosine distance to extract contextual embeddings of dialog responses and applying them to a Nearest Neighbor Algorithm defined in metric space using metricpreserving cosine distance. (2) We measure the performance of a metric-preserving function that combines Jaccard distance for navigational terms such as specific entity names. Through this, we show the potential improvement of the response retrieval by using various metrics together in the metric treebased Nearest Neighbor Search.
Outlier-aware Cross-Market Product Recommendation

Cross Market Recommendation (CMR) is a method of recommending in a resource-scarce market by using modelagnostic meta-learning. Generally, more interactions give more clues to identify the user preferences, so CF performs better with outlier users (who have more item interactions) than normal users. However, constructing each adapt batch set (support set) and evaluation batch set (query set) for meta-learning in CMR causes the model to underfit in outlier users. We aim at this phenomenon and propose a new hybrid strategy to solve this problem. By simply combining MAML and CF to target general users and outliers, respectively. We also validate our method with the benchmark dataset and the proposed model shows better performance compared to the original model.
Utilizing Priming to Identify Optimal Class Ordering to Alleviate Catastrophic Forgetting

In order for artificial neural networks to begin accurately mimicking biological ones, they must be able to adapt to new exigencies without forgetting what they have learned from previous training. Lifelong learning approaches to artificial neural networks attempt to strive towards this goal, yet have not progressed far enough to be realistically deployed for natural language processing tasks. The proverbial roadblock of catastrophic forgetting still gate-keeps researchers from an adequate lifelong learning model. While efforts are being made to quell catastrophic forgetting, there is a lack of research that looks into the importance of class ordering when training on new classes for incremental learning. This is surprising as the ordering of "classes" that humans learn is heavily monitored and incredibly important. While heuristics to develop an ideal class order have been researched, this paper examines class ordering as it relates to priming as a scheme for incremental class learning. By examining the connections between various methods of priming found in humans and how those are mimicked yet remain unexplained in life-long machine learning, this paper provides a better understanding of the similarities between our biological systems and the synthetic systems while simultaneously improving current practices to combat catastrophic forgetting. Through the merging of psychological priming practices with class ordering, this paper is able to identify a generalizable method for class ordering in NLP incremental learning tasks that consistently outperforms random class ordering.
Improving Generation of Sentiment Commonsense by Bias Mitigation

Commonsense knowledge graphs (CSKG) are crucial for artificial intelligence systems to understand natural language. Recently, with the construction of COMET (Commonsense Transformer) and ATOMIC2020, a comprehensive coverage commonsense reasoning knowledge graph, CSKG research is increasingly vital in natural language understanding and reasoning. Since sentiment commonsense knowledge is understudied yet, our work focuses on improving the generation of sentiment commonsense in ATOMIC2020. We first show a problem in natural language generation that degrades the accuracy due to the unbalanced sentiment distribution in the dataset. Next, we propose the EDA (Easy Data Augmentation) and UDA(Unsupervised Data Augmentation) based methods that improve the accuracy through biased mitigation of the unbalanced dataset. Our experimental results show that EDA method has little effect on the accuracy, while UDA-based method has some accuracy improvements in ROUGE-I, ROUGE-2, and ROUGE-L.
Speech Recognition for Turkic Languages Using Cross-Lingual Transfer Learning from Kazakh

This paper investigates the effectiveness of transfer learning in building automatic speech recognition models for nine Turkic languages (Azerbaijani, Bashkir, Chuvash, Kyrgyz, Sakha, Tatar, Turkish, Uyghur, and Uzbek), by leveraging large-scale training data from the Kazakh language. The performance of the models built using transfer learning from Kazakh was compared with the performance of the models for three non-Turkic languages (Indonesian, Japanese, and Swedish) to which transfer learning from Kazakh was also applied. We also compared the performance of the models with the results of models trained on English data. A total of 64 models were created. Most of the models built using transfer learning from Kazakh performed better than the monolingual baselines, with the most notable improvement observed for the Sakha model, which achieved a 45.5% and 22.8% reduction in the word error rate and character error rate on the test set, respectively. The datasets and codes used to train the models are available for download from https://github.com/IS2AI/CLTL_Turkic_ASR.
JIDECA: Jointly Improved Deep Embedded Clustering for Android activity

