Reviewerly/exHarmony huggingface dataset:
Persuasive Apps for Sustainable Waste Management: A Comparative Systematic Evaluation of Behavior Change Strategies and State-of-the-Art

With the proliferation of ubiquitous computing and mobile technologies, mobile apps are tailored to support users to perform target behaviors in various domains, including a sustainable future. This article provides a systematic evaluation of mobile apps for sustainable waste management to deconstruct and compare the persuasive strategies employed and their implementations. Specifically, it targeted apps that support various sustainable waste management activities such as personal tracking, recycling, conference management, data collection, food waste management, do-it-yourself (DIY) projects, games, etc. The authors who are persuasive technology researchers retrieved a total of 244 apps from App Store and Google Play, out of which 148 apps were evaluated. Two researchers independently analyzed and coded the apps and a third researcher was involved to resolve any disagreement. They coded the apps based on the persuasive strategies of the persuasive system design framework. Overall, the findings uncover that out of the 148 sustainable waste management apps evaluated, primary task support was the most employed category by 89% (n = 131) apps, followed by system credibility support implemented by 76% (n = 112) apps. The dialogue support was implemented by 71% (n = 105) apps and social support was the least utilized strategy by 34% (n = 51) apps. Specifically, Reduction (n = 97), personalization (n = 90), real-world feel (n = 83), surface credibility (n = 83), reminder (n = 73), and self-monitoring (n = 50) were the most commonly employed persuasive strategies. The findings established that there is a significant association between the number of persuasive strategies employed and the apps' effectiveness as indicated by user ratings of the apps. How the apps are implemented differs depending on the kind of sustainable waste management activities it was developed for. Based on the findings, this paper offers design implications for personalizing sustainable waste management apps to improve their persuasiveness and effectiveness.
Cloud-Assisted Privacy-Preserving Method for Healthcare Using Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm

The security of medical data in the cloud is the key consideration of cloud customers. While publishing the medical data, the cloud distributor may suffer from data leakages and attacks such that the data may leak. In order to resolve this, this article devises the developed Adaptive Fractional Brain Storm Integrated Whale Optimization Algorithm (AFBS_WOA), which is the hybridization of Adaptive Fractional Brain Storm Optimization (AFBSO) and Whale Optimization algorithm (WOA). The developed AFBS_WOA algorithm generates the key matrix coefficient for retrieving the perturbed database in order to preserve the privacy of healthcare data in the cloud. The developed AFBS-WOA scheme utilized the fitness function involving utility and privacy measures for calculating the secret key. Here, the privacy-preserved database is obtained by multiplying the input database with a key matrix based on developed AFBS-WOA using the Tracy–Singh product. For data retrieval, the secret key is shared with the service provider in order to retrieve the database, and then the data are accessed. Moreover, the experimental result demonstrates that the developed AFBS_WOA model attained the maximum utility and privacy measure of 0.1872 and 0.8755 using the Hungarian dataset.
Deep Convolutional Neural Network for Handwritten Bangla and English Digit Recognition

As handwritten digit recognition is one of the most complex tasks in pattern detection for a machine, many works were done and many methods were applied to solve this problem. But, traditional methods have reached the final destination and deep learning techniques are now showing the new effective path in this field. This paper describes the application of artificial intelligence to work out the problem of handwritten English &#x0026; Bangla digit recognition. The most effective model called Convolutional Neural Network (CNN) is used in this work to detect English and Bangla handwritten digits. After building up this model which contains 19 layers, MNIST and NumtaDB data set of the handwritten digit is used for training. At the stage of testing, constructed CNN model shows the fruitful result for both English and Bangla handwritten digit recognition segments. The trained network is highly capable to give the expected result. The constructed CNN model gets 98.48&#x0025; accuracy for English handwritten digit recognition and 86.76&#x0025; accuracy for handwritten Bangla digit recognition.
Visual Perceptual Quality Assessment Based on Blind Machine Learning Techniques

