Reviewerly/exHarmony huggingface dataset:
Multiple Anchor Staged Alignment Algorithm – Sensitive (MASAA – S)

Sequence alignment is common nowadays as it is used in computational biology or Bioinformatics to determine how closely two sequences are similar. There are many computational algorithms developed over the course of time to not only align two sequences. The first algorithms developed were based on a technique called Dynamic Programming which rendered them slow but produce optimal alignment. Today, however heuristic approach algorithms are popular as they are faster and yet produce near optimal alignment. In this paper, we are going to improve on a heuristic algorithm called MASAA (Multiple Anchor Staged Local Sequence Alignment Algorithm) - which we published previously. This new algorithm appropriately called MASAA - S stands for MASAA Sensitive. The algorithm is based on suffix tree data structure to identify anchors first, but to improve sensitivity, we employ adaptive seeds, and shorter perfect match seeds in between the already identified anchors. When the Anchors are separated by a greater distance than a threshold 'd', we exclude such anchors. We tested this algorithm on a randomly generated sequences, and Rosetta dataset where the sequence length ranged up to 500 thousand.
Neurophysiological Effects of Presence in Calm Virtual Environments

Presence, the feeling of being there, is an important factor that affects the overall experience of virtual reality. Presence is measured through post-experience subjective questionnaires. While questionnaires are a widely used method in human-based research, they suffer from participant biases, dishonest answers, and fatigue. In this paper, we measured the effects of different levels of presence (high and low) in virtual environments using physiological and neurological signals as an alternative method. Results indicated a significant effect of presence on both physiological and neurological signals.
Selective personalization and group profiles for improved web search personalization

Personalization is a common technique used in Web search engines to improve the effectiveness of retrieval. While personalizing some queries yields significant improvements in user experience by providing a ranking in line with the user preferences, it fails to improve or even degrades the effectiveness for less ambiguous queries. A potential personalization metric could improve search engines by selectively applying personalization. One such measure, click entropy uses the query history and the clicked documents for the query, which might be sparse for some queries. In this article, the topic entropy measure is improved by integrating the user distribution into the metric, robust to the sparsity problem. Furthermore, a topic model-based ranking for the personalization method is proposed using grouped user profiles. Experiments reveal that the proposed potential prediction method correlates with human query ambiguity judgments and the group profile-based ranking method improves the mean reciprocal rank by 8%.
Refining and Measuring the Construct of Evaluative Thinking: An Exploratory Factor Analysis of the Evaluative Thinking Inventory

Background: Evaluative thinking has emerged as a key construct in evaluation, especially for evaluation practitioners and researchers interested in evaluation capacity building (ECB). Yet, despite increasing calls for more research on evaluation and, more specifically, for more research on ECB, until recently little empirical inquiry on the dimensions of evaluative thinking has been conducted.&#x0D; Purpose: To address that lack, the purpose of the study presented in this paper is to refine the construct of evaluative thinking by exploring its underlying dimensions and to ascertain the internal consistency of an instrument developed to measure evaluative thinking, the Evaluative Thinking Inventory (ETI).&#x0D; Setting: The ETI was developed as part of an ECB initiative focused on non-formal science, engineering, technology, and math (STEM) education in the United States, and was tested as part of a study focused on evaluating gifted education programs, also in the United States.&#x0D; Intervention: Not applicable.&#x0D; Research design: Survey research and exploratory factor analysis (EFA).&#x0D; Data collection &amp; analysis: The ETI was administered to participants in a study measuring the effectiveness of a tool used to conduct internal evaluations of gifted education programs. SPSS was used to conduct an EFA on 96 completed ETIs. Cronbach’s alpha was used to estimate the internal consistency of the instrument.&#x0D; Findings: The analysis of the ETI revealed a two-factor model of evaluative thinking (i.e., believe in and practice evaluation and pose thoughtful questions and seek alternatives). This study also provided internal consistency evidence for the ETI showing alpha reliabilities for the two factors ranging from 0.80 to 0.82. The ETI has potentially wide applicability in research and practice in ECB and in the field of evaluation more generally.&#x0D; Keywords: evaluative thinking; evaluation capacity building; research on evaluation; exploratory factor analysis.
Recommending Learning Objects with Arguments and Explanations

The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system’s operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results.
A Scene-to-Speech Mobile based Application: Multiple Trained Models Approach

