Reviewerly/exHarmony huggingface dataset:
Novel Based Ensemble Machine Learning Classifiers for Detecting Breast Cancer

Nowadays, for many industries, innovation revolves around two technological improvements, Artificial Intelligence (AI) and machine learning (ML). ML, a subset of AI, is the science of designing and applying algorithms that can learn and work on any activity from past experiences. Of all the innovations in the field of ML models, the most significant ones have turned out to be in medicine and healthcare, since it has assisted doctors in the treatment of different types of diseases. Among them, early detection of breast cancer using ML algorithms has piqued the interest of researchers in this area. Hence, in this work, 20 ML classifiers are discussed and implemented in Wisconsin’s Breast Cancer dataset to classify breast cancer as malignant or benign. Out of 20, 9 algorithms are coded using Python in Colab notebooks and the remaining are executed using the Waikato Environment for Knowledge Analysis (WEKA) software. Among all, the stochastic gradient descent algorithm was found to yield the highest accuracy of 98%. The algorithms that gave the best results have been considered in the development of a novel ensemble model and the same was implemented in both WEKA and Python. The performance of the ensemble model in both platforms is compared based on metrics like accuracy, precision, recall, and sensitivity and investigated in detail. From this experimental comparative study, it was found that the ensemble model developed using Python has yielded an accuracy of 98.5% and that developed in the WEKA has yielded 97% accuracy.
Detecting Anomaly Data for IoT Sensor Networks

The Internet of Things, or IoT, has been widely recognized as a new perception paradigm for interacting between the digital world and the physical one. Acting as the interface and integral part of the Internet of Things, sensors embedded within the network are the principal components that collect the unprocessed data, and these sensors are usually deployed in unattended, hostile, or harsh areas, which inevitably makes the sensor readings prone to faults and even anomalies. Therefore, the quality of sensor readings will ultimately affect the quality of various data-oriented IoT services, and the sensor data are of vital importance affecting the performance of the system. However, the data anomaly detection is a nontrivial task for IoT because sensors are usually resource-constrained devices with limited computing, communication, and capacity. Therefore, an efficient and lightweight detecting method is needed to meet the requirements. In this study, we deal with the anomaly data by detecting the source sensor nodes through combination methods of the local outlier factor and time series. Simulations show that the proposed method can effectively detect the anomaly data and presents a better normal data rate.
Novel content based medical image retrieval based on BoVW classification method

Amount of digital image is increasing exponentially nowadays. Storage requirements for these images increase from Gigabytes to Pet bytes. Searching and retrieving the relevant images from such larger volume of images datasets based on its contents plays dynamic role in various applications for computer vision. There are many common retrieval systems are there to provide solutions for this problem. Times taken to retrieve the images are high and accuracy of retrieved images is less in the existing systems. The limitation of Hadoop maps reduce is the lack of performing real time tasks efficiently. A proficient content-based image retrieval framework based on Spark Map Reduce with bag of visual word is to be proposed to perform with high accuracy for big data. The Apache spark programming can be utilized to productively recover pictures with less retrieval time and retrieve the accurate images from the big database that resemble the query image. The proposed system obtained an accuracy of 97.7% and it is better than the existing classification method for Retrieve the image from the large database.
Satisfaction Level of Engineering Students in Face-to-Face and Online Modalities under COVID-19—Case: School of Engineering of the University of León, Spain

University education in times of COVID-19 was forced to seek alternative teaching/learning methods to the traditional ones, having to abruptly migrate to the online modality, changes that have repercussions on student satisfaction. That is why this study aims to compare the level of student satisfaction in face-to-face and “forced” online modalities under COVID-19. A quantitative, cross-sectional methodology was applied to two groups of students: Under a face-to-face modality (n = 116) and under an online modality (n = 120), to which a questionnaire was applied under a Likert scale, with four dimensions: Course design structure, content, resources, and instructor. Non-parametric statistics, specifically the Mann–Whitney U-test, were used to compare the groups. The results showed that there are significant differences in the level of satisfaction of students in the face-to-face and online “forced” modalities (p = 0.01984 &lt; 0.05), and the dimensions of the level of satisfaction that presented significant differences were course design structure (p = 0.04523 &lt; 0.05) and content (p = 0.00841 &lt; 0.05). The research shows that students in the face-to-face modality express a higher level of satisfaction, which is reflected in the dimension design structure of the course, specifically in its workload indicator, as well as in the dimension content, in its indicators, overlapping with other courses and materials.
Methods to Improve the Efficiency of Rural Physical Education Teaching Resources Allocation and Utilization in the Context of Artificial Intelligence

