Reviewerly/exHarmony huggingface dataset:
An Efficient VM Scheduling Framework for Interactive Streaming Service

Cloud computing has become widely used to provide many services such as analyzing and streaming data to increase scalability and minimize up-front IT infrastructure costs. However, to make the best use of cloud infrastructures in terms of performance and cost, efficient virtual machine (VM) management is required. In this paper, we propose a VM scheduling framework for automatic and cost-effective management of VMs for streaming services. The framework controls and manages the life-cycle and status of multiple VMs in the cloud platform automatically. We implement the VM scheduling framework based on the google cloud platform (GCP). The experimental results show that the streaming services based on the proposed framework can provide lower costs with fewer performance overheads than the streaming services without the framework.
Modeling and Simulation of a Wheelchair System with Motion Control

This paper presents mathematical modeling and simulation results of a wheelchair system with motion control, which constitutes a crucial part of a research project on vision-based smart wheelchair control and navigation. Disabled people with neuromuscular or reflex disabilities have difficulties in operating standard electric wheelchairs properly. In addition to, driving wheelchair in outdoor has critical risks. The work will contribute greatly mobility problem of disabled people by visual information technology and efficient motion control design simulation. To reach this purpose, the mathematical model of wheelchair is constructed that shows main characteristics and dynamical behavior of system. Results of the study is observed by a designed computer simulation model. Simulation test outcomes provide developing control algorithm that well organized motor control. Computer simulation results include controlling the position and direction of wheelchair, linear displacement, velocity information and disturbance effect on driving. Consequencely, the mathematical model has the ability to show the representing autonomous wheelchair dynamics in open-loop and closed-loop and simulation outputs prove effeciency of the model and designed control system. Results of the study will be used in the research project that aims to solve the autonomous wheelchair control problem.
A Stochastic Network-Interdiction Model For Cyber Security

We propose a general defender-attacker model for security of computer networks, using attack graphs to represent the possible attacker strategies and defender options. The defender’s objective is to maximize the security of the network under a limited budget. In computer security, the attacker could be operating from the safety of a foreign country, and the cost of changing attack strategies may be quite low. To capture the ability of the attacker to launch multiple attempts, we represent the attacker’s success on each arc of the attack graph probabilistically, and formulate the resulting problem as a two- stage stochastic NI problem. We solve the resulting problem using two-stage stochastic optimization with recourse, and explore the attacker and defender strategies.
Sentiment Analysis using SVM and Naïve Bayes Classifiers on Restaurant Review Dataset

Consumer reviews on the food and services of a restaurant is a significant thing to monitor for restaurant businesses. Sentiment Analysis, having another name of Opinion Mining, is a technique that was used in order to identify people's opinions and attitudes towards certain subjects, and the most widely used application of sentiment analysis is analyzing consumer reviews of their products and services. This paper will assess sentiment analysis' performance with SVM and Naïve Bayes classifiers on a dataset of restaurant reviews. A grid search with different hyperparameters of the classifiers and feature selection methods is done to compare their effects on performance. Each model will be evaluated based on accuracy, F1 score, and confusion matrix. The trained models can be further finetuned to aid restaurant businesses in tracking their business performance and reputation.
Detection of volcanic microearthquakes based on homomorphic deconvolution and STA/LTA

Maintaining the seismic monitoring of volcanoes has resulted in a greater understanding of the relationship between the volcano and its environment. This monitoring also provides information on the relationship between the seismic activity of a volcano and possible eruptions. Therefore, automatic detection systems of microearthqueakes are of great importance in this task. In this sense, The aim of this work is to develop a microearthquake detector that search to estimate the signal of the seismic source by reducing the noise present in the registered signal by sensors. For this purpose, it has been proposed the use of homomorphic deconvolution to estimate the signal of the volcanic microearthquake from the signal received by the different seismic sensors that monitor the Cotopaxi volcano. The homomorphic deconvolution technique is accompanied by the calculation of the root mean square energy as a function g(x) for the Short Time Average over Long Time Average (STA/LTA) algorithm. The proposed detector was evaluated with several datasets, including a record of approximately 5 days (7000 min) with 350 seismic events, the data set was provided and labeled by experts from IGEPN, where, a 98.29% of detection probability and an 99.31% of accuracy it was obtained. Additionally, the volcanic microearthquake signals show a gain of 10dB on average after homomorphic deconvolution, when estimating the Signal to Noise Ratio (SNR).
Automatic Door Detection of Convenient Stores based on Association Relations

Although deep learning-based object detection models such as YOLOv4 performs with high accuracy and high frame rate, it was unable to distinguish between glass doors and glass walls of a convenience store. In this paper, we introduce a method that incorporates YOLOv4 and the association relations between objects in the scene to enhance the accuracy and robustness of door entrance detection of convenient stores.
Exploring Different Game Mechanics for Anti-phishing Learning Games

