Miracle-dz/UniFAR-FTUs huggingface dataset:
Dialog policy optimization for low resource setting using Self-play and Reward based Sampling

Reinforcement Learning is considered as the state of the art approach for dialogue policy optimization in task-oriented dialogue systems. However, these models demand a large corpus of dialogues to learn effectively. Training Reinforcement Learning agent with low data amount tends to overﬁt the agent. Although synthesizing dialogue agendas with dialogue Self-play using rule-based agents and crowdsourcing has demonstrated promising re-sults with the low amount of samples, these methods hold limitations. For instance, rule-based agents acquire speciﬁc domain and language while crowdsourcing demands a high price and domain experts, especially in local languages. In this paper, we address these limitations by proposing a novel approach for synthetic agenda generation by acknowledging the underlying probability distribution of the user agendas and a reward-based sampling method that prioritizes failed dialogue acts. Evaluations conducted shows leveraged performance without overﬁtting, compared to the baseline method. Also, the reward-based sampling method improves the overall mean task success rate by an average of 11.307%.
Extraction of Novel Character Information from Synopses of Fantasy Novels in Japanese using Sequence Labeling

In this paper, we propose a method of extracting novel character information such as characters’ names, gender, age, occupations, and a part of relationships between characters from synopses of fantasy novels in Japanese using sequence labeling by comparing sequence labeling models based on CRF (Conditional Random Fields) and deep learning with CRF. From the experimental results, we conﬁrmed that the BiLSTM-CRF model with the information of part-of-speech of words has achieved the best performance, the precision of 85.40%, the recall of 91.47%, and F1-measure of 88.30% for extracting characters’ names. The BiLSTM-CRF model has achieved the best overall performance for extracting all tags.
Combining Thai EDUs: Principle and Implementation

Due to the lack of explicit end-of-sentence marker in Thai, Elementary Discourse Units (EDUs) are usually preferred over sentences as the basic linguistic units for processing Thai language. However, some segmented EDUs lack of structural or semantic information. To obtain a well-form unit, which represents a complete idea, this paper proposes combining EDUs with rhe-torical relations selected depending on our proposed syntactic and semantic criteria. The combined EDUs can be then used without considering other parts of the text. Moreover, we also annotated data with the criteria. After that, we trained a deep learning model inspired by coreference resolution and dependency parsing models. As a result, our model achieves the F1 score of 82.72%.
Imbalanced Chinese Multi-label Text Classification Based on Alternating Attention

In this work, we construct an imbalanced Chinese multi-label text classiﬁcation dataset, IMCM. The imbalance is mainly reﬂected in: (1) The degree of discrimination among labels is different. (2) The distribution of labels is moderately imbalanced. Then, we adopt several methods for multi-label classiﬁcation and conduct thorough evaluation of them, which show that even the most competitive models struggle on this dataset. Therefore, to tackle these imbalanced problems, we proposed an alternating attention model, AltXML. Two attention heads which alternately reading sequence enable the model capture different parts of the document rather than one point. Experimental results show that our proposed model signiﬁcantly outperforms the state-of-the-art baselines in our IMCM dataset, and also achieves quite good results in several public datasets.
How State-Of-The-Art Models Can Deal With Long-Form Question Answering

Question answering is an essential task in nat-ural language processing. According to our review, the datasets for this task often contain short answers. We raise a research question of whether state-of-the-art models can perform well when a longer answer is needed. We pro-pose a dataset that contains much longer answers called FitQA 1 and conduct a brief performance analysis among current state-of-the-art models on this dataset. The robust trans-former architecture like ALBERT achieved 90.9% F1 on SQuAD 2.0 but only got 47.3% F1 score on FitQA. Our hypothesis is that for longer context, the model needs to be guided to focus on longer dependent words. We conduct a curriculum-learning-based framework. Experimental results show that our approach could improve the performance with the appropriate answer length up to 55.3% on F1. Acknowledgments: This work was supported by JST CREST Grant Number JPMJCR1513 and the Asian Ofﬁce of Aerospace R&D (AOARD), Air Force Ofﬁce of Scientiﬁc Research (Grant no. FA2386-19-1-4041)
Generation and Evaluation of Concept Embeddings Via Fine-Tuning Using Automatically Tagged Corpus

Word embeddings are used in various ﬁelds of natural language processing. The use of word embeddings and concept or word sense embeddings demonstrated effectiveness in many tasks, such as machine translation and text summarization. However, it is difﬁcult to obtain a sufﬁciently large concept-tagged corpus, as the annotation of concept-tags is time-consuming. Therefore, in this paper, we pro-pose a method for generating concept embeddings of Word List by Semantic Principles, a Japanese thesaurus, using both a corpus tagged by an all-words word sense disambiguation (WSD) system and a manually tagged corpus. We generated concept embeddings via ﬁne-tuning using both an automatically tagged corpus and a small manually tagged corpus. In this paper, we propose a novel method of evaluating concept embed-dings using the tree structure of Word List by Semantic Principles. Experiments revealed the effectiveness of ﬁne-tuning. The best performance was achieved when the concept embeddings were initially trained with a corpus tagged by an all-words WSD system and re-trained with a manually tagged corpus.
Association between declarative memory and language ability in older Chinese by education level

