Reviewerly/exHarmony huggingface dataset:
Combining Oculo-motor Indices to Measure Cognitive Load of Synthetic Speech in Noisy Listening Conditions.

Gaze-based assistive technologies (ATs) that feature speech have the potential to improve the life of people with communication disorders. However, due to a limited understanding of how different speech types affect the cognitive load of users, an evaluation of ATs remains a challenge. Expanding on previous work, we combined temporal changes in pupil size and ocular movements (saccades and fixation differentials) to evaluate cognitive workload of two types of speech (natural and synthetic) mixed with noise, through a listening test. While observed pupil sizes were significantly larger at lower signal-to-noise levels, as participants listened and memorised speech stimuli; saccadic eye-movements were significantly more frequent for synthetic speech. In the synthetic condition, there was a strong negative correlation between pupil dilation and fixation differentials, indicating a higher strain on participants’ cognitive resources. These results suggest that combining oculo-motor indices can aid our understanding of the cognitive implications of different speech types.
PROBLEMS OF PATRIOTIC EDUCATION OF STUDENTS IN A HIGHER MEDICAL EDUCATIONAL INSTITUTION IN THE MODERN WORLD

At present, one of the priority directions in the higher education system is the fostering of patriotism among the younger generation of their homeland. It is the emergence and formation of patriotic feelings that sets new goals for teachers of educational institutions both on a regional and national scale. Among the many tasks of patriotic education, one can single out the main ones that require implementation in the framework of the interaction of information and analytical structures between students and government bodies through the introduction of various legislative acts. Having determined the goals and objectives, it is easy to build a certain algorithm of actions, structure or stages of activities necessary to form the civic position of the individual. However, at present, there are problems of various levels in the educational process of the formation of patriotic values of youth. Patriotism is also an indispensable condition for the formation of personality, an important component of the modern life of society. Without ideals and role models, without a sense of pride and dignity, love for their country, any society will inevitably come to moral decline and self-destruction. Only joint efforts of the family, the teaching staff and the state in matters of upbringing the younger generation will be able to ensure the full development of the individual with a clear civic position.
A New Dataset for Natural Language Understanding of Exercise Logs in a Food and Fitness Spoken Dialogue System

Health and fitness are becoming increasingly important in the United States, as illustrated by the 70% of adults in the U.S. that are classified as overweight or obese, as well as globally, where obesity nearly tripled since 1975. Prior work used convolutional neural networks (CNNs) to understand a spoken sentence describing one's meal, in order to expedite the meal-logging process. However, the system lacked a complementary exercise-logging component. We have created a new dataset of 3,000 natural language exercise-logging sentences. Each token was tagged as an Exercise, Feeling, or Other, and mapped to the most relevant exercise, as well as a score of how they felt on a scale from 1 to 10. We demonstrate the following: for intent detection (i.e., logging a meal or exercise), logistic regression achieves over 99% accuracy on a held-out test set; for semantic tagging, contextual embedding models achieve 93% F1 score, outperforming conditional random field models (CRFs); and recurrent neural networks (RNNs) trained on a multiclass classification task successfully map tagged exercise and feeling segments to database matches. By connecting how the user felt while exercising to the food they ate, in the future we may provide personalized and dynamic diet recommendations.
A Subtype Classification of Hematopoietic Cancer Using Machine Learning Approach

Hematopoietic cancer is the malignant transformation in immune system cells. This cancer usually occurs in areas such as bone marrow and lymph nodes, the hematopoietic organ, and is a frightening disease that collapses the immune system with its own mobile characteristics. Hematopoietic cancer is characterized by the cells that are expressed, which are usually difficult to detect in the hematopoiesis process. For this reason, we focused on the five subtypes of hematopoietic cancer and conducted a study on classifying by applying machine learning algorithms both contextual approach and non-contextual approach. First, we applied PCA approach for extracting suited feature for building classification model for subtype classification. And then, we used four machine learning classification algorithms (support vector machine, k-nearest neighbor, random forest, neural network) and synthetic minority oversampling technique for generating a model. As a result, most classifiers performed better when the oversampling technique was applied, and the best result was that oversampling applied random forest produced 95.24% classification performance.
Methodological Challenges of Creating a Next-Generation Machine Learning-Based Game Engine for Generating Maps and Vehicle Behavior

The article presents conceptual work in the area of technological challenges for the construction of a next-generation racing game engine. The team creating the game decided to implement the project based on the Unreal solution. The main innovation of the product is to be: realism of driving experience and the use of machine learning elements to improve work under the engine. In terms of realism, the team will make real measurements using race vehicle telemetry devices. The Machine Learning component is intended for two purposes as a map detailing element and for setting the parameters of physics of objects in the game. The paper aims to present the ideal project idea to the critical analysis and thus improve the concept.
Secured Data Encryption and Decryption for Transmission in Device Independent Systems

