Reviewerly/exHarmony huggingface dataset:
Robust extreme learning machine neural approach for uncertain nonlinear hyper‐chaotic system identification

Abstract This paper proposes a novel nonlinearly parameterized advanced single‐hidden layer neural extreme learning machine (ASHLN‐ELM) model in which the hidden and output weighting values are simultaneously updated using adaptively robust rules that are implemented based on Lyapunov stability principle. The proposed scheme guarantees the fast convergence speed of the state‐estimation residual errors bounded to null regarding to the influence of time‐varied disturbances. Additionally, proposed method needs no any knowledge related to desired weighting values or required approximating error. Typical uncertain hyper‐chaotic benchmark systems are used as to verify the new ASHLN‐ELM approach and to demonstrate the efficiency and the robustness of proposed method.
Psycho-Linguistic Changes Associated With Historical Celebrities in Henan Using Classical Chinese Big Data

Chinese civilization has a long history, and the Central Plains, with Henan as the core, is one of its birthplaces. Understanding the psycho-linguistic changes in Henan is of great significance for understanding the evolution and formation of national cultural psychology. The traditional method is mainly qualitative or speculative, which makes the consistency among the research results low. Moreover, because most of the research are conducted in a specific period or on a specific figure, they are relatively scattered and unsystematic. To systematically and quantitatively study the psychological changes in the Central Plains represented by Henan Province, this article aimed to examine the self-reported discourses of historical celebrities in Henan in official history and their psycho-linguistic changes based on the classical Chinese Linguistic Inquiry and Word Count (classical Chinese LIWC, CC-LIWC) psycholinguistic dictionary and the classical Chinese words segmentation system, using big data. The research found that the frequency of male words in Henan historical celebrities ( F = 2.938, p &amp;lt; 0.05), of differ words ( F = 4.767, p &amp;lt; 0.01), of motion words ( F = 4.042, p &amp;lt; 0.01), and of time words ( F = 5.412, p &amp;lt; 0.01) are significantly different among the five dynasties. The conclusion is that during the Spring and Autumn and the Warring States periods, 100 schools of thought contended, and the status of “scholars” in Henan rose. The frequency of male and differ words was at that point significantly higher than during the other dynasties. From “The Contention of a Hundred Schools of Thought” to “The Supremacy of Confucianism,” the scholars in Henan were in decline, and differential cognitive tendencies had diminished since the Han dynasty. During the period of the Three Kingdoms and Jin and Southern and Northern Dynasties, political powers and territories changed, and the historical celebrities in Henan show a remarkable tendency that were related to time and space. The psycho-linguistic changes found in this study are highly consistent with the development of social history, which indicates that the guidance of the social, political, and cultural environment has an important influence on the psycho-linguistic changes of social class. This is the first time that the text analysis system has been applied in classical Chinese to carry out quantitative research on the psycho-linguistic changes of ancient Chinese people, which provides new ideas and new methods for humanistic research.
Breaking TrustZone memory isolation and secure boot through malicious hardware on a modern FPGA-SoC

Abstract FPGA-SoCs are heterogeneous embedded computing platforms consisting of reconfigurable hardware and high-performance processing units. This combination offers flexibility and good performance for the design of embedded systems. However, allowing the sharing of resources between an FPGA and an embedded CPU enables possible attacks from one system on the other. This work demonstrates that a malicious hardware block contained inside the reconfigurable logic can manipulate the memory and peripherals of the CPU. Previous works have already considered direct memory access attacks from malicious logic on platforms containing no memory isolation mechanism. In this work, such attacks are investigated on a modern platform which contains state-of-the-art memory and peripherals isolation mechanisms. We demonstrate two attacks capable of compromising a Trusted Execution Environment based on ARM TrustZone and show a new attack capable of bypassing the secure boot configuration set by a device owner via the manipulation of Battery-Backed RAM and eFuses from malicious logic.
Transformer-based Lexically Constrained Headline Generation

