Reviewerly/exHarmony huggingface dataset:
Design Thinking for Developing a Case-based Reasoning Emotion-Sensing Robot for Interactive Interview

As the application of design and technology has become more interdisciplinary and integrated, the development of interactive service robots (ISRs), which are designed according to unique situational requirements, has emerged as a popular trend. Research has shown that if affective computing technologies and machine learning mechanisms can be introduced to enhance interaction and feedback between ISRs and users, ISRs may be better aligned with both the service scenarios and the future development of innovative services. Based on an interdisciplinary integration framework, this study combined the concept and methodologies of design thinking, emotion detection technologies, and case-based reasoning (CBR), based on the use case of a simulated interview for empirical research, and developed a prototype emotion-sensing robot (ESR) system for the planning and testing of emotion sensing. Three emotion detection and analysis indicators, namely, happiness index of facial expressions, blink rate, and semantic emotions conveyed by the text on their resume, were proposed as the basis for analyzing emotional perception in this study. The experimental results were then used to analyze the effectiveness of the technologies as well as the value, utility, and affordance of the interactive interview bot system.
Efficient Search over Genomic Short Read Data

Modern DNA sequencing technology produces large volumes of genome strings for various biological and medical applications. To mitigate the space overhead required for storing these genome data, previous research has developed compression schemes to save storage resources and to improve the locality for bioinformatics applications. These approaches, however, typically need significant additional processing to support efficient genome string search, an important step for downstream applications in a bioinformatics pipeline. In this paper, we propose to store raw DNA sequence data in a compressed but searchable format, enabling efficient string lookups using database indexes. To build this format, we partition genome strings by computing hash-based minimizers to group overlapping strings together. We carefully optimize our hash function to avoid large buckets caused by repetitive sequences. These buckets are then effectively compressed using local constructed references, and an index is built upon them to guide exact match lookups. Both data compression and string lookups exploit the enhanced locality as a result of the partitioning. We implement storage and search functions over the storage format as a multithreaded library, and perform extensive experiments on real genome sequencing data. The results show that our approach efficiently executes genome string lookups over highly compressed raw read data.
Algorithm Based on KNN and Multiple Regression for the Missing-Value Estimation of Sensors

Missing sensor data are unavoidable when sensors are used to monitor a system. These missing data largely affect the sensor applications. When missing data exist, the best method is estimation. Herein, we introduce the k-nearest neighbor on multiple-regression algorithm (KMRA), which builds on the KNN and multiple regression. In the process of estimation, KMRA considers both spatial correlations from its neighbor sensor and time correlations from its own time serials. After computing these two correlations, the algorithm combines them into a unified result of estimation. As KMRA involves spatial and time correlations, it has the efficiency and practicability as an algorithm. Examination results show that KMRA can precisely estimate the missing data.
Proof-of-Burn.

Proof-of-burn has been used as a mechanism to destroy cryptocurrency in a verifiable manner. Despite its well known use, the mechanism has not been previously formally studied as a primitive. In this paper, we put forth the first cryptographic definition of what a proof-of-burn protocol is. It consists of two functions: First, a function which generates a cryptocurrency address. When a user sends money to this address, the money is irrevocably destroyed. Second, a verification function which checks that an address is really unspendable. We propose the following properties for burn protocols. Unspendability, which mandates that an address which verifies correctly as a burn address cannot be used for spending; binding, which allows associating metadata with a particular burn; and uncensorability, which mandates that a burn address is indistinguishable from a regular cryptocurrency address. Our definition captures all previously known proof-of-burn protocols. Next, we design a novel construction for burning which is simple and flexible, making it compatible with all existing popular cryptocurrencies. We prove our scheme is secure in the Random Oracle model. We explore the application of destroying value in a legacy cryptocurrency to bootstrap a new one. The user burns coins in the source blockchain and subsequently creates a proof-of-burn, a short string proving that the burn took place, which she then submits to the destination blockchain to be rewarded with a corresponding amount. The user can use a standard wallet to conduct the burn without requiring specialized software, making our scheme user friendly. We propose burn verification mechanisms with different security guarantees, noting that the target blockchain miners do not necessarily need to monitor the source blockchain. Finally, we implement the verification of Bitcoin burns as an Ethereum smart contract and experimentally measure that the gas costs needed for verification are as low as standard Bitcoin transaction fees, illustrating that our scheme is practical.
Robust Approach for Emotion Classification Using Gait

