Reviewerly/exHarmony huggingface dataset:
Design Flow and Implementation of an AI-Enabled Smart Guidance System for Art Galleries

Audio guides are commonly utilized to enrich the experience of art gallery visitors and to fully engage them with the artwork by providing background, contexts, and other information related to the corresponding artists. However, this method may be monotonous to the public and cannot automatically change to the appropriate recording when users move to the next artwork. The traditional operation of audio guides is only enabled by the user’s visual recognition of the artwork to activate appropriate commentaries manually. In this paper, we explore the potential enhancement of the art gallery visitors’ experience by applying Artificial Intelligence (AI), machine learning, and computer vision techniques to recognize the artwork’s main features automatically and provide the corresponding artist and other information to the visitors. We train a model using a Deep Convolutional Neural Network (CNN) and Kaggle’s Painter by Numbers dataset, and host the trained model on a low-power AI accelerator equipped with a camera to recognize the artwork in real-time. The results are then transmitted to a database, which is utilized by a custom Android application on the smartphone to provide updated artworkrelated information on the display or by audio. Our system targets not only the general public for an interactive experience in the art gallery but also visually impaired people for proper guidance with the smartphone’s camera and its voice feedback capabilities.
COUNTING TO INFINITY: GRADED MODAL LOGIC WITH AN INFINITY DIAMOND

Abstract We extend the languages of both basic and graded modal logic with the infinity diamond, a modality that expresses the existence of infinitely many successors having a certain property. In both cases we define a natural notion of bisimilarity for the resulting formalisms, that we dub $\mathtt {ML}^{\infty }$ and $\mathtt {GML}^{\infty }$ , respectively. We then characterise these logics as the bisimulation-invariant fragments of the naturally corresponding predicate logic, viz., the extension of first-order logic with the infinity quantifier. Furthermore, for both $\mathtt {ML}^{\infty }$ and $\mathtt {GML}^{\infty }$ we provide a sound and complete axiomatisation for the set of formulas that are valid in every Kripke frame, we prove a small model property with respect to a widened class of weighted models, and we establish decidability of the satisfiability problem.
Deep Feature Learning Based Fault Detection with High-Frequency Signals

High-frequency signals, which play an important role in detecting the occurrence of early faults, are characteristic of massive sparse features. Existing feature extraction methods mainly focus on selecting from a large quantity of statistics or designing elaborate hand-crafted features. With the help of little prior domain knowledge, this paper proposes a deep feature learning approach which employs discrete wavelet transform (DWT) as an embedding layer for a bidirectional gated recurrent unit network with attention mechanism (AM-BiGRU). Experiments on a real-world case study of partial discharge (PD) verify that our approach outperforms the state-of-the-art hand-crafted features and other data-driven methods which select statistics as shallow features. In addition, the visualization result of attention layer weightings confirms that our approach could automatically locate the area where faults may occur, which makes our deep model explicable to a certain extent.
Health Performance Interval Prediction of Pumped Storage Unit Based on Multi-Objective Optimization

The health performance of pumped storage unit (PSU) affects the stable and safe operation of pumped storage station and even the power system. Thus, it is essential to predict the health tendency accurately and helpful to achieve predictive maintenance as well. An interval prediction model based on information fusing and multi-objective optimization is proposed to describe the randomness and uncertainty of the health performance. To effectively obtain the operating characteristic of PSU, the health state model is developed based on Gaussian process regression (GPR) and the monitoring data. Then, to comprehensively quantify the health degree, an integrate health index (IHI) is developed with entropy weight to fuse the healthy information of different objects. Taken the prediction interval coverage probability (PICP) and prediction interval normalized average width (PINAW) as objective constraints, the global optimization strategy is proposed based on kernel extreme learning machine (KELM) and multi-objective particle swarm optimization (MOPSO). In the final, the comparative experiments are conducted with the data collected from a PSU in China. The results show that the proposed model can deduce the health interval with reliability and accuracy.
Subject-Object Aspect-Based Sentiment Analysis Model Based on News Texts