This paper explores a variant of automatic headline generation methods, where a generated headline is required to include a given phrase such as a company or a product name.Previous methods using Transformer-based models generate a headline including a given phrase by providing the encoder with additional information corresponding to the given phrase.However, these methods cannot always include the phrase in the generated headline.Inspired by previous RNN-based methods generating token sequences in backward and forward directions from the given phrase, we propose a simple Transformerbased method that guarantees to include the given phrase in the high-quality generated headline.We also consider a new headline generation strategy that takes advantage of the controllable generation order of Transformer.Our experiments with the Japanese News Corpus demonstrate that our methods, which are guaranteed to include the phrase in the generated headline, achieve ROUGE scores comparable to previous Transformer-based methods.We also show that our generation strategy performs better than previous strategies.
Interactive Teaching Mode of College English Based on Internet of Things

In terms of English teaching in China, English teaching mode, there are some drawbacks. The interaction between teachers and students is very insufficient, which cannot fully mobilize the enthusiasm of students. The purpose of this study is to explore a new way for English teaching mode, hoping to explore better English teaching methods and improve students’ English performance. This paper focuses on the characteristics of College English (CE) interactive teaching mode based on the Internet of Things technology, discusses the practical application of the Internet of Things technology in College English interactive teaching, and makes a simple research on the interactive teaching mode of College English based on the Internet of Things (IoT). Students from two natural classes in our city were selected as the research objects and divided into an experimental group and control group. The overall English levels of the two classes were similar, and the study lasted for a semester. Research tools include questionnaires, writing tests, and interviews. Statistical software was used to analyze the data in detail. Before and after the experiment, the test results of the experimental class and the control class were tested by independent sample t-test and paired sample t-test. The experimental results show that the College English interactive teaching mode based on the Internet of Things is welcomed by students, and the experimental effect is significant. The overall average score of the experimental group increased by 13.6 points, while that of the control group increased by 4.2 points. The gap between the two groups was very obvious. As far as the scores of each part of the English test paper are concerned, the scores of each part of the experimental group are also higher than those of the control group.
Towards Increasing Data Availability for Meteorological Services: Inter-Comparison of Meteorological Data from a Synoptic Weather Station and Two Automatic Weather Stations in Kenya

Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others.However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination.In sub-Saharan Africa, such networks are limited due to low investment and capacity.To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals.Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited.This is mainly due to low confidence, by data users, in electronically observed data.In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales.To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters.Results show there is com-
On the curved exponential family in the Stochastic Approximation Expectation Maximization Algorithm

The Expectation-Maximization Algorithm (EM) is a widely used method allowing to estimate the maximum likelihood of models involving latent variables. When the Expectation step cannot be computed easily, one can use stochastic versions of the EM such as the Stochastic Approximation EM. This algorithm, however, has the drawback to require the joint likelihood to belong to the curved exponential family. To overcome this problem, [16] introduced a rewriting of the model which “exponentializes” it by considering the parameter as an additional latent variable following a Normal distribution centered on the newly defined parameters and with fixed variance. The likelihood of this new exponentialized model now belongs to the curved exponential family. Although often used, there is no guarantee that the estimated mean is close to the maximum likelihood estimate of the initial model. In this paper, we quantify the error done in this estimation while considering the exponentialized model instead of the initial one. By verifying those results on an example, we see that a trade-off must be made between the speed of convergence and the tolerated error. Finally, we propose a new algorithm allowing a better estimation of the parameter in a reasonable computation time to reduce the bias.
Leveraging Pre-Trained Representations to Improve Access to Untranscribed Speech from Endangered Languages

Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR).Yet many endangered languages lack sufficient data for pretraining such models, or are predominantly oral vernaculars without a standardised writing system, precluding finetuning.Query-by-example spoken term detection (QbE-STD) offers an alternative for iteratively indexing untranscribed speech corpora by locating spoken query terms.Using data from 7 Australian Aboriginal languages and a regional variety of Dutch, all of which are endangered or vulnerable, we show that QbE-STD can be improved by leveraging representations developed for ASR (wav2vec 2.0: the English monolingual model and XLSR53 multilingual model).Surprisingly, the English model outperformed the multilingual model on 4 Australian language datasets, raising questions around how to optimally leverage self-supervised speech representations for QbE-STD.Nevertheless, we find that wav2vec 2.0 representations (either English or XLSR53) offer large improvements (56-86% relative) over state-of-the-art approaches on our endangered language datasets.
Integrating human experience in deep reinforcement learning for multi-UAV collision detection and avoidance