School physical education is an important part of school education, and physical education resources are the overall measure of a school's physical education level. Rural schools are limited by environmental constraints, especially the lack of physical education resources verification. Therefore, the allocation and utilization efficiency improvement of rural physical education resources are an important element of rural physical education construction. With the mature development of artificial intelligence technology, it has great potential for application in the field of cultural education. Artificial intelligence technology has the advantages of ease of use, strong self-adaptation, and excellent generalization ability, and it is selected for data processing in this paper. Using artificial intelligence technology, the study of sports informatization to help rural sports development and rational utilization of resources can help rural sports education development, rural sports industry prosperity, rural sports talent revitalization, rural sports governance system improvement, and governance capacity enhancement. The artificial intelligence raindrop algorithm, which can automate the statistics of all relevant factors and derive results based on data fusion, was selected as the core algorithm of this paper. Therefore, this paper proposes an intelligent algorithm for allocating and improving the efficiency of rural sports teaching resources so as to improve the informatization level of the rural sports industry, promote industrial integration based on local needs, accelerate the "online urban-rural integration" in rural sports education, rural fitness guidance, and rural recreation and medical care, and rely on multiple entities. We will support the new model of sports informatization to empower rural sports teaching development by relying on the collaboration of multiple entities.
Guidelines for evaluating wearables’ quality of experience in a mulsemedia context

Abstract Quality of Experience (QoE) is inextricably linked to the user experience of multimedia computing and, although QoE has been explored in relation to other types of multimedia devices, thus far its applicability to wearables has remained largely ignored. Given the proliferation of wearable devices and their growing use to augment and complement the multimedia user experience, the need for a set of QoE guidelines becomes imperative. This study meets that need and puts forward a set of guidelines tailored exclusively towards wearables’ QoE. Accordingly, an extensive experimental investigation has been undertaken to see how wearables impact users’ QoE in multiple sensorial media (mulsemedia) context. Based on the exploratory study, the findings have shown that the haptic vest (KOR-FX) enhanced user QoE to a certain extent. In terms of adoption, participants reported they would generally incorporate the heart rate (HR) monitor wristband (Mio Go) into their daily lives as opposed to the haptic vest. Other findings revealed that human factors play a part in user’s attitudes towards wearables and predominantly age was the major influencing factor. Moreover, the participants’ HR varied throughout the experiments, suggesting an enhanced level of engagement whilst viewing the multimedia video clips. Furthermore, the results suggest that there is a potential future for wearables, if the QoE is a positive one and if the design of such devices are appealing as well as unobtrusive.
Edge data integrity verification scheme supporting data dynamics and batch auditing

With the rapid development of 5G technology and wireless networks, new applications such as VR/AR-based immersive online games have developed rapidly. These applications are very sensitive to network delays. To address the time delay of data transfer, the vendors of these delay-sensitive applications often deploy data replicas on edge servers to provide users with a low-latency application experience. However, the edge computing environment is highly distributed and dynamic, which makes the data replicas stored on edge servers often face intentional or unintentional damage. In addition, the computing resources of edge servers are very limited compared to cloud servers. Therefore, how to efficiently audit the integrity of data replicas stored on a large number of edge servers becomes an urgent problem to be solved. This paper first designs a data integrity verification scheme called EDI-DA in edge environment to help application vendors to check the integrity of data replicas stored on distributed edge servers. Then, we extend EDI-DA to allow application vendors to verify the integrity of different edge data replicas simultaneously, i.e., our EDI-DA supports batch auditing of multiple original data. Next, we propose an improved data structure I-SLT based on which EDI-DA supports full data dynamics, including insertion, deletion and modification. Finally, security and performance analyses indicate the security and practicability of our EDI-DA.
Detecting explicit lyrics: a case study in Italian music