Recently, clustering research using deep neural networks to learn latent vectors is growing rapidly. In particular, people are interested in developing clustering methods based on the result of deep embedded clustering (DEC). Among several clustering applications, we examine Android activity clustering using the Rico dataset. We propose a Jointly Improved Deep Embedded Clustering for Android activity (JIDECA) method for better activity clustering. We generate various activity latent vectors using a DNN autoencoder and a CNN autoencoder with the Rico dataset. Simultaneously, JIDECA learns latent vectors and performs clustering with local structure preservation for each modality. In addition, JIDECA uses cross-modality alignment loss to make each single modality similar. Our experimental results show that JIDECA outperforms both single modal methods and multimodal methods for real activity images.
An Approach for Fusing Two Training-Datasets with Partially Overlapping Classes

Supervised deep learning techniques in image processing require training data, typically consisting of manually labeled ground truth annotations. Since manual labeling is costly, using as many existing training datasets as possible is necessary. This paper introduces a novel approach for combining training datasets into a new one. The naive approach to this is a plain concatenation of the existing datasets. However, this approach fails with partially overlapping datasets when certain annotated instances specific to one dataset also appear in the other dataset -without their annotations. Therefore, we present a novel method for combining existing training datasets using a pseudo-labeling technique with uncertainty quantification. The effectiveness of our method is evaluated by fusing two datasets consisting of partially overlapping traffic sign annotations in street view images. The results demonstrate that the pseudo-labeling errors weigh less than those introduced by the naive fusion. Furthermore, our work provides evidence for practitioners to use a pseudo-labeling-based fusion technique with uncertainty quantification rather than naively combining training datasets into a new one.
Unsupervised Relation Extraction with Sentence level Distributional Semantics

Relation Extraction (RE) aims to identify the relationship between pairs of named entities in natural-language sentences. An unsupervised RE approach extracts relations in the absence of training data. Recently, many state-of-the-art unsupervised approaches have used word embeddings for RE. Such approaches ignore the semantic structure of the complete sentence. On the other hand, in this paper, we propose a novel approach that utilizes sentence encoding for unsupervised relation extraction. Our model classifies the sentence encoding of contextually similar natural-language sentences into clusters using an unsupervised approach, where each cluster consists of one or more potential relations. We queried the cluster for a candidate relation, and used a confidence value/threshold to extract accurate relations without semantic drift. We validated our approach by comparing it with both the unsupervised and bootstrapping approaches. Our experimental results suggest that our model achieves a better F-score on state-of-the-art datasets than the other unsupervised approaches.
MTFT: Multi-Tenant Fair Throttling

Distributing resources fairly to tenants is important in cloud data center. However, traditional Fair I/O schedulers are not suitable for container environments. It is because of the unfairness and performance degradations. Blk-throttle method was used to distribute bandwidth fairly without performance degradations. But this method has the disadvantage that work-conservation is impossible due to static. In this work, the Multi-Tenant Fair Throttling Tool (MTFT), a dynamic blk-throttle controller, was implemented that maintains fairness and minimizes performance degradations during simultaneous execution.
Extended Abstract: Predicting the Morality of a Character Using Character-Centric Embeddings

Story generation and analysis have been researched for decades using AI and Natural Language Processing technology. However, while ethics is becoming essential for developing AI applications, little research deals with morality in the narrative. We present the framework to build embeddings for representing characters in terms of the character’s morality. In addition, we propose a morality judgment task using storybooks. We conduct experiments, and the results suggest that word embedding models can learn a character’s morality.
Exploratory Inference Chain: Exploratorily Chaining Multi-hop Inferences with Large Language Models for Question-Answering

