Wireless power transfer for charging electric vehicles (EVs) using the inductive charging mechanism enables EVs to recharge their batteries wirelessly while in motion or during halts at traffic signals. Additionally, resonant inductive charging (RIC) can wirelessly transfer energy with high efficiency and over longer distances. To achieve these objectives, we propose a RIC-enabled system for traffic signal scenarios capable of selecting EVs based on their required energy needs for travel and the remaining traffic signal time. We formulate an optimization problem that allows the selected EVs to maximize their battery energy during traffic signal halts, consequently optimizing the defined utility function. Given the dynamic nature of the problem, it falls under the category of NP-hard problems, and to address this, we propose a novel artificial intelligence framework. Our approach utilizes both the halt time and energy demand information, resulting in the development of the Traffic Signal-Aware Electric Vehicle Selection and Resource Allocation algorithm. We employ long short-term memory based deep learning model to predict battery energy needs and generate energy demand score information. The generated demand score, along with the remaining traffic signal time, serves as conditions for the final selection and allocation of charging resources to EVs. Finally, experimental results confirm the effectiveness of our proposed method, demonstrating superior performance compared to the deep neural network model. Furthermore, in terms of battery energy, our approach achieves a 55.1% increase compared to baseline-B and an 8.4% increase compared to baseline-C.

The General Meeting Understanding and Generation Challenge challenge track 2 in ICASSP 2023 Signal Processing Grand Challenge aims at extractive summarization of meeting transcripts. The main characteristic of this challenge is abridged as long spoken documents that are extremely difficult to manipulate with pre-trained language models. In this paper, we propose a post-trained DeBERTA which does not only adapt to spoken language but also manages long documents. According to the experimental results, the proposed method shows a 6% higher test score than the challenge baseline model.

Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. Thus, a reliable XAI twin system becomes essential to discretizing the physical behavior of the Internet of Everything (IoE) and identifying the reasons behind that behavior for enabling ZSM. To address the challenges of extensible, modular, and stateless management functions in ZSM, a novel neuro-symbolic XAI twin framework is proposed that to enable trustworthy ZSM for a wireless IoE. The proposed neuro-symbolic XAI twin framework consists of two learning systems: 1) implicit learner that acts as an unconscious learner in physical space and 2) explicit leaner that can exploit symbolic reasoning based on implicit learner decisions and prior evidence. The physical space of the XAI twin executes a neural-network-driven multivariate regression to capture the time-dependent wireless IoE environment while determining unconscious decisions of IoE service aggregation, such as uplink, downlink, and service provisioning. Subsequently, the virtual space of the XAI twin constructs a directed acyclic graph (DAG)-based Bayesian network that can infer a symbolic reasoning score over unconscious decisions through a first-order probabilistic language model. Furthermore, a Bayesian multiarm bandit-based learning problem is proposed for reducing the gap between the expected explained score and the current obtained score of the proposed neuro-symbolic XAI twin. Experimental results show that the proposed neuro-symbolic XAI twin can achieve around 96.26% accuracy while guaranteeing from 18% to 44% more trust score in terms of reasoning and closed-loop automation.

One of the core envisions of the sixth-generation (6G) wireless networks is to accumulate artificial intelligence (AI) for autonomous controlling of the Internet of Everything (IoE). Particularly, the quality of IoE services delivery must be maintained by analyzing contextual metrics of IoE such as people, data, process, and things. However, the challenges incorporate when the AI model conceives a lake of interpretation and intuition to the network service provider. Therefore, this paper provides an explainable artificial intelligence (XAI) framework for quality-aware IoE service delivery that enables both intelligence and interpretation. First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user. Second, a regression problem is devised to solve the formulated problem, where explainable coefficients of the contextual matrices are estimated by Shapley value interpretation. Third, the XAI-enabled quality-aware IoE service delivery algorithm is implemented by employing ensemble-based regression models for ensuring the interpretation of contextual relationships among the matrices to reconfigure network parameters. Finally, the experiment results show that the uplink improvement rate becomes 42.43% and 16.32% for the AdaBoost and Extra Trees, respectively, while the downlink improvement rate reaches up to 28.57% and 14.29%. However, the AdaBoost-based approach cannot maintain the CQI of IoE service users. Therefore, the proposed Extra Trees-based regression model shows significant performance gain for mitigating the trade-off between accuracy and interpretability than other baselines.

