Reviewerly/exHarmony huggingface dataset:
Towards a customizable immersive virtual reality serious game for earthquake emergency training

Earthquake emergencies require a variety of behavioral responses in order to ensure the safety of occupants, which is different from simply exiting a building in fire emergencies. This makes it more complex to train building occupants in order to acquire skills that align to best practices for immediate earthquake response and post-earthquake evacuation. In recent years, Immersive Virtual Reality (IVR) and Serious Games (SGs) have become popular as training tools for earthquake emergencies. IVR SGs have been introduced to train individuals for specific building layouts or settings with fixed training objectives. However, the lack of flexibility in existing IVR SGs makes it challenging to have widespread uptake as trainees require different training objectives, pedagogical strategies, context, and content. As a result, the effectiveness of IVR SGs training is jeopardized if the customization ability is limited. To overcome this limitation, this paper presents a customization framework for IVR SGs suited to earthquake emergency training, using the concept of adaptive game-based learning. Trainees can receive training in context by customizing virtual environments, storylines, and teaching methods. A case study was undertaken to validate the proposed framework. Results showed the potential to carry out the customization process with ease, to generate a customized training experience, and to deliver the customized training for optimum learning.
Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset

Now a day's sentiment analysis is the most used research topic. The sentiment analysis result is based on different investigation for example politics, terrorism, economy, international affairs, movies, fashion, justice, humanity. Social media are the main resource for collecting people's opinion and their sentiment about a different trending topic. People use many abusing words in social media to express their emotion. Using sentiment analysis, we will build a platform where one can easily identify the opinions are either positive or negative or neutral. This research paper will contain supervised learning which is under the machine learning approach. We run an experiment on different queries from humanity to terrorism and find out an interesting result. First of all, we have preprocessed the dataset to convert unstructured airline review into structured review form. After that, we convert structured review into a numerical value. We have to preprocess the data before using it. Stop word removal, @ removal, Hashtag removal, POS tagging, calculating sentiment score have done in preprocessing part. Then an algorithm has been applied to classify the opinion as either it is positive or negative. In this research paper, we will briefly discuss supervised machine learning. Support vector machine as well as Naïve Bayes algorithm and compares their overall accuracy, precession, recall value. The result shows that in the case of airline reviews Support vector machine gave way better result than Naïve Bayes algorithm.
An Optimal Least Square Support Vector Machine Based Earnings Prediction of Blockchain Financial Products

The booming applications of bitcoin Blockchain technologies made investors concerned about the return and risk of financial products. So, the return rate of bitcoin must be foreseen in prior. This research article devises an effective return rate prediction technique for Blockchain financial products based on Optimal Least Square Support Vector Machine (OLS-SVM) model. The parameter optimization of the LS-SVM model was performed using hybridization of Grey Wolf Optimization (GWO) with Differential Evolution (DE), called optimal GWO (OGWO) algorithm. The hybridization process is performed to eliminate the local optima problem of GWO and enhance the diversity of the population. To verify the goodness of the proposed model, the Ethereum (ETH) return rate was chosen as the target and experimental analysis was performed on it to verify the predictive results on the time series. The experimental outcome was analyzed in terms of two performance measures namely Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The obtained simulation outcome infers that the OLS-SVM model yielded better predictive outcome of the return rate of financial products.
Detection, assessment and mitigation of vulnerabilities in open source dependencies

Abstract Open source software (OSS) libraries are widely used in the industry to speed up the development of software products. However, these libraries are subject to an ever-increasing number of vulnerabilities that are publicly disclosed. It is thus crucial for application developers to detect dependencies on vulnerable libraries in a timely manner, to precisely assess their impact, and to mitigate any potential risk. This paper presents a novel method to detect, assess and mitigate OSS vulnerabilities. Differently from state-of-the-art approaches that depend on metadata to identify vulnerable OSS dependencies, our solution is code-centric, and combines static and dynamic analyses to determine the reachability of the vulnerable portion of libraries, in the context of a given application. Our approach also supports developers in choosing among the existing non-vulnerable library versions, with the goal to determine and minimize incompatibilities. , the open source implementation of our code-centric and usage-based approach is the tool recommended to scan Java software products at SAP; it has been successfully used to perform more than one million scans of about 1500 applications. In this paper we report on the lessons learned when maturing the tool from a research prototype to an industrial-grade solution. To evaluate , we conducted an empirical study to compare its detection capabilities with those of OWASP Dependency Check (), scanning 300 large enterprise applications under development with a total of 78165 dependencies. Reviewing a sample of the findings reported only by one of the two tools revealed that all findings are true positives, while 88.8% of the findings of for vulnerabilities covered by our code-centric approach are false positives. For vulnerabilities not caused by code but due, e.g., to erroneous configuration, 63.3% of findings are true positives.
Investigation of Low-Dose CT Image Denoising Using Unpaired Deep Learning Methods