This paper presents the construction of a new objective method for estimation of visual perceiving quality. The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption. Two main processes have been used to build our models: The first one uses deep learning with a convolutional neural network process, without any preprocessing. The second objective visual quality is computed by pooling several image features extracted from different concepts: the natural scene statistic in the spatial domain, the gradient magnitude, the Laplacian of Gaussian, as well as the spectral and spatial entropies. The features extracted from the image file are used as the input of machine learning techniques to build the models that are used to estimate the visual quality level of any image. For the machine learning training phase, two main processes are proposed: The first proposed process consists of a direct learning using all the selected features in only one training phase, named direct learning blind visual quality assessment DLBQA. The second process is an indirect learning and consists of two training phases, named indirect learning blind visual quality assessment ILBQA. This second process includes an additional phase of construction of intermediary metrics used for the construction of the prediction model. The produced models are evaluated on many benchmarks image databases as TID2013, LIVE, and LIVE in the wild image quality challenge. The experimental results demonstrate that the proposed models produce the best visual perception quality prediction, compared to the state-of-the-art models. The proposed models have been implemented on an FPGA platform to demonstrate the feasibility of integrating the proposed solution on an image sensor.
Neural Network Solution of Inverse Problems of Geological Prospecting with Discrete Output

The inverse problems of exploration geophysics are to reconstruct the spatial distribution of the properties of the medium in the Earth's thickness from the geophysical fields measured on its surface.In particular, this paper deals with the problems of gravimetry, magnetometry, and magnetotelluric sounding, as well as their integration, i.e., the simultaneous use of several geophysical fields to restore the desired distribution.To implement the integration, a 4-layer 2D model was used, where the inverse problem was to determine the lower boundary of the layers, and each layer was characterized by variable values of the depth of the lower boundary along the section and fixed values of density, magnetization, and resistivity, both for the layer and for the entire data set.To implement the neural network solution of the inverse problem, a data set was generated by solving the direct problem, where for each pattern, the distribution of layer depth values was set randomly in a given range and with a given step, i.e. it took discrete values from a certain set.In this paper, we consider an approach involving the use of neural networks to solve the problem of multiclass classification, where class labels correspond to discrete values of the determined layer depths.The results of the solution are compared with the results of the solution of the same inverse problem in the formulation of the regression problem, in terms of the error in determining the depth of the layers.
Credibility via Coupling

This study investigates Model Intercomparison Projects (MIPs) as one example of a coordinated approach to establishing scientific credibility. MIPs originated within climate science as a method to evaluate and compare disparate climate models, but MIPs or MIP-like projects are now spreading to many scientific fields. Within climate science, MIPs have advanced knowledge of: a) the climate phenomena being modeled, and b) the building of climate models themselves. MIPs thus build scientific confidence in the climate modeling enterprise writ large, reducing questions of the credibility or reproducibility of any single model. This paper will discuss how MIPs organize people, models, and data through institution and infrastructure coupling (IIC). IIC involves establishing mechanisms and technologies for collecting, distributing, and comparing data and models (infrastructural work), alongside corresponding governance structures, rules of participation, and collaboration mechanisms that enable partners around the world to work together effectively (institutional work). Coupling these efforts involves developing formal and informal ways to standardize data and metadata, create common vocabularies, provide uniform tools and methods for evaluating resulting data, and build community around shared research topics.
University Employment Quality Evaluation System Based on Multicriteria Decision and Data Analysis

In the educational sector, an evaluation index is required to draw up planning. The establishment of an evaluation index is useful to properly predict the employment quality of graduates. Such valuable indices help educational administrative departments to formulate talent training standards. Multicriteria decision making is a decision-making tool that can be used in the formulation of the evaluation index. This research work proposes an effective evaluation model to assess the employment quality of graduate students. The model uses 10 evaluation indicators which are considered to be the standard employment quality. The proposed evaluation method utilizes the entropy method and fuzzy comprehensive evaluation. Correlation between the employment quality evaluation index and employment quality is computed. The analytic hierarchy model is used to solve the weight of each employment quality evaluation index to the employment quality evaluation coefficient. According to the value characteristics of the 14 employment indicators, the expert method is used to assign scores to the sample data on each indicator. Thus, the indicator scores of the sample corresponding to the item are obtained. Through the evaluation of the employment quality of a certain university, the evaluation results are consistent with the actual employment quality of graduates. The employment quality evaluation model of college graduates established in this paper provides effective means and applications.
BLENDED LEARNING AS AN INNOVATIVE FORM OF THE EDUCATIONAL PROCESS ORGANIZATION IN HIGHER EDUCATION INSTITUTIONS

