Humanistic Next-Generation Artificial Intelligence Capable of Association

The third artificial intelligence (AI) boom focused on the “handling of large amounts of data” and “automated learning.” One may think that AI can do anything because it is capable of automated learning, but there are still many problems that AI must tackle. The “necessity of a large amount of data” is and will become an even more significant problem. Obtaining an accurate solution from small amounts of data requires imagination and the detection of trends from a small number of phenomena. One approach is to add artificial data. For example, data can be created by intentionally including noise, and the variation may be expanded by a crossover. Different data can be generated by association or inference. Needless to say, these are artificial data and are not correct cases. “Humanistic AI” must be implemented by devising a scheme to allow accurate learning from small amounts of data. I think that the days when robots are considered enemies are transient and robots will soon be recognized as good partners that support humans instead of being rivals

Construction and Expansion of Dictionary of Idiomatic Emotional Expressions and Idiomatic Emotional Expression Corpus

Objective: In the study of sentiment estimation from language, methods focusing on words, phrases, sentence patterns, and sentence-final expressions have been proposed. However, it is difficult to deal with a wide variety of emotional expressions by only assigning emotions to words and phrases. In particular, it is difficult to analyze metaphorical expressions and idiomatic expressions on a word-by-word basis, and it is impossible to register all expressions in a dictionary because new expressions can be created by flexibly replacing words. However, it is difficult to determine the constraints on the words to be replaced, and not all expressions can be registered in the dictionary as sentence patterns. Methods: In this paper, we construct a dictionary of idiomatic sentiment expressions, which contains idioms expressing emotions. In this paper, we construct a pseudo-emotional corpus by collecting utterances containing emotional idioms from social media and automatically assigning emotions expressed by the idioms. Results: This corpus includes expressions other than idioms, and can be an effective resource for estimating emotions in sentences that do not contain idioms. In this study, we create an emotion estimation model for utterances based on the constructed corpus, and conduct evaluation experiments to explore the problems of the idiomatic emotion corpus. In addition, using the constructed sentiment corpus, we investigate how to expand the dictionary of sentiment expressions in idiomatic phrases by using deep learning methods. Conclusion: Using the corpus of idiomatic sentiments constructed by the proposed method as training data, models with and without idioms were constructed by machine learning models. The results show that the F-values of all emotions with idioms exceed 0.8. On the other hand, when idioms were not included, the F-values tended to decrease overall. However, the F-values of emotions such as "shame" and "excitement" were around 0.7, indicating that the characteristics of emotional expressions other than idioms were expressed

Endotoxin-Induced L-Arginine Pathway Produces Nitric Oxide and Modulates the Ca2+-Activated K+ Channel in Cultured Human Dermal Papilla Cells

Endotoxin induces an enzyme that synthesizes nitric oxide (NO) from L-arginine (NO synthase) in vascular smooth muscle cells, macrophages, and fibroblasts, leading to the release of NO. We evaluated the release of NO and its intracellular action on the Ca2+-activated K+ channel (Kca channel) in cultured human dermal papilla cells by use of the electron paramagnetie response (EPR) spin trapping method and the patch clamp technique. In dermal papilla cells pretreated for 24h with endotoxin (1 μg/ml), application of 1mM L-arginine generated NO, although no measurable release of NO was observed in cells without endotoxin pretreatment, as determined by the EPR spin trapping method. With the patch clamp technique, we found that the Kca channel of dermal papilla cells had high conductance and was voltage dependent. In addition, after endotoxin pretreatment, the extracellular application of 100 μM L-arginine modulated the Kca channel in the cellattached patch confignrations. In inside-out patch configuration, however, NO produced by L-arginine itself did not modulate the Kca channel. This modulation of the Kca channel was suppressed by pretreatment with 100 μm Nω-nitro-L-arginine methyl ester, an inhibitor of inducible and constitutive NO synthases. Methylene blue, a blocker of guanylate cyclase, inhibited the L-arginine-induced activation of the Kca channel. These results indicate that the endotoxin-induced L-arginine pathway generates NO, which consequently modulates the Kca channel in cultured human dermal papilla cells by increasing of cyclic GMP-dependent phosphorylation

チノウ エージェント オヨビ コウガクブ ナビゲーション システム ノ カイハツ

Recent years, a huge amount of information is available through the internet, and many information retrievers have been developed. However, these retrievers only show retrieved results without hearty communication. In this paper, an intelligent agent is developed. It recognizes a user’s utterance using a speech recognizer, and retrieves information from the World Wide Web. Finally, the agent makes an appropriate answer from retrieved results, and give it to the user. In order to communicate with a user warmheartedly, the agent also recognizes user’s emotion from a voice and a facial expression, and the agent represents it’s own emotion using voice and behaviour. We also develop the intelligent campus navigation robot using the proposed intelligent agent. The robot can give a user campus information, chat with a user, and communicate with a user warmheartedly

Comparisons of Brightness Temperatures of Landsat-7/ETM+ and Terra/MODIS around Hotien Oasis in the Taklimakan Desert

The brightness temperature (BT) of Taklimakan Desert retrieved from the data of Landsat-7/ETM+ band 6 and Terra/MODIS band 31 and 32 indicates the following features: (1) good linear relationship between the BT of ETM+ and that of MODIS, (2) the observation time adjusted BT of ETM+ is almost equal to that of MODIS, (3) the BT of Terra/MODIS band 31 is slightly higher than that of band 32 over a reservoir while opposite feature is recognized over desert area, (4) the statistical analysis of 225 sample data of ETM+ in one pixel of MODIS for different landcovers indicates that the standard deviation and range of BT of ETM+ corresponding to one pixel of MODIS are 0.45∘C, 2.25∘C for a flat area of desert, while respective values of the oasis farmland and shading side of rocky hill amount to 2.88∘C, 14.04∘C, and 2.80∘C, 16.04∘C

Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer

This paper provides the classification of the review texts on a smartphone application posted on social media. We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus. The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT)

Relationship Between Personality Patterns and Harmfulness : Analysis and Prediction Based on Sentence Embedding

This paper hypothesizes that harmful utterances need to be judged in the context of whole sentences, and the authors extract features of harmful expressions using a general-purpose language model. Based on the extracted features, the authors propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representation of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model

Flame Prediction Based on Harmful Expression Judgement Using Distributed Representation

In recent years, flaming-that is, hostile or insulting interaction-on social media has been a problem. To avoid or minimize flaming, enabling the system to automatically check messages before posting to determine whether they include expressions that are likely to trigger flaming can be helpful. We target two types of harmful expressions: insulting expressions and expressions that are likely to cause a quarrel. We first constructed an original harmful expressions dictionary. To minimize the cost of collecting the expressions, we built our dictionary semi-automatically by using word distributed representations. The method used distributed representations of harmful expressions and general expressions as features, and constructed a classifier of harmful/general expressions based on these features. An evaluation experiment found that the proposed method was able to extract harmful expressions with an accuracy of approximately 70%. The proposed method was also able to extract unknown expressions; however, it tended to wrongly extract non-harmful expressions. The method is able to determine unknown harmful expressions not included in the basic dictionary and can identify semantic relationships among harmful expressions. Although the method cannot presently be applied directly to multi-word expressions, it should be possible to add such a capability by introducing time-series learning