Successful few-shot question-answering with large language models (LLMs) has been reported for a variety of tasks. In the usual approach, an answer is generated by a single call to an LLM, but it has been pointed out that the performance of multi-hop inference by LLMs is not sufficient. Thus, an LLM is unable to perform the complex processing necessary to get an answer, which leads to poor performance. Moreover, the inference process is opaque. Against this, approaches that call an LLM multiple times have been proposed, but many of these approaches can only be used for a limited number of effective tasks, and LLMs essentially require complex processing.To address these problems, we propose the Exploratory Inference Chain (EIC) framework that combines the implicit processing of LLMs with explicit inference chains, and this is based on the dual process theory of human cognitive processes. The EIC framework first generates the information needed to answer a multi-hop question as keywords and then performs 1-hop inference for each keyword. If the inference is not sufficient, additional inferences are performed. This process is repeated, and when sufficient inferences are obtained, they are aggregated, and the final answer is generated. This makes the information per inference by LLM simplified, and logical inference is achieved through an explicit inference chain.We conducted experiments on two multi-hop QA datasets and confirmed through a quantitative evaluation that our EIC framework performed better than existing approaches. Moreover, a qualitative evaluation confirmed that our approach can effectively perform inference so as to get closer to the answer in question-answering tasks that require knowledge. In addition, compared with existing approaches, the EIC framework improves the interpretability of the output.
Modeling Recommendation and Project Deployment Automation for Semantic Knowledge Graphs

Semantic technology continues to advance but adoption still has existing and emerging challenges due to varying amounts of individual knowledge for performing development tasks, limited semantic-expert resources, as well as fast-paced time lines to produce industry products. Semantic modeling is usually time-consuming even for those with the expertise. Ad-hoc and rushed implementations may result in less ideal interoperability in downstream work, slowing down adoption of knowledge graphs in intelligent systems. The objective of this contribution is to use automation of certain expert tasks to propel semantic development efforts toward solutions that avoid compounding semantic tech debt for data alignment-based tasks and subsequent knowledge graph-based projects. This work aims to function in tandem with other existing internal data curation and deployment efforts to offer an automation-based framework and tool with results automatically deployable to a chosen project space, semantic editor, or triple store. This ultimately encourages development with artifacts that have already been assembled with all semantic dependencies to encourage using them alongside other industry standards. It also reduces expensive and redundant modeling efforts, helps to teach non-semantic experts about the implications of their modeling choices with recommendations and paradigm delineations, and eases remaining data alignment work so that results more easily follow semantic best practices.
Automatic Identification of Chinese Paired Discourse Connectives

This paper describes our approach to automatically identify paired Discourse Connectives (DCs) in Chinese texts. Discourse Connectives (DCs) are terms that connect two text spans and signal the discourse relations between them. Most DCs consist of a consecutive words (eg. as a result); however paired DCs are composed of non-consecutive words that together signal the discourse relation (eg. on one hand … on the other hand). Although paired DCs are not common in English, they are very frequent in Chinese. The contribution of this paper in two-fold: First, we propose a methodology for the automatic identification of Chinese paired DCs. Second, we present a new corpus based on the Chinese Discourse Treebank (CDTB) [1] annotated with paired DCs. To identify paired DCs, we experimented with two main approaches: hypothesis testing and supervised machine learning. Although the hypothesis testing approaches led to lower than expected results, the simple machine learning models achieved F-scores between 72.5%–75.6% with no fine-tuning.
Edge-Cloud Collaboration Architecture for Efficient Web-Based Cognitive Services

Many web services are often latency-sensitive, have high network capabilities, and have computing resource limitations, as demonstrated by deep neural network (DNN) model-based web applications. In this work, a DNN model-based web application providing visual cognitive services is implemented and explored to address latency-sensitive problems, taking into account the broad practicability and importance of web object recognition tasks in intelligent applications and modern systems. We propose a collaborative architecture of enduser, edge server, and cloud server, employing a binary offloading strategy to reduce the upload rate of images while ensuring good detection performance, thereby reducing the response time. Detection scenario-oriented data augmentation is carried out on the dataset to improve detection accuracy. Finally, we compare the performance of the proposed approach with traditional object recognition services running entirely on the cloud. The experimental results show that the detection scenariooriented strategy significantly improves the detection accuracy of fine-trained YOLOv5 models on the PASCAL VOC 2012 dataset. Compared with the traditional cloud-based architecture, the proposed edge-cloud collaboration architecture can detect more objects and reduce response time efficiently.
MENDEL: Time series anomaly detection using transfer learning for industrial control systems

