Reviewerly/exHarmony huggingface dataset:
SpaVerb-WN—A megastudy of naming times for 4562 Spanish verbs: Effects of psycholinguistic and motor content variables

Abstract Several studies have been carried out in various languages to explore the role of the main psycholinguistic variables in word naming, mainly in nouns. However, reading of verbs has not been explored to the same extent, despite the differences that have been found between the processing of nouns and verbs. To reduce this research gap, we present here SpaVerb-WN, a megastudy of word naming in Spanish, with response times (RT) for 4562 verbs. RT were obtained from at least 20 healthy adult participants in a reading-aloud task. Several research questions on the role of syllable frequency, word length, neighbourhood, frequency, age of acquisition (AoA), and the novel variable ‘motor content’ in verb naming were also examined. Linear mixed-effects model analyses indicated that (1) RT increase in with increasing word length and with decreasing neighbourhood size, (2) syllable frequency does not show a significant effect on RT, (3) AoA mediates the effect of motor content, with a positive slope of motor content at low AoA scores and a negative slope at high AoA scores, and (4) there is an interaction between word frequency and AoA, in which the AoA effect for low-frequency verbs gradually decreases as frequency increases. The results are discussed in relation to existing evidence and in the context of the consistency of the spelling–sound mappings in Spanish.
RACP: A network with attention corrected prototype for few-shot speaker recognition using indefinite distance metric

Few-shot speaker recognition task is to identify speakers from limited support samples. We argue that query samples and support samples are both informative for classification. To help Prototypical Networks capture information from query samples, this paper proposes the relation-based indefinite distance metric attentive correction prototype network (RACP). Since the mean prototype deviates from the ideal prototype, we calculate attention scores for each query sample to customize the attention prototype. Then, to compensate for the missed query samples information, the prototype is further refined by correction data that is constructed by combining query samples with the global class attention score. Later, the indefinite distance metric of Relation Networks is introduced on Prototypical Networks, and the relation scores between the sample prototypes and the query samples are calculated for final prediction. Compare with existing methods, RACP can consider both query samples and support samples instead of ignoring the query ones. We compare RACP with strong baselines (e.g. GMM-SVM, MAML, Prototypical Networks, Res32, and VGG11). Ablation study and generalizability study of different scenarios are also conducted on different datasets. Results show that RACP achieves better performance and generalization ability.
Robust vision-based method for wing deflection angle measurement with defocus images

The accuracy of existing vision-based angle measurement methods is extremely reduced when the images appear defocus. To solve this problem, a robust center location method for defocus circular targets is proposed and sub-pixel accuracy of control points can be obtained even with defocus images. Based on this location method, two vision-based angle measurement methods are proposed to measure deflection angles for fixed-axis and moving-axis wings, respectively. These methods are simple and easy to install with no need of prior information of wings' structure or rotation axes. Simulation and real experiments show that the proposed methods have good robustness to image noise and can significantly reduce the measurement errors of defocus images. The proposed methods almost keep unchanging to the results at good focus status, and the measurement accuracy under severe defocus blur state is about 60% higher than that of the state-of-art method for comparison.
A VR-Enabled Chatbot Supporting Design and Manufacturing of Large and Complex Power Transformers

Virtual reality (VR) immersive technology allows users to experience enhanced reality using human–computer interfaces (HCI). Many systems have implemented VR with improved HCI to provide strategic market advantages for industry and engineering applications. An intelligent chatbot is a conversational system capable of natural language communication allowing users to ask questions and receive answers online to enhance customer services. This research develops and implements a system framework for a VR-enabled large industrial power transformer mass-customization chatbot. The research collected 1272 frequently asked questions (FAQs) from a power transformer manufacturers’ knowledge base that is used for question matching and answer retrieval. More than 1.2 million Wikipedia engineering pages were used to train a word-embedding model for natural language understanding of question intent. The complex engineering questions and answers are integrated with an immersive VR computer human interface. The system enables users to ask questions and receive explicit and detailed answers combined with 3D immersive images of industrial sized power transformer assemblies. The user interfaces can be projected into the VR headwear or computer screen and manipulated with a controller. The unique immersive VR consultation chatbot system is to support real-time design consultation for the design and manufacturing of complex power transformers.
Cost-Sensitive Learning and Ensemble BERT for Identifying and Categorizing Offensive Language in Social Media