The concept of Scene-to-Speech (STS) is to recognize elements in a captured image or a video clip to speak loudly an informative textual content that describes the scene. The contemporary progression in convolution neural network (CNN) allows us to attain object recognition procedures, in real-time, on mobile handled devices. Considerable number of applications has been developed to perform object recognition in scenes and say loudly their relevant descriptive messages. However, the employment of multiple trained deep learning (DL) models is not fully supported. In our previous work, a mobile application that can capture images and can recognize the objects contained in them was developed. It constructs descriptive sentences and speak them in Arabic and English languages. The notion of employing multi-trained DL models was used but no experimentation was conducted. In this article, we extend our previous work to perform required assessments while using multiple trained DL models. The main aim is to show that the deployment of multiple models approach can reduce the complexity of having one large compound model, and can enhance the prediction time. For this reason, we examine the prediction accuracy for single DL model-based recognition and multiple DL model-based recognition scenarios. The assessments results showed significant improvement in the prediction accuracy and in the prediction time. In the other hand, from the end user aspect, the application is designed primarily for visually impaired people to assist them in understanding their surroundings. In this context, we conduct a usability study to evaluate the usability of the proposed application with normal people and with visually impaired people. In fact, participants showed large interest in using the mobile application daily.
Inducing Schema.org markup from Natural Language Context

Schema.org creates, supports and maintain schemas for structured data on the web pages. For a non-technical author, it is difficult to publish contents in a structured format. This work presents an automated way of inducing Schema.org markup from natural language context of web-pages by applying knowledge base creation technique. As a dataset, Web Data Commons was used, and the scope for the experimental part was limited to RDFa. The approach was implemented using the Knowledge Graph building techniques - Knowledge Vault and KnowMore.
An optimise ELM by league championship algorithm based on food images

This paper presents an optimisation of extreme learning machine by league championship algorithm based on food images. Extreme Learning Machine (ELM) is an effective classifier because of the performance which is higher than other classifiers’ aspects. However, some important drawbacks still work as a hindrance like failure of optimal selection weights for the weights of the input-hidden layer and the output of the threshold. In spite of the wide number of problem-solving attempts, there was no solution to be considered effective. This paper presents the approach of hybrid learning and the League Championship Algorithm is used by for the purpose of selecting the input weights and the thresholds outputs. The experimental outcomes showed that the performance of proposed technique is superior as compared according to different scenarios of the measures to benchmark. The proposed method has achieved an overall accuracy of 95% for UEC food 100 dataset and 94% for UEC food 256 dataset comparing with 94% and 80% for baseline approaches.
Learning Representations from Spatio-Temporal Distance Maps for 3D Action Recognition with Convolutional Neural Networks

This paper addresses the action recognition problem using skeleton data. In this work, a novel method is proposed, which employs five Distance Maps (DM), named as Spatio-Temporal Distance Maps (ST-DMs), to capture the spatio-temporal information from skeleton data for 3D action recognition. Among five DMs, four DMs capture the pose dynamics within a frame in the spatial domain and one DM captures the variations between consecutive frames along the action sequence in the temporal domain. All DMs are encoded into texture images, and Convolutional Neural Network is employed to learn informative features from these texture images for action classification task. Also, a statistical based normalization method is introduced in this proposed method to deal with variable heights of subjects. The efficacy of the proposed method is evaluated on two datasets: UTD MHAD and NTU RGB+D, by achieving recognition accuracies91.63% and 80.36% respectively.
Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks

In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using combination of pre-trained convolutional neural network and an external GAN, that is, Pix2Pix conditional GAN, to determine the transformations between adversarial examples and clean data, and to automatically synthesize new adversarial examples. These adversarial examples are employed to strengthen the model, attack, and defense in an iterative pipeline. Our simulation results demonstrate the success of the proposed method.
The effect of brands’ social network content quality and interactivity on purchase intention: Evidence from Jordan

