Reviewerly/exHarmony huggingface dataset:
Three Player Otrio will be Strongly Solved

Otrio is a 3D extension of classic Tic-Tac-Toe played with two to four players. Three-player Otrio has a big state space due to the additional dimension and players, and stands on the line between strongly and weakly solvable games. In this paper, we analyze the feasibility of strongly solving 3-player Otrio by giving a tighter upper bound and a fairly accurate approximation of the state-space complexity. We propose a new indexing scheme to subdivide the state space into slices that are manageable by a commodity computer, and then develop an algorithm to conduct a retrograde analysis of the game. We find that the state-space complexity of three player Otrio is in the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">12</sup> and that fully solving it would require approximately 248 days of CPU time per player cooperation configuration.
Online Water Usage Monitoring under Anomalous Interference in Residential Households

As the issue of water shortage is increasing nowadays due to climate change, water consumption monitoring has become more critical in home automation services in recent years. In order to lower water bills, residents need to adjust their water usage behaviors to reduce their water consumption, highlighting the importance of the water behavior disaggregation task. However, existing works may fail to precisely disaggregate behaviors when anomaly data exists in received water data since they usually assume it is a clean dataset. In order to deal with this issue, we propose a two-phase framework to online disaggregate water usage behaviors in consideration of the occurrence of water anomaly data. A density-based clustering and different pretrained classification models are combined to detect anomalies efficiently and effectively recognize different usage behaviors. As studied on the real-world dataset, we demonstrate that the proposed framework can achieve good performance on datasets with or without anomalies.
Human Values Estimation on News Articles through BERT-extracted Opinion Expressions

This paper proposes a human values estimation method with an opinion extraction approach. We assume that opinion sentences reflect human values the most. For a given article, the proposed method extracts the opinion sentences from the texts and estimates the human values included in the opinion sentences. The opinion sentence extraction is conducted by classifying each sentence as an opinion sentence or a non-opinion sentence. The proposed method concatenates sentences from the same article to extend the input texts as an upsampling approach while estimating the human values. The upsampling approach enriches the information volume of the training data. The distribution of human values from each opinion sentence shows the article's general human values. We conducted two evaluation experiments. We used the editorial articles from Mainichi Newspaper as the corpus data. The first experiment evaluated the performance of opinion sentence extraction. The training accuracy of opinion sentence extraction was 92%. In the evaluation test, the model reached a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> score of 85.5%. The results showed that opinion sentences could be extracted with high accuracy. The second experiment evaluated the performance of human values estimation. There are five categories for human values estimation. The experiment was conducted with the same editorial articles from Mainichi Newspaper as the corpus data. We applied a training data enhancement approach by increasing the sentence number of training input and achieved a training accuracy up of over 50%. The results showed that the human values of opinion sentences could be estimated with high accuracy.
Generate Multi-Perspective Summarization with Pairwise Alignment Mechanism

Cyberterrorism is often defined as attack using communication networks to cause destruction or generate fear in society to achieve political or ideological goals. Nowadays, due to the accessibility and anonymity, social media has become the breeding grounds for spreading cyberterrorism. To counter cyberterrorism efficiently and effectively, it is critical for the legal enforcement to summarize information on the internet. Particularly, to analyze intelligence comprehensively and make decision without bias, there are multiple perspectives to be considered. However, it remains two challenges to be solved for multi-perspective summarization, which are lacking of annotations and various perspectives with conflicts. To address the challenges, we propose Pairwise Alignment Mechanism and Perspective Consensus Strategy to incorporate different perspectives into the summary. The experiment results show that our methodology can significantly leverage the coverage of multiple perspectives and maintain the consistency of the generated summary.
Removement of MisLeading Data in Transfer Learning Using BERT

When using machine learning to solve natural language processing tasks, the domains in which the model is trained and the domains in which the learned model is applied are different. Domain shift problem reduces model performance. Transfer learning using Bidirectional Encoder Representations from Transformers(BERT) is an effective method used for solving this problem. However, even with this method, we face the problem known as “negative transfer”, which occurs when some source labeled data adversely affect the learning in the target domain. In this study, we propose a for removing misleading data, causing negative transfer, for document classification tasks. We demonstrated the effectiveness of our proposed method in an experiment using the Webis-CLS-10 dataset.
Decoupled Knowledge Propagation Network and Sampling Strategies for Recommendation System

