Reviewerly/exHarmony huggingface dataset:
Security of Mobile Multimedia Data:The Adversarial Examples for Spatio-temporal Data

With the rapid development of the internet, the number of mobile multimedia devices is increasing among people. As the popularity of these mobile devices, a large amount of multimedia data is produced. How to utilize these multimedia data is essential in the wireless communication area. In recent years, deep learning has achieved good performance in Multimedia data analysis. However, recent research shows that deep learning models are vulnerable to the crafted samples. The crafted samples are called adversarial examples. In the paper, we explore the security of deep learning in mobile multimedia computing systems. We use the Spatio-temporal data (GPS trajectory data) to conduct experiments. The deep learning networks attacked are trajectory mode classification models. Since both Spatio-temporal data and image data are continuous, we adopt the algorithms of Computer Vision (Fast Gradient Sign Method (FGSM)) to generate adversarial examples. We propose an Adversarial Sample generation model based on a Convolutional AutoEncoder (ASCAE). Firstly, we design a convolutional AutoEncoder (CAE) to convert trajectory data into image format data. Then, we utilize image format data to train trajectory mode classification models. Based on image format data and the trained models, we use FGSM algorithm to generate adversarial examples. Finally, we utilize adversarial examples to attack the trained models. Experiment results demonstrate that the adversarial examples successfully attack the deep learning models. Besides, the adversarial examples have good transferability. Therefore, in mobile multimedia computing systems, adversarial examples are a serious data security problem for deep learning models.
The Linguistic Characteristics and Functions of Hashtags: #Is it a New Language?

Defined as a form of tagging that allows social media users to embed metadata in their posts, hashtags initially served to categorize topics and make them searchable online.Originating first on Twitter in 2007, hashtags have spread to other platforms, such as Instagram, Facebook, and Youtube.In addition to functioning as topic markers, hashtags have developed more complex linguistic functions.The ubiquity of this feature in the online medium, which now occupies a significant portion of our everyday communication is thus worthy of investigation.Although this topic has been researched in different disciplines, such as information diffusion, marketing, as well as sociology and public opinion, hashtags have not yet received enough attention from linguistic research.Using a sample of hashtags from a corpus of Instagram posts by Egyptian and Arab participants, this research thus aims to examine the characteristics of hashtags from a linguistic perspective, with particular focus on hashtags in the Arabic language.The study primarily seeks to determine the morpho-syntactic features of these recently emerging linguistic items according to the taxonomy proposed by Caleffi (2015).It also explores the pragmatic functions of hashtags based on Zappavigna's (2015) view of hashtags as technologically discursive tools.The analysis points out that most of the hashtags in the data serve the experiential function and come as suffixes.The findings reveal both similarities and differences between English and Arabic hashtags.
An Integrated System for Occupational Category Classification based on Resume and Job Matching

Resume plays an important role in the recruitment process; it decides the candidate's first impression on the recruiter. However, according to recent studies, resume analysis is still a time-consuming task. The primary focus of the model proposed – Smart Resume Selector (SRS) in this paper is to automatically choose the right candidate for a job profile which reduces the efforts, time, and extra cost spent. The SRS model proposed in this paper first segments a resume to extract vital information. The resume is then converted into tokens which are compared with the defined array of information and skill set as per the company’s requirement. An overall score is calculated for each resume and its area of expertise out of the basic 5 occupational categories. After performing the tests on over 200 resumes collected from datasets from Indeed, LinkedIn, AngelList and other datasets available online and comparing the desired output with the experimental output, an accuracy of 83.5% was achieved in the general category resumes and 89% in a specific category type. These findings will help to shortlist the candidate initially based on their resume for further process. It will automate the process of resume screening making it easy and time-efficient.
Design of Artificial Intelligence Education Program based on Design-based Research

Recently, the artificial intelligence(AI) is used in various environments in life, and research on this is being actively conducted in education. In this paper, we designed a Design-Based Research(DBR)-based AI programming education program and analyzed the application of the program for the improvement of understanding of AI in elementary school. In the artificial intelligence education program in elementary school, we should consider that it should be used in conjunction with software education through programming activities, rather than creating interest through simple AI experiences. The designed education program reflects the collaborative problem-solving procedures following the DBR process of analysis - design - execution - redesign, allowing the real-world problem-solving activities using AI experiences and block-type programming language. This paper also examined the examples of education programs to improve understanding of AI by using Machine Learning for Kids and to draw implications for developing and operating such a program.
Automatic Identification of Local Features Representing Image Content with the Use of Convolutional Neural Networks

