Reviewerly/exHarmony huggingface dataset:
The Effects of Class Imbalance and Training Data Size on Classifier Learning: An Empirical Study

This study discusses the effects of class imbalance and training data size on the predictive performance of classifiers. An empirical study was performed on ten classifiers arising from seven categories, which are frequently employed and have been identified to be efficient. In addition, comprehensive hyperparameter tuning was done for every data to maximize the performance of each classifier. The results indicated that (1) naïve Bayes, logistic regression and logit leaf model are less susceptible to class imbalance while they have relatively poor predictive performance; (2) ensemble classifiers AdaBoost, XGBoost and parRF have a quite poorer stability in terms of class imbalance while they achieved superior predictive accuracies; (3) for all of the classifiers employed in this study, their accuracies decreased as soon as the class imbalance skew reached a certain point 0.10; note that although using datasets with balanced class distribution would be an ideal condition to maximize the performance of classifiers, if the skew is larger than 0.10, a comprehensive hyperparameter tuning may be able to eliminate the effect of class imbalance; (4) no one classifier shows to be robust to the change of training data size; (5) CART is the last choice among the ten classifiers.
Incidence of Digital Competences in the Completion Rates of MOOCs: Case Study on Energy Sustainability Courses

Contribution: This article analyzes the correlation between users' digital competencies and their tendencies to successfully complete energy sustainability massive online open courses (MOOCs). In addition to reviewing whether digital competencies are a predictor of the effective completion of the course, this article analyzes whether participants acquire higher levels of digital competence through interaction in the course. Background: Completion rates of MOOCs typically range between 5% and 8%, with respect to registered participants. According to the literature, low rates may be due to factors such as students' lack of motivation or digital competence limitations. Research Questions: RQ1: Is there a correlation between the level of digital competence declared by the participants and their tendency to successfully complete the MOOC? RQ2: Does participation in a MOOC improve participants' digital competencies? Methodology: Two surveys, one pretest and one post-test (before and after the MOOCs), were applied to assess the digital competence levels of the participants. The total population of participants in the 12 MOOCs was 123 124 unique users, from which 9075 participants (pretest) - 7.37% of the universe - and 6029 participants (post-test) - 35.70% of the universe - were extracted as a sample. To determine its internal consistency, an exploratory factorial analysis was performed on both instruments and a Cronbach's alpha greater than 0.8 was obtained in all of its dimensions. Findings: A significant level of moderate to high correlation between the declared levels of digital competence and the trend toward successful completion of the MOOCs under study was observed. However, a significant increase was not demonstrated in the levels of digital competence acquired in the interaction with MOOCs. Conclusions: The level of digital competence of a participant in a MOOC was a valid predictor of their tendency to finish it. Although no increase in the levels of digital competence acquired through MOOCs was demonstrated, this may be because the subject matter of the MOOCs was alien to the indicators and dimensions of the digital competence. Further research could analyze the effectiveness of MOOCs in terms of digital competition at the acquired levels of competition.
WebObs: The Volcano Observatories Missing Link Between Research and Real-Time Monitoring

Volcanological observatories have common needs and often common practical issues for multi-disciplinary data monitoring applications. Real-time access to integrated data, technical metadata, modeling and estimation of uncertainties are fundamental for an efficient interpretation. But in fact, the heterogeneity of instruments or acquisition systems and the inherent problems to produce rapid models using real-time data lead to difficulties that may hinder crisis management. In an attempt to globally address these questions, the French volcanological and seismological observatories have developed a specific operational software system over the past 19 years. Based on GNU/Linux open source tools and a Web interface, the WebObs system mainly offers: (1) a modular database for equipment network management; (2) a dozen of evolving dedicated periodic tasks for each monitoring technique like seismology, deformations and geochemistry that use standard data formats with automated execution of periodic tasks that produce high-quality graphs on preset moving time intervals, data exports, optional event notifications including e-mail alerting, instruments status controls based on their data validity; (3) web-form interfaces for manual data input/editing and export; (4) a user request form to adjust the tasks parameters for a single execution and to produce customized graphs and data exports. This system hence constitute a web-based tool that perform integrated, centralized and automated real-time volcano monitoring. It has therefore become a strong support for data analysis and exchange between researchers, engineers, and technicians during periods of unrest as well as periods of long-term quiescence. WebObs is also widely open for development of interdisciplinary modeling and enhanced data processing. This allows scientists to test new methods with real-time data flux and to instantaneously share their results in the community.
An ontological approach for pathology assessment and diagnosis of tunnels