Machine learning is commonly used to detect anomalies in industrial control systems (ICS). In general, building an anomaly detection model requires massive training data and computational resources. Therefore, an ideal solution is to use a pre-trained model instead of building each model completely from scratch. However, we cannot directly use a pre-trained model because each ICS dataset has its own unique features and characteristics. This paper proposes a practical transfer learning technique dubbed MENDEL (tiMe sEries aNomaly Detection using transfEr Learning) to efficiently build anomaly detection models, respectively, for different ICS domains. MENDEL first applies principal components analysis (PCA) to each model to obtain a fixed number of reduced features compatible with other models and then finds a reasonable mapping between different models’ reduced features systemically for effective transfer learning. We evaluate the performance of MENDEL on two datasets (SWaT and WADI) with two models (InterFusion and USAD). Our evaluation results show that MENDEL can overall achieve high F1 scores even when a model is retrained with only a small proportion of the training dataset. For example, when we first train InterFusion with the SWaT train dataset and then retrain the trained model with only 10% of the entire WADI train dataset, the retrained InterFusion achieves an F1 score of 72%, which is better than an F1 score of 44% achieved by InterFusion with the entire SWAT training dataset.
Fast Integration for Multiple Graphs with Neumann Approximation

Graph-based models have gained much interest in the domain of machine learning as they offer the advantage of handling data that reside on complex structures. From various models that encounter graph-structured data, graph-based semi-supervised learning (SSL) have shown successful results in multiple applications. The key idea behind SSL is the spreading process of labels through the edges and the problem boils down to keeping the graph Laplacian intact. Meanwhile, with the rapid growth in availability of data, there exist multiple descriptions of graphs for the same set of data points. Each graph contains complementary information to one another, and it would be beneficial to integrate all the available information. In this paper, we propose an SSL-based fast graph integration method that employs approximation in the maximum likelihood estimation process of finding the combination. The proposed approximation utilizes the connection between the covariance and its Neumann series, which allows us to avoid explicit matrix inversion. Empirically, the proposed method achieved competitive performance with significant improvements in computational time when compared to other method.
Criminal Deduction Using Similarity Analysis Between Mystery Stories

The " Knowledge Graph Reasoning Challenge" an annual event in which participants try to infer a mystery novel using Knowledge Graph and other tools, has been attracting a lot of attention. The purpose of this research is to propose and implement a new method using BERT for this challenge. To prove this, we focused on the degree of similarity between the two novels and estimated the culprit. As a result of the analysis focusing on the words and actions of the culprit, we succeeded in extracting the culprit's name. However, the successful extraction was based on a specific condition, and further discussion is needed to pursue more precise accuracy in the future.
MMTS: Multimodal Teacher-Student learning for One-Shot Human Action Recognition

Human action recognition (HAR) is applied to many real-world applications, such as visual surveillance, video retrieval, and autonomous driving vehicles. It can utilize various modalities such as RGB, infrared, depth, or skeleton. Among these, we selected and used a skeleton suited to real-time application because it requires less input than RGB data. Furthermore, we focused on a one-shot setting. The skeleton data tends to have a smaller dataset size than other modalities, so it is hard to expect the powerful generalization ability to make representation from unseen data (i.e. novel class). Therefore, to solve this problem, we proposed a skeleton-text multimodal learning method by borrowing a powerful pretrained text encoder that was trained using a large-scale dataset. Our method utilizes the teacherstudent approach through the skeleton-text dataset and only uses the skeleton for inferences. The proposed method is more suitable for one-shot skeleton-based HAR compared to the existing multimodal learning method. Our approach outperformed the stateof-the-art methods for the one-shot action recognition protocol on the NTU RGB+D120 dataset.