Existing anti-phishing learning games rely on the same simple game mechanics that do not allow for detailed assessment of the players’ acquired knowledge and skills. They focus mostly on factual and conceptual knowledge to remember or understand. To extend the research field, this paper presents two new games: The first game implements an extended classification mechanic to better assess the player’s decision process, while the second game implements a different game mechanic, which requires players to combine given URL parts to construct their own phishing URLs. Both games aim to address higher-order cognitive processes as well as procedural knowledge. The games’ functionality and user experience were evaluated by a group of 40 CS students, resulting in general improvements of the games.
Extracting Impacts of Non-pharmacological Interventions for COVID-19 From Modelling Study

COVID-19 pandemic continues to rampage in the world. Before the achievement of global herd immunity, non-pharmacological interventions(NPIs) are crucial to mitigate the pandemic. Although various NPIs have been put into practice, there are many concerns about the impacts and effectiveness of these NPIs. COVID-19 modelling study (CMS) in epidemiology can provide evidence to solve the aforementioned concerns. It is time-consuming to collect evidence manually when dealing with the vast amount of CMS papers. Accordingly, we seek to accelerate evidence collection by developing an information extraction model to automatically identify evidence from CMS papers. This work presents a novel COVID-19 Non-pharmacological Interventions Evidence (CNPIE) Corpus, which contains 597 abstracts of COVID-19 modelling study with richly annotated entities and relations of the impacts of NPIs. We design a semi-supervised document-level information extraction model (SS-DYGIE++) which can jointly extract entities and relations. Our model outperforms previous baselines in both entity recognition and relation extraction tasks by a large margin. The proposed work can be applied towards automatic evidence extraction in the public health domain for assisting the public health decision-making of the government.
Analysis of Social Phenomenon on Tattoo Using Big Data and Social Network Analysis - News texts for the last year as collated -

The purpose of this study was to identify the social phenomena of tattoo, which have been of constant interest in our society, through analysis of social networks collected from big data on what the social phenomena implied in keywords emphasized in newspaper articles over the past year. To this end, by analyzing keywords about tattoos that frequently appeared in newspaper articles, we could see what the main interests of social phenomena related to tattoos were. Data on tattoos were collected from newspaper articles over the past year and analyzed how they formed meaning regarding the relationship structure and centrality between the keywords at issue through social network analysis. These findings provide basic data on social discussions and policy directions related to tattoos in practice and discussions related to ways to improve them. This study is an extension from existing quantitative research by analyzing the social phenomena of tattoos through Bigdata and social network analysis. Apart from statistical surveys or subjective qualitative research, we have approached them with content analysis using big data and social network analysis. The conclusion of this study is as follows. First, as a result of analyzing the word cloud regarding tattoos, it was confirmed that “rose” and “300” were the most prominent, and there were keywords that could analyze various other social phenomena. Second, as a result of analysis by connection centrality, it was proved that the social interest and popularity of tattoos increased. Third, as a result of analysis by eigenvector centrality, the popularity of tattoos was proved. It objectified academic research by attempting research from a different perspective from the analysis of research trends and provided visualized research results of readers.
Imparare dagli errori: apprendenti russofoni di italiano LS/L2

The study deals with written errors in Italian language made by Russian learners and their use in Italian LS/L2 lessons. The cultural-linguistic background of Russian learners of Italian and the differences in the morphosyntax of Italian and Russian are partly accounted for causes of such errors. The paper focuses on the didactic advantages of ranking multiple choice test on the base of the degree of incorrectness of the proposed choice: it supports the importance of using systematic errors as a source to create ad hoc multiple choice, and to trigger students’ conscious second language learning.
Solving Travelling Salesman Problem Using a Modified Grey Wolf Optimizer

Grey wolf optimizer (GWO) is a recent swarm intelligence metaheuristic that mimics the leadership hierarchy of grey wolves. GWO was originally developed to address continuous optimization problems however, various versions of GWO algorithms are available in the literature that have been successfully applied to a wide range of problems. This work presents a modified version of the algorithm for solving the traveling salesman problem, a well-known NP-hard problem that is widely adopted in real-world applications. The performance of the proposed method has been tested over several TSP instances from TSPLIB and the result has been compared with other well-known meta-heuristics algorithms. The experimental findings showed that the proposed algorithm has promising performance and was able to overcome other algorithms in 55.56% of cases.
School-enterprise Cooperation College Students' Innovation and Entrepreneurship Improvement under the "Internet" Perspective