Tunnel maintenance requires complex decision making, which involves pathology diagnosis and risk assessment, to ensure full safety while optimising maintenance and repair costs. A Decision Support System (DSS) can play a key role in this process by supporting the decision makers in identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key aspect is to identify which spatial stretches within a tunnel contain pathologies of similar kinds within neighbouring tunnel segments. This paper presents PADTUN, a novel intelligent decision support system that assists with pathology diagnosis and assessment of tunnels with respect to their disorders and diagnosis influencing factors. It utilises semantic web technologies for knowledge capture, representation, and reasoning. The core of PADTUN is a family of ontologies which represent the main concepts and relations associated with pathology assessment, and capture the decision process concerning tunnel maintenance. Tunnel inspection data is linked to these ontologies to take advantage of inference capabilities offered by semantic technologies. In addition, an intelligent mechanism is presented which exploits abstraction and inference capabilities. Thus PADTUN provides the world's first semantically based intelligent DSS for tunnel maintenance. PADTUN was developed by an interdisciplinary team of tunnel experts and knowledge engineers in real-world settings offered by the NeTTUN EU Project. An evaluation of the PADTUN system is performed using real-world tunnel data and diagnosis tasks. We show how the use of semantic technologies allows addressing the complex issues of tunnel pathology inferencing, aiding in, and matching transportation experts' expectations of decision support. The methodology is applicable to any linear transport structures, offering intelligent ways to aid with complex decision processes related to diagnosis and maintenance.
Detection and recognition of human body posture in motion based on sensor technology

The recognition of human motion posture is of great values in the field of sports. In this paper, inertial sensor technology is employed to recognize four postures including dribbling, passing, catching, and shooting in basketball. The data are collected by four nine‐axis inertial sensors worn on the arm. The time domain and frequency domain features are extracted after the process of smoothing and normalizing. Then 30 eigenvectors are obtained by dimensionality reduction of principal component analysis (PCA), and the support vector machine (SVM) method is used for posture recognition. The experimental results show that after PCA dimensionality reduction, the recognition accuracy of the features as the SVM input is significantly high. Compared with the recognition accuracy of back propagation neural network (BPNN) (85.4%), the average recognition accuracy of SVM is 96%, which verifies the reliability of the method proposed in this study. This research proves the effectiveness of sensor technology in basketball posture recognition, which can provide a reliable guidance for basketball training. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
A Generalized Earley Parser for Human Activity Parsing and Prediction

Detection, parsing, and future predictions on sequence data (e.g., videos) require the algorithms to capture non-Markovian and compositional properties of high-level semantics. Context-free grammars are natural choices to capture such properties, but traditional grammar parsers (e.g., Earley parser) only take symbolic sentences as inputs. In this paper, we generalize the Earley parser to parse sequence data which is neither segmented nor labeled. Given the output of an arbitrary probabilistic classifier, this generalized Earley parser finds the optimal segmentation and labels in the language defined by the input grammar. Based on the parsing results, it makes top-down future predictions. The proposed method is generic, principled, and widely applicable. Experiment results clearly show the benefit of our method for both human activity parsing and prediction on three video datasets.
Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features

Skeleton-based action recognition is a widely used task in action related research because of its clear features and the invariance of human appearances and illumination. Furthermore, it can also effectively improve the robustness of the action recognition. Graph convolutional networks have been implemented on those skeletal data to recognize actions. Recent studies have shown that the graph convolutional neural network works well in the action recognition task using spatial and temporal features of skeleton data. The prevalent methods to extract the spatial and temporal features purely rely on a deep network to learn from primitive 3D position. In this paper, we propose a novel action recognition method applying high-order spatial and temporal features from skeleton data, such as velocity features, acceleration features, and relative distance between 3D joints. Meanwhile, a method of multi-stream feature fusion is adopted to fuse these high-order features we proposed. Extensive experiments on Two large and challenging datasets, NTU-RGBD and NTU-RGBD-120, indicate that our model achieves the state-of-the-art performance.
End-to-end training of CNN ensembles for person re-identification

We propose an end-to-end ensemble method for person re-identification (ReID) to address the problem of overfitting in discriminative models. These models are known to converge easily, but they are biased to the training data in general and may produce a high model variance, which is known as overfitting. The ReID task is more prone to this problem due to the large discrepancy between training and test distributions. To address this problem, our proposed ensemble learning framework produces several diverse and accurate base learners in a single DenseNet. Since most of the costly dense blocks are shared, our method is computationally efficient, which makes it favorable compared to the conventional ensemble models. Experiments on several benchmark datasets demonstrate that our method achieves state-of-the-art results. Noticeable performance improvements, especially on relatively small datasets, indicate that the proposed method deals with the overfitting problem effectively.
Efficient Parallel Inflated 3D Convolution Architecture for Action Recognition