State-of-the-art knowledge-graph-based models, such as RippleNet and CKAN, have been successfully applied to recommendation systems. These models make use of knowledge graph to expand the entity information, which is similar to ripples, so as to strengthen the correlation between the user's preference and the candidate item. In RippleNet and CKAN, however, the networks for learning the features of user preference and candidate item are mutually coupled, which might limit the recommendation performance, especially for multi-levels propagation. On the other hand, when entities are expanded over the knowledge graph, the amount of entities increases exponentially with the number of diffusion levels. In earlier research, random sampling was adopted to restrict the number of triples, which however may limit the coverage of the expanded knowledge for model learning as well as the prediction performance. To tackle these problems, we propose a decoupled knowledge propagation network that separates the user's preference and the candidate item to distinguish the candidate items better. Experiments conducted on the MovieLens 1M recommendation dataset and the Microsoft Satori knowledge graph show the proposed mode outperforms RippleNet and CKAN. In addition, three sampling strategies, including balanced sampling, non-duplication, and dynamic sampling were proposed to deal with the sampling issue, and experiments show such sampling strategies are effective and superimposed for the prediction performance.
Entropy-Based Two-Phase Optimization Algorithm for Solving Wordle-like Games

In the past, a method called Two-Phase Optimization Algorithm (TPOA) was designed by Shan-Tai Chen to solve the game of Mastermind and the AB game efficiently. In this paper, we proposed a modified version called Entropy-Based TPOA (EBTPOA) for Wordle-like games. It is a combination of his algorithm and our previous work on Nerdle with greedy method. It focuses on not only effectiveness but also efficiency while finding optimal results. In Wordle-like games, EBTPOA performs better with fewer guess times on average than TPOA. In Wordle, EBTPOA hits the answer optimally within 3.42117 times on average, and 5 times in the worst case, and the best opening word is “SALET”. In Nerdle, EBTPOA hits the answer within 3.01947 times on average, and 4 times in the worst case, and the best opening equation is ’’ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$52-34=18$</tex> ’’. These are the best results up to date, and particularly none has achieved the result for Nerdle so far. As for efficiency, by expanding with fewer branches and limiting the depth of exploration, EBTPOA can obtain the optimal result with a lower time complexity compared to related works.
Proposal of Consistent Learning Model with Exchange Monte Carlo

This paper proposes a consistent learning model based on Exchange Monte Carlo Method. The paper also gives discussion with respect to experiments on the synthesized case. Learning model is currently focusing on the model with the interface for input and output. On that model, preparing a dataset remains in human's work, and there is still not sufficient research how to prepare a valuable dataset efficiently. Exchange Monte Carlo is used widely for both purposes of optimization and estimation of a probability distribution, and it has the ability to combine any probability model in one and sample the model's state efficiency from the combined probability model. From this point of view, when we consider the three models, i.e., real space, learnt model, and model of parameter distribution, we can combine them and construct the consistent model that explains the phenomena of learning consistently. It is supposed that those samples give us valuable dataset and set of the learnt parameter, when the parameter space also modeled with Bayesian inference framework. With these ideas mentioned so far, this paper proposes a generalized consistent probability model of the real space, learning model, and parameters' distribution of the learning model. To challenge to the sampling problem on high-dimensionality of the consistent model, Exchange Monte Carlo and Hamiltonian dynamics are employed. Experiments show the proposed method works on the synthesized case, that is, the original distribution is approximated well and parameter is optimized too, only by sampling from the consistent model without preparing dataset.
Deep Recognition of Facial Expressions in Movies

Consumer feedback is used in lots of fields for various purposes. However, traditional paper questionnaires or online surveys cannot fully meet these demands for obtaining accurate and useful feedback from consumers. Therefore, we propose a deep learning based deep recognition of facial micro expressions in order to receive more realistic feedback from users in this study. To achieve this goal, we integrate several approaches including: 1. using trained face detection model to capture face image from input. 2. training a high accurate 468-point landmark detection model with multiple face dataset. Based on the FACS (Facial Action Coding System) table, we categorize these landmarks into 13 groups of facial regions. These regions with specific emotion labels are used as our target units of AU (Action Unit) detection. 3. training CNN model to detect and analysis AUs from facial landmark data. 4. implying FACS to evaluate the facial expressions and emotions. and 5. using a straightforward GUI plotter to show the digitized emotions. The experiment results show that not only the primary emotion but also the secondary emotion of users in movies can be detected and evaluated successfully. Therefore, our system has a great potential for detecting micro expressions in a more accurate and comprehensive manner.
Exploring Graph Neural Network in Administrative Medical Dataset