The article actualizes the problem of introduction blended learning in higher education institutions during the quarantine period caused by the spread of corona virus infection COVID-19. Today, the whole world is in the conditions of strict quarantine measures, and learning in isolation has become a challenge for all participants in the educational process.The authors reveal the essence of the concept "blended learning". It is proved that blended learning is an innovative form of the educational process organization in HEI, a qualitatively new approach that transforms the structure and content of education, changing the traditional roles of a teacher and applicants of higher education. It is noted that blended learning due to its versatility and availability of a wide range of learning tools forms different models. That’s why, to solve the problem of using a certain model in the educational process, the educational opportunities of each of them are considered. In addition, the authors note that blended learning makes it possible to combine digital educational resources with a variety of online services for the implementation of educational activities in higher education institutions. It is concluded that an important aspect of blended learning is the separation of educational material studied in the classroom with that which will be studied in the electronic informative-educational environment. It is also necessary clearly defined the activities that a student will perform, teaching methods in various forms of educational work organization, the system of monitoring, control and self-control. Blended learning combines two important components - knowledge, teacher skills and technological tools that allow you to organize learning at a high level.
Development of Augmented Reality Application for Onsite Inspection of Expressway Structures Using Microsoft HoloLens

This research introduces an augmented reality (AR)-based approach for the onsite inspection of expressway structures by developing an AR application, namely HoloSpector, deployed on the first generation of the Microsoft HoloLens headset. It was tested by a focus group of 10 postgraduate students, followed by three inspectors from the Expressway Authority of Thailand (EXAT), to investigate the practical applicability of the application. A questionnaire was employed as a research tool for measurement and assessment of the application and the HoloLens in this study. The results of this study indicated that the developed digital approach was satisfactory, easy to use and learn, useful, user-friendly and practical for EXAT expressway inspections; the users also intended to use it. Compared to the conventional approach, the current data communication and management could be significantly improved; this digital approach has the potential to help save resources, time and cost and increase work productivity.
Continuous Mobile User Authentication Using Combined Biometric Traits

Current state of the art authentication systems for mobile devices primarily rely on single point of entry authentication which imposes several flaws. For example, an attacker obtaining an unlocked device can potentially use and exploit it until the screen gets locked again. With continuous mobile user authentication, a system is embedded into the mobile devices, which continuously monitors biometric features of the person using the device, to validate if those monitored inputs match and therefore were made by the previously authenticated user. We start by giving an introduction towards the state of the art of currently used authentication systems and address related problems. For our main contribution we then propose, implement and discuss a continuous user authentication system for the Android ecosystem, which continuously monitors and records touch, accelerometer and timestamp data, and run experiments to gather data from multiple subjects. After feature extraction and normalization, a Hidden Markov Model is employed using an unsupervised learning approach as classifier and integrated into the Android application for further system evaluation and experimentation. The final model achieves an Area Under Curve of up to 100% while maintaining an Equal Error Rate of 1.34%. This is done by combining position and accelerometer data using gestures with at least 50 data points and averaging the prediction result of 25 consecutive gestures.
Social Presence as a Moderator of the Effect of Agent Behavior on Emotional Experience in Social Interactions in Virtual Reality

