Reviewerly/exHarmony huggingface dataset:
Analysis of the Effect of Data Scaling on the Performance of the Machine Learning Algorithm for Plant Identification

Data scaling has an important role in preprocessing data that has an impact on the performance of machine learning algorithms. This study aims to analyze the effect of min-max normalization techniques and standardization (zero-mean normalization) on the performance of machine learning algorithms. The stages carried out in this study included data normalization on the data of leaf venation features. The results of the normalized dataset, then tested to four machine learning algorithms include KNN, Naïve Bayesian, ANN, SVM with RBF kernels and linear kernels. The analysis was carried out on the results of model evaluations using 10-fold cross-validation, and validation using test data. The results obtained show that Naïve Bayesian has the most stable performance against the use of min-max normalization techniques as well as standardization. The KNN algorithm is quite stable compared to SVM and ANN. However, the combination of the min-max normalization technique with SVM that uses the RBF kernel can provide the best performance results. On the other hand, SVM with a linear kernel, the best performance is obtained when applying standardization techniques (zero-mean normalization). While the ANN algorithm, it is necessary to do a number of trials to find out the best data normalization techniques that match the algorithm.
Outlier-Robust Student's-$t$-Based IMM-VB Localization for Manned Aircraft Using TDOA Measurements

This article proposes a novel outlier-robust localization algorithm that is based on time difference of arrival measurements at an airport for multilateration surveillance. An outlier-robust filtering scheme is derived based on Student's-t-distribution, where the state, a scale matrix, and a degree of freedom parameter are estimated simultaneously using variational Bayesian (VB) inference. An interacting multiple model (IMM) filter with different system models is implemented to handle the multimodal dynamics of the aircraft, yielding the IMM-VB algorithm. Specifically, the likelihood function is newly derived using VB inference for the combination procedure in the proposed IMM-VB algorithm. The experimental results obtained from a flight test using a commercial aircraft at an airport demonstrate that the proposed IMM-VB algorithm has better localization accuracy and robustness to outlier measurements than the existing state-of-the-art approaches.
The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection

Thermal cameras are popular in detection for their precision in surveillance in the dark and for privacy preservation. In the era of data driven problem solving approaches, manually finding and annotating a large amount of data is inefficient in terms of cost and effort. With the introduction of transfer learning, rather than having large datasets, a dataset covering all characteristics and aspects of the target place is more important. In this work, we studied a large thermal dataset recorded for 20 weeks and identified nine phenomena in it. Moreover, we investigated the impact of each phenomenon for model adaptation in transfer learning. Each phenomenon was investigated separately and in combination. the performance was analyzed by computing the F1 score, precision, recall, true negative rate, and false negative rate. Furthermore, to underline our investigation, the trained model with our dataset was further tested on publicly available datasets, and encouraging results were obtained. Finally, our dataset was also made publicly available.
On a conjecture about a class of permutation quadrinomials

Very recently, Tu et al. presented a sufficient condition on (a1,a2,a3), see Theorem 1.1, such that f(x)=x3⋅2m+a1x2m+1+1+a2x2m+2+a3x3 is a class of permutation polynomials over F2n with n=2m and m odd. In this present paper, we prove that the sufficient condition is also necessary.
The role of foreign direct investment, financial development, democracy and political (in)stability on economic growth in West Africa

Purpose In this research, we examine the role of financial development, FDI, democracy and political instability on economic growth in West Africa. Design/methodology/approach The study uses the dynamic fixed effects technique on the secondary data obtained from 1996 to 2016. Findings Our empirical findings suggest that even though no significant relationship is established in the short run, the long-run coefficient of FDI is found to be significant and positive; a 1% increase in FDI inflow into the West African sub-region results in a 0.26% increase in economic growth. The coefficient of democracy is significant neither in the short run nor in the long run, but political instability is found to significantly and negatively impact the growth of the countries. Finally, the estimate of financial development–growth nexus follows the supply-leading hypothesis. Research limitations/implications This research affirms the proposition that FDI is a relevant means of technology and knowledge transfers, thus resulting in increasing returns to production as a result of productive spillovers, which drives the growth of the economy. Consequently, an efficient institution – where the rule of law, political stability and economic freedom are top priorities – is a key to accelerate the growth of the West African economy. Similarly, we confirm the validity of the supply-leading hypothesis in West Africa. As such, by deepening the financial system, the growth of the subregion is propelled because an efficient financial system is a basis for sustainable development. Practical implication The applicable policies are those that promote growth through FDI, financial development, democracy and political instability. The governments of West African countries are enjoined to promote policies that attract FDI into the subregion and promote financial sector credits so that economic performances may be enhanced. In addition, the governments of West African subregion should fully entrench democratic practices and enhance a stable and sustainable political environment. This will not only restore investor confidence but will also facilitate the inflow of FDI into the West African economy. Originality/value Our study is the first to jointly examine these important growth determinants, especially in the context of West Africa. This becomes necessary in order to open the eyes of policy makers to the need for entrenched full democracy and to proffer sustainable cures to the frequent unrests in the subregion. The use of Pesaran (2007) technique of unit root is also a deviation from several existing studies. One advantage of this technique over others is that being a second-generation test, it tests variable unit root in the presence of cross-sectional dependence.
DocCit2Vec: Citation Recommendation via Embedding of Content and Structural Contexts