The ability to gauge emotion via their gait is a key component in enabling machines to understand people in images, videos and in real-time. Emotion recognition using machinery has been done through various methods. Facial Expressions; necessitates an obstructed view of the face, Psychological Parameters; require constant and prolonged monitoring of the subject, and Sentiment Analysis; demands text be written by that individual. However, in real-time situations, these features are not readily available which makes it difficult to classify emotion. Thus, this paper proposes a technique for classification of emotions without having to compromise on the accuracy of the detection while at the same time utilising the most trivial of information; walking. A University of York dataset of videos segregated emotion-wise was chosen to perform pose estimation through which important features were extracted. These features were processed in multiple machine learning classification models like; Decision Tree, Naive Bayes, SVM, K-Nearest Neighbours and Artificial Neural Network, to classify into five emotions. It was observed that this approach demonstrates a higher accuracy than most theories proposed in the past.
Perfect squares representing the number of rational points on elliptic curves over finite field extensions

Let q be a perfect power of a prime number p and E(Fq) be an elliptic curve over Fq given by the equation y2=x3+Ax+B. For a positive integer n we denote by #E(Fqn) the number of rational points on E (including infinity) over the extension Fqn. Under a mild technical condition, we show that the sequence {#E(Fqn)}n>0 contains at most 10200 perfect squares. If the mild condition is not satisfied, then #E(Fqn) is a perfect square for infinitely many n including all the multiples of 12. Our proof uses a quantitative version of the Subspace Theorem. We also find all the perfect squares for all such sequences in the range q<50 and n≤1000.
The Construction of Learning Resource Recommendation System Based on Recognition Technology

Under the guidance of educational informationize 2.0 and Chinese education modernization 2035 baton, teaching information resources will show exponential growth. It is difficult to manually process and recommend to learners including text, pictures, audio, video, and other rich media learning resources. This study makes an in-depth analysis of the existing recommendation system of learning resources, which shows that the existing recommendation system is unsatisfactory and unable to accurately recommend learning resources for learners. Therefore, it is urgent to establish a recommendation system for learning rich media resources based on recognition technology. In this study, we will analyze the key technologies and construct the learning resource recommendation system based on identification technology by combining the feature analysis of these technologies, to introduce the operation mode of this system in detail, finally summarizing the research of this paper and discussing the next step of the research.
PAC-Bayesian Meta-learning with Implicit Prior and Posterior

We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a prior distribution of the model of interest. Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot learning setting to upper-bound generalisation errors on unseen tasks and samples. We also propose a generative-based approach to model the shared prior and the posterior of task-specific model parameters more expressively compared to the usual diagonal Gaussian assumption. We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on few-shot classification (mini-ImageNet and tiered-ImageNet) and regression (multi-modal task-distribution regression) benchmarks.
MODEL FOR QUALITY EVALUATION IN SECONDARY EDUCATION

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 3745-3755ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.1031Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Multi-objective Task Scheduling Using Chaotic Quantum-Behaved Chicken Swarm Optimization (CQCSO) in Cloud Computing Environment

Task scheduling is a challenging process with the increasing number of requests from the clients in a cloud system. Achieving efficient task scheduling with multiple objectives is much required in this modern era. A novel Chaotic Quantum-behaved Chicken Swarm Optimization (CQCSO) based task scheduling approach is presented in this paper. CQCSO is developed by applying chaotic theory and quantum theory to the standard Chicken Swarm Optimization to overcome its problem of premature convergence and local optima. CQCSO algorithm models the task scheduling as an optimization problem and solves it by formulating a multi-objective fitness function using task completion time, response time and throughput to ensure maximum Quality-of-service (QoS) satisfaction and minimum SLA violations. CQCSO identifies the task order and optimally schedules them to the suitable virtual machines with better performance. Experiments were conducted in CloudSim to evaluate the CQCSO approach and it provided efficient task scheduling than the prior existing algorithms.
Device-independent quantum key distribution based on asymmetric CHSH inequalities

The simplest device-independent quantum key distribution protocol is based on the Clauser-Horne-Shimony-Holt (CHSH) Bell inequality and allows two users, Alice and Bob, to generate a secret key if they observe sufficiently strong correlations. There is, however, a mismatch between the protocol, in which only one of Alice's measurements is used to generate the key, and the CHSH expression, which is symmetric with respect to Alice's two measurements. We therefore investigate the impact of using an extended family of Bell expressions where we give different weights to Alice's measurements. Using this family of asymmetric Bell expressions improves the robustness of the key distribution protocol for certain experimentally-relevant correlations. As an example, the tolerable error rate improves from 7.15% to about 7.42% for the depolarising channel. Adding random noise to Alice's key before the postprocessing pushes the threshold further to more than 8.34%. The main technical result of our work is a tight bound on the von Neumann entropy of one of Alice's measurement outcomes conditioned on a quantum eavesdropper for the family of asymmetric CHSH expressions we consider and allowing for an arbitrary amount of noise preprocessing.
To Find the Best-Suited Model for Sentiment Analysis of Real-Time Twitter Data