The demand for a tool for summarizing emerging topics is increasing in modern life since the tool can deliver well-organized information to its users. Even though there are already a number of successful search systems, the system which automatically summarizes and organizes the content of emerging topics is still in its infancy. To fulfill such demand, we introduce an automated report generation system that generates a well-summarized human-readable report for emerging topics. In this report generation system, emerging topics are automatically discovered by a topic model and news articles are indexed by the discovered topics. Then, a topical summary and a timeline summary for each topic is generated by a topical multi-document summarizer and a timeline summarizer respectively. In order to enhance the apprehensibility of the users, the proposed report system provides two report modes. One is Today's Briefing which summarizes five discovered topics of every day, and the other is Full Report which shows a long-term view of each topic with a detailed topical summary and an important event timeline.

Two major problems of Korean part-of-speech (POS) tagging are that the word-spacing unit is not mapped one-to-one to a POS tag and that morphemes should be recovered during POS tagging. Therefore, this article proposes a novel two-step Korean POS tagger that solves the problems. This tagger first generates a sequence of lemmatized and recovered morphemes that can be mapped one-to-one to a POS tag using an encoder-decoder architecture derived from a POS-tagged corpus. Then, the POS tag of each morpheme in the generated sequence is finally determined by a standard sequence labeling method. Since the knowledge for segmenting and recovering morphemes is extracted automatically from a POS-tagged corpus by an encoder-decoder architecture, the POS tagger is constructed without a dictionary nor handcrafted linguistic rules. The experimental results on a standard dataset show that the proposed method outperforms existing POS taggers with its state-of-the-art performance.

Hyun-Je Song, Seong-Bae Park. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Dialog state tracking in spoken dialog system is the task that tracks the flow of a dialog and grasps what a user wants from the utterance precisely. Since the dialog success is related to catching the want of the user, dialog state tracking is a necessary component for spoken dialog systems. This paper proposes a neural dialog state tracker with the attention mechanism for focusing on valuable words and the hierarchical softmax for efficient training of the tracker. In addition, the proposed tracker combines a natural language understanding module and a dialog state module in an end-to-end style. As a result, the error propagation within a dialog system is minimized. To prove the effectiveness of the proposed model, we do experiments on dialog state tracking in the human-human task-oriented dialogs. Our experimental results show that the proposed method outperforms both the neural tracker without the attention mechanism and that without the hierarchical softmax.

The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines. The proposed method formulates the translation as a generation task. That is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an attention mechanism is also used to cope with long natural language queries.

Knowledge base completion aims to complete a knowledge base by filling up missing facts of the knowledge base. Neural knowledge base embeddings proposed to solve this task measure the plausibility of all candidate triples, and then select top-ranked triples by the plausibility as new facts for the knowledge base. The plausibility by neural embeddings allows true facts to be ranked at high positions, but not at top positions. This is because neural knowledge base embeddings are limited to using only the information within the knowledge base. Therefore, this paper proposes a re-ranking model for precise knowledge base completion. As a re-ranking model, a neural network which uses knowledge base schema and web statistic additionally is adopted. As a result, the proposed re-ranking model has an effect of using additional information for knowledge base completion. Thus, the candidate triples are first ranked by a neural knowledge base embedding, and then the result is re-ranked by the neural network. The experimental results show that the proposed re-ranking model improves the base neural embeddings up to 16% in Hits@1. This implies that the re-ranking model places true facts at top positions effectively.

Hee-Geun Yoon, Hyun-Je Song, Seong-Bae Park, Se-Young Park. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

This paper proposes a topic-based method to reflect calls and proximities simultaneously into finding interaction patterns from a mobile log. For this purpose, the proposed method regards calls and proximities as a homogeneous information type that are drawn from the same temporal space expressed by the same distribution, but with different parameters. The number of proximities in a mobile log usually overwhelms that of calls and the proximities are observed regularly. Therefore, the proposed method models a single directional influence from proximities to calls, where both call and proximity are modeled by the Latent Dirichlet Allocation (LDA). According to the experiments on the data set from MIT's Reality Mining project, the proposed method outperforms the method that treats calls and proximities independently, which proves the plausibility of the proposed method.

Readability is used to provide users with highquality service in text recommendation or text visualization.With the increasing use of handheld devices, reading device is regarded as an important factor for readability.Therefore, this paper investigates the relationship between readability and reading devices such as a smart phone, a tablet, and paper.We suggest readability factors that are strongly related with the readability of a specific device by showing the correlations between various factors in each device and human-rated readability.Our experimental results show that each device has its own readability characteristics, and thus different weights should be imposed on readability factors according to the device type.In order to prove the usefulness of the results, we apply the device-dependent readability to news article recommendation.