Deep neural networks have received increasing attention in human action recognition.Previous research has established that utilizing 3D convolution is a reasonable approach to learn spatio-temporal representation.Nevertheless, constructing effective 3D ConvNets usually need an expensive pre-training process that performing on a huge-scale video dataset.To avoid this burdensome situation, one major issue is to determine whether the pre-trained parameters of 2D convolution networks can be directly bootstrapped into 3D.In this paper, we devise a 2D-Inflated operation and a parallel 3D ConvNet architecture to solve this problem.The 2D-Inflated operation is used for converting pre-trained 2D ConvNets into 3D ConvNets, which avoiding video data pre-training.We further explore the optimal quantity of 3D ConvNet in the parallel architecture, and the results suggest that 6-nets architecture is an excellent solution for recognition.Another contribution of our study is two practical and valid skills, accumulated gradient descent and video sequence decomposition.Either of those techniques can promote the improvement of performance.The recognition results of UCF101 and HMDB51 reveal that, without the video data pre-training, our 3D ConvNets still can achieve competitive performance to the other generic and recent methods of using 3D ConvNets in the RGB image domain.
Robust H∞ dynamic output feedback control for interval type-2 T-S fuzzy multiple time-varying delays systems with external disturbance

This research focuses on the H∞ stability analysis and fuzzy dynamic output feedback control for a class of interval type-2 T-S fuzzy systems. Interval type-2 T-S fuzzy model is employed to approximate the uncertain system with multiple time-varying delays and external disturbance. Interval type-2 T-S fuzzy dynamic output feedback controller is designed by employing the interval type-2 T-S fuzzy model. Some stability criterions in terms of linear matrix inequalities (LMIs) are presented by introducing the fuzzy Lyapunov function. The closed-loop system is described as a descriptor system by employing the descriptor redundancy approach. Some less conservative stability criterions un-including the product terms between system gains and controller gains are presented and the fuzzy-basis-dependent matrix with system gains transformation is introduced. The stability criterions are extended to a finite set of LMIs constraints. Finally, simulation examples and comparison figures are presented to show the effectiveness of the proposed approach.
The role of Bitcoin on developed and emerging markets – on the basis of a Bitcoin users graph analysis

This paper joins discussions about Bitcoin's relationship to investments in developed and emerging markets. Our study contributes to the ongoing discussion of the role of Bitcoin for investors. In our research, we analyse relationships on financial markets with Bitcoin user graph characteristics. We examine the relationships between Bitcoin rates of return, stock indices, and variables characterising Bitcoin users who carried out the largest number of Bitcoin buy and sell transactions and users who carried out transactions with the highest amounts. We mainly focus on the problem whether the Bitcoin rate of return is mostly driven by capital market situation or major Bitcoin users behaviour. Our results confirmed that there is not, in general, significant relationship between financial market situation (if such relationship does exist, it is found in emerging markets). However, our findings indicate a significant impact of the behaviour of major Bitcoin users on Bitcoin rate of return.
An efficient hardware implementation of the elliptic curve cryptographic processor over prime field,

Summary Elliptic curve cryptography (ECC) schemes are widely adopted for the digital signature applications due to their key sizes, hardware resources, and higher security per bit than Rivest‐Shamir‐Adleman (RSA). In this work, we proposed a new hardware architecture for elliptic curve scalar multiplication (ECSM) in Jacobian coordinates over prime field, . This is a combination of point doubling and point addition architecture, implemented using resource sharing concept to achieve high speed and low hardware resources, which is synthesized both in field‐programmable gate array (FPGA) and application‐specific integrated circuit (ASIC). The proposed ECSM takes 1.76 and 2.44 ms on Virtex‐7 FPGA platform over 224‐bit and 256‐bit prime field, respectively. Similarly, ASIC (GF 40 nm complementary metal‐oxide semiconductor [CMOS]) technology implementation provides energy efficient with a latency of 0.46 and 0.6 ms over prime field and , respectively. This design provides better area‐delay product and high throughput value in both FPGA and ASIC when compared with other designs.
Neural Networks for Detecting Multimode Wigner Negativity