Low-dose computed tomography (LDCT) is desired due to prevalence and ionizing radiation of CT, but suffers elevated noise. To improve LDCT image quality, an image-domain denoising method based on cycle-consistent generative adversarial network (CycleGAN) is developed and compared with two other variants, IdentityGAN and GAN-CIRCLE. Different from supervised deep learning methods, these unpaired methods can effectively learn image translation from the low-dose domain to the full-dose (FD) domain without the need of aligning FDCT and LDCT images. The results on real and synthetic patient CT data show that these methods can achieve peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) comparable to, if not better than, the other state-of-the-art denoising methods. Among CycleGAN, IdentityGAN, and GAN-CIRCLE, the later achieves the best denoising performance with the shortest computation time. Subsequently, GAN-CIRCLE is used to demonstrate that the increasing number of training patches and of training patients can improve denoising performance. Finally, two nonoverlapping experiments, i.e., no counterparts of FDCT and LDCT images in the training data, further demonstrate the effectiveness of unpaired learning methods. This work paves the way for applying unpaired deep learning methods to enhance LDCT images without requiring aligned FD and low-dose images from the same patient.
CNN-SVM Learning Approach Based Human Activity Recognition

Although it has been encountered for a long time, the human activity recognition remains a big challenge to tackle. Recently, several deep learning approaches have been proposed to enhance the recognition performance with different areas of application. In this paper, we aim to combine a recent deep learning-based method and a traditional classifier based hand-crafted feature extractors in order to replace the artisanal feature extraction method with a new one. To this end, we used a deep convolutional neural network that offers the possibility of having more powerful extracted features from sequence video frames. The resulting feature vector is then fed as an input to the support vector machine (SVM) classifier to assign each instance to the corresponding label and bythere, recognize the performed activity. The proposed architecture was trained and evaluated on MSR Daily activity 3D dataset. Compared to state of art methods, our proposed technique proves that it has performed better.
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals

Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs. The reliability of spoofing CMs is typically gauged using the equal error rate (EER) metric. The primitive EER fails to reflect application requirements and the impact of spoofing and CMs upon ASV and its use as a primary metric in traditional ASV research has long been abandoned in favour of risk-based approaches to assessment. This paper presents several new extensions to the tandem detection cost function (t-DCF), a recent risk-based approach to assess the reliability of spoofing CMs deployed in tandem with an ASV system. Extensions include a simplified version of the t-DCF with fewer parameters, an analysis of a special case for a fixed ASV system, simulations which give original insights into its interpretation and new analyses using the ASVspoof 2019 database. It is hoped that adoption of the t-DCF for the CM assessment will help to foster closer collaboration between the anti-spoofing and ASV research communities.
Humanoid robots in higher education: Evaluating the acceptance of Pepper in the context of an academic writing course using the UTAUT

Abstract This study investigates the acceptance of social robots by higher education students in the social sciences. Pepper, a humanoid social robot from SoftBank Robotics, provided a sample of its capabilities during a first semester, large‐scale, university course, “Introduction to academic writing.”From this course, 462 freshmen participated in our survey. The unified theory of acceptance and use of technology (UTAUT) acts as the conceptual framework, and partial least squares structural equation modelling (PLS‐SEM) as the method for data analysis. The four perceived characteristics—trustworthiness, adaptiveness, social presence and appearance—all predict the intention to use the robot for learning purposes; anxiety regarding making mistakes in handling the robot and about privacy issues are not significant predictors. An importance‐performance map analysis indicated adaptiveness as the robot’s most important characteristic for predicting student behavioural intention. Overall, however, the study shows that students do not have the intention to rely on social robots for learning purposes at the current level of state‐of‐the‐art technology: behavioural intention reaches only 36.6% of the theoretical maximum.
Multisensory integration and the experience of scene instability, presence and cybersickness in virtual environments