News as an important part of the open source intelligence has always played an important role in international relations and national security fields. However, the fine-grained sentiment analysis work such as Aspect-Based Sentiment Analysis (ABSA) tasks focused on the service industry and e-commerce comments and the Target Aspect-Based Sentiment Analysis (T-ABSA) tasks focused on the Twitter datasets, these two types of tasks usually limit the context to the fixed subjects or objects, simplifying the model by limiting several aspects simultaneously. In addition, the sentiment analysis work based on the news texts most focused on the chapters level. Therefore, based on the characteristics of news texts that it contains multiple subjects and objects, this paper proposed the Subject-Object Aspect-Based Sentiment Analysis (SO-ABSA) model, which can do fine-grained emotional element extraction in the context of indefinite subjects, objects and aspects. More emotional elements can be mined through SO-ABSA model. The proposed model can extract the uncertain entities efficiently and improve the accuracy of subjects and objects extraction. Moreover, the uncertain aspects also can be extracted flexibly and the sentiment analysis result can represent the subjects’ emotional tendency towards specific aspects. To evaluate our method, we built a subject-object oriented dataset (SOOD) with data sourced from 30,000 news articles. We propose a subject-object aspect emotion analysis model and evaluate the model on the SOOD dataset. The experimental results show the effectiveness of our model.
Distributed AI-Driven Search Engine on Visual Internet-of-Things for Event Discovery in the Cloud

Millions of connected devices like connected cameras and streaming videos are introduced to smart cities every year, which are valuable source of information. However, such rich source of information is mostly left untapped. Thus, in this paper, we propose distributed deep neural networks (DNNs) over edge visual Internet of Things (VIoT) devices for parallel, real-time video scene parsing and indexing in conjunction with BigQuery retrieval on stored data in the cloud. The IoT video streams parsed into adaptive meta-data of person, attributes, actions, object, and relations using pre-trained DNNs. The meta-data cached at the edge-cloud for real-time analytics and also continuously transferred to the cloud for data fusion and BigQuery batch processing. The proposed distributed deep learning search platform bridges the gap between edge-to-cloud continuum computation by utilizing state-of-the-art distributed deep learning and BigQuery search algorithms for the geo-distributed Visual Internet of Things (VIoT). We show that our proposed system supports real-time event-driven computing at 122 milliseconds on virtual IoT devices in parallel, and as low as 2.4 seconds batch query response time on multi-table JOIN and GROUP-BY aggregation.
Differentially Private Reinforcement Learning with Linear Function Approximation

Motivated by the wide adoption of reinforcement learning (RL) in real-world personalized services, where users' sensitive and private information needs to be protected, we study regret minimization in finite-horizon Markov decision processes (MDPs) under the constraints of differential privacy (DP). Compared to existing private RL algorithms that work only on tabular finite-state, finite-actions MDPs, we take the first step towards privacy-preserving learning in MDPs with large state and action spaces. Specifically, we consider MDPs with linear function approximation (in particular linear mixture MDPs) under the notion of joint differential privacy (JDP), where the RL agent is responsible for protecting users' sensitive data. We design two private RL algorithms that are based on value iteration and policy optimization, respectively, and show that they enjoy sub-linear regret performance while guaranteeing privacy protection. Moreover, the regret bounds are independent of the number of states, and scale at most logarithmically with the number of actions, making the algorithms suitable for privacy protection in nowadays large-scale personalized services. Our results are achieved via a general procedure for learning in linear mixture MDPs under changing regularizers, which not only generalizes previous results for non-private learning, but also serves as a building block for general private reinforcement learning.
Automatic Detection of Situational Context Using AI from Minimal Sensor Modality

There is a vast improvement in the sector of human healthcare and education delivery using artificial intelligence (AI) such as recommender system. For this purpose, many modern technology are being used like smart wearable device to make the diagnosis of different types of human disease and automated tutoring systems. For these AI algorithms, there can be high error rates if situational contexts are ignored. Currently there is no automated approach to detect situational context. In this work, we propose a novel approach to automatically detect situational context using clustering algorithm type AI from minimal sensor modality. We begin the process by converting a few sensor data to a multitude of axes, then determine situational context from these axes by using clustering algorithm. Here, we evaluated the machine learning Clustering algorithm performance on the simulated data and compared the characteristics of their performance. The results show 89.4% accuracy for Seven situational contexts and 89.8% accuracy for eight situational contexts. This preliminary work shows the feasibility of detecting situational context automatically from a few sensor data by converting the sensor data to multiple axes and applying clustering algorithm.
How Is Technology Useful in the Study of Ancient Music?

Abstract A variety of possible applications of modern technology for music-archaeological purposes are discussed: from studying and evaluating musical finds and acoustical environments through the presentation of pitch structures down to databases, their statistical evaluation and the necessity and promises of dedicated coding.
Research on Mongolian acoustic model based on BLSTM-CTC for Inner Mongolia Electric Power