In general, a company will have its branches at different locations and every branch will have different hardware and software configurations. Different configurations have different issues in transferring the data from various branches to corporate office and vice versa. To overcome the challenge, this application remains as an alternative solution. Data transferred through the network remains insecure due to its plain text format. Regarding the data restoration, it becomes incompatible for software as well as hardware. For security concern, the security process handling in data encryption and data decryption using cryptic service provider for RSA encryption and decryption using public and private key. The keys were stored in server as a XML format. This encryption is an asymmetric encryption. This data also accessed through device independent system like mobile, PDA, etc. The quality of file to trace in progress prominence of the file which users works on and after getting the files, the file will be sent to quality analysis person and it will be obtained back with the status before restoring a database. The quality of file will be checked and sent back to the administrator. The unsatisfactory files are then sent back to client for rectification.
Direct-imaging DOEs for high-NA multi-spot confocal surface measurement

Abstract Diffractive lens arrays with overlapping apertures can produce spot arrays with high numerical apertures (NAs). Combined with low-NA objectives, they can measure a large area with high lateral resolution. However, for surface measurements, the axial resolution of such setups is still fundamentally limited by the objectives. In this work, we propose a new design of diffractive optical elements (DOEs) to overcome this problem. The proposed Direct-imaging DOEs can perform 3D high-NA multi-spot surface measurements. Laterally, a non-vanishing contrast up to 1448 lp/mm is measured with a USAF resolution target. Axially, an average height of 917.5 nm with a standard deviation of 49.9 nm is measured with a calibrated step height target of 925.5 nm.
An Implementation of Linear Temporal Logic for Driving Safety Suitable for the Concept of Local Dynamic Map

Safely driving on the road is a highly complicated information process for autonomous driving systems, which requires knowledge in the form of the hierarchical structure to bind them together. According to the past works on Cooperative Intelligent Transport Systems (C-ITS), the concept of Local Dynamic Map (LDM) is one of the critical technologies. It can coordinate environmental information with different time scales to update by mapping onto a consistent geometric structure in a hierarchy. A possible representation of the LDM concept can be associated with a relational database or semantic web technology such as Resource Description Framework (RDF) and Web Ontology Language (OWL). However, moving objects on the road can take infinitely many states. In this study, we propose an integration with Linear Temporal Logic (LTL) to represent events with potential risks to express them on LDM. In the computer experiment, risky situations were examined by using LDM update rules with LTL at turning and roundabout.
Implementation of Audio Image Steganography—A Technique to Secure Data

The modern era comes with a very crucial job of encrypting the important information before sharing it in order to avoid its misuse. Steganography is the most efficient way of encrypting the data and hiding it in a carrier audio or image file, with minimal increase in the size of the file, and sharing the data securely between two parties. The objective of this paper is to implement audio and image steganography, i.e., concealing of data within an audio file or an image file. The aim is to develop an application for encoding and decoding. Various cryptography techniques like Caesar cipher have been used to achieve steganography. MATLAB tool has been used to achieve the same.
Brief Analysis on Human Activity Recognition

Human activity recognition is regarded as one of the most prominent fields of exploration in computer science research. The process to elucidate human body movements to determine human gestures has been widely applied in surveillance, health assistance and interaction of human with computers. A variety of methodologies have been adopted by different researchers in this domain like wearable devices, device-free tools and object trackers to successfully recognize human gestures. This paper gives a brief analysis on processing of sensors data in HAR by using two deep neural network (DNN) models: Convolutional and recurrent neural network and summarizing their respective accuracies. The data being efficiently used to create the appropriate model is provided by the Wireless Sensor Data Mining (WSDM) lab. The data collection was done from 30 people using a wearable sensor and performing six different activities: (1) Sitting (2) Walking (3) Downstairs (4) Upstairs (5) Standing (6) Jogging and performing them with n number of repetitions. In the end, we discuss the approaches ‘Convolutional Neural Network’ and ‘Recurrent Neural Network’ and on the basis of their precision of how they recognize the activities and movements.
Comparison of Ensemble Kalman Filter, Unscented Kalman Filter, and Fractional Kalman Filter for estimating the concentration of CO and NO 2