Multisensory integration is critical for supporting human spatial perception as we move around our environment. In this study, we used a modern head-mounted display (HMD) as a tool for studying the effects of visual-vestibular conflict on human spatial perception, feelings of presence and cybersickness in virtual reality (VR). Thirty participants were recruited and exposed to different levels of sensory conflict as they moved their heads in pitch. This sensory conflict was generated by artificially delaying updates to the visual representations of their virtual environment. On each trial we measured our participants': 1) perceived scene instability using an angular gauge figure task; 2) spatial presence using a subjective rating scale, and 3) cybersickness severity using the fast motion sickness (FMS) scale. As display lag was increased, perceived scene instability and cybersickness were both found to increase, whereas reported presence decreased. Importantly, the difference in the participant's virtual and physical head orientation (DVP) for the trial was also found to predict reported scene instability and cybersickness. Faster head rotations (at 1.0 Hz compared with 0.5 Hz) amplified both of these adverse effects (presumably due to the increase in DVP). These findings are consistent with the view that measurable low-level visual-vestibular conflicts can account for the cybersickness experienced during HMD VR.
An Efficient Recursive Differential Grouping for Large-Scale Continuous Problems

Cooperative co-evolution (CC) is an efficient and practical evolutionary framework for solving large-scale optimization problems. The performance of CC is affected by the variable decomposition. An accurate variable decomposition can help to improve the performance of CC on solving an optimization problem. The variable grouping methods usually spend many computational resources obtaining an accurate variable decomposition. To reduce the computational cost on the decomposition, we propose an efficient recursive differential grouping (ERDG) method in this article. By exploiting the historical information on examining the interrelationship between the variables of an optimization problem, ERDG is able to avoid examining some interrelationship and spend much less computation than other recursive differential grouping methods. Our experimental results and analysis suggest that ERDG is a competitive method for decomposing large-scale continuous problems and improves the performance of CC for solving the large-scale optimization problems.
Characterizing public emotions and sentiments in COVID-19 environment: A case study of India

Coronavirus 2019, or COVID-19, is a contagious disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With origins in Wuhan, China, this disease has since spread globally, resulting in the ongoing 2019–2020 coronavirus pandemic. As of May 3, 2020, the Ministry of Health and Family Welfare confirmed a total of 39,980 positive COVID-19 cases and 1,301 deaths in India (more than 3.42 million positive COVID-19 cases resulting in more than 243,000 deaths worldwide). To flatten the curve, India has been locking down its country from March 24 to May 17, 2020. This study collected "COVID-19 in India" related tweets (totaling 410,643 tweets in English) from March 22 to April 21, 2020 to gauge the unknowns and contexts associated with public sentiments during the lockdown. This work contributes to the growing body of studies on COVID-19 social media mining by extracting emotions and sentiments over time, which could potentially shed some lights on the contexts of expressions during pandemic.
Ongoing Debate Between Foreign Aid and Economic Growth in Nigeria: A Wavelet Analysis

Abstract This study aims to reexamine the interconnection between economic growth, foreign aid, trade, gross fixed capital formation, and inflation rate in one model for the case of Nigeria, which has not yet been analyzed utilizing the new econometric techniques, employing time series data covering the years between 1980 and 2018. No previous research has employed a wavelet coherence technique to gather information on the dynamic connection and/or causality between these economic indicators at dissimilar frequencies and various time frames. The main objectives are to address the questions: (a) Is there long‐run relationship between the indicators under consideration? (b) What are the main determinants of economic growth in the long run? (c) How are the indicators related at dissimilar frequencies and various time frames? The empirical findings confirm that (a) there is a long‐run relationship between the indicators under consideration; (b) in the long run, economic growth is influenced significantly by foreign aid, trade openness, gross fixed capital formation, and inflation rate; (c) the outcomes of the wavelet coherence technique give evidence to support the long‐run estimations of this study; and (d) the outcomes of wavelet coherence are supported by the Toda‐Yamamoto causality test results.
A wavelet - Particle swarm optimization - Extreme learning machine hybrid modeling for significant wave height prediction