Purpose This paper aims to realize a fully distributed multi-UAV collision detection and avoidance based on deep reinforcement learning (DRL). To deal with the problem of low sample efficiency in DRL and speed up the training. To improve the applicability and reliability of the DRL-based approach in multi-UAV control problems. Design/methodology/approach In this paper, a fully distributed collision detection and avoidance approach for multi-UAV based on DRL is proposed. A method that integrates human experience into policy training via a human experience-based adviser is proposed. The authors propose a hybrid control method which combines the learning-based policy with traditional model-based control. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the approach. Findings A fully distributed multi-UAV collision detection and avoidance method based on DRL is realized. The reward curve shows that the training process when integrating human experience is significantly accelerated and the mean episode reward is higher than the pure DRL method. The experimental results show that the DRL method with human experience integration has a significant improvement than the pure DRL method for multi-UAV collision detection and avoidance. Moreover, the safer flight brought by the hybrid control method has also been validated. Originality/value The fully distributed architecture is suitable for large-scale unmanned aerial vehicle (UAV) swarms and real applications. The DRL method with human experience integration has significantly accelerated the training compared to the pure DRL method. The proposed hybrid control strategy makes up for the shortcomings of two-dimensional light detection and ranging and other puzzles in applications.
E-learning in elementary schools: educational system change during COVID-19 pandemic

Introduction. New digital technology in education and its constantly evolving paradigm have completely transformed the model of learning and the learning methods. E-learning has become an important tool for teaching and learning environments. Moreover, virtual learning has become a required alternative teaching method in educational system change during the COVID-19 pandemic, especially for learners in elementary schools. Aim. This research aimed to explore the necessities, lack, and want of learning English, which is done virtually at home. Methodology and research methods. This research employs descriptive methods using questionnaires and interviews as instruments to get responses from teachers and students about their learning needs. In the course of the needs analysis process, the importance and priority of teachers’ and students’ needs are discussed. The participants of this study were second-grade elementary school students, and the sample was 40 students and 2 English teachers for 10 months. Results. The research results demonstrate a gap between the needs of learners and current conditions; therefore, the whole language approach, the multimedia, and technology-based English teaching materials will become an inevitable need and even become a solution in the world of education. Scientific novelty. Further research is needed to study educational system change during the COVID-19 pandemic or after, especially in designing teaching materials in various disciplines and at different educational stages. Future studies could be generalised by these research findings to other populations in different learning contexts, and the findings presented in thisarticle should be further explored. In particular, technology-based and multimedia-based teaching materials must be explored more deeply. Furthermore, further research should focus on utilising a different model for needs analysis in language education to verify the need for effective English learning in changing the education system during the pandemic and after. Practical significance. It is thought that this study will contribute to the stakeholders in terms to provide empirical evidence of what necessities, lack, and want should be considered when a teacher will design and develop teaching materials during the COVID-19 period or afte.
Adaptation Knowledge Discovery Using Positive and Negative Cases

Case-based reasoning usually exploits positive source cases, each of them consisting in a problem and a correct solution to this problem. Now, the general issue of exploiting also negative cases—i.e., problem-solution pairs where the solution answers incorrectly the problem—can be raised. Indeed, such cases are “naturally” generated by a CBR system as long as it sometimes proposes incorrect solutions. This paper aims at addressing this issue for adaptation knowledge (AK) discovery: how positive and negative cases can be used for this purpose. The idea is that positive cases are used to propose adaptation rules and that negative cases are used to filter out some of these rules. In a preliminary work, this kind of AK discovery has been applied using frequent closed itemset (FCI) extraction on variations within the case base and tested on a toy Boolean use case, with promising first results. This paper resumes this study and evaluates it on 4 benchmarks, which confirms the benefit of exploiting negative cases for AK discovery. This involves some adjustments in the data preparation and in adaptation rule filtering, in particular because FCI extraction works only with Boolean features, hence some methodology lessons learned for AK discovery with positive and negative cases.
Ethical aspects of voice assistants: a critical discourse analysis of Indonesian media texts