Episodic memory and semantic memory are two subsystems of declarative memory which is considered to be related to language ability. We investigated the interdependence of the episodic and semantic memory and their association with syntactic complexity in two groups of Chinese older adults with higher and lower education levels. The results indicate that episodic memory and semantic memory are significantly correlated with each other, but only episodic memory shows a significant relationship with syntactic complexity. Educational attainment has a substantial influence on the performance of memory tasks, but no influence on the syntactic complexity. The study provides new evidence from Chinese older adults for clarifying the association between declarative memory and syntactic processing and the benefits from education on memory preservation in later life.
Neural Language Generation for a Turkish Task-Oriented Dialogue System

Rapidly growing language and speech-enabled technologies contribute to the development of task-oriented dialogue systems. The demand for better user engagement has been increasing at an accelerating pace and this brings new remarkable challenges including the generation of informative and natural system utterances. In this work, our ultimate goal is to develop a Turkish task-oriented dialogue system that enables users to navigate over a map in order to get informed about dining venues that best match their preferences and make reser-vations based on received recommendations. This paper presents the pipeline architecture of our dialogue system with a particular focus on the language generator. We utilize an open source framework for building the components of our system and develop a sequence-to-sequence (Seq2Seq) neural model for language generation. This pioneering work is the ﬁrst that proposes the use of a neural generation model in a Turkish conversational sys-tem. Our evaluations suggest that Turkish neural generation from meaning representations given in the form of dialogue acts is effective, but still in need of further improvements.
Composing Word Vectors for Japanese Compound Words Using Bilingual Word Embeddings

This study conducted an experiment to compare the word embeddings of a compound word and a word in Japanese on the same vector space using bilingual word embeddings. Because Japanese does not have word delim-iters between words; thus various word deﬁ-nitions exist according to dictionaries and corpora. We divided one corpus into words on the basis of two deﬁnitions, namely, shorter and ordinary words and longer compound words, and regarded two word-sequences as a parallel corpus of different languages. We then generated word embeddings from the corpora of these languages and mapped the vectors into the common space using monolingual mapping methods, a linear transformation matrix, and VecMap. We evaluated our methods by synonym ranking using a thesaurus. Furthermore, we conducted experiments of two comparative methods: (1) a method where the compound words were divided into words and the word embeddings were averaged and (2) a method where the word embeddings of the latter words are regarded as those of the compound words. The VecMap results with the supervised option outperformed that with the identical option, linear transformation matrix, and the latter word method, but could not beat the average method.
Improving Semantic Similarity Calculation of Japanese Text for MT Evaluation

In recent years, the quality of machine translation has significantly improved, and it is considered that translation of a novel becomes possible. However, when performing translation for novels by a machine translation, it needs an evaluation method that performs comparison using a vector of distributed representations. BERTScore is one of the methods for performing automatic evaluation using a distributed representation. In this research, we examined the optimal setting for applying the BERTScore to the evaluation of translations of Japanese novels, and improved the method by introducing penalties for named entities based on idf values calculated from large corpora. The introduction of the penalty has made it possible to mitigate the false matching of personal names caused by distributed representation. We verified the method by calculating the Pearson correlation between the modified BERTScore and human-rated scores. Furthermore, we set four BERT models and two kinds of corpora to calculate idf value, and investigated which setting is most suitable for evaluation of novel translation. As a result, the setting with the model based on novel corpus, the idf based novel corpus and the penalty had the highest correlation with human-rated scores.
Complete Genome Sequences of Curtobacterium, Pantoea, Erwinia, and Two Pseudomonas sp. Strains, Isolated from Apple Flower Stigmas from Connecticut, USA

The genome sequences of 5 bacterial strains isolated from apple flower stigmas are reported. The strains represent species of Curtobacterium, Pantoea, and Erwinia and two species of Pseudomonas. These data will provide information for future taxonomic studies and information for investigating the metabolic and functional characteristics of apple flower-colonizing bacteria. ABSTRACT The genome sequences of 5 bacterial strains isolated from apple flower stigmas are reported. The strains represent species of Curtobacterium, Pantoea, and Erwinia and two species of Pseudomonas. These data will provide information for future taxonomic studies and information for investigating the metabolic and functional characteristics of apple flower-colonizing bacteria.
Complete Genome Sequence of Rhodococcus qingshengii Strain CL-05, Isolated from Concrete