Predictions of Significant wave height (Hs) of oceans is highly required in advance for coastal and ocean engineering applications. Therefore, this study aims to precisely predict the ocean wave height via developing a novel hybrid algorithm. Wavelet, Particle Swarm Optimization (PSO), and Extreme Learning Machine (ELM) methods were used and integrated to design the wavelet-PSO-ELM (WPSO-ELM) model for estimating the wave height belongs to coastal and deep-sea stations. A comparative analysis among the ELM, Kernel ELM (KELM), and PSO-ELM models were performed with and without wavelet integration. In addition, wave height prediction time leads up to 72 h were assessed. The meteorological data, including wave height for one year, have been utilized and evaluated to design and validate the proposed model; the data obtained from buoys situated off the south-east coast of the US. The results demonstrated that the WPSO-ELM outperforms other models to predict the wave height in both hourly and daily lead times; in addition, the wavelet increased the accuracy of the prediction models, with the goal that coefficient of determination (R2), willmott's index of agreement (d), root mean square error (RMSE), and mean absolute error (MAE) were obtained for the lead time 12 h equivalent 0.794, 0.784, 0.374 m, and 0.297 m, respectively for the WPSO-ELM, and 0.643, 0.736, 0.495 m and 0.363 m, respectively for the PSO-ELM. Comparing the obtained results revealed the better performance of the WPSO-ELM model in predicting wave height for coastal and deep-sea regions up to 36 h' lead times.
Neural Re-rendering of Humans from a Single Image

Human re-rendering from a single image is a starkly underconstrained problem, and state-of-the-art algorithms often exhibit undesired artefacts, such as over-smoothing, unrealistic distortions of the body parts and garments, or implausible changes of the texture. To address these challenges, we propose a new method for neural re-rendering of a human under a novel user-defined pose and viewpoint, given one input image. Our algorithm represents body pose and shape as a parametric mesh which can be reconstructed from a single image and easily reposed. Instead of a colour-based UV texture map, our approach further employs a learned high-dimensional UV feature map to encode appearance. This rich implicit representation captures detailed appearance variation across poses, viewpoints, person identities and clothing styles better than learned colour texture maps. The body model with the rendered feature maps is fed through a neural image-translation network that creates the final rendered colour image. The above components are combined in an end-to-end-trained neural network architecture that takes as input a source person image, and images of the parametric body model in the source pose and desired target pose. Experimental evaluation demonstrates that our approach produces higher quality single-image re-rendering results than existing methods.
Aspect-Based Sentiment Analysis Using a Hybridized Approach Based on CNN and GA

Sentiment analysis is a computational analysis of unstructured textual data, used to assess the person's attitude from a piece of text. Aspect-based sentimental analysis defines the relationship among opinion targets of a document and the polarity values corresponding to them. Since aspects are often implicit, it is an extremely challenging task to spot them and calculate their respective polarity. In recent years, several methods, strategies and improvements have been suggested to address these problems at various levels, including corpus or lexicon-based approaches, term frequency and reverse document frequency approaches. These strategies are quite effective when aspects are correlated with predefined groups and may struggle when low-frequency aspects are involved. In terms of accuracy, heuristic approaches are stronger than frequency and lexicon based approaches, however, they consume time due to different combinations of features. This article presents an effective method to analyze the sentiments by integrating three operations: (a) Mining semantic features (b) Transformation of extracted corpus using Word2vec (c) Implementation of CNN for the mining of opinion. The hyperparameters of CNN are tuned with Genetic Algorithm (GA). Experimental results revealed that the proposed technique gave better results than the state-of-the-art techniques with 95.5% accuracy rate, 94.3% precision rate, 91.1% recall and 96.0% f-measure rate.
Fine-Grained Image Captioning With Global-Local Discriminative Objective

Significant progress has been made in recent years in image captioning, an active topic in the fields of vision and language. However, existing methods tend to yield overly general captions and consist of some of the most frequent words/phrases, resulting in inaccurate and indistinguishable descriptions (see Fig. 1). This is primarily due to (i) the conservative characteristic of traditional training objectives that drives the model to generate correct but hardly discriminative captions for similar images and (ii) the uneven word distribution of the ground-truth captions, which encourages generating highly frequent words/phrases while suppressing the less frequent but more concrete ones. In this work, we propose a novel global-local discriminative objective that is formulated on top of a reference model to facilitate generating fine-grained descriptive captions. Specifically, from a global perspective, we design a novel global discriminative constraint that pulls the generated sentence to better discern the corresponding image from all others in the entire dataset. From the local perspective, a local discriminative constraint is proposed to increase attention such that it emphasizes the less frequent but more concrete words/phrases, thus facilitating the generation of captions that better describe the visual details of the given images. We evaluate the proposed method on the widely used MS-COCO dataset, where it outperforms the baseline methods by a sizable margin and achieves competitive performance over existing leading approaches. We also conduct self-retrieval experiments to demonstrate the discriminability of the proposed method.