Abstract Preventing the reproduction of songs whose textual content is offensive or inappropriate for kids is an important issue in the music industry. In this paper, we investigate the problem of assessing whether music lyrics contain content unsuitable for children (a.k.a., explicit content). Previous works that have computationally tackled this problem have dealt with English or Korean songs, comparing the performance of various machine learning approaches. We investigate the automatic detection of explicit lyrics for Italian songs, complementing previous analyses performed on different languages. We assess the performance of many classifiers, including those–not fully exploited so far for this task–leveraging neural language models, i.e., rich language representations built from textual corpora in an unsupervised way, that can be fine-tuned on various natural language processing tasks, including text classification. For the comparison of the different systems, we exploit a novel dataset we contribute, consisting of approximately 34K songs, annotated with labels indicating explicit content. The evaluation shows that, on this dataset, most of the classifiers built on top of neural language models perform substantially better than non-neural approaches. We also provide further analyses, including: a qualitative assessment of the predictions produced by the classifiers, an assessment of the performance of the best performing classifier in a few-shot learning scenario, and the impact of dataset balancing.
Gaze-Based Interaction Intention Recognition in Virtual Reality

With the increasing need for eye tracking in head-mounted virtual reality displays, the gaze-based modality has the potential to predict user intention and unlock intuitive new interaction schemes. In the present work, we explore whether gaze-based data and hand-eye coordination data can predict a user’s interaction intention with the digital world, which could be used to develop predictive interfaces. We validate it on the eye-tracking data collected from 10 participants in item selection and teleporting tasks in virtual reality. We demonstrate successful prediction of the onset of item selection and teleporting with an 0.943 F1-Score using a Gradient Boosting Decision Tree, which is the best among the four classifiers compared, while the model size of the Support Vector Machine is the smallest. It is also proven that hand-eye-coordination-related features can improve interaction intention recognition in virtual reality environments.
Developing a new model in solar radiation estimation with genetic algorithm method

Abstract In this study, a new model was developed to estimate solar radiation using the genetic algorithm method in the MATLAB program. When the literature was reviewed, it is observed that there are a lot of different methods used to estimate solar radiation. In the literature review, it was seen that the genetic algorithm approach was not used in solar radiation estimation before. In this study, the coefficients of the Angstrom equation were calculated through a genetic algorithm by using the 5‐year solar radiation data of Adana province, and accordingly, a new model was developed. The suggested model was compared to various models published in the literature for determining the solar radiation density of the given region. It was evaluated that the new developed model using a genetic algorithm has acceptable performance for estimating solar radiation.
On the digitalisation of higher education in times of the pandemic crisis: techno-philic and techno-sceptic attitudes of social science students in Crete (Greece)

Drawing upon empirical evidence from a research carried out at the University of Crete, this paper investigates the social sciences students' perception towards their experiences regarding the emergency online learning amidst the COVID-19 pandemic. The study reveals aspects of digital divides, as well as divergent perceptions of students, ranging from techno-philic attitudes that enthusiastically welcome the pivot to online learning, to ambivalent opinions expressing moderate satisfaction and techno-sceptic views, criticising pandemic pedagogies through digitalised forms of learning. The analysis sheds light on the contextual factors associated with the dystopic condition of the protracted economic crisis and the pandemic, that lie behind the claims of many students, revealing a main tension between contrasting perceptions of digital education. Students with positive attitudes towards online learning tend to highlight the advantages in regard to time and space flexible modalities of digital education, embracing it as an inclusive practice that responds to the social and educational needs of students, especially at times of crisis. Techno-sceptic attitudes criticise online learning models fοr lowering the academic standards, separating students from the real-life world on campus and repositioning them in digital settings where common physical experience, affective body language and classroom socialisation are missing. The article offers insight in the ongoing discussion of the emergency remote learning and underlines the political and pedagogical significance of the accelerating digitalisation of the universities in the post-COVID era.
Leveraging fusion of sequence tagging models for toxic spans detection

The upsurge of prolific blogging and microblogging platforms enabled the abusers to spread negativity and threats greater than ever. Negative and hateful comments are averting users from sharing their opinion freely on social media platforms. It often breaks people’s confidence and causes extensive damage to their mental health. Hence, identifying these toxic contents and taking appropriate measures against them is crucial to preserve a safe environment on social media. Numerous state-of-the-art approaches classify the whole content as toxic or non-toxic, but they don’t distinguish the precise toxic portion from the whole content. Detecting the toxic portions is essential as it substantially aids to moderate the toxic contents through excluding the abusive parts. This paper describes our proposed approach to detect the toxic portions from text contents efficiently and accurately. We explore an ensemble of sequence labeling models including the word embedding-based Spark NLP NER (named entity recognition) deep learning model, spaCy NER model with custom toxic tags, and ALBERT NER model to identify the toxic spans. The NER-based models usually intend to capture the contextual attributes of phrases and spans that are essential for named entity recognition. As the toxic span detection task also requires us to apprehend the phrasal context for detecting toxic span, the similarity between these two tasks inspires us to exploit these NER models. Finally, we determine the final toxic spans using a prevalence-based fusion of the predictions generated by these models. The fusion strategy enables us to consolidate the diversity of these models for perceiving the phrasal context in all aspects. Experimental results achieved on the SemEval-2021 toxic spans detection dataset depict that our model meticulously captures the toxic fragment and achieves a competitive result among the other state-of-the-art methods.
A pose-aware dynamic weighting model using feature integration for driver action recognition