Image analysis has many practical applications and proper representation of image content is its crucial element. In this work, a novel type of representation is proposed where an image is reduced to a set of highly sparse matrices. Equivalently, it can be viewed as a set of local features of different types, as precise coordinates of detected keypoints are given. Additionally, every keypoint has a value expressing feature intensity at a given location. These features are extracted from a dedicated convolutional neural network autoencoder. This kind of representation has many advantages. First of all, local features are not manually designed but are automatically trained for a given class of images. Second, as they are trained in a network that restores its input on the output, they may be expected to minimize information loss. Consequently, they can be used to solve similar tasks replacing original images; such an ability was illustrated with image classification task. Third, the generated features, although automatically synthesized, are relatively easy to interpret. Taking a decoder part of our network, one can easily generate a visual building block connected with a specific feature. As the proposed method is entirely new, a detailed analysis of its properties for a relatively simple data set was conducted and is described in this work. Moreover, to present the quality of trained features, it is compared with results of convolutional neural networks having a similar working principle (sparse coding).
Balancing Cost and Benefit with Tied-Multi Transformers

We propose a novel procedure for training multiple Transformers with tied parameters which compresses multiple models into one enabling the dynamic choice of the number of encoder and decoder layers during decoding.In training an encoder-decoder model, typically, the output of the last layer of the N -layer encoder is fed to the M -layer decoder, and the output of the last decoder layer is used to compute loss.Instead, our method computes a single loss consisting of N × M losses, where each loss is computed from the output of one of the M decoder layers connected to one of the N encoder layers.Such a model subsumes N × M models with different number of encoder and decoder layers, and can be used for decoding with fewer than the maximum number of encoder and decoder layers.Given our flexible tied model, we also address to a-priori selection of the number of encoder and decoder layers for faster decoding, and explore recurrent stacking of layers and knowledge distillation for model compression.We present a cost-benefit analysis of applying the proposed approaches for neural machine translation and show that they reduce decoding costs while preserving translation quality.
Differential evolution detection models for SMS spam

With the growth of mobile phones, short message service (SMS) became an essential text communication service. However, the low cost and ease use of SMS led to an increase in SMS Spam. In this paper, the characteristics of SMS spam has studied and a set of features has introduced to get rid of SMS spam. In addition, the problem of SMS spam detection was addressed as a clustering analysis that requires a metaheuristic algorithm to find the clustering structures. Three differential evolution variants viz DE/rand/1, jDE/rand/1, jDE/best/1, are adopted for solving the SMS spam problem. Experimental results illustrate that the jDE/best/1 produces best results over other variants in terms of accuracy, false-positive rate and false-negative rate. Moreover, it surpasses the baseline methods.
A Tale of Two Universities: Electronic Resources Troubleshooting Comparisons

This article presents findings from a comparison of electronic resources troubleshooting tickets submitted during a one-year period at Texas A&M University and University of Tennessee Knoxville. The authors used a classification previously developed by Mark Hemhauser and Rebecca Goldfinger at University of Maryland, College Park to categorize the types electronic resources access problems reported at their respective institutions. The findings suggest that academic libraries deal with similar electronic resources access problems at a similar rate. The authors recommend adopting this classification to categorize types of electronic resources access problems profession-wide in order to increase efficiency in reporting and resolving electronic resources access problems.
The ADAPT System Description for the STAPLE 2020 English-to-Portuguese Translation Task

This paper describes the ADAPT Centre's submission to STAPLE (Simultaneous Translation and Paraphrase for Language Education) 2020, a shared task of the 4th Workshop on Neural Generation and Translation (WNGT), for the English-to-Portuguese translation task.In this shared task, the participants were asked to produce high-coverage sets of plausible translations given English prompts (input source sentences).We present our English-to-Portuguese machine translation (MT) models that were built applying various strategies, e.g.data and sentence selection, monolingual MT for generating alternative translations, and combining multiple nbest translations.Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Portuguese translation task.
A Critical Realist Perspective on Organizations’ Approach to Blockchain