The number of academic papers being published is increasing rapidly, and recommending sufficient citations to assist researchers in writing papers is a non-trivial task.Conventional recommendation approaches may not be optimal, as the recommended papers may already be known to the users or may be solely relevant to the surrounding context but not to other concepts discussed in the manuscript.In this study, we propose a novel embedding algorithm, namely DocCit2Vec, along with the new concept of ''structural context'', to address the aforementioned issues.The proposed models are compared extensively with network-based, document-based, and combined approaches in experiments of citation recommendation and classification tasks.Three implications are concluded.First, the document-based methods demonstrated overwhelmingly superior performances for citation recommendation than the network-based methods, as the latter lack consideration of the word information.Second, DocCit2Vec exhibited significant improvement for citation recommendation among the document-based methods.Third, the ability to conduct classification tasks could be significantly enhanced by adding attention layer to DocCit2Vec.
An Adaptive Robustness Evolution Algorithm with Self-Competition for Scale-Free Internet of Things

Internet of Things (IoT) includes numerous sensing nodes that constitute a large scale-free network. Optimizing the network topology for increased resistance against malicious attacks is an NP-hard problem. Heuristic algorithms, particularly genetic algorithms, can effectively cope with such problems. However, conventional genetic algorithms are prone to falling into premature convergence owing to the lack of global search ability caused by the loss of population diversity during evolution. Although this can be alleviated by increasing population size, additional computational overhead will be incurred. Moreover, after crossover and mutation operations, individual changes in the population are mixed, and loss of optimal individuals may occur, which will slow down the evolution of the population. Therefore, we combine the population state with the evolutionary process and propose an Adaptive Robustness Evolution Algorithm (AREA) with self-competition for scale-free IoT topologies. In AREA, the crossover and mutation operations are dynamically adjusted according to population diversity to ensure global search ability. Moreover, a self-competitive mechanism is used to ensure convergence. The simulation results demonstrate that AREA is more effective in improving the robustness of scale-free IoT networks than several existing methods.
Should stock investors include cryptocurrencies in their portfolios after all? Evidence from a conditional diversification benefits measure

Abstract Higher media coverage and stronger investor interest in cryptocurrency market may create closer linkages with traditional assets, leading to deteriorated diversification benefits. Cryptocurrencies have recently emerged as an alternative digital asset class; however, very little is known about their portfolio performances. In this study, we investigate the time‐varying investment benefits of cryptocurrencies for stock portfolios using a correlation‐based conditional diversification benefits (CDB) measure. We construct six portfolios consisting of cryptocurrencies, developed and emerging equity markets and find that the time‐varying correlations between cryptocurrencies and stock markets are generally low. However, the level of correlations significantly increases in turbulent periods, such as Brexit referendum and Coincheck hack. The dynamic CDB measures suggest that adding cryptocurrencies to equity market portfolios enhances portfolio diversification; however, the benefits of diversification have diminished after late 2017. Our results offer significant insights and potential implications for market participants.
A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images

As Deep Convolutional Neural Networks (DCNNs) have shown robust performance and results in medical image analysis, a number of deep-learning-based tumor detection methods were developed in recent years. Nowadays, the automatic detection of pancreatic tumors using contrast-enhanced Computed Tomography (CT) is widely applied for the diagnosis and staging of pancreatic cancer. Traditional hand-crafted methods only extract low-level features. Normal convolutional neural networks, however, fail to make full use of effective context information, which causes inferior detection results. In this paper, a novel and efficient pancreatic tumor detection framework aiming at fully exploiting the context information at multiple scales is designed. More specifically, the contribution of the proposed method mainly consists of three components: Augmented Feature Pyramid networks, Self-adaptive Feature Fusion and a Dependencies Computation (DC) Module. A bottom-up path augmentation to fully extract and propagate low-level accurate localization information is established firstly. Then, the Self-adaptive Feature Fusion can encode much richer context information at multiple scales based on the proposed regions. Finally, the DC Module is specifically designed to capture the interaction information between proposals and surrounding tissues. Experimental results achieve competitive performance in detection with the AUC of 0.9455, which outperforms other state-of-the-art methods to our best of knowledge, demonstrating the proposed framework can detect the tumor of pancreatic cancer efficiently and accurately.
Designing a Competent Chatbot to Counter the COVID-19 Pandemic and Empower Risk Communication in an Emergency Response System