User attribute is an essential factor for personalized recommendation and targeted advertising. Therefore, there have been a number of studies to identify user attributes automatically from SNS postings, since the postings reveal various attributes of writers. Many kinds of machine learning methods have been applied to automatic identification of user attributes as a candidate solution, but they suffer from two major problems. First, there are many postings in SNS that do not deliver any information about writers. Then, learning from SNS postings results in a biased model by these irrelevant postings. Second, the postings of a SNS user are somewhat related one another. However, most machine learning methods ignore this information, since they assume that data are independently and identically distributed. In order to solve these problems in user attribute identification, this paper proposes a novel method based on non-i.i.d. multi-instance learning. Since multi-instance learning treats all postings by a user as a bag and learns user attribute identification with such bags, not with postings, the first problem is solved. In addition, the proposed method assumes that the postings by a single user have a structure. By incorporating this assumption into the multi-instance learning, the second problem is solved. Our experimental results show that consideration of these two problems in automatic user attribute identification results in performance improvement.

The interest of users in handheld devices is strongly related to their location. Therefore, the user location is important, as a user context, for news article recommendation in a mobile environment. This paper proposes a novel news article recommendation that reflects the geographical context of the user. For this purpose, we propose the Explicit Localized Semantic Analysis (ELSA), an ESA-based topical representation of documents. Every location has its own geographical topics, which can be captured from the geo-tagged documents related to the location. Thus, not only news articles but locations are also represented as topic vectors. The main advantage of ELSA is that it stresses only the topics that are relevant to a given location, whereas all topics are equally important in ESA. As a result, geographical topics have different importance according to the user location in ELSA, even if they come from the same article. Another advantage of ELSA is that it allows a simple comparison of the user location and news articles, because it projects both locations and articles onto an identical space composed of Wikipedia topics. In the evaluation of ELSA with the New York Times corpus, it outperformed two simple baselines of Bag-Of-Words and LDA as well as two ESA-based methods. Rt10 of ELSA was improved up to 46.25% over other methods, and its [email protected] was always higher than those of the others regardless of k.

With the increasing interest in location-based services, location comparison gains more and more attentions. One of the best ways to represent a location is to use topics that are generated near the location. In order to compare locations through such geographical topics, two conditions need to be met. One is that the topic set should be fixed but cover various aspects of all possible locations, and the other is that geographical topics often depend on each other. This paper proposes Probabilistic Explicit Semantic Analysis (PESA) that meets these conditions. PESA represents a location as a weighted topic vector where each topic is a Wikipedia concept. The number of Wikipedia concepts is fixed, but their enormous quantity allows PESA to be used to compare various locations. In addition, link information within Wikipedia articles is used to compute prior probabilities of topics considering their dependencies. That is, it enables PESA to model the topic dependency. PESA was evaluated using eighteen locations in three distinct geographical categories and compare it with LDA and ESA. The experimental results that PESA outperformed LDA and ESA highlighting its superiority in location comparison.

Text classification is a well-known problem for various applications. For last decades, it is beleived that a large corpus is one of the most important aspects for better classification. However, even though a great number of documents is available for training a classifier, it is practically impossible to achieve an ideal performance, since the distributions of labeled and unlabeled documents are often different. To overcome this problem, this paper describes a novel Naïve Bayes classifier for text classification under distribution difference between training and test data. The proposed method approximates test distribution by weighting labeled documents to cope with the distribution difference. Unlike other transfer learning which estimates the weights of labeled documents, the proposed method considerd both the documents and their estimated class labels. Therefore, the proposed method naturally combines the advantage of semi-supervised learning with those of transfer learning.

One of the critical problems in natural language interfaces is the discordance between the expressions covered by the interface and those by the knowledge base. In the graph-based knowledge base such as an ontology, all possible queries can be prepared in advance. As a solution of the discordance problem in natural language interfaces, this paper proposes a method that translates a natural language query into a formal language query such as SPARQL. In this paper, a user query is translated into a formal language by choosing the most appropriate query from the prepared queries. The experimental results show a high accuracy and coverage for the given knowledge base.

BACKGROUND: Introduction of spaced speeds opened a way of sensitivity improvement in homology search without loss of search speed. Since then, the efforts of finding optimal seed which maximizes the sensitivity have been continued today. The sensitivity of a seed is generally computed by its hit probability. However, the limitation of hit probability is that it computes the sensitivity only at a specific similarity level while homologous regions usually distributed in various similarity levels. As a result, the optimal seed found by hit probability is not actually optimal for various similarity levels. Therefore, a new measure of seed sensitivity is required to recommend seeds that are robust to various similarity levels. RESULTS: We propose a new probability model of sensitivity hit integration which covers a range of similarity levels of homologous regions. A novel algorithm of computing hit integration is proposed which is based on integration of hit probabilities at a range of similarity levels. We also prove that hit integration is computable by expressing the integral part of hit integration as a recursive formula which can be easily solved by dynamic programming. The experimental results for biological data show that hit integration reveals the seeds more optimal than those by PatternHunter. CONCLUSION: The presented model is a more general model to estimate sensitivity than hit probability by relaxing similarity level. We propose a novel algorithm which directly computes the sensitivity at a range of similarity levels.