Abstract Estimation distribution of air pollution is necessary to determine the level of pollution in a location so it can be used to recommend emission minimization. This research use some modification of Kalman Filter method for estimation distribution of air pollution. Three estimation methods namely Fractional Kalman Filter (FKF), Ensemble Kalman Filter (EnKF), Unscented Kalman Filter (UKF). The simulation result of three methods will be compared. Distribution of carbon monoxide and nitrogen dioxide in Surabaya was estimated using three methods. This reserach showed that Fractional Kalman Filter method is the best estimator among the others because error value is the smallest.
Curriculum-Oriented Multi-goal Agent for Adaptive Learning

Adaptive learning is an important part of Intelligent Tutoring System (ITS). Given that students have different learning targets and knowledge concepts proficiency, a smart intelligent tutor should be able to provide personalized learning materials to them, and help students master target knowledge and skills with learning materials as less as possible. Reinforcement Learning (RL) algorithms are good at solving sequence decision problems, so they are widely used in learning material recommendation. However, the existing intelligent tutoring systems based on reinforcement learning usually consider only one learning target. Moreover, the agent needs to learn in the case of sparse rewards, resulting in inefficient learning. To this end, we propose a curriculum-oriented multi-goal reinforcement learning method, which combines an off-policy RL algorithm with Hindsight Experience Replay (HER) to enable the agent to learn from past failed experiences to alleviate the problem of sparse rewards. Besides, our method is applicable to the case of multi-goal learning, and the agent learns specific strategy for each goal. Additionally, according to different learning stages of the agent, we set different learning pseudo goals adaptively for it to accelerate learning speed.
Becoming Blended

‘How Many Ways Can an Articulate Alien Analyse an Animated Robot?' is a performative, becoming-blended, active learning lecture that has evolved into one modelled on the principles of active blended learning. The author assembles the thinking of Gilles Deleuze, Felix Guattari, Paulo Freire, and bell hooks, amongst others, to create a discussion about how working with students actively, collaboratively, and in modes of blended delivery can enhance critical thinking and student engagement. The structure of this chapter echoes the way the lecture is organised, as in a three-act play. This enables a form of immersive experience, as the acts and actions of the lecture, dissected throughout the chapter, unfold.
Fine-Tuning BERT for COVID-19 Domain Ad-Hoc IR by Using Pseudo-qrels

This work analyzes the feasibility of training a neural retrieval system for a collection of scientific papers about COVID-19 using pseudo-qrels extracted from the collection. We propose a method for generating pseudo-qrels that exploits two characteristics present in scientific articles: a) the relationship between title and abstract, and b) the relationship between articles through sentences containing citations. Through these signals we generate pseudo-queries and their respective pseudo-positive (relevant documents) and pseudo-negative (non-relevant documents) examples. The article retrieval process combines a ranking model based on term-maching techniques and a neural one based on pretrained BERT models. BERT models are fine-tuned to the task using the pseudo-qrels generated. We compare different BERT models, both open domain and biomedical domain, and also the generated pseudo-qrels with the open domain MS-Marco dataset for fine-tuning the models. The results obtained on the TREC-COVID collection show that pseudo-qrels provide a significant improvement to neural models, both against classic IR baselines based on term-matching and neural systems trained on MS-Marco.
Attention-Based Multi-Branch Network for Low-Light Image Enhancement

Low-light conditions make the obtained images suffer a series of degradation, such as low contrast, noise interference and color distortion. Many previous learning-based methods have made remarkable progress, but they may still produce unsatisfactory results for ignoring noise in low-light regions. An attention-based multi-branch network is proposed, which can adequately enhance the image and suppress latent noise. The proposed method firstly estimates illumination component and reflectance component through a decomposition process. Then the illumination component is brightened to reconstruct the global lighting distribution, and the reflectance component is restored to remove noise and maintain details. A lightweight but effective attention block is employed to guide the restoration of the reflectance component, so as to concentrate on the distribution of lighting in different regions and effectively suppress noise in the dim environment. Extensive experiments on several datasets show the proposed method can achieve good results compared with classic and state-of-the-art methods.
Information - Theoretic Methods for Anomaly Detection

Maintaining the security of digital systems with a huge amount of data is one of the main concerns of IT specialists in these times. Anomaly detection in systems is one of the solutions to overcome this challenge. Anomaly detection means ¯nding patterns that are not normal or deviate from normal behavior in a system. Anomaly detection has various applications in bio-informatics, image processing, cyber security, security for databases, etc. There are many groups of methods that are used for anomaly detection including statistical methods, neural network methods and information theoretic methods. In this paper we survey pros and cons of anomaly detection based on information theoretic techniques
The effectiveness of implementing ANEKA-THK-based countenance evaluation application at IT vocational schools in Bali

