Reviewerly/exHarmony huggingface dataset:
Aspect Term Extraction Improvement Based on a Hybrid Method

The sentiment analysis task has been given great attention in the recent years, especially by enterprises and customers in commercial domain. In fact, companies tend to identify the customers' opinions regarding their services, industrialized products, etc. In this context, the Aspect Based Sentiment Analysis (ABSA) was introduced to determine the clients' viewpoints and extract the different aspects (e.g., price, quality, etc.) of entity (e.g., laptops) and assign them a sentiment polarity. In the present work, we are interested only in the aspect extraction (AE) task which is the most crucial and difficult task in the ABSA domain. We propose a hybrid method that combines the strengths of the linguistic knowledge and those of deep learning methods to solve the problem of AE for the French language. We also enhance this method by means of a new pruning algorithm which is mainly based on an out-domain dataset. The developed hybrid method has significantly improved the current state of the art and has given encouraging results when applied on respectively the Amazon mobile phone reviews (86.39% of F-measure) and the SemEval-2016 restaurant dataset (76.62% of F-measure).
Learning and Explanation of Extreme Multi-label Deep Classification Models for Media Content

Providing rich and accurate metadata for indexing media content is a crucial problem for all the companies offering streaming entertainment services. These metadata are typically used to improve the result of search engines and to feed recommendation algorithms in order to yield recommendation lists matching user interests. In particular, the problem of labeling multimedia content with informative tags (able to accurately describe the topics associated with such content) is a relevant issue. Indeed, the labeling procedure is time-consuming and susceptible to errors process as it is usually performed by domain experts in a fully manual fashion. Recently, the adoption of Machine Learning based techniques to tackle this problem has been investigated but the lack of clean and labeled training data leads to the yield of weak predictive models. To address all these issues, in this work we define a Deep Learning based framework for semi-automatic multi-label classification integrating model prediction explanation tools. In particular, Model Explanation techniques allow for supporting the operator to perform labeling of the contents. A preliminary experimentation conducted on a real dataset demonstrates the quality of the proposed solution.
Anomaly detection in rolling bearings based on the Mel‐frequency cepstrum coefficient and masked autoencoder for distribution estimation

It is difficult to establish a classification and recognition model of machinery and equipment based on labeled samples in the actual industrial environment because of the imperfect fault modes and data missing. To solve this problem, a semisupervised anomaly detection method based on masked autoencoders of distribution estimation (MADE) is designed. First, the Mel-frequency cepstrum coefficient (MFCC) is employed to extract fault features from vibration signals of rolling bearings. Then, a group of mask matrices are set on each hidden layer to overcome the perfect reconstruction problem of the autoencoders' input, and the full-connection probability of reconstruction is used to replace the reconstruction error and adopted as the anomaly score. Finally, the diagnostic threshold is determined according to the Youden index. Experimental results show that the MADE method can extract fault-sensitive features from a noisy industrial environment and introduce mask matrices renders to make the network autoregressive, thus solving the problem of perfect reconstruction of autoencoders. It is verified based on three rolling bearing datasets that the accuracy, precision, recall, and F1-score of the proposed method are confirmed to be all 100%. Moreover, the accuracy of the proposed method is 17.19% higher than that of the memory-inhibition method on the rolling bearing dataset provided by the Center for Intelligent Maintenance Systems (IMS) in University of Cincinnati (USA). The accuracy of the proposed method is also improved compared with other state-of-the-art anomaly detection methods.
Sentiment Polarity and Emotion Detection from Tweets Using Distant Supervision and Deep Learning Models

Automatic text-based sentiment analysis and emotion detection on social media platforms has gained tremendous popularity recently due to its widespread application reach, despite the unavailability of a massive amount of labeled datasets. With social media platforms in the limelight in recent years, it's easier for people to express their opinions and reach a larger target audience via Twitter and Facebook. Large tweet postings provide researchers with much data to train deep learning models for analysis and predictions for various applications. However, deep learning-based supervised learning is data-hungry and relies heavily on abundant labeled data, which remains a challenge. To address this issue, we have created a large-scale labeled emotion dataset of 1.83 million tweets by harnessing emotion-indicative emojis available in tweets. We conducted a set of experiments on our distant-supervised labeled dataset using conventional machine learning and deep learning models for estimating sentiment polarity and multi-class emotion detection. Our experimental results revealed that deep neural networks such as BiLSTM and CNN-BiLSTM outperform other models in both sentiment polarity and multi-class emotion classification tasks achieving an F1 score of 62.21% and 39.46%, respectively, an average performance improvement of nearly 2–3 percentage points on the baseline results.
XAI to Explore Robustness of Features in Adversarial Training for Cybersecurity