In terms of intelligent voice customer service of Inner Mongolia Electric Power, there are a large number of Mongolian speakers. The Mongolian speech recognition in it mainly applies Q&A mode which uses sentences for realizing human-machine dialogue. However, in the process of training the Mongolian acoustic model based on deep neural network-hidden markov model (DNN-HMM), the fragment information of Mongolian speech is mainly applied because of different lengths of speech sentences, it ignores integrity of speech sentences. In this regard, this paper proposes a Mongolian acoustic model based on Bi-directional Long Short-Term Memory-Connectionist Temporal Classification (BLSTM-CTC), which unifies length of input sentences and models complete sentences by inserting BLANK features and labels. The results of comparison experiment of speech recognition between BLSTM-CTC and DNN-HMM shows lower word error rate and sentence error rate of speech recognition based on BLSTM-CTC, especially in later, with reduces by 3.57% and 4.09% respectively. That indicates modeling ability of BLSTM-CTC, especially the modeling ability for sentences, is obviously higher than the DNN-HMM.
The Power of Questioning Strategy on Students Learning English in the Classroom

The goals of this study were to determine the types of questioning strategies used in the classroom and the impact such strategies have on students' acquisition of English. The kids of senior high school participated in this study as the topic. A qualitative methodology was used for the study that was carried out here. The research included in this study collects data via observation, questionnaires, and interviews. The data that were acquired were then examined by first lowering the amount of data, then displaying the data, and finally deriving conclusions from the data. The results of the investigation revealed that the instructor had used several techniques and methods while questioning the students. These included putting more emphasis on the questions, translating the questions into Bahasa Indonesia, repeating the questions, and moving closer to the students. in addition, the sorts of questions that were used were open/close questions, recall questions, rhetorical questions, and yes/no questions. The instructor was responsible for using these types of questions throughout each individual teaching session. It is dependent on the content that the instructor conveyed to the students as to whether open-ended or closed-ended questions or display questions were the most common. Aside from that, the instructor will only utilize the recall and referential questions a limited number of times. The findings of the study demonstrated that there were positive benefits from using the method and the sorts of questions, namely, favourable answers from students and excellent relationships between the instructor and the students.
Calibration and Imaging Pipeline Processing Baseline-dependent Averaged Visibilities

In this paper, we present a calibration and imaging pipeline whose processing functions directly operate on visibilities to which Baseline-Dependent Averaging (BDA) has been applied. This avoids the need to expand BDA-ed visibilties to full resolution. We demonstrate that this reduces the computational needs for calibration (both visibility prediction and solving) and (de)gridding by processing fewer visibilities without causing unexpected degradation of the results. This first prototype of a BDA-enabled calibration and imaging pipeline allows further exploration of BDA-enabled processing, for which we raise several questions.
Neural automated writing evaluation for Korean L2 writing

Abstract Although Korean language education is experiencing rapid growth in recent years and several studies have investigated automated writing evaluation (AWE) systems, AWE for Korean L2 writing still remains unexplored. Therefore, this study aims to develop and validate a state-of-the-art neural model AWE system which can be widely used for Korean language teaching and learning. Based on a Korean learner corpus, the proposed AWE is developed using natural language processing techniques such as part-of-speech tagging, syntactic parsing, and statistical language modeling to engineer linguistic features and a pre-trained neural language model. This study attempted to determine how neural network models use different linguistic features to improve AWE performance. Experimental results of the proposed AWE tool showed that the neural AWE system achieves high reliability for unseen test data from the corpus, which implies metrics used in the AWE system can help differentiate different proficiency levels and predict holistic scores. Furthermore, the results confirmed that the proposed linguistic features–syntactic complexity, quantitative complexity, and fluency–offer benefits that complement neural automated writing evaluation.
Keyword Detection of Japanese Media Teaching Based on Support Vector Machines and Speech Detection

The keyword detection of Japanese speech in streaming media has a certain effect on our study of Japanese information and a certain promotion effect on Japanese teaching. Currently, there is a problem of stability in the detection model of Japanese speech keywords. In order to improve the detection effect of Japanese speech keywords in streaming media, based on SVM, this study constructed a detection model of Japanese speech keywords in streaming media based on support vector machine. Moreover, this study analyzes the problem of SVM probability output and the comprehensive problem of SVM confidence, etc. In addition, by comparing the effect of confidence synthesis with the arithmetic average method, we found that the confidence obtained by SVM can obtain a higher recognition rate under the same rejection rate and improve the overall performance of the system. Finally, this study uses the difference comparison test to analyze the performance of the model proposed in this study. The research results show that the algorithm proposed in this paper has good performance and can be used as a follow-up system algorithm.
Careful Seeding for the K-Medoids Algorithm with Incremental K++ Cluster Construction