Abstract Successful implementation of an application on a large scale depends on the effectiveness of the operational test results on the application. The main objective of this research was to demonstrate the effectiveness of the implementation of the ANEKA-THK (ANEKA-Tri Hita Karana)-based Countenance evaluation application at IT Vocational Schools in Bali. This research was development research with the development model was the Borg &amp; Gall that focuses on the operational test phase to obtain the effectiveness level of implementing the evaluation application. A total of 114 people were included as respondents in this research. Questionnaires were used as tools for assessment in the operational test by respondents. The analysis technique was used in this research was quantitative descriptive, so that the results were obtained in the form of an effectiveness percentage of the implementation of ANEKA-THK-based Countenance evaluation application was 88.67% from usage test results by experts and 89.93% by teachers and students.
Research on the Innovation of Education and Teaching Management Models in Colleges and Universities in the “Internet +” Era

Through questionnaire surveys, inductive deduction and other methods, this research conducts surveys of college students' online learning in the “Internet+” era, finds out the problems, analyzes the significance of “Internet+” to university innovative education and teaching, and starts from the “Internet+” era. The theory, educational mechanism, teacher team construction, and the exploration of educational teaching methods of the university's innovative education have explored and constructed an innovative management model for university education and teaching in the “Internet +” era.
Graph-Based Depth Estimation in a Monocular Image Using Constrained 3D Wireframe Models

In this paper, the problem of estimating the depth of an object from its monocular image is addressed. Here, basically, an algorithm is developed, which performs shape matching, and as a result, achieve accurate depth maps of objects. In the algorithm, first, an optimal camera position is determined. Then, the 3D model is projected onto the image plane of the camera, yielding a projected 2D image of the 3D model. An objective function determines a score based on graph-based feature matching, and the depth map is extracted from the geometrical information of the 3D model. Finally, the depth map and the original image are combined to create a 3D point cloud simulation of the object. Experimental analysis shows the efficacy of the proposed method.
Physics teachers’ perceptions and anxieties about the use of technology-integrated learning resources on magnetic field material: A preliminary research on augmented reality-integrated STEM learning

Abstract The educational world development faces the challenges of the 21 st -century where technology plays an important role in learning so that technology-integrated teaching material is needed. The subjects of this study were 48 physics subject teachers in several schools in Lampung province. The research method used was a mixed method. The data was collected using questionnaires and interviews. The results of the study revealed that the teachers have never made learning resources assisted by the integration between STEM and augmented reality technology. The teachers also revealed that they were anxious that the learning resources that they will use cannot fully meet the competency standards if they are not equipped with learning technology that supports the abstract concepts learning. Thus, the teachers expected technology-assisted learning resources in which texts, images, 3D animations, and videos can be integrated. For further researchers, it is necessary to develop STEM learning resources assisted by augmented reality technology.
Information Retrieval based Improvising Search using Automatic Query Expansion

Current search engines like Google, Yahoo give the search results, indeed users are facing problems in information retrieval. The main problem is because of word mismatch and availability of many resources. Petabytes of information is available because of Internet. From that huge available data, for a naïve user it becomes hectic to distinguish between relevant and irrelevant information to individual interest. Also, another reason of getting irrelevant information is incompatibility between terms that users are using and keywords present in documents. Query expansion is an adding keyword to the original query. The main issue in query expansion is selection of appropriate terms from user's original query. Vocabulary database helps to solve this issue. Identification of the similar words and language entities that are similar in meaning is done by vocabulary which is frequently incorporated in information retrieval system. Thesaurus has been used across a large area of in information retrieval also many applications and natural language processing. In this work, to improve performance of a search query, BM25 model is used for query expansion. Cosine similarity is used to determine similarity between two keywords. Rocchio algorithm is used to calculate the relevance feedback. Experimental result shows better results using Rocchio algorithm.
Multiplicative orders of Gauss periods and the arithmetic of real quadratic fields

We obtain divisibility conditions on the multiplicative orders of elements of the form ζ+ζ−1 in a finite field by exploiting a link to the arithmetic of real quadratic fields.
ScalSPARQL: A New Scalable system for the mapping of SPARQL Queries to NoSQL languages Based on MDE approach

In recent years, NoSQL databases have solved the problem of managing big data, on the other hand, RDF data has become increasingly used. SPARQL is the language used for querying RDF data. In this paper, we will present a new proposal for a system for mapping SPARQL queries to queries from NoSQL languages like Cassandra QL, MongoDB QL, etc. The idea of this technique is based on the principle of engineering metamodels, of which we first proposed the SPARQL metamodel. And secondly, we have proposed metamodels of NoSQL languages. The third part consists of carrying out a transformation between these two metamodels, the source metamodel of SPARQL and the target metamodel of NoSQL languages.