Risk communication is incorporated into an emergency response system. In Thailand, the Department of Disease Control (DDC) manages emergencies through the Emergency Operation Center (EOC). As a part of the EOC, the risk communication unit provides a hotline service that delivers information to and receives complaints and concerns from the general public. During the Coronavirus Disease 2019 (COVID-19) pandemic, a chatbot, which is a type of artificial intelligence (AI) was used to support the hotline service. This paper focuses on how to design an informative chatbot for the COVID-19 pandemic period that disseminates information to the general public. The chatbot, named “COVID-19 Preventable”, was created based on the Design Science Research Methodology (DSRM) under two cycles of design and development. At the early stage of development, information from reliable sources was transformed into a question and answer system and imported to natural language processing in the Dialogflow on Google Cloud. The chatbot was the first official chatbot to communicate on COVID-19 on behalf of public health authorities. It consists of seven prompt features, namely, a situation report, how to protect yourself from COVID-19, fake news, self-screening for COVID-19, a list of nearest hospitals, the hotline number to call, and report notification. The uniquely informative and dynamic chatbot is likely to be an alternative channel for disseminating timely information on COVID-19.
Markov Models for Anomaly Detection in Wireless Body Area Networks for Secure Health Monitoring

The use of Wireless Body Area Networks (WBANs) in healthcare for pervasive monitoring enhances the lives of patients and allows them to fulfill their daily life activities while being monitored. Various non-invasive sensors are placed on the skin to monitor several physiological attributes, and the measured data are transmitted wirelessly to a centralized processing unit to detect changes in the health of the monitored patient. However, the transferred data are vulnerable to various sources of interference, sensor faults, measurement faults, injection and alteration by malicious attackers, etc. In this article, we propose a change point detection model based on a Markov chain for centralized anomaly detection in WBANs. The model is derived from the Root Mean Square Error (RMSE) between the forecasted and measured values for whole attributes. The RMSE transforms the monitored attributes into a univariate times series which is divided into overlapping sliding window. The joint probability of the sequence of RMSE values in each sliding window is calculated to decide whether a change has occurred or not. When an effective change is detected over k consecutive windows, the number of deviated attributes is used to distinguish faulty measurements from a health emergency. We apply our proposed approach on real physiological data from the Physionet database and compare it with existing approaches. Our experimental results prove the effectiveness of our proposed approach, as it achieves high detection accuracy with a low false alarm rate (5.2%).
Language in a Time of COVID-19: Literacy Bias Ethnic Minorities Face During COVID-19 from Online Information in the UK

To investigate the readability and presence of translated online information readily available to the British public during COVID-19. A cross-sectional study was performed. The terms "Coronavirus", "COVID-19", "Lockdown", "Social Distancing", "Handwashing", "Furlough Scheme" and "Sick pay" were inputted into the popular search engine, Google. Websites were categorised by their source (i.e. Government, Non-Governmental Organisation, NHS and Commercial) and theme (i.e. general COVID-19 information, population practise and employment rulings). Reliable calculators for assessing readability (Simple Measure of Gobbledygook, Gunning Fog Index, Flesch-Kincaid Grade Level, Coleman-Liau Index and Automated Readability Index) were used. The median scores with the interquartile range from each calculator of the pooled data were observed. The presence of accompanying translated material and graphic information was also counted and presented as counts and percentages. The number of readable websites (i.e. a score ≤ 8) for each source and theme category were also recorded. UK Internet servers. The median scores of the pooled data (n = 148) had shown that the majority of websites were unreadable to the average British reader according to each calculator used (SMOG 1.3%; GF 6.8%; FK 35.8%; CL 2.6%; ARI 40%). Only 3.4% and 6.8% of the pooled websites had readily available translated material and accompanying graphic material, respectively. Readability of COVID-19 information is below national standards and that there is a lack of accompanying translated and graphics-based material online. This may lead to an amplified level of misunderstanding in BAME populations about the COVID-19 pandemic and the rulings put in place.
When is a bishop not like a rook? When it’s like a rabbi! Multi-prototype BERT embeddings for estimating semantic relationships