Social media has become one of the most effective tools for marketing and promotion of brands.Given this importance of social media, it is therefore critical for brands to develop quality content for their brand pages and frequently interact with consumers.Using theoretical lens of Stimulus-Organism-Response (S-O-R) paradigm, this research aims to study the impact of content quality and brand interactivity on purchase intention via mediation of brand awareness.To this end, we collected data from n = 282 millennial shoppers of sportswear to test hypothesized relationships.Our findings suggest that content quality and brand interactivity significantly impact purchase intention.It is further revealed that brand awareness mediates the relationship between content quality and purchase intention as well as brand interactivity and purchase intention.This research provides new theoretical insights into social media marketing and consumer behavior research by linking content quality and brand interactivity to purchase intention.
USING SOCIAL NETWORKS IN TEACHING ESP TO ENGINEERING STUDENTS

The present study examines students' and teachers' views on and attitudes to the use of different social networks in teaching English for specific purposes (ESP) to future engineers. It focuses on the types of work social networks can be used for, on language skills they help develop. The study was conducted with a survey method. The research was held at the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" and the sample was 170 full-time students of Energy Management and Energy Saving Institute and 60 lecturers of the Department of English for Engineering. It was conducted during December-February of 2019 – 2020 academic year. Within the context of the research, the students' and teachers' surveys were conducted. They were asked about their experience in using social networks and their application concerning university-related work and studies. The analysis of responses in the questionnaires showed that students and their teachers have a positive perception of using social media networks for English for specific purposes teaching in the classroom and outside. From the conducted research, we can conclude that using social networks is effective in ESP teaching and these advanced technologies can improve students' ESP proficiency, enhance their motivation for studying foreign languages, and can be used as corpora of authentic materials. The results of this research may be used for other studies concerning ESP teaching and technology application in this process.
Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability.

With regard to the wider area of AI/LT platform interoperability, we concentrate on two core aspects: (1) cross-platform search and discovery of resources and services; (2) composition of cross-platform service workflows. We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms. We illustrate the approach using the five emerging AI/LT platforms AI4EU, ELG, Lynx, QURATOR and SPEAKER.
Exploring NLP and Information Extraction to Jointly Address Question Generation and Answering

Question Answering (QA) and Question Generation (QG) have been subjects of an intensive study in recent years and much progress has been made in both areas. However, works on combining these two topics mainly focus on how QG can be used to improve QA results. Through existing Natural Language Processing (NLP) techniques, we have implemented a tool that addresses these two topics separately. We further use them jointly in a pipeline. Thus, our goal is to understand how these modules can help each other. For QG, our methodology employs a detailed analysis of the relevant content of a sentence through Part-of-speech (POS) tagging and Named Entity Recognition (NER). Ensuring loose coupling with the QA task, in the latter we use Information Retrieval to rank sentences that might contain relevant information regarding a certain question, together with Open Information Retrieval to analyse the sentences. In its current version, the QG tool takes a sentence to formulate a simple question. By connecting QG with the QA component, we provide a means to effortlessly generate a test set for QA. While our current QA approach shows promising results, when enhancing the QG component we will, in the future, provide questions for which a more elaborated QA will be needed. The generated QA datasets contribute to QA evaluation, while QA proves to be an important technique for assessing the ambiguity of the questions.
PneuModule: Using Inflatable Pin Arrays for Reconfigurable Physical Controls on Pressure-Sensitive Touch Surfaces

We present PneuModule, a tangible interface platform that enables users to reconfigure physical controls on pressure-sensitive touch surfaces using pneumatically-actuated inflatable pin arrays. PneuModule consists of a main module and extension modules. The main module is tracked on the touch surface and forwards continuous inputs from attached multiple extension modules to the touch surface. Extension modules have distinct mechanisms for user input, which pneumatically actuates the inflatable pins at the bottom of the main module through internal air pipes. The main module accepts multi-dimensional inputs since each pin is individually inflated by the corresponding air chamber. Also, since the extension modules are swappable and identifiable owing to the marker design, users can quickly customize the interface layout. We contribute to design details of inflatable pins and diverse pneumatic input control design examples for PneuModule. We also showcase the feasibility of PneuModule through a series of evaluations and interactive prototypes.
Research on Integrated Learning Fraud Detection Method Based on Combination Classifier Fusion (THBagging): A Case Study on the Foundational Medical Insurance Dataset