Blockchain technology, a decentralized infrastructure validating tamper-resistant transactions, was introduced roughly one decade ago. Till today, numerous financial serviceproviders (FSPs) claim to have developed and test use cases as well as prototypes of blockchain-based solutions. However, hitherto organizations do not scale their existing solutions. As a result, the actual business value of blockchain falls short of the expected business value of the technology. Information systems (IS) researchers increasingly criticize this situation and develop recommendations for FSPs how to foster the adoption of the technology, thereby trying to realize their dream of a fully decentralized blockchain-economy. This paper asserts, however, that despite having good intentions, IS researchers currently neglect an essential characteristic of blockchain technology, i.e., equivocality. IS researchers should consider that FSPs apply the technology not only in different ways but also because of a plethora of reasons and causes, which is why we cannot treat FSPs as equal based on the assumption that they are all ‘adopters’. Using a critical realist perspective, we found divergences between FSPs’ actions taken toward the adoption of blockchain technology, i.e., what they do, and why and how they adopt the technology. Our results disclose the need to develop more differentiated recommendations that take into account the impact of sensemaking activities as well as the divergent motives of FSPs which induce them to adopt blockchain technology.
Estimation of Effort in Software Projects using Genetic Programming

Software effort estimation refers to the estimation of effort that is required in given software project.It starts at the proposal stage and can sometimes continue till the last stages of a software project.Projects normally have a budget, and continual cost estimation is necessary to ensure that spending is in line with the budget.There is need of finding a good model which can establish an accurate relationship between the software a project and cost drivers.It is important for project managers and the researchers working in the domain to explore, analyze and understand the strengths and weaknesses of various software cost estimation methods.This paper focusses on using Genetic Programming for software effort estimation.The implementation involves evolution of individuals for obtaining best results over several generations.Metrics are chosen to evaluate the model based on the literature survey.Standard software engineering datasets are used in this project so that suitability and possible relations that arise could be realized.Kfold validation is used to sum up with more reliable values of evaluation.The design, implementation and result presentation are completed successfully and recorded clearly.
Automatically extracting features for face classification using multi-objective genetic programming

This paper proposes a new multi-objective feature extraction algorithm using genetic programming (GP) for face classification. The new multi-objective GP-based feature extraction algorithm with the idea of non-dominated sorting, which aims to maximise the objective of the classification accuracy and minimise the objective of the number of extracted features. The results show that the proposed algorithm achieves significantly better performance than the baseline methods on two different face classification datasets.
A Novel Fuzzy C-means Clustering Algorithm Based on Local Density

Fuzzy C-means (FCM) clustering algorithm is a fuzzy clustering algorithm based on objective function. FCM is the most perfect and widely used algorithm in the theory of fuzzy clustering. However, in the process of clustering, FCM algorithm needs to randomly select the initial cluster center. It is easy to generate problems such as multiple clustering iterations, low convergence speed and unstable clustering. In order to solve the above problems, a novel fuzzy C-means clustering algorithm based on local density is proposed in this paper. Firstly, we calculate the local density of all sample points. Then we select the sample points with the local maximum density as the initial cluster center at each iteration. Finally, the selected initial cluster center are combined with the traditional FCM clustering algorithm to achieve clustering. This method improved the selection of the initial cluster center. The comparative experiment shows that the improved FCM algorithm reduces the number of iterations and improves the convergence speed.
Definition Framework of Educational Process Construction Supported by an Intelligent Tutoring System

The motivation behind this paper is the need for educational processes that can be constructed and adapted to the needs of the learners, the preferences of the tutors, the requirements of the system administrators and the system designers of ITS. In this vein, the paper explores the theory to study the problem of educational process construction. This study introduces a new multi-level view of educational processes. Based on the proposed view, a faceted definition framework conducts a comparative study between different ITS to understand and classify issues in educational process construction.
Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Clustering algorithms can be used to build geographic mapping systems to determine crime-prone locations. This study aims to establish a geographical mapping system to determine crime-prone locations that can help police control certain locations that often occur crime and provide information to people in crime-prone locations. Criminal groups are calculated based on crime data from November 2018 to October 2019 which occurred in 9 districts in Kudus Regency. The crime grouping process uses the k-means method used to classify based on regional vulnerability and uses a weighted product method that functions as a vulnerability ranking that is vulnerable to crime selection. The grouping results obtained from this study are that there are 1 very vulnerable area, 5 areas in the vulnerable category, and 3 safe areas. While the weighted product method produces Melatilor area as a vulnerable area to be defeated by a score of 0.182093. This research provides benefits for the public to see crime-prone areas so that they can be more vigilant, while for the police to analyze crime so as to speed up the process of resolving crime and increase and improve crime prevention measures.
Developing Educational Technologies In School Technology Education