Traffic accidents caused by distracted driving are on the rise, posing a serious threat to the safety of people’s lives and property. Recognition and early warning of the driver’s actions is particularly important. Considering the differences in local details of driver actions, we use the keypoint information of drivers that reflects the category differences. Specifically, we explicitly model keypoints features and propose a pose-aware driver action recognition model. We design a pose-based feature fusion module incorporating the attention mechanism, to fuse the global features and keypoint features of different scales in driving images. In addition, we propose an input-dependent weighting module to enhance the discrimination of the fused features. We use dynamic convolution to apply channel attention to convolution weights. According to the input driving image, the corresponding weights are adaptively generated for multiple convolution kernels, and the final weighted summation is carried out. This is a soft gate scheme, which opens a new perspective for driver action recognition. The proposed model achieves 90.7% and 95.3% accuracy on StateFarm dataset and SEU-Driving dataset, which are improved by 1.4% and 3.1% respectively compared to SOTA (MSA-CNN).
Semi-supervised Knowledge Distillation for Tiny Defect Detection

Image anomaly detection can automatically detect defects using images of products, which is crucial for product quality controls. Because of insufficient abnormal data, unsupervised image anomaly detection based on knowledge distillation has attracted broad attention recently. However, fully unsupervised methods suffer from detecting tiny anomalies that widely exist in industrial products because the features of tiny anomalies and normal features extracted by the teacher network are similar. This paper extends current unsupervised anomaly detection methods into a semi-supervised manner, simultaneously leveraging normal data and a limited amount of abnormal data. An automobile plastic parts dataset is established to prove the effectiveness of the proposed method. Experiments show that the proposed method can accurately detect small anomalies and largely surpass a powerful baseline (6% in AU-ROC, 10% in F1-score, 11% in Accuracy).
Engagement by Design: A Card-based approach to design crowdsourcing initiatives

Nowadays, applications that use crowdsourcing are becoming more and more widespread. However, for the solution to be successful, it needs to go beyond usage, users must be engaged with them. User’s engagement (UE) is the quality of user experience in applications that highlights the good aspects of their interaction and failing at engage users can be crucial to a system. We have developed an 18-cards deck that provides domain-specific insights for designers on user engagement in crowdsourcing initiatives. We have applied the cards in three online design workshops, offer participants insights, findings that were lifted in the workshop, and discuss how they support to advance of the design research and design practice in crowdsourcing initiatives in the context of engagement.
Factors influencing the acquisition of English skills in an English learning environment using Rain Classroom

Mobile technologies are widely used in education, both as standalone tools and in conjunction with other applications. The WeChat-based app Rain Classroom is one such application. Rain Classroom includes learning activities not only during class but also before and after class. Previous research has suggested strong demand for WeChat-based apps that can facilitate learning. However, relevant studies have not empirically evaluated the effect of Rain Classroom on English learning performance. This study examined the effects of using Rain Classroom for English learning. A total of 152 students were divided into control and experimental groups, each with 76 students. The experimental group had significantly better English reading, writing and listening scores than the control group did. The effects of the perceived features of Rain Classroom and learner satisfaction on learning performance were also investigated. Both factors had a significant positive influence on learning performance. The Rain Classroom has been proven to be an effective English learning tool. On the basis of the study findings, relevant implications are offered.
Interactive Visual Exploration of Big Relational Datasets

We propose and implement a tool for the access, analysis, and visual exploration of very large relational databases and data warehouses. To this end, we represent the database as a graph in which each table is represented by a tree rooted at the key and in which there is an edge from table T to table T' if there is an attribute of T which is a foreign key of T'. The interface allows the user to define analytic queries by clicking on nodes and edges of the graph. The user actions are then translated into SQL group-by queries that are evaluated by the underlying database management system. Additionally, the interface allows the user to visualize the analysis results, and to interact with visualizations in order to explore the database content.