In recent years, the number of fraud cases in basic medical insurance has increased dramatically. We need to use a more efficient method to identify the fraudulent users. Therefore, we deploy the cloud edge algorithm with lower latency to improve the security and enforceability in the operation process. In this paper, a new feature extraction method and model fusion technology are proposed to solve the problem of basic medical insurance fraud identification. The feature second-level extraction algorithm proposed in this paper can effectively extract important features and improve the prediction accuracy of subsequent algorithms. In order to solve the problem of unbalanced simulation allocation in the medical insurance fraud identification scenario, a sample division method based on the idea of sample proportion equilibrium is proposed. Based on the above methods of feature extraction and sample division, a new training and fitting model fusion algorithm (tree hybrid bagging, THBagging) is proposed. This method makes full use of the balanced idea of the tree model algorithm based on Boosting to fuse, and finally achieves the effect of improving the accuracy of basic medical insurance fraud identification.
Vision-based patient identification recognition based on image content analysis and support vector machine for medical information system

Abstract In this paper, a vision-based patient identification recognition system based on image content analysis and support vector machine is proposed for medical information system, especially in dermatology. This proposed system is composed of three parts: pre-processing, candidate region detection, and digit recognition. To consider the efficiency of the proposed scheme, image normalization is performed. The color information is used to identify camera-captured screen images. In the pre-processing part, the effect of noise in captured screen images is reduced by a bilateral filter. The color and spatial information is used to initially and roughly locate the candidate region. To reduce the skew effect, a skew correction algorithm based on the Hough transform is developed. A template matching algorithm is used to find special symbols for locating the region of interest (ROI). For digit segmentation, digits are segmented in the ROI based on the vertical projection and adaptive thresholding. For the digit recognition, some features are measured from each digit segment and a classifier based on the support vector machine is applied to recognize digits. The experiment’s results show that the proposed system could effectively not only use color information to distinguish the captured screen images from the skin images but also detect the ROIs. After the digit segmentation, the accuracy rates of digit recognition are 98.4% and 94.2% for the proposed system and the Tesseract Optical Character Recognition (OCR) software, respectively. These results demonstrate that the proposed system outperforms the Tesseract OCR software in terms of the accuracy rate of digit recognition.
Speaker/Style-Dependent Neural Network Speech Synthesis Based on Speaker/Style Embedding

The paper presents a novel architecture and method for training neural networks to produce synthesized speech in a particular voice and speaking style, based on a small quantity of target speaker/style training data. The method is based on neural network embedding, i.e. mapping of discrete variables into continuous vectors in a low-dimensional space, which has been shown to be a very successful universal deep learning technique. In this particular case, different speaker/style combinations are mapped into different points in a low-dimensional space, which enables the network to capture the similarities and differences between speakers and speaking styles more efficiently. The initial model from which speaker/style adaptation was carried out was a multi-speaker/multi-style model based on 8.5 hours of American English speech data which corresponds to 16 different speaker/style combinations. The results of the experiments show that both versions of the obtained system, one using 10 minutes and the other as little as 30 seconds of target data, outperform the state of the art in parametric speaker/style-dependent speech synthesis. This opens a wide range of application of speaker/style dependent speech synthesis based on small quantities of training data, in domains ranging from customer interaction in call centers to robot-assisted medical therapy.
Towards field-of-view prediction for augmented reality applications on mobile devices

By allowing people to manipulate digital content placed in the real world, Augmented Reality (AR) provides immersive and enriched experiences in a variety of domains. Despite its increasing popularity, providing a seamless AR experience under bandwidth fluctuations is still a challenge, since delivering these experiences at photorealistic quality with minimal latency requires high bandwidth. Streaming approaches have already been proposed to solve this problem, but they require accurate prediction of the Field-Of-View of the user to only stream those regions of scene that are most likely to be watched by the user. To solve this prediction problem, we study in this paper the watching behavior of users exploring different types of AR scenes via mobile devices. To this end, we introduce the ACE Dataset, the first dataset collecting movement data of 50 users exploring 5 different AR scenes. We also propose a four-feature taxonomy for AR scene design, which allows categorizing different types of AR scenes in a methodical way, and supporting further research in this domain. Motivated by the ACE dataset analysis results, we develop a novel user visual attention prediction algorithm that jointly utilizes information of users' historical movements and digital objects positions in the AR scene. The evaluation on the ACE Dataset show the proposed approach outperforms baseline approaches under prediction horizons of variable lengths, and can therefore be beneficial to the AR ecosystem in terms of bandwidth reduction and improved quality of users' experience.