Here, we report the complete genome sequence of Rhodococcus qingshengii strain CL-05, which was isolated from pavement concrete in Newark, Delaware. The genome consists of a 6.29-Mbp chromosome and one plasmid (123,183 bp), encodes a total of 5,859 predicted proteins, and has a GC content of 62.5%. ABSTRACT Here, we report the complete genome sequence of Rhodococcus qingshengii strain CL-05, which was isolated from pavement concrete in Newark, Delaware. The genome consists of a 6.29-Mbp chromosome and one plasmid (123,183 bp), encodes a total of 5,859 predicted proteins, and has a GC content of 62.5%.
Metagenome-Assembled Genomes Isolated from a Human Fecal Diarrhea Sample

Diarrheal infection is the second leading infectious disease that is killing children under the age of 5 years. This study investigates the microbial community within a fecal sample from a diarrhea-affected child through shotgun metagenomic sequencing. ABSTRACT Diarrheal infection is the second leading infectious disease that is killing children under the age of 5 years. This study investigates the microbial community within a fecal sample from a diarrhea-affected child through shotgun metagenomic sequencing.
Complete Genome Sequence of Buttiauxella agrestis DSM 9389

We report here the complete genome sequence of Buttiauxella agrestis DSM 9389, which harbors eight 16S rRNA genes classified into three types. The genome sequence of this strain showed a higher average nucleotide identity value (97.3%) than that of the highly membrane vesicle-producing strain B. agrestis ATCC 33320T. ABSTRACT We report here the complete genome sequence of Buttiauxella agrestis DSM 9389, which harbors eight 16S rRNA genes classified into three types. The genome sequence of this strain showed a high average nucleotide identity (97.3%) with that of the highly membrane vesicle-producing strain B. agrestis ATCC 33320T.
Complete Genome Assembly of Multidrug-Resistant Yersinia enterocolitica Y72, Isolated in Sweden

Here, we report the complete genome sequence of a Swedish clinical strain of Yersinia enterocolitica, Y72. With emerging Yersinia outbreaks circulating in Nordic countries, the Y72 genome will provide more insights on the genetic relatedness and antibiotic resistance dissemination in future studies. ABSTRACT Here, we report the complete genome sequence of a Swedish clinical strain of Yersinia enterocolitica, Y72. With emerging Yersinia outbreaks circulating in Nordic countries, the Y72 genome will provide more insights on the genetic relatedness and antibiotic resistance dissemination in future studies.
Draft Genome Sequences of Legionella Presumptive Novel Species Isolated during Environmental Surveillance in Artificial Water Systems

We present the draft genome sequences of three Legionella strains that were isolated from a hotel water distribution system. Legionella species identification was performed by macrophage infectivity potentiator (mip) and RNA polymerase β subunit (rpoB) gene sequencing. Whole-genome sequencing and average nucleotide identity results supported the hypothesis of new Legionella species isolation. ABSTRACT We present the draft genome sequences of three Legionella strains that were isolated from a hotel water distribution system. Legionella species identification was performed by macrophage infectivity potentiator (mip) and RNA polymerase β subunit (rpoB) gene sequencing. Whole-genome sequencing and average nucleotide identity results supported the hypothesis of new Legionella species isolation.
Transcriptome RNA Sequencing Data Set of Differential Gene Expression in Escherichia coli BW25113 Wild-Type and slyA Mutant Strains

Escherichia coli laboratory strains remain instrumental for the development of tools and techniques in molecular microbiology. The transcriptional regulator SlyA, associated with host-derived oxidative stress, antibiotic resistance, and virulence, is prominent in Enterobacteriaceae. Here, we announce a transcriptome data set detailing the global gene expression in E. coli BW25113 and its slyA mutant. ABSTRACT Escherichia coli laboratory strains remain instrumental for the development of tools and techniques in molecular microbiology. The transcriptional regulator SlyA, associated with host-derived oxidative stress, antibiotic resistance, and virulence, is prominent in Enterobacteriaceae. Here, we announce a transcriptome data set detailing the global gene expression in E. coli BW25113 and its slyA mutant.
Real-time DDoS flood attack monitoring and detection (RT-AMD) model for cloud computing

In recent years, the advent of cloud computing has transformed the field of computing and information technology. It enabled customers to rent virtual instances and take advantage of various services on-demand with the lowest costs. Despite the advantages offered by cloud computing, it faces several threats; an example is DDoS attack which is considered one of the most serious ones. This paper proposes a real-time monitoring and detection of DDoS attacks on the cloud using machine learning approach. Naïve Bayes, K-Nearest Neighbor, and Random Forest machine learning classifiers have been selected to build predictive models. This model will be evaluated on the cloud for its accuracy and efficiency.
Machine Learning approach to Secure Software Defined Network: Machine Learning and Artificial Intelligence