This paper attempts to find the best-suited model for real-time sentiment analysis of tweets. A lot of research is being done on Twitter data and sentiment analysis, but all of them analyze the pre-existing datasets. This causes many problems as Twitter data is mostly dependent on time, location, topic, trends, etc., and the same trained model cannot be used for real-time analysis. Another problem is that a complex algorithm will take a lot of time, and some tweets can be missed. For example, we need to create a model to track tweets to predict terrorist attacks. A model like this should be fast so that we do not miss even a single tweet. Many other factors like traffic and the language of the tweet should also be kept in mind. This type of model cannot be trained on pre-existing datasets. In this paper, tweets are extracted from Twitter in real time and stored in a dataset. Feature extraction and NLP models are applied to the data, and then, machine learning models are run through the data. Support vector machine with bag of words provided the best results, whereas logistic regression with bag of words was the fastest.
Mathematical modeling of the frequencies of letters for their occurrence in corpora, words (types) and in the initial positions of words of corpora

Abstract In the present paper an attempt has been made to determine the mathematical model for the frequencies of occurrence of letters in the corpora, in the word types of the corpora and in the initial positions of words of the corpora while both the word tokens and word types have been taken into account. In the current study corpora written in American English have been used by the selection of the entities from ‘The Open American National Corpus (OANC)’.
A case of severe Plasmodium knowlesi malaria in a post-splenectomy patient.

Malaria is a parasitic disease that is caused by the Plasmodium parasite. Worldwide, it remains a significant public health problem especially in the Africa region where it contributes to more than 90% of cases and malaria death. However, zoonotic (simian) Plasmodium knowlesi parasite is a widely prevalent cause of malaria in the South East Asian countries. It is known to cause severe human disease due to its 24hour erythrocytic cycles. Thus far, cases of severe falciparum malaria have been reported in asplenic patients. Here, we report a case of severe P.knowlesi malaria in a 51-year-old man who is a postsplenectomy patient.
Grasshopper Optimization Algorithm - Modifications and Applications

This chapter discusses practical aspects of the algorithm inspired by the social behavior of grasshoppers -- insects which are known for their tendency to form huge swarms. The technique which mimics their movement strategies known as Grasshopper Optimization Algorithm relies on three components resembling the influence of social interactions and the wind advection. The first part of this chapter presents the original version of the algorithm aimed at solving continuous optimization tasks. It is followed by the description of modifications -- adapting its scheme to the alternative optimization domains, using different movement strategies or treating GOA as a component of hybrid metaheuristic. Finally, we also present demonstrative use case of GOA for data clustering, ubiquitous task of unsupervised machine learning.
Quality-diversity optimisation

tutorial Quality-diversity optimisation Share on Authors: Antoine Cully Imperial College London, London, UK Imperial College London, London, UKView Profile , Jean-Baptiste Mouret Université de Lorraine,, France Université de Lorraine,, FranceView Profile , Stéphane Doncieux Sorbonne Université, Paris Sorbonne Université, ParisView Profile Authors Info & Claims GECCO '20: Proceedings of the 2020 Genetic and Evolutionary Computation Conference CompanionJuly 2020 Pages 701–723https://doi.org/10.1145/3377929.3389852Online:08 July 2020Publication History 1citation123DownloadsMetricsTotal Citations1Total Downloads123Last 12 Months21Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
EDUCATIONAL POTENTIAL OF DIGITAL TECHNOLOGIES

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 1501-1506ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.0490Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
Design and implementation of system-on-chip for peripheral component interconnect express encryption card based on multiple algorithms