Adversarial training is an effective learning approach to harden deep neural models against adversarial examples. In this paper, we explore the accuracy of adversarial training in cybersecurity. In addition, we use an XAI technique to analyze how certain input features may have an effect on decisions yielded with adversarial training giving the security analyst much better insight into robustness of features. Finally, we start the investigation of how XAI can be used for robust features selection within adversarial training in cybersecurity problems.
Scientific and technological creative stage design using artificial intelligence

• In this manuscript, we employed Artificial Intelligence (AI) technology to perform novel research on traditional stage design in order to merge traditional Chinese opera with current intelligent technology. • We concentrated on the development of scientific and technical equipment for stage design, the collaboration between intelligent technology and the establishment of opera culture, and the stage design effect and overall stage effect following the adoption of AI technology. • According to the results of the performance analysis research, merging traditional Chinese opera with AI resulted in substantial performance efficiency when compared to conventional procedures. To combine traditional Chinese opera with modern intelligent technology, we use Artificial Intelligence (AI) technology to conduct innovative research on traditional stage design. In this paper, we mainly focused on the production of scientific and technological equipment for stage design, the cooperation between intelligent technology and opera culture creation, and the stage design effect and the overall stage effect after the introduction of AI technology. We elaborate on the background of AI in traditional opera art creation space and associate it with the internet collaborative creation from multiple angles through the survey as satisfaction, staff salary difference, calculation comparison, etc. The performance analysis study reveals that combining traditional Chinese opera with AI resulted in significant performance efficiency compared to conventional techniques.
New <scp>CGSP</scp> Carbonate Matrix Reference Materials for <scp>LA‐ICP‐MS</scp> Analysis

Four CGSP (Chinese Geological Standard Powders) carbonate matrix element reference materials were prepared by a coprecipitation method. The major and trace elements were dissolved from natural rare earth carbonate raw materials in strong acid and precipitated with ammonium bicarbonate or ammonia to form carbonate matrix phases. The carbonate reference materials were ground and pressed into tablets for microanalysis. The element homogeneity and stability in CGSP reference materials were tested by LA‐ICP‐MS using a calibration strategy without an internal standard element and evaluated mainly following ISO Guide 35: 2017 and JJF 1343‐2012. Although the homogeneity of most of elements in the CGSP series were comparable to the repeatability obtained from homogeneous glasses, some elements (e.g., Mo, Se, Ga, Ge, Cd, Ni, Co, U) were found to have some degree of heterogeneity. All of elements passed the t ‐test evaluation in a 32‐month stability examination except for Mo in CGSP‐A, Na, Ge, Mo in CGSP‐B, Ni, Cd, Sn in CGSP‐C, and Al, P, Tb, Ho in CGSP‐D. The certified element mass fraction values and uncertainties were determined with the characterisation of a non‐operationally defined measurand using various bulk analysis methods in eight laboratories. GGSPs will provide a new carbonate calibration reference option for microanalysis.
Real-time Image Enhancement with Attention Aggregation

Image enhancement has stimulated significant research works over the past years for its great application potential in video conferencing scenarios. Nevertheless, most existing image enhancement approaches are still struggling to find a good tradeoff that reduces the computational cost as much as possible while maintaining plausible result quality. Recently, curve-based mapping methods are proposed and have shown great potential for real-time and high-quality image enhancement of arbitrary resolutions. In this article, we take advantage of the curve-based mapping representation and focus on further improving the enhancement quality and robustness, while minimizing additional computational costs. Specifically, we (1) carefully re-formulate the curve function to improve learning stability, and (2) aggregate different semantic attention into the curve regression process, which can overcome the major problems of curve-based methods that generate moderate results with low contrast. The semantic attention is jointly learned with the supervision from class activation mapping of pre-trained feature extractors, thus reducing the manual annotation cost of semantic labels. Experiments have shown that our proposed method significantly improves curve-based methods both qualitatively and quantitatively, achieving visually plausible results compared with other deep neural network-based enhancement methods, and maintains a very low computational cost, i.e., taking 18.7 ms for a 360p image on a single P40 GPU. Extensive experiments demonstrate that our method is also capable of video enhancement tasks.
Track‐Before‐Resolving: Doppler ambiguity resolution at low SNR