Some people often abuse freedom of expression on social media to carry out offensive actions. So we need mechanisms to keep social media conducive. This research aims to identify and categorize offensive language on social media, which consists of three subtasks: Offensive language identification (subtask A), Automatic categorization of offense types (subtask B), and Offense target identification (subtask C). These three subtasks use the OLID dataset (Offensive Language Identification Dataset) [1]. The previous research which utilized fine-tuning BERT achieved competitive performance at the OLID dataset (Zampieri. et al. 2019). In this research, we improve fine-tuning BERT performance using cost-sensitive learning and ensemble technique. The evaluation results on test data beat state of the art on subtask B (F1-score 0.7776), second position on subtask A (F1-score 0.8207), and second position on subtask C (F1-score 0.6574).
Diagnostic Evaluation of Policy-Gradient-Based Ranking

Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains, such as web search, recommender systems, dialogue systems, machine translation, and even computational biology, to name a few. In light of recent advances in neural networks, there has been a strong and continuing interest in exploring how to deploy popular techniques, such as reinforcement learning and adversarial learning, to solve ranking problems. However, armed with the aforesaid popular techniques, most studies tend to show how effective a new method is. A comprehensive comparison between techniques and an in-depth analysis of their deficiencies are somehow overlooked. This paper is motivated by the observation that recent ranking methods based on either reinforcement learning or adversarial learning boil down to policy-gradient-based optimization. Based on the widely used benchmark collections with complete information (where relevance labels are known for all items), such as MSLRWEB30K and Yahoo-Set1, we thoroughly investigate the extent to which policy-gradient-based ranking methods are effective. On one hand, we analytically identify the pitfalls of policy-gradient-based ranking. On the other hand, we experimentally compare a wide range of representative methods. The experimental results echo our analysis and show that policy-gradient-based ranking methods are, by a large margin, inferior to many conventional ranking methods. Regardless of whether we use reinforcement learning or adversarial learning, the failures are largely attributable to the gradient estimation based on sampled rankings, which significantly diverge from ideal rankings. In particular, the larger the number of documents per query and the more fine-grained the ground-truth labels, the greater the impact policy-gradient-based ranking suffers. Careful examination of this weakness is highly recommended for developing enhanced methods based on policy gradient.
A novel methodology for Arabic news classification

Abstract The automated news classification concerns the assignment of news to one or more predefined categories. The automated classified news helps the search engines to mine and categorize the type of news that the user asks for. Most of the researchers focused on the classification of English news and ignore the Arabic news due to the complexity of the Arabic morphology. This article presents a novel methodology to classify the Arabic news. It relies on the use of features extraction and the application of machine learning classifiers which are the Naive Bayes (NB), the Logistic Regression (LR), the Random Forest (RF), the Xtreme Gradient Boosting (XGB), the K‐Nearest Neighbors (KNN), the Stochastic Gradient Descent (SGD), the Decision Tree (DT), and the Multi‐Layer Perceptron (MLP). The methodology is applied to the Arabic news dataset provided by Mendeley. The accuracy of the classification is more than 95%. This article is categorized under: Algorithmic Development &gt; Text Mining Technologies &gt; Machine Learning Technologies &gt; Classification
Toward Early Abnormalities Detection on Digestive System: Multi-Features Electrogastrogram (EGG) Signal Classification based on Machine Learning

Electrogastrogram (EGG) is one of the bio-signals that can be developed as a tool for early detection of digestive abnormalities. The use of features extraction and machine learning can be applied to accelerate the development of the system detection. In this paper, five features extraction and two classifiers are used as comparative study. The feature extraction includes Mean Absolute Value (MAV), Average Amplitude Change (AAC), Waveform Length (WL), Maximum Fractal Length (MFL), and Root Mean Square (RMS). ANN and SVM were designed as the proposed classifier. There are two classes that are designed for classification, namely Fasting and Postprandial stages. From the experimental results, it was found that the highest accuracy value is acquired when using SVM classifier and used five features extraction. The classification reached 82.3% that showed significant result. From the experimental results, it is found that EGG function as early diseases detection on digestive system is very promising i.e., Covid-19 effect to digestive system.
Primitive normal pairs with prescribed norm and trace

Let q be a prime power and n be a positive integer. In this article, we consider the problem of the existence of a primitive normal element α in Fqn of a prescribed primitive norm a and non-zero trace b over Fq, such that f(α) is also a primitive normal element in Fqn, where f(x)∈Fqn[x] is an arbitrary polynomial with minor restrictions. For this existence problem, we obtain a sufficient condition and show that for n≥5 there exist at least (q−1)ϕ(q−1) primitive normal elements α such that f(α) is also a primitive normal element in all but finitely many fields Fqn. In particular, for q=7k;k≥1, deg(f(x))=2 and n≥8, we find that there are only 5 fields in which the existence of such elements is not guaranteed.
CRISP-DM for Data Quality Improvement to Support Machine Learning of Stunting Prediction in Infants and Toddlers