This paper investigates contextual language models, which produce token representations, as a resource for lexical semantics at the word or type level.We construct multi-prototype word embeddings from bert-base-uncased (Devlin et al., 2018).These embeddings retain contextual knowledge that is critical for some type-level tasks, while being less cumbersome and less subject to outlier effects than exemplar models.Similarity and relatedness estimation, both type-level tasks, benefit from this contextual knowledge, indicating the context-sensitivity of these processes.BERT's token level knowledge also allows the testing of a type-level hypothesis about lexical abstractness, demonstrating the relationship between token-level behavior and type-level concreteness ratings.Our findings provide important insight into the interpretability of BERT: layer 7 approximates semantic similarity, while the final layer (11) approximates relatedness.
Generationary or “How We Went beyond Word Sense Inventories and Learned to Gloss”

Mainstream computational lexical semantics embraces the assumption that word senses can be represented as discrete items of a predefined inventory.In this paper we show this needs not be the case, and propose a unified model that is able to produce contextually appropriate definitions.In our model, Generationary, we employ a novel span-based encoding scheme which we use to fine-tune an English pre-trained Encoder-Decoder system to generate glosses.We show that, even though we drop the need of choosing from a predefined sense inventory, our model can be employed effectively: not only does Generationary outperform previous approaches in the generative task of Definition Modeling in many settings, but it also matches or surpasses the state of the art in discriminative tasks such as Word Sense Disambiguation and Word-in-Context.Finally, we show that Generationary benefits from training on data from multiple inventories, with strong gains on various zeroshot benchmarks, including a novel dataset of definitions for free adjective-noun phrases.
Accelerating CNN Training by Pruning Activation Gradients

Sparsification is an efficient approach to accelerate CNN inference, but it is challenging to take advantage of sparsity in training procedure because the involved gradients are dynamically changed. Actually, an important observation shows that most of the activation gradients in back-propagation are very close to zero and only have a tiny impact on weight-updating. Hence, we consider pruning these very small gradients randomly to accelerate CNN training according to the statistical distribution of activation gradients. Meanwhile, we theoretically analyze the impact of pruning algorithm on the convergence. The proposed approach is evaluated on AlexNet and ResNet-{18, 34, 50, 101, 152} with CIFAR-{10, 100} and ImageNet datasets. Experimental results show that our training approach could substantially achieve up to $$5.92 \times $$ speedups at back-propagation stage with negligible accuracy loss.
Hyperparameter Optimization Using Scikit-Learn

In the previous chapter, you learned what hyperparameters are and how they affect the performance of an algorithm. Now that you know how important it is to tune hyperparameters, this chapter introduces you to some simple yet powerful uses of algorithms implemented in the scikit-learn library for hyperparameter optimization. Scikit-learn is one of the most widely used open source libraries for machine learning practices. It’s simple to use and really effective in predictive analysis.
The Michelangelo Effect: Art Improves the Performance in a Virtual Reality Task Developed for Upper Limb Neurorehabilitation

The vision of an art masterpiece is associated with brain arousal by neural processes occurring quite spontaneously in the viewer. This aesthetic experience may even elicit a response in the motor areas of the observers. In the neurorehabilitation of patients with stroke, art observation has been used for reducing psychological disorders, and creative art therapy for enhancing physical functions and cognitive abilities. Here, we developed a virtual reality task which allows patients, by moving their hand on a virtual canvas, to have the illusion of painting some art masterpieces, such as The Creation of Adam of Michelangelo or The birth of Venus of Botticelli. Twenty healthy subjects (experiment 1) and four patients with stroke (experiment 2) performed this task and a control one in which they simply colored the virtual canvas. Results from User Satisfaction Evaluation Questionnaire and the NASA Task Load Index highlighted an appropriate level of usability. Moreover, despite the motor task was the same for art and control stimuli, the art condition was performed by healthy subjects with shorter trajectories ( p = 0.001) and with a lower perception of physical demand ( p = 0.049). In experiment 2, only the patients treated with artistic stimuli showed a reduction in the erroneous movements performed orthogonally to the canvas ( p &amp;lt; 0.05). This finding reminds the so-called Mozart effect that improves the performance of subjects when they listen to classic music. Thus, we called this improvement in the performance when interacting with an artistic stimulus as Michelangelo effect.