Data Mining - Concepts and Techniques.

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

Emotion and Adaptation

Emotion is the vital path to achieving strong artificial intelligence. Therefore, it is significant to study the emotional guiding and controlling theory to enhance system intelligence. Conversational datasets in social media contain useful information, including individuals exhibiting continuously changing emotion states in response to external stimuli, and are the foundation for research on artificial emotions. We define dialog-based emotional guidance in reinforcement learning to research the emotional guidance of the discrete emotion model. This article proposes three strategies to obtain the optimal policy: 1) given the current emotional transition matrix, use the emotional Markov decision process (E-MDP) algorithm to calculate the optimal stimulus policy for each target emotion; 2) given the emotional transition sequences, use the emotional Monte Carlo algorithm to calculate the optimal stimulus policy; and 3) given the emotional transition sequences, use the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula> -learning algorithm to calculate the optimal stimulus policy. Besides, we improve the Markov chain Monte Carlo algorithm to sample emotional transition sequences and design a metric to evaluate the effectiveness of policies. Experimental results on three datasets show that these methods can more effectively guide emotion than traditional methods. Particularly, E-MDP achieves the best results while others can be more widely used in real-world scenarios. 

Dynamic Emotional Transition Sampling and Emotional Guidance of Individuals Based on Conversation

Indoor localization remains a hot topic and receives tremendous research efforts during the last few decades. While most previous efforts focus on the designing issue, little effort has been paid to the impact of different environmental parameters on the system performance. To this end, we present an extensive empirical study with real-world experiments to provide sufficient data for analysis. By seizing the fundamental relationship between a given scenario and environmental parameters, we show that the optimal parameter settings can be achieved for the scenario regardless of the localization algorithms.

Optimal parameter setting for indoor localization via big data analysis

The study of generative dialogue systems has become a hotspot with the recently well-studied natural language understanding and generation techniques. However, most works have been focusing on those widely used languages, such as English and Chinese, with huge dialogue corpora being collected and thoroughly analyzed for training the dialogue generation models. In addition, retaining the human-like diversity and spontaneity of speech in these corpora is challenging for building dialogue systems. In this paper, we propose a method to build a lage Japanese dialogue corpus by using the conversations posted on Twitter and to annotate the dialogue- and utterance-level topic labels and the corresponding probabilistic scores automatically by analyzing the similarity between the word clusters and the dialogue clusters of the corpus in the same semantic space. The advantage of this method is that it does not require the expensive time and effort of human workers for mimicking dialogues and annotating labels, which is specifically useful for those less widely used languages, such as Japanese. We compare four filtering settings with respect to the lower-bound of utterance length for corpus creation and topic annotation and report the effect of the utterance length to the quality of our dialogue corpus, based on a manual evaluation. Based on this corpus, we further propose two topic-based dialogue generation tasks, that is, the next-response-topic prediction task and the next-topic-based response generation task. The Japanese dialogue corpus is available on GitHub1.

Creating a Japanese Dialogue Corpus with Multi-level Topic Analysis

A significant proportion of out of vocabulary (OOV) words are named entities and technical terms. Typical analyses find around 50% of OOV words to be named entities. Yet these can be the most important words in the queries. For example, in the list of queries for TREC 2001 cross-language track, all 25 queries con- tained proper names. Cross-language retrieval performance (average precision) reduced more than 50% when named entities in the que- ries were not translated. One way to deal with OOV words when the two languages have different alphabets is to transliterate the un- known words, that is, to render them in the orthography of the sec- ond language. Transliteration is the process of formulating a repre- sentation of words in one language using the alphabet of another language. In the present study, we present different approaches for transliteration of proper noun pair's extraction from parallel corpora based on different similarity measures between the English and the romanized Arabic proper nouns under consideration. The strength of our new system is that it works well for low-frequency proper noun pairs. We evaluate the presented new approaches using two different English-Arabic parallel corpora. Most of our results out- perform previously published results in terms of precision, recall, and F-Measure.

Chapter 17 Similarity-based model for transliteration

In order to improve the emotion recognition rate, this paper proposes an electroencephalograph (EEG) emotion recognition model using a library for support vector machine (LIBSVM) as the classifier In this paper, we collected EEG signals from 10 volunteers along with the participants’ self-assessment of their affective state after each stimulus, in terms of Valence and arousal After these signals are filtered, we calculate the features of Lempel-Ziv complexity, wavelet detail coefficient, and the co-integration relationship degree first At the same time, EMD is carried out to calculate the average approximate entropy of the first four Intrinsic Mode Functions (IMFs) At last, all the features extracted will input into the LIBSVM for training and testing, and complete emotion recognition In this paper, two classifications are carried out on the two dimensions of Valence and Arousal, respectively The experimental results show that the average emotional recognition rate is 8364% and 7511%, respectively, which proves that the proposed scheme has a certain feasibility

Fuji Ren

Papers

Dynamic Emotional Transition Sampling and Emotional Guidance of Individuals Based on Conversation

Optimal parameter setting for indoor localization via big data analysis

Creating a Japanese Dialogue Corpus with Multi-level Topic Analysis

Chapter 17 Similarity-based model for transliteration

Emotion Recognition Based on EEG Signals Using LIBSVM as the Classifier