Purpose Voice assistant (VA) technology is one of the fastest-growing artificial intelligence applications at present. However, the burgeoning scholarship argues that there are ethical challenges relating to this new technology, not the least related to privacy, which affects the technology’s acceptance. Given that the media impacts public opinion and acceptance of VA and that there are no studies on media coverage of VA, the study focuses on media coverage. In addition, this study aims to focus on media coverage in Indonesia, a country that has been underrepresented in earlier research. Design/methodology/approach The authors used critical discourse analysis of media texts, focusing on three levels (text, discourse practice and social practice) to study how VA technology was discussed in the Indonesian context and what power relations frame the representation. In total, 501 articles were collected from seven national media in Indonesia from 2010 to 2020 and the authors particularly focus on the 45 articles that concern ethics. Findings The ethical topics covered are gender issues, false marketing, ethical wrongdoing, ethically positive effects, misuse, privacy and security. More importantly, when they are discussed, they are presented as constituting no real critical problem. Regarding discursive practices, the media coverage is highly influenced by foreign media and most of the articles are directed to well-educated Indonesians. Finally, regarding social practices, the authors hold that the government ideology of technological advancement is related to this positive portrayal of VAs. Originality/value First, to provide the first media discourse study about ethical issues of VAs. Second, to provide insights from a non-Western context, namely, Indonesia, which is underrepresented in the research on ethics of VAs.
A Study on the Changes in the Appraisal Industry in the Era of the 4th Industrial Revolution - Focus on the Factors Affecting Intention to Adopt Big Data in the Appraisal Field

The appraisal industry is the first structural change since the implementation of the public land price system in 1989.Since the environment surrounding the appraisal market has changed so far, structural adjustment from the inside is inevitable even though it was not enforced through legislation.This study aimed to identify factors that affect intention to adopt big data in the appraisal field based on the perception of values and environmental factors suggested by Paret et al., Quinn & Baily, Elizabeth & Michael, and Kyung, and propose a relevant guideline to institutions and corporations that consider adopting Big Data.Based on empirical analysis using SEM (structure equation modeling), the result was as follows: first, institutions and corporations that are considering adopting big data in the appraisal field should focus on improving the work process, capturing opportunities, and providing information for decision-making.Second, they must fully review internal factors of the Appraisal industry such as IT development trends and changing environment and select crucial business partnerships to gain competitiveness in the market.
Sparse anchoring guided high-resolution capsule network for geospatial object detection from remote sensing imagery

As the optical remote sensing techniques keep developing with a rapid pace, remote sensing images are positively considered in many fields. Accordingly, a great number of algorithms have been exploited for remote sensing image interpretation purposes. Thereinto, object recognition acts as an important ingredient to many applications. However, to achieve highly accurate object recognition is still challengeable caused by the orientation and size diversities, spatial distribution and density variations, shape and aspect ratio irregularities, occlusion and shadow impacts, as well as complex texture and surrounding environment changes. In this paper, a sparse anchoring guided high-resolution capsule network (SAHR-CapsNet) is proposed for geospatial object detection based on remote sensing images. First, formulated with the multibranch high-resolution capsule network architecture assisted by multiscale feature propagation and fusion, the SAHR-CapsNet can extract semantically strong and spatially accurate feature semantics at multiple scales. Second, integrated with the efficient capsule-based self-attention module, the SAHR-CapsNet functions promisingly to attend to target-specific spatial features and informative channel features. Finally, adopted with the capsule-based sparse anchoring network, the SAHR-CapsNet performs efficiently in generating a fixed number of lightweight, high-quality sparse region proposals. Quantitative assessments and comparative analyses on two challenging remote sensing image datasets demonstrate the applicability and effectiveness of the developed SAHR-CapsNet for geospatial object detection applications.