Background: Exposure therapy involves exposure to feared stimuli and is considered to be the gold-standard treatment for anxiety disorders. While its application in Virtual Reality (VR) has been very successful for phobic disorders, the effects of exposure to virtual social stimuli in Social Anxiety Disorder are heterogeneous. This difference has been linked to demands on realism and presence, particularly social presence, as a pre-requisite in evoking emotional experiences in virtual social interactions. So far, however, the influence of social presence on emotional experience in social interactions with virtual agents remains unknown. Objective: We investigated the relationship between realism and social presence and the moderating effect of social presence on the relationship between agent behavior and experienced emotions in virtual social interaction. Methods: Healthy participants ( N = 51) faced virtual agents showing supportive and dismissive behaviors in two virtual environments (short interactions and oral presentations). At first, participants performed five blocks of short one-on-one interactions with virtual agents (two male and two female agents per block). Secondly, participants gave five presentations in front of an audience of 16 agents. In each scenario, agent behavior was a within subjects factor, resulting in one block of neutral, two blocks of negative, and two blocks of positive agent behavior. Ratings of agent behavior (valence and realism), experience (valence and arousal), and presence (physical and social) were collected after every block. Moderator effects were investigated using mixed linear models with random intercepts. Correlations were analyzed via repeated measures correlations. Results: Ratings of valence of agent behaviors showed reliable relationships with experienced valence and less reliable relationships with experienced arousal. These relationships were moderated by social presence in the presentation scenario. Results for the interaction scenario were weaker but potentially promising for experimental studies. Variations in social presence and realism over time were correlated but social presence proved a more reliable moderator. Conclusion: Our findings emphasize the role of social presence for emotional experience in response to specific agent behaviors in virtual social interactions. While these findings should be replicated with experimental designs and in clinical samples, variability in social presence might account for heterogeneity in efficacy of virtual exposure to treat social anxiety disorder.
A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network

Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect.
Graph attention mechanism with global contextual information for multi-label image recognition

Recent works have shown that multi-label image recognition is still a challenging task in computer vision due to the complicatedness and diversity of multi-label images. However, the existing works ignore the co-occurrence correlation and global contextual information between image space and objects. We present a model to solve these problems. On the one hand, we devise the graph attention mechanism to compute the hidden representations of different categories in multi-label images. It can specify different weights to different neighbor objects and well model the label dependency. On the other hand, we iterate the global contextual information by the second-order covariance pooling to enhance nonlinear modeling capability and use basic residual network to extract features. The proposed model is thoroughly evaluated on PASCAL VOC 2007 and MS-COCO datasets. Compared with classical ML-GCN, the model can better combine the image features and label embedding. Meanwhile, experiments show that it outperforms the state-of-the-art methods such as residual multi-layer perceptron, EfficientNet, and vision transformer.
Sentiment Analysis Using Hybrid Approach

Abstract: Most important part of information gathering is to focus on how people think. There are so many opinion resources such as online review sites and personal blogs are available. In this paper we focused on the Twitter. Twitter allow user to express his opinion on variety of entities. We performed sentiment analysis on tweets using Text Mining methods such as Lexicon and Machine Learning Approach. We performed Sentiment Analysis in two steps, first by searching the polarity words from the pool of words that are already predefined in lexicon dictionary and in Second step training the machine learning algorithm using polarities given in the first step. Keywords: Sentiment analysis, Social Media, Twitter, Lexicon Dictionary, Machine Learning Classifiers, SVM.
WRITER IDENTIFICATION: THE EFFECT OF IMAGE RESIZING ON CNN PERFORMANCE

Abstract. Introducing Deep Learning has been successful in improving the performance of automated writer identification systems. However, using very large patch sizes as input to CNN consumes a lot of machine resources and requires a lot of training time. To overcome this problem, many researchers use resized images.In this paper, we will try to make a comparative study between several patches sizes which were then resized to a normalized size of 32 × 32. Our aim is to elaborate the best recommendations for choosing the image resizing in order to increase the CNN performance. Thus, we will carry our tests on three databases. The first is CVL, a Latin dataset with 310 writers, the second is CERUG-CH a Chinese dataset with 105 writers and the last is KHATT that contains the Arabic writings of 1000 writers. To see if the type of CNN model impacts the results conducted on resized images, we deploy two models: ResNet-18 and MobileNet. The main finding is that the best results correspond to the resizing values of the images which makes it possible to have the average line height of the writings closer to the height of the CNN patches.
Using animation to mediate scientific discourse

Interactive annotation and commenting tools provide a means to ground animations in experimental evidence and to support scientific discourse and progress.