The characterization of quantum features in large Hilbert spaces is a crucial requirement for testing quantum protocols. In the continuous variable encoding, quantum homodyne tomography requires an amount of measurement that increases exponentially with the number of involved modes, which practically makes the protocol intractable even with few modes. Here, we introduce a new technique, based on a machine learning protocol with artificial neural networks, that allows us to directly detect negativity of the Wigner function for multimode quantum states. We test the procedure on a whole class of numerically simulated multimode quantum states for which the Wigner function is known analytically. We demonstrate that the method is fast, accurate, and more robust than conventional methods when limited amounts of data are available. Moreover, the method is applied to an experimental multimode quantum state, for which an additional test of resilience to losses is carried out.
Machine Speech Chain

Despite the close relationship between speech perception and production, research in automatic speech recognition (ASR) and text-to-speech synthesis (TTS) has progressed more or less independently without exerting much mutual influence. In human communication, on the other hand, a closed-loop speech chain mechanism with auditory feedback from the speaker's mouth to her ear is crucial. In this paper, we take a step further and develop a closed-loop machine speech chain model based on deep learning. The sequence-to-sequence model in closed-loop architecture allows us to train our model on the concatenation of both labeled and unlabeled data. While ASR transcribes the unlabeled speech features, TTS attempts to reconstruct the original speech waveform based on the text from ASR. In the opposite direction, ASR also attempts to reconstruct the original text transcription given the synthesized speech. To the best of our knowledge, this is the first deep learning framework that integrates human speech perception and production behaviors. Our experimental results show that the proposed approach significantly improved performance over that from separate systems that were only trained with labeled data.
Lightweight kernel isolation with virtualization and VM functions

Commodity operating systems execute core kernel subsystems in a single address space along with hundreds of dynamically loaded extensions and device drivers. Lack of isolation within the kernel implies that a vulnerability in any of the kernel subsystems or device drivers opens a way to mount a successful attack on the entire kernel.
ALSTM: An attention-based long short-term memory framework for knowledge base reasoning

Knowledge Graphs (KGs) have been applied to various application scenarios including Web searching, Q&A, recommendation system, natural language processing and so on. However, the vast majority of Knowledge Bases (KBs) are incomplete, necessitating a demand for KB completion (KBC). Methods of KBC used in the mainstream current knowledge base include the latent factor model, the random walk model and recent popular methods based on reinforcement learning, which performs well in their respective areas of expertise. Recurrent neural network (RNN) and its variants model temporal data by remembering information for long periods, however, whether they also have the ability to use the information they have already remembered to achieve complex reasoning in the knowledge graph. In this paper, we produce a novel framework (ALSTM) based on the Attention mechanism and Long Short-Term Memory (LSTM), which associates structure learning with parameter learning of first-order logical rules in an end-to-end differentiable neural networks model. In this framework, we designed a memory system and employed a multi-head dot product attention (MHDPA) to interact and update the memories embedded in the memory system for reasoning purposes. This is also consistent with the process of human cognition and reasoning, looking for enlightenment for the future in historical memory. In addition, we explored the use of inductive bias in deep learning to facilitate learning of entities, relations, and rules. Experiments establish the efficiency and effectiveness of our model and show that our method achieves better performance in tasks which include fact prediction and link prediction than baseline models on several benchmark datasets such as WN18RR, FB15K-237 and NELL-995.
Inspection or Play? A Study of How Augmented Reality Technology Can Be Utilized in Advertising

Abstract This study presents one of the earliest empirical studies that evaluates the effects of different interactivity and message design aspects of augmented reality (AR) advertising on consumer response. Specifically, this research examined whether and how AR interaction type (i.e., instrumental versus hedonic), ad context (i.e., realistic versus imaginative), and product type (think versus feel) jointly influence perceived ad informativeness and brand liking. A laboratory experiment with 213 college students was conducted using print ads with marker-based AR technology. The results suggested the effectiveness of instrumental AR interaction and the mediating role of telepresence. Based on the study findings, theoretical and strategic implications are discussed.
Software development effort estimation: a systematic mapping study

The field of software-development effort estimation explores ways of defining effort through prediction approaches. Even though this field has a crucial impact on budgeting and project planning in industry, the number of works classifying and examining currently available approaches is still small. This article, therefore, presents a comprehensive overview of these approaches, and pinpoints research gaps, challenges and trends. A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 120 primary studies were selected, analysed and categorised, after applying a careful filtering process from a sample of 3746 candidate studies to answer six research questions. Over 70% of the selected studies adopted multiple effort estimation approaches; over 45% adopted evaluation research as a research method; over 90% of the participants were students, rather than professionals; most studies had their quality assessed as high, and were most commonly published in journals. Our study benefits practitioners and researchers by providing a body of knowledge about the current literature, serving as a starting point for upcoming studies. This article reports challenges worth investigating, regarding the use of cognitive load and team interaction.