Many Machine Learning (ML) projects ended up only as proof concept and failed to be produced. Therefore, this research focused on well-defined processes that must be followed, adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) with the specifications and requirements of supervised and unsupervised learning which includes a methodology for Classification/grouping. The Data Understanding and Data Preparation phases, used transactional data on examination of infants and toddlers in 2018-2021 on the iPosyandu application. At the Business Understanding stage, the ML was intended to predict stunting, so that data quality of iPosyandu can be informed and then recommendations and feature improvements and assistance for end-users can be made. The output of Data Understanding and Data Preparation was in the form of baby & toddler examination dataset, which was used in the Machine Learning modeling stage, especially to classify and predict nutritional/stunting status. Of the 192 tables contained in the iPosyandu application, there were 5 main tables that were needed to define the dataset. 75,652 data on infants and toddlers were checked with 49,615 data of examinations in 3173 Posyandu, which resulted in clean data of 39,411 rows of datasets for all examinations and 13,868 rows of datasets for the last examination of infants and toddlers. The dataset was combined with the nutritional status of infants and toddlers resulting from the calculation of the baby’s weight, length of the baby’s body, and the comparison of the baby’s height and weight. The dataset was tested into the ML using the Orange Application and produced a Classification model that can be used for prediction. From the results of the modeling evaluation, it can be seen that the Naïve Bayes Algorithm had an advantage with a predictive value of 0.851 while the Tree algorithm was 0.848 and the Neural Net was 0.845. From the overall evaluation, it can be concluded that there is a need to improve data quality by improving the application and improving the literacy of the end-users, so that the data has better quality and ready to be used as a ML dataset. The selected features can be aggregated to simplify the modeling process so as to obtain the expected model.
Minimum t-norm leads to unrealizable fuzzy PID controllers

In this paper, we show that Minimum triangular norm (Min t-norm) always leads to unrealizable fuzzy PID (FPID) controllers. To show this, mathematical models of two FPID controllers are obtained. Two fuzzy sets, Negative (N) and Positive (P), for fuzzification of each of the three input variables (error, change of error, and double change of error), and four fuzzy sets N, Negative Small (NS), Positive Small (PS), and P on the output variable (change of control effort) are considered. Min t-norm, Maximum (Max) s-norm, triangular membership functions, Larsen Product (LP)/ Mamdani Minimum (MM) inference, and Centre of Area (Centre of Gravity) (CoA (CoG)) defuzzification are used for mathematical modeling of the controllers. Then upon thoroughly analyzing the properties of the mathematical models of the controllers, we show that Min t-norm leads to unrealizable FPID controllers.
An Approach for Detecting Bangla Spam Comments on Facebook

Since digital technology is expanding and spreading constantly, sharing posts, texts, and comments appears to be a reliable form of communication and the quickest way to obtain information. Nowadays, most transactions, whether general or business, are conducted via Facebook pages and groups as a means of communication. However, spammers have destroyed these pages. They are constantly destroying this platform by making annoying comments that disrupt others who use Facebook for communication and entertainment. Malicious remarks or spams are causing significant challenges for Facebook users, particularly those who do business on Facebook platform. Due to this reason, we look into how a machine can detect spam automatically. Since detecting spam from malicious Bangla text content still requires a great deal of attention. For this purpose, we have collected 2759 comments from Facebook. For classification, the Decision Tree, KNN, SVC, Gradient Boosting and Multinomial Na&#x00EF;ve Bayes algorithms are used. Finally, we present a comparison between the results of our datasets and the results of collected datasets.
Human activity recognition using machine learning techniques in a low-resource embedded system

Human activity recognition aims to infer a person&#x0027;s actions from a set of observations captured by several sensors. Data acquisition, processing and inference on edge devices add a complexity factor to the task, as they involve a trade-off between hardware efficiency and performance. We present a prototype of a wearable device that identifies a person&#x0027;s activity: walking, running or staying still. The system consists of a Texas Instruments MSP-EXP430G2ET launchpad, connected to a BOOSTXL-SENSORS boosterpack with a BMI160 accelerometer. The designed prototype can take acceleration measurements, process them and either transmit them to a computer or classify the activity in the microcontroller. Additionally, our system has LEDs to display coloured signals according to the inferred activity in real-time. The classification algorithm is based on the calculation of statistical features (mean, standard deviation, maximum and minimum) for each accelerometer axis, the application of a dimensionality reduction algorithm (LDA, Linear Discriminant Analysis) and an SVM (Support Vector Machines) classification model.
Syntactic discontinuous reduplication with antonymic pairs: a case study from Italian