Purpose The data protection is always a vital problem in the network era. High-speed cryptographic chip is an important part to ensure data security in information interaction. This paper aims to provide a new peripheral component interconnect express (PCIe) encryption card solution with high performance, high integration and low cost. Design/methodology/approach This work proposes a System on Chip architecture scheme of high-speed cryptographic chip for PCIe encryption card. It integrated CPU, direct memory access, the national and international cipher algorithm (data encryption standard/3 data encryption standard, Rivest–Shamir–Adleman, HASH, SM1, SM2, SM3, SM4, SM7), PCIe and other communication interfaces with advanced extensible interface-advanced high-performance bus three-level bus architecture. Findings This paper presents a high-speed cryptographic chip that integrates several high-speed parallel processing algorithm units. The test results of post-silicon sample shows that the high-speed cryptographic chip can achieve Gbps-level speed. That means only one single chip can fully meet the requirements of cryptographic operation performance for most cryptographic applications. Practical implications The typical application in this work is PCIe encryption card. Besides server’s applications, it can also be applied in terminal products such as high-definition video encryption, security gateway, secure routing, cloud terminal devices and industrial real-time monitoring system, which require high performance on data encryption. Social implications It can be well applied on many other fields such as power, banking, insurance, transportation and e-commerce. Originality/value Compared with the current strategy of high-speed encryption card, which mostly uses hardware field-programmable gate arrays or several low-speed algorithm chips through parallel processing in one printed circuit board, this work has provided a new PCIe encryption card solution with high performance, high integration and low cost only in one chip.
TEACHERS’ PROFESSIONAL DEVELOPMENT: MAIN FACTORS FOSTERING PROGRESS OF EDUCATION

Appears in: EDULEARN20 Proceedings Publication year: 2020Pages: 8580-8587ISBN: 978-84-09-17979-4ISSN: 2340-1117doi: 10.21125/edulearn.2020.2108Conference name: 12th International Conference on Education and New Learning TechnologiesDates: 6-7 July, 2020Location: Online Conference
THE RESEARCH OF PSYCHOLOGICAL WELL-BEING PREDICTORS: COMPARATIVE STUDY OF TEACHERS IN LATVIA AND RUSSIA

THE RESEARCH OF PSYCHOLOGICAL WELL-BEING PREDICTORS: COMPARATIVE STUDY OF TEACHERS IN LATVIA AND RUSSIA
Deconvolution of Image Sequences with a Learning FFT-based Approach

In this work, we address the problem of fast and accurate non-blind motion deblurring of a sequence of frames. We propose an approach based on single image non-blind deconvolution methods by extending them from two spatial dimensions onto a 2D+ time space. The resulted algorithms utilize recent advances of deep learning and heavily rely on learning the spatial and time correlations of frames from data which allows to perform the deblurring in an end-to-end fashion without the manual tuning of parameters. Developed algorithms were trained and tested on blurred video sequences, and as a result an improvement in restoration quality by around 1dB in terms of PSNR was achieved, comparing to a single-frame deconvolution. The proposed algorithms perform video restoration in a close to real-time speed, having potential for real applications in both portable consumer electronics and machine vision systems.
ARTIFICIAL INTELLIGENCE APPLICATION TECHNIQUES FOR DEEP ASSESSMENT OF MULTIPLE CHOICE EDUCATIONAL SYSTEMS

Learning is the quintessential differentiating aspect that favors humans on this planet. The ability to learn the accumulated knowledge from one human to the other and furthermore to prissiest this knowledge throughout the ages have been at the canter of all human civilizations. Education developed as a systematic way to institutionalize the learning process and scale it. Any evolving system needs to be able to adjust and adapt to improve over time. The main way by which any control system adjusts is to have a feedback loop, a way to assess and measure the current status in order to adapt. This process translates to assessments and testing in education, however, this process requires considerable manual effort. In recent years, especially with the advancement of electronic testing, multiple-choice assessments became prevalent as it can be automatically evaluated removing the need for human intervention. The main limitation of this method is the dichotomy of the result in contrast with a fully qualified answer. The conundrum is that the fully qualified answer requires an order of magnitude effort to grade, which is mostly manual and not easy to scale. In this paper, a novel approach is presented in which Artificial Intelligence is implemented in order to demonstrate how a multiple-choice assessment can be constructed so that a deeper analysis of the answers is attained. The paper further asserts the potential to render partial grades resulting in a superior assessment of the learner’s understanding.
Artificial Neural Networks for Text-to-SQL Task: State of the Art

Databases store a significant amount of data from the world, however, to access this data, users must understand a query language such as SQL. In order to facilitate this task and to make the interaction with databases possible for all the world, researches has recently appeared to approach systems that understand the natural language questions and automatically convert them into SQL queries. The purpose of this article is to provide the state of the art text-to-SQL task in which we present the main models and existing solutions based on Artificial Neural Networks (ANN), precisely on Deep Learning (DL) and Natural Language Processing (NLP). We also specify the experimental settings of each approach, their limits as well as a comparison of the best existing ones.