This paper proposes a novel method to translate tags attached to multimedia contents for cross-language retrieval. The main issue in this problem is the sense disambiguation of tags given with few textual contexts. In order to solve this problem, the proposed method represents both tags and its translation candidates as networks of co-occurring tags since a network allows richer expression of contexts than other expressions such as co-occurrence vectors. The method translates a tag by selecting the optimal one from possible candidates based on a network similarity even when neither the textual contexts nor sophisticated language resources are available. The experiments on the MIR Flickr-2008 test set show that the proposed method achieves 90.44% accuracy in translating tags from English into German, which is significantly higher than the baseline methods of a frequency based translation and a co-occurrence-based translation.

Information extraction from world wide web has been paid great attention to. Since a table is a well-organized and summarized knowledge expression for a domain, it is of great importance to extract information from the tables. However, many tables in web pages are used not to transfer information but to decorate the pages. Therefore, it is one of the most critical tasks in web table mining to discriminate the meaningful tables from the decorative ones. The main obstacle of this task comes from the difficulty of generating relevant features for the discrimination. This paper proposes a novel method to discriminate them using a composite kernel which combines a parse tree kernel and a linear kernel. Since a web table is represented as a parse tree by a HTML parser, the parse tree kernel can be naturally used in determining the similarity between trees, and the linear kernel with content features is used to make up for the weak points of the parse tree kernel. The support vector machines with the composite kernel distinguish with high accuracy the meaningful tables from the decorative ones. A series of experiments show that the proposed method achieves the state-of-the-art performance.

The ontology alignment has two kinds of major problems. First, the features used for ontology alignment are usually defined by experts, but it is highly possible for some critical features to be excluded from the feature set. Second, the semantic and the structural similarities are usually computed independently, and then they are combined in an ad-hoc way where the weights are determined heuristically. This paper proposes the modified parse tree kernel (MPTK) for ontology alignment. In order to compute the similarity between entities in the ontologies, a tree is adopted as a representation of an ontology. After transforming an ontology into a set of trees, their similarity is computed using MPTK without explicit enumeration of features. In computing the similarity between trees,the approximate string matching is adopted to naturally reflect not only the structural information but also the semantic information. According to a series of experiments with a standard data set, the kernel method outperforms other structural similarities such as GMO. In addition, the proposed method shows the state-of-the-art performance in the ontology alignment.

An automatic word spacing is one of the important tasks in Korean language processing and information retrieval. Since there are a number of confusing cases in word spacing of Korean, there are some mistakes in many texts including news articles. This paper presents a high-accurate method for automatic word spacing based on self-organizing η-gram model. This method is basically a variant of η-gram model, but achieves high accuracy by automatically adapting context size.In order to find the optimal context size, the proposed method automatically increases the context size when the contextual distribution after increasing it dose not agree with that of the current context. It also decreases the context size when the distribution of reduced context is similar to that of the current context. This approach achieves high accuracy by considering higher dimensional data in case of necessity, and the increased computational cost are compensated by the reduced context size. The experimental results show that the self-organizing structure of η-gram model enhances the basic model.

This paper proposes a hybrid of hand-crafted rules and a machine learning method for chunking Korean. In the partially free word-order languages such as Korean and Japanese, a small number of rules dominate the performance due to their well-developed postpositions and endings. Thus, the proposed method is primarily based on the rules, and then the residual errors are corrected by adopting a memory-based machine learning method. Since the memory-based learning is an efficient method to handle exceptions in natural language processing, it is good at checking whether the estimates are exceptional cases of the rules and revising them. An evaluation of the method yields the improvement in F-score over the rules or various machine learning methods alone.

Most corpus-based approaches to natural language processing suffer from lack of training data. This is because acquiring a large number of labeled data is expensive. This paper describes a learning method that exploits unlabeled data to tackle data sparseness problem. The method uses committee learning to predict the labels of unlabeled data that augment the existing training data. Our experiments on word sense disambiguation show that predictive accuracy is significantly improved by using additional unlabeled data.