The k-medoids algorithm is a popular variant of the k-means algorithm and widely used in pattern recognition and machine learning. A main drawback of the k-medoids algorithm is that it can be trapped in local optima. An improved k-medoids algorithm (INCKM) was recently proposed to overcome this drawback, based on constructing a candidate medoids subset with a parameter choosing procedure, but it may fail when dealing with imbalanced datasets. In this paper, we propose a novel incremental k-medoids algorithm (INCKPP) which dynamically increases the number of clusters from 2 to k through a nonparametric and stochastic k-means++ search procedure. Our algorithm can overcome the parameter selection problem in the improved k-medoids algorithm, improve the clustering performance, and deal with imbalanced datasets very well. But our algorithm has a weakness in computation efficiency. To address this issue, we propose a fast INCKPP algorithm (called INCKPP sample ) which preserves the computational efficiency of the simple and fast k-medoids algorithm with an improved clustering performance. The proposed algorithm is compared with three state-of-the-art algorithms: the improved k-medoids algorithm (INCKM), the simple and fast k-medoids algorithm (FKM) and the k-means++ algorithm (KPP). Extensive experiments on both synthetic and real world datasets including imbalanced datasets illustrate the effectiveness of the proposed algorithm.
Copyright Transaction Mode and Copyright Protection Risk Analysis of Green Industry from the Perspective of Information Environment

At present, the requirements of the current environment and background require us to have higher requirements for the data mode of copyright transaction. According to the previous way to conduct copyright transactions, it will cause a lot of unnecessary trouble, and according to the establishment of the copyright transaction database, we analyse the data types and patterns of copyright transactions and related copyright transaction protection measures and risk prevention. According to the copyright transaction database, we adopt BS network model, CIF copyright transaction data set model, CI data set model, data interactive query technology, and block matching technology to deal with the copyright transaction data model and copyright transaction risk prevention under the background of Informa ionization as follows: (1) we use BS network model, CIF copyright transaction data set model, and CI data set model to analyse and detect the copyright transaction data before transaction, which better reflects the network model effect of self-enhancing operation rate of copyright transaction data and effectively avoids overfitting problem. (2) By using interactive data query technology, the data presented in the preparation work before the transaction is more detailed, the samples are normalized, and the collected transaction data are tested for accuracy, which can eliminate potential dangers. (3) The method of similarity feature reconstruction of copyright transaction data set completes the extraction of association matching features of copyright transaction data system, which provides strong support for us to prevent risks and effectively match data in copyright transaction.
Digitizing Wildlife: The Case of a Reptile 3-D Virtual Museum

In this article, we design and develop a 3-D virtual museum with holistic metadata documentation and a variety of reptile behaviors and movements. First, we reconstruct the reptile's mesh in high resolution, and then create its rigged/skinned digital counterpart. We acquire the movement of two subjects using an optical motion capture system, accelerometers, and RGB-vision cameras; these movements are then segmented and annotated to various behaviors. The 3-D environment, virtual reality (VR), and augmented reality (AR) functionalities of our online repository serve as tools for interactively educating the public on animals, which are difficult to observe and study in their natural environment. It also reveals important information regarding animals' intangible characteristics (e.g., behavior), that is critical for the preservation of wildlife. Our museum is publicly accessible, enabling motion data reusability, and facilitating learning applications through gamification. We conducted a user study that confirms the naturalness and realism of our reptiles, along with the ease of use and usefulness of our museum.
Natural Language Processing for Detecting Undefined Values in Specifications

In this project, we present an algorithm with the use of the parse trees and parts of speech tagging in detecting missing quantified values in user requirement specifications and how the natural language processing results impact the quality of the detections. A comparative adjective found in quantifier phrases is tagged by the parts of speech tagger as a comparative adjective but is phrased as a noun in the parse tree. This could happen because dependencies for phrases of the parse trees may favor nearby words causing the sentence to be phrased incorrectly. We conclude that the overall accuracy of the Artificial Intelligence/Machine Learning parse trees are inadequate to detect quantitative statements as quantifier phrases even when it is very clear of their meaning. When this occurs in the parse tree, we search for patterns in the parts of speech tags to reveal the missing quantitative values. We found this solution effective because the parts of speech tagger has over 56% accuracy tagging sentences with a much higher rate for single words.
Classroom and Extracurricular Activities as the Basis of Project and Problem Methods of Teaching a Foreign Language in a Non-linguistic University

The article reveals the influence of classroom and extracurricular projects on the motivation of students of non-linguistic directions of higher education to learn a foreign language. The phenomenon of motivation is considered in the article as an important part of modern education in higher school. Also, in this scientific report, the methods of project and problem-based teaching of a foreign language are described and a comparative analysis of the presented methods is carried out. A feature of these teaching techniques is that students are involved both in a team (at the same time they are involved in the creative process, since there is an element of competition) and individually. The article talks about the importance of using both presented methods, without mutual exclusion, to increase students’ interest in learning a foreign language.