Administrative medical dataset contains medical records of patients. Using administrative medical dataset can extract disease association to help finding comorbidity. Previous methods only use statistics on administrative medical dataset such as computing probabilities of disease occurrence and are limited by the capability of statistics. To enhance hidden information usage of administrative medical dataset, we propose two different methods based on graph neural networks to exploit hidden information in administrative medical dataset. One is using graph neural networks to generate disease embeddings and pass through kNN algorithm to find similar diseases for suggestion to physicians. The other one is that we formulate sequence prediction problem and use gated graph neural network to model every disease sequence by forming session graphs. Different from previous methods for sequence prediction that only consider current sequence, we also consider all sequences in dataset at the same time. Besides, we use position-aware soft-attention mechanism to aggregate disease embeddings to session embeddings and predict the next disease of a patient. We conduct extensive experiments on two methods and show its ability to outperform several baselines.
Introducing Simplicity in Document Summarization by Leveraging Coreference and Pseudo-Summary

Document summarization has rapidly gained importance due to the exponentially increasing data. Generally, studies in document summarization focused on generating summaries having high coverage and fluency. Such summaries can be challenging for readers with limited language proficiency. This paper introduces the simplification concept in document summarization tasks. Our method is divided into two phases to handle the challenges of the task. The first phase is to tackle the problem of long documents with unnecessary details that can affect key information or coverage of the summaries. Thus, we propose a method to condense key information by utilizing coreference resolution. The second phase uses the condensed documents as inputs. This phase handles the challenge of having no dataset with a simplification concept in summarization tasks. Therefore, this research proposes an unsupervised training framework without relying on golden summaries. The training first outputs summaries with high coverage called pseudo-summaries. Then, it is used as a reference to generate final summaries with words that are more familiar and commonly used, resulting in easier-to-understand summaries for readers.
Identifying Reply-to Relation in Textual Group Chat using Unlabeled Dialogue Scripts and Next Sentence Prediction

With instant message (IM) software becoming an important part of work and study, more and more research has begun to aim at supporting people's communication by analyzing their chat messages, such as topic provision and relationship sustainment. As the essential step for achieving these research works, the first task is to identify the relations between those large amounts of chat messages, especially in the situation of group chats. In this paper, we propose a method to identify the “reply-to” relations in IM's group chat from unlabeled textual data by using the next sentence prediction (NSP) approach. First, we proposed a method of automatically sampling two messages with and without the “reply-to” relation from unlabeled dialogue scripts to prepare the training data. Second, we built and trained three settings of the NSP model through the training data to identify the “reply-to” relations between two text chat messages. These NSP models were based on the pre-trained Japanese BERT (bidirectional encoder representation from transformers) model. Last, we evaluated the trained models through actual text group chat data with manual labels. The evaluation data contains 444 textual chat messages from three chat groups, each group has three chat members. Evaluation results showed that the models reached a max accuracy up to 69.6%, higher than past methods, and the top F1 score is 0.558.
Automatic Evaluation of Discussion Quality Using Topic Relevance and Participants' Performance

People often have discussions with others in working cooperatively. Discussions are conducted with given topics. Participants in discussions give their opinions to have an agreement on an issue. They listen to others' opinions and give their views. The duration of a discussion is limited, so a discussion can not be held for a long time. Since the participants have to get an agreement in a limited duration, the quality of discussion must keep high. However, it is difficult for the participants to assess the quality of the discussion. An automatic evaluation method for the quality of discussion is required. This paper proposes an automatic evaluation method of the discussion quality. We assume if a discussion has much information related to discussion topics, the quality of the discussion must be high. We also assume if all participants give their opinions related to the discussion topics, the quality of the discussion must be high. Based on the assumptions, the proposed method takes discussion texts and keywords related to discussion topics to evaluate topic relevance and participant's performance. The proposed method generates an equation of the discussion quality using multiple regression analysis. If a new discussion is given, the generated equation can automatically assess the discussion's Quality. In evaluation experiments, we used 20 discussion texts and obtained an equation for the quality of discussion. We found that the obtained equation could assess the quality of discussion with high accuracy (a multiple <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R$</tex> was 0.92).
Proposal for Visualizing Sound Information in Movies