In the article, the author highlights the content of solving some practical topics in labor and vocational education in school technology education, such as mathematics, physics, biology, as well as practical issues related to agriculture and technology.
SENTIMENT ANALYSIS OF COSMETIC REVIEW USING NAIVE BAYES AND SUPPORT VECTOR MACHINE METHOD BASED ON PARTICLE SWARM OPTIMIZATION

Sentiment analysis is an area of ​​approach that solves problems by using reviews from various relevant scientific perspectives. Reading a review before buying a product is very important to know the advantages and disadvantages of the products we will use, besides reading a cosmetic review can find out the quality of the cosmetic brand is feasible or not be used. Before consumers decide to buy cosmetics, consumers should know in detail the products to be purchased, this can be learned from the testimonials or the results of reviews from consumers who have bought and used the previous product. The number of reviews is certainly very much making consumers reluctant to read reviews. Eventually, the reviews become useless. For this reason, the authors classify based on positive and negative classes, so consumers can find product comparisons quickly and precisely. The implementation of Particle Swarm Optimization (PSO) optimization can improve the accuracy of the Support Vector Machine (SVM) and Naïve Bayes (NB) algorithm can improve accuracy and provide solutions to the review classification problem to be more accurate and optimal. Comparison of accuracy resulting from testing this data is an SVM algorithm of 89.20% and AUC of 0.973, then compared to SVM based on PSO with an accuracy of 94.60% and AUC of 0.985. The results of testing the data for the NB algorithm are 88.50% accuracy and AUC is 0.536, then the accuracy is compared with the PSO based NB for 0.692. In these calculations prove that the application of PSO optimization can improve accuracy and provide more accurate and optimal solutions
OPPORTUNITIES AND CHALLENGES FOR EXTENDED REALITY TECHNIQUES IN HIGHER EDUCATION

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 481-490ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0207Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Traceable Attribute-Based Secure Data Sharing with Hidden Policies in Mobile Health Networks

The growing need to store, share, and manage medical and health records has resulted in electronic medical health sharing system (mHealth), which provides intelligent medical treatment for people. Attribute-based encryption (ABE) is regarded as a new cryptology to enhance fine-grained access control over encrypted sharing data in mHealth. However, some existing attribute-based mHealth systems not only violate the one-to-many application characteristics of attribute-based encryption mechanism but also destroy the anonymity of user. In this study, an efficient scheme is proposed to tackle the above defaults and offer two-way anonymity of data owner and data user by introducing a pseudoidentity. The computation of hidden access policy is reduced by removing the bilinear pairing, whereas the interaction between cloud storage and data user is avoided to save bandwidth during trapdoor generation. We also consider the temporal factor of the uploaded information by introducing access validity. Security and performance analyses show that the proposed scheme is efficient without reducing security.
Convolutional Neural Networks on FPGA and GPU on the Edge: A Comparison

When asked to implement a neural network application, the decision concerning what hardware platform to use may not always be easily made. This thesis studies various relevant platforms with regard ...
Use and users of a social science research data archive

This study focuses on the use and users of Finnish social science research data archive. Study is based on enriched user data of the archive from years 2015–2018. Study investigates the number and type of downloaded datasets, the number of citations for data, the demographics of data downloaders and the purposes data are downloaded for. Datasets were downloaded from the archive 10346 times. Majority of the downloaded datasets are quantitative. Quantitative datasets are also more often cited, but the number of citations vary and does not always correlate with the number of downloads. Use of the archive varies by user’s country, organization, and discipline. Datasets from the archive were downloaded most often for study work, bachelor’s and master’s theses, and research purposes. It is likely that reusing research data will increase in the near future as more data will become available, scholars are more informed about research data management, and data citation practices are established.
USDL: Inexpensive Medical Imaging Using Deep Learning Techniques and Ultrasound Technology

Abstract In this study, we present USDL, a novel model that employs deep learning algorithms in order to reconstruct and enhance corrupted ultrasound images. We utilize an unsupervised neural network called an autoencoder which works by compressing its input into a latent-space representation and then reconstructing the output from this representation. We trained our model on a dataset that compromises of 15,700 in vivo images of the neck, wrist, elbow, and knee vasculature and compared the quality of the images generated using the structural similarity index (SSIM) and peak to noise ratio (PSNR). In closely simulated conditions, the architecture exhibited an average reconstruction accuracy of 90% as indicated by our SSIM. Our study demonstrates that USDL outperforms state of the art image enhancement and reconstruction techniques in both image quality and computational complexity, while maintaining the architecture efficiency.