This study introduces a new scheme for resolving the ambiguity in a pulse-Doppler radar. While ambiguity resolution is typically performed before tracking, the proposed approach, Track-Before-Resolving, is resolving ambiguity utilising tracking filters. A specific Doppler ambiguity resolution algorithm is provided for demonstrating that the Track-Before-Resolving method is useful. In order to resolve the ambiguity, which is equivalent to finding the ambiguity number, this algorithm investigates the maximum probability calculated in the multiple model estimation, each with a tracking filter of a different ambiguity number. Simulation shows that the proposed algorithm performs effectively, even at low SNR.
Accounting for retest effects in cognitive testing with the Bayesian double exponential model via intensive measurement burst designs

Monitoring early changes in cognitive performance is useful for studying cognitive aging as well as for detecting early markers of neurodegenerative diseases. Repeated evaluation of cognition via a measurement burst design can accomplish this goal. In such design participants complete brief evaluations of cognition, multiple times per day for several days, and ideally, repeat the process once or twice a year. However, long-term cognitive change in such repeated assessments can be masked by short-term within-person variability and retest learning (practice) effects. In this paper, we show how a Bayesian double exponential model can account for retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance. We also highlight how this approach allows for the inclusion of person-level predictors and draw intuitive inferences on cognitive change with Bayesian posterior probabilities. We use older adults' performance on cognitive tasks of processing speed and spatial working memory to demonstrate how individual differences in peak performance and change can be related to predictors of aging such as biological age and mild cognitive impairment status.
How Mobiles Affect International Trade, FDI and Economic Growth: Comparative Analysis of Income Level

Mobile is a key sector promoting international trade, FDI and economic growth in the modern era. The objective of this paper is to examine the dynamic nexus between Mobile, trade, FDI and economic growth by employing a panel vector error correction model. Empirical results suggest that Mobile has more positively contributed to international trade in the low income country group than other groups, and it has a more positive impact on FDI inflow in the higher-income country group than other groups, it has a more powerful impact on economic growth in the middle-income country group than other groups.
Face Recognition—Eigenfaces

The aim of the project is to recognize a person’s face by making a comparison between its characteristics and those of other people’s faces which are already known. To perform this task, Eigenfaces method is implemented together with a previous preparation of the images. Initially, outlier images are identified within a face and landscape dataset, using metrics such as Manhattan, Euclidean, Chebyshev and Minkowsky distances $$\left( {p = \frac{5}{2}\,{\text{and}}\,p = \frac{\sqrt 2 }{2}} \right)$$ . Then the results are compared to determine which one of the metrics has better performance in the identification of rare images. In total 2.470 images (2.260 faces and 210 natural landscapes) were used. Secondly, the methodology of Eigenfaces was implemented with a total of 3059 images of faces and then, by distance measurements, distances of new images projected on the subspace were compared with those used to form the Eigenfaces. Thirdly, a lineal discriminant analysis (LDA) was used on the subspace generated through PCA. Finally, for the prediction of faces of women, men and landscapes, supervised and unsupervised classification methods were used and compared. For the supervised classification, the Multinomial Logistic Regression and Linear Discriminant Analysis (LDA) were used, and the unsupervised analysis was done using K-means, T-Distributed Stochastic Neighbor Embedding (TDSNE) and Agglomerative Clustering.
An Introduction to Artificial Intelligence in Healthcare

The chapter presents a brief overview of Artificial Intelligence (AI) in healthcare. We start with the why and the how of AI in medicine. The most known and commonly used AI algorithms are presented. We continued the chapter with the pros and cons of AI, highlighting the main advantages and disadvantages it has. The chapter finishes with some of the newest real-life applications of AI in different healthcare sectors from diagnostic, drug development to administrative work.
Speakers use more redundant references with language learners: Evidence for communicatively-efficient referential choice

According to the communicative efficiency hypothesis, speakers should produce more linguistic material when comprehension difficulty increases. Here, we investigate a potential source of comprehension difficulty – listeners’ language proficiency – on speakers’ productions, using referential choice as a case study. Referential choice is influenced by communicative efficiency: pronouns are used less than full noun phrases (NPs) for less predictable referents (Tily & Piantadosi, 2009). However, the extent to which it is influenced by the listener is debated. Here, we compare participants’ descriptions of the same picture book to children, adult L2 learners and adult native speakers. We find that speakers use more full NPs when their interlocutors are learners – child and adult learners alike, illustrating an effect of listeners’ proficiency (regardless of age) on production choices. Importantly, the increased use of full NPs relative to pronouns is found controlling for discourse-related differences (e.g., previous mention), suggesting a direct relation between listeners’ perceived language proficiency and referential choice.
A Near-Optimal Truthful Online Auction for Efficient Crowdsourced Data Trading with Dynamic Data Owners and Dynamic Data Requests