Abstract This article aims at giving a comprehensive account of a so far undescribed reduplicative pattern in Italian named syntactic discontinuous reduplication with antonymic pairs (SDRA). This pattern, characterized by the non-contiguous repetition of the same element within a larger fixed configuration defined by two spatial antonyms, can be schematized as &lt;X i Adv 1 X i Adv 2 &gt;, where Adv 1 and Adv 2 are antonyms (e.g., di qua ‘here’ ∼ di là ‘there’). After describing its formal and functional properties, based on naturally occurring data extracted from the Italian Web 2016 corpus, the SDRA is analyzed as an independent ‘construction’ in the Construction Grammar sense. This construction is claimed to convey a general value of ‘plurality’ and to have developed a polysemy network of daughter constructions expressing more specific functions such as ‘distributivity,’ ‘related variety,’ and ‘dispersion.’ In addition, we propose considering the SDRA a ‘multiple source construction,’ originating from the blending of two independent constructions: syntactic reduplication and irreversible binomials with antonymic adverbs. Finally, we discuss SDRA-like patterns in other typologically different languages (Russian, Modern Hebrew, Mandarin Chinese, German), pointing out similarities and differences, and paving the way to a more systematic study of discontinuous reduplication in a crosslinguistic perspective.
A Calibration Method for a Self-Rotating, Linear-Structured-Light Scanning, Three-Dimensional Reconstruction System Based on Plane Constraints

This paper proposes a calibration method for a self-rotating, linear-structured-light (LSL) scanning, three-dimensional reconstruction system based on plane constraints. The point cloud of plane target collected by the self-rotating, LSL scanning, 3D reconstruction system should be constrained to the basic principle of the plane equation; it can quickly and accurately calibrate the position parameters between the coordinate system of the LSL module and the coordinate system of the self-rotating, LSL scanning, 3D reconstruction system. Additionally, the transformation equation could be established with the calibrated optimal position parameters. This paper obtains the above-mentioned position parameters through experiments and uses the calibrated self-rotating, LSL scanning, 3D reconstruction system to perform three-dimensional scanning and reconstruction of the test piece. The experimental results show that the calibration method can effectively improve the measurement accuracy of the system.
Prediction of multicore CPU performance through parallel data mining on public datasets

In the present scenario, high-performance computing needs more attention towards multicore computing. While designing the CPU, we need to consider hardware for processing speed, cache bandwidth, minimum memory requirements, etc. So for the selection of the best combination of CPU data mining tools may play an important role. In this era, data mining attracts more interest on parallel computing to enhance the performance of multicores. This paper demonstrates a parallel strategy similar to the traditional parallel programming paradigm to improve the performance of multicores by using a data mining approach. We have selected one approach EM with Gaussian and analyze the impact of its parallel execution on selected multicore clusters obtained through data mining. We have also evaluated our finding with a virtual environment of having 32 different families of CPUs, showing a speedup of up to ≈1.02x. This paper considers data clusters, cache mapping techniques and ranking of MPI programming techniques.
An Integration Model for Text Classification using Graph Convolutional Network and BERT

Abstract Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks, and has effectively completed tasks that are considered to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and cannot capture local information. The Bidirectional Encoder Representation from Transformers (BERT) has been shown to have the ability to capture the contextual information in a sentence or document, but its ability to capture global information about the vocabulary of a language is relatively limited. The latter is the advantage of GCN. Therefore, in this paper, Mutual Graph Convolution Networks (MGCN) is proposed to solve the above problems. It introduces semantic dictionary (WordNet), dependency and BERT. MGCN uses dependency to solve the problem of context dependence and WordNet to obtain more semantic information. Then the local information generated by BERT and the global information generated by GCN are interacted through the attention mechanism, so that they can influence each other and improve the classification effect of the model. The experimental results show that our model is more effective than previous research reports on three text classification data sets.
Sliced Wasserstein Distance for Neural Style Transfer

Neural Style Transfer (NST) aims to render a content image with the style of another image in the feature space of a Convolution Neural Network (CNN). A fundamental concept of NST is to define the features extracted from a CNN as a distribution so that the style similarity can be computed by measuring the distance between distributions. Conceptually, Wasserstein Distance (WD) is ideal for measuring the distance between distributions as it theoretically guarantees the similarity of style distributions with the WD between them equaling 0. However, due to the high computation cost of WD, previous WD-based methods either oversimplify the style distribution or only use a lower bound of WD, therefore, losing the theoretical guarantee of WD. In this paper, we propose a new style loss based on Sliced Wasserstein Distance (SWD), which has a theoretical approximation guarantee. Besides, an adaptive sampling algorithm is also proposed to further improve the style transfer results. Experiment results show that the proposed method improves the similarity of style distributions, and such improvements result in visually better style transfer results.