This paper proposes network security enhancement solution aiming to improving the level of performance in the detection of cyber-attacks on Software Defined Network (SDN) it will prevent against Denial of Service Attack. We are going to employ two solution and comparing on the SDN attack detection performance. The first approach is the performance accuracy of the SDN with IDS procedural, and the second approach is the integration of SDN with Machine Learning. The project serves the organization generally in the field of information security, network security and cybersecurity awareness. The system performance evaluation results prove the system is capable to provide the effective DDoS attack detection and provide security enhancement in Software Defined Network.
Predicting RNA secondary structure based on machine learning and genetic algorithm

In recent years, RNA secondary structure prediction is an important issue in structural bioinformatics, and RNA pseudoknotted secondary structure prediction represents an NP-hard problem. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a genetic algorithm method to improve the accuracy with large-scale RNA sequence and structure data.
A New distributed PKI for WSN-Based Application in Smart Grid

Smart Grid is an evolutionary system that addresses the problems of the traditional power grid. The deployment of wireless sensors network enhances the Smart Grid aspects. However, it is facing many challenges, particularly the security. Tremendous effort is done to provide solutions dealing with security in Smart Grid environment. In this paper, we conduct a comprehensive review of the existing security approaches in Smart Grid environments in order to investigate their efficiency in meeting the security requirements and coping with different attacks. Then, we propose a new distributed Public Key Infrastructure for wireless sensors network-Based Application in Smart Grid that aims to cope with the single point of failure risk in traditional Public Key Infrastructure and provides many security services.
Methodology for testing LPWAN networks with mesh topology

The paper presents a methodology for testing LPWAN network with a mesh topology. Network testing is essential to identify and resolve problems before deploying the network on a large scale. Deploying a network without preliminary testing increases the number of wasted resources and does not guarantee the expected network performance. The methodology provided in this article allows testing the network and determining the main parameters, which will allow identifying the faults of the network before implementation. The proposed methodology is based on the IETF RFC 2544 specification. Model-based testing allows automating the testing process. For this, a simulation model of an LPWAN network with mesh topology based on LoRaWAN was developed in the OMNeT++ simulation environment.
Functional connexin35 increased in the myopic chicken retina

Abstract Our previous research showed that increased phosphorylation of connexin (Cx)36 indicated extended coupling of AII amacrine cells (ACs) in the rod-dominant mouse myopic retina. This research will determine whether phosphorylation at serine 276 of Cx35-containing gap junctions increased in the myopic chicken, whose retina is cone-dominant. Refractive errors and ocular biometric dimensions of 7-days-old chickens were determined following 12 h and 7 days induction of myopia by a −10D lens. The expression pattern and size of Cx35-positive plaques were examined in the early (12 h) and compensated stages (7 days) of lens-induced myopia (LIM). At the same time, phosphorylation at serine 276 (functional assay) of Cx35 in strata 5 (S5) of the inner plexiform layer was investigated. The axial length of the 7 days LIM eyes was significantly longer than that of non-LIM controls (P < 0.05). Anti-phospho-Ser276 (Ser276-P)-labeled plaques were significantly increased in LIM retinas at both 12 h and 7 days. The density of Ser276-P of Cx35 was observed to increase after 12 h LIM. In the meanwhile, the areas of existing Cx35 plaques did not change. As there was more phosphorylation of connexin35 at Ser276 at both the early and late stages (12 h) and 7 days of LIM chicken retinal activity, the coupling with ACs could be increased in myopia development of the cone-dominated chicken retina.
A wearable wrist haptic display for motion tracking and force feedback in the operational space

Abstract Force feedback is often beneficial for robotic teleoperation, as it enhances the user’s remote perception. Over the years, many kinesthetic haptic displays (KHDs) have been proposed for this purpose, which have different types of interaction and feedback, depending on their kinematics and their interface with the operator, including, for example, grounded and wearable devices acting either at the joint or operational space (OS) level. Most KHDs in the literature are for the upper limb, with a majority acting at the shoulder/elbow level, and others focusing on hand movements. A minority exists which addresses wrist motions. In this paper, we present the Wearable Delta (W$ \Delta $), a proof-of-concept wearable wrist interface with hybrid parallel–serial kinematics acting in the OS, able to render a desired force directly to the hand involving just the forearm–hand subsystem. It has six degrees of freedom (DoFs), three of which are actuated, and is designed to reduce the obstruction of the range of the user’s wrist. Integrated with positions/inertial sensors at the elbow and upper arm, the W$ \Delta $ allows the remote control of a full articulated robotic arm. The paper covers the whole designing process, from the concept to the validation, as well as a multisubject experimental campaign that investigates its usability. Finally, it presents a section that, starting from the experimental results, aims to discuss and summarize the W$ \Delta $ advantages and limitations and look for possible future improvements and research directions.