Data is an extremely important asset in a modern scientific and commercial society. The life force behind powerful artificial intelligence (AI) or machine learning (ML) algorithms is data, especially lots of data, which makes data trading significantly essential to unlocking the power of AI or ML. Data owners who offer crowdsourced data and data consumers who request data blocks negotiate with each other to make an agreement on data assignment and trading prices via a data trading platform; consequently, both sides gain profit from the process of data trading. A great many existing studies have investigated various kinds of data sharing or trading as well as protecting data privacy or constructing a decentralized data trading platform due to mistrust issues. However, existing studies neglect an important characteristic, i.e., dynamics of both data owners and data requests in trading crowdsourced data collected by IoT devices. To this end, we first construct an auction-based model to formulate the data trading process and then propose a near-optimal online data trading algorithm that not only resolves the problem of matching dynamic data owners and randomly generated data requests but also determines the data trading price of each data block. The proposed algorithm achieves several good properties, such as a constant competitive ratio for near-optimal social efficiency, incentive compatibility, and individual rationality of participants, via rigorous theoretical analysis and extensive simulations. We further design a decentralized data trading platform in order to construct a practical data trading process incorporating the proposed data trading algorithm.
Cognitive Resonance and the Architecture Issues of Cognitive Information Systems

The high-speed and constants changes in information technology and business needs to drive the development and evolution of Cognitive Information Systems (CIS). There are limited items of research on the general model related to the analysis and design of CIS. This paper attempts to create a joint design scheme, where tries to joining the various models for CISs and Understanding-based management systems (UBMSS). The examined components substantiate elements of CIS analysis and design; however, they weren’t delineated as modeling elements and not depicted as enabling instruments that are needed to create a consistent and integrated system. The vital ingredients for modeling as follows: semi-structured documents, business processes, constituents of knowledge management, the enterprise, and the information architecture, together with the self-directing software components—Artificial Intelligence (AI)—that yield functions. For CIS modeling, the constituents, which were mentioned previously, were combined into a unified framework. That framework follows the object-oriented paradigm and architecture approach. The research aims to describe and present a framework that covers an overarching model and assists our understanding of the properties of CIS and UBMSS, permitting the formulation of a practical development method for Cognitive Information and Cognitive Management Systems (CMSs).
A novel multi‐strategy self‐optimizing SAPSO algorithm for PMSM parameter identification

To address the unsatisfactory performance of particle swarm optimization (PSO), a novel multi-strategy self-optimizing simulated annealing particle swarm optimization (SOSAPSO) method for permanent magnet synchronous motor (PMSM) parameter identification is proposed. The full-rank mathematical model and the fitness function are developed. In SOSAPSO, the velocity term of the PSO is simplified and dynamic opposition-based learning (DOBL) is introduced in the inertia weight update process to avoid population monotonicity. Moreover, A Cauchy–Gaussian hybrid variation strategy based on similarity and density is devised to achieve self-learning in deep regions. Meanwhile, the simulated annealing (SA) with a memory and tempering mechanism is introduced into SOSAPSO, and the greedy optimization algorithm (GOA) is used to enhance local fine-exploitation capabilities when SOSAPSO evolution is stalled. The test results indicate the proposed method can effectively avoid local convergence problems and has better robustness and convergence speed.
English to Indian Sign Language Gloss Conversion Using a Rule-Based Approach

Indian Sign Language (ISL) is the most widely used language by the deaf community in India. ISL has its own grammatical structure and syntactic rules. It varies significantly from other regional or spoken languages. This paper presents a system for converting English text to ISL gloss structure, which could be considered as a low-resource machine translation task. A constituency parse tree-based approach is followed for performing the grammatical transformation of sentences. The transformed sentences are then converted to ISL gloss structure, based on ISL grammar rules. The proposed system could be used as the primary component of English to ISL video generation systems and also for supporting bilingual education of the Deaf.
Bitcoin Price Forecasting Through Crypto Market Variables: Quantile Regression and Machine Learning Approaches

The cryptocurrency market is an innovative alternative to the traditional currency markets that has increased the interest of investors, academics, and governments. In the last years, recent studies have tried to model the price of Bitcoin for investment and forecast purposes. Therefore, the objective of this study was to evaluate different crypto market variables through a quantile regression model and thus identify the best predictors for Bitcoin price forecasting by machine learning models. The main finding was that the Gaussian Process Regression models allowed the best performance metrics through the following predictors: high and low Bitcoin price, ask-sum, and Bitcoin price lagged. Likewise, the Bitcoin price was predicted for the next seven days, and it was observed a significant approximation by the confidence intervals of Gaussian Process Regression.