This paper proposes a method for visualizing sound information accompanying movies. Sometimes we have a situation where we want to watch a movie with the sound off, such as when watching multiple movies at the same time, while traveling by public transport, etc. As alternatives to audio, subtitles are commonly used to represent sound information in a movie. However, using subtitles only is difficult to give us the same impression as audio, such as the emotion of the speakers, music played, and noise in the movie. As another alternative to representing sound information, this paper aims to visualize the sound information in movies. The proposed method employs speech bubbles, frame borders, and concentration lines, which are inspired by the representation used in comics. This paper shows the effectiveness and characteristics of the proposed method by comparing it with a subtitle-based method by user experiments.
Improving LiDAR Semantic Segmentation on Minority Classes and Generalization Capability for Autonomous Driving

LiDARs have emerged as an important sensor in autonomous driving systems because they offer more accurate geometric measurements than cameras and radars. Therefore, LiDARs have been commonly combined with cameras or radars to tackle many perception problems in autonomous driving, such as object detection, semantic segmentation, or navigation. For semantic segmentation of LiDAR data, due to the class imbalance issue of large-scale scene, there is a performance gap between majority classes and minority classes of large-scale dataset. The minority classes usually include the crucial classes to the autonomous driving, such as “person”, “motorcyclist”, “traffic-sign”. To improve the performance of minority classes, we adopt U-Net++ as the architecture, KPConv as convolution operator, and use both dice loss and cross entropy as loss functions. We get 5.1% mIoU improvement on SemanticKITTI of all classes and 9.5% mIoU improvement of minority classes. Moreover, due to the different resolution of LiDAR sensors, we show the generalization capability of our model by training it on 64-beam dataset and testing on 32-beam and 128-beam dataset. We get 3.3% mIoU improvement on 128-beam dataset and 1.9% mIoU improvement on 32-beam dataset.
LiDAR-Based Orientation Monitoring of an Unknown Object During Manipulation with an Industrial Robot

Picking unknown objects in an automated process is a particular challenge in industrial robotics. Besides the actual pickup process, it is also important to check whether the picked object retains its orientation in the gripper while being manipulated in order to perform reliable collision-free path planning and ensure that the subsequent process continues without failure. This paper discusses the prerequisites, development, implementation, integration and evaluation of such monitoring of an unknown handling object in industrial robotics using LiDAR sensor technology.
Preliminary Design, Implementation and Observations of the Multi-player Game Tri-Othello

To study game characteristics, e.g., effective searching strategies and consequence of rules on playing styles, of multi-player games, we extend the popular two-player game Othello to be played by three players and is called Tri-Othello. Previous multi-player games are either too much knowledge involved, e.g., Poker and Mahjong, so that the focus is usually on designing heuristics, or not too much different by simply extending from their two-player versions so that not much extra game related information can be found. Tri-Othello is a simple, fun-to-play, asymmetry, imperfect information game where one player, initially unknown to the opponents, has a goal different from the rest. Through the design and implementation, we understand better how that piece of hided information can be used towards the advantage of particular players. From experimental results, we also understand better why and when multi-player alpha-beta pruning cannot be as effective as its two-player version.
The Maintenance of Frequent Episodes

Data mining technology is of great help in data analysis. Mining frequent episode is one of the important task in this field, which allows users to predict future events based on the current events. The traditional approaches for mining frequent episodes use hierarchical concept, that is, generate candidate episode first, and then scan the sequence data to determine whether they are frequent episode, is very time consuming to repeatedly scan the sequence data and search for candidate episodes. This paper proposes a method for mining episode in a data stream. Our method just scans new added data to update existing frequent episodes without scanning original data and searching for candidate episodes, which is more efficient than the other methods.
Spatial-Temporal Evidence Combination Method for Multi-sensor Target Recognition Based on Prospect Theory

Evidence theory has been widely used in the field of spatial-temporal information fusion, and since target recognition information fusion exhibits obvious dynamic and sequential characteristics. In order to realize the target recognition information fusion, spatial-temporal evidence is combined based on prospect theory and credibility decay model. Firstly, introducing the prospect value function to evaluate and adjust for evidence credibility, after that, an weight for each spatial evidence can be obtained to achieved the spatial information fusion. Finally, evidence combining by evidence discount criterion and Dempster combination rule to achieve sequential combination of temporal evidence. In this way, judgments for updated data of target recognition are data-driven, and makes more reasonable. The numerical simulation indicate that the proposed method fully reflects the dynamic and sequential characteristics of spatial-temporal evidence combination. Moreover, it has a strong capability to handle conflict information.