With the gradual penetration of the Internet into people's lives and work, people can choose more and more ways of working, and the employment of college students is not limited to finding jobs, but can meet living standards through innovation and entrepreneurship. In this context, Internet+ provides new possibilities for the cultivation of highly efficient college students’ innovative and entrepreneurial talents, and the combination of colleges provides opportunities for college students to transform into applied talents. The school-enterprise combination of innovation and entrepreneurship activities also laid a solid foundation for the development after the birthday of the university.
Bayesian learning via neural Schrödinger–Föllmer flows

Abstract In this work we explore a new framework for approximate Bayesian inference in large datasets based on stochastic control. We advocate stochastic control as a finite time and low variance alternative to popular steady-state methods such as stochastic gradient Langevin dynamics. Furthermore, we discuss and adapt the existing theoretical guarantees of this framework and establish connections to already existing VI routines in SDE-based models.
Introduction: Shifting Attention

In recent years, attention has become a matter of increasing public concern. New digital technologies have transformed human attention materially and discursively, reorganizing perceptual practices and inciting debates about them. The essays in this special issue emerged from a set of panels focused on attention at the 4S conference in New Orleans in 2019. They are all, in various ways, concerned with shifts among attention’s many meanings: between payment and care, instinct and agency, or vulnerability and power. Drawing on Science and Technology Studies (STS) sensibilities, these pieces examine how scientific and technical actors are invested in theorizing and capturing attention, while simultaneously engendering new forms of care, resistance, and critique. At a moment where the attention economy appears to be in transformative crisis, this collection maps a set of incipient directions that ask us to pay attention to not only attention itself but also to the many sociotechnical settings where experts and publics are shifting attention’s meaning and value.
110 kV insulator contamination diagnosis method based on UV imaging

Insulator is one of the main electrical equipment in power grid. Under the action of long-term electrical, thermal, environmental and mechanical stress, the problem of external insulation deterioration, aging and even damage will inevitably appear, which endangers the safe and stable operation of power system. The equipment with poor external insulation performance is often accompanied by the distortion of surface partial electric field, resulting in surface discharge. As an instrument to detect the abnormal discharge on the surface of external insulation, UV imager can detect the hidden danger or damage of external insulation as soon as possible, which is of great significance to reduce the failure rate of equipment and ensure the safe operation of power system. In this paper, the artificial fog chamber is built to simulate the actual working environment of insulators, and the artificial pollution test is carried out. The ultraviolet imager is used to capture the insulator discharge image, so as to realize the diagnosis of 110kV insulator pollution state.
Accessing spoken language corpora: an overview of current approaches

In this paper, we present an overview of freely available web applications providing online access to spoken language corpora. We explore and discuss various solutions with which the corpus providers and corpus platform developers address the needs of researchers who are working with spoken language. The paper aims to contribute to the long-overdue exchange and discussion of methods and best practices in the design of online access to spoken language corpora.
Exploring Affordances and Student Perceptions of MALL in Familiar Environments

This study aimed to explore affordances and student perceptions of mobile-assisted language learning (MALL) in familiar environments to facilitate English as a foreign language (EFL) learning. Twenty-five undergraduate students participated in this study. Students learned EFL in class and then they worked on learning tasks in familiar learning environments outside of classroom. The students used resources from learning environments such as objects, people, situations, and scenarios to complete assigned tasks by describing them. They used a mobile learning system installed on Tablet PCs during learning process. We explored affordances and student perceptions of MALL in familiar environments to facilitate English as a foreign language (EFL) learning. The results showed that personal, specific, innovate, familiar, predictable, and meaningful were affordances of familiar environments for EFL learning. The students had positive perceptions of technology. Based on our results, several implications along with practical suggestions were prepared for EFL teachers and researchers.
Sentiment Analysis of the Coronavirus Vaccine on Social Media

The rapid spread of Internet use has allowed people to access social media more easily. Especially after the Coronavirus epidemic, some restrictions and people’s working from home caused them to use social media intensively. While the Coronavirus epidemic continues its affect all over the world, developments related to vaccines are followed with interest by people. Conversations and shares about vaccines and vaccine brands continue to increase. It’s a fact that social media has attracted the attention of society even to any ordinary subject and has almost become a decision-making mechanism. The thoughts and feelings of many people about the vaccine can be extremely decisive for society. People’s attitudes towards vaccines can change throughout the pandemic process. Therefore, this study focuses to learn and monitor people’s feelings about the vaccine over a period of time. On the Reddit platform, which has no character limit for posts, the data is pre-processed to classify comments into emotional tags. The data was subjected to a series of training processes to classify with higher accuracy using the Natural Language Understanding (NLU) library. Then people’s emotional changes related to the vaccine monitored on a monthly basis.
Adaptive Probabilistic Neuro-Fuzzy System and its Hybrid Learning in Medical Diagnostics Task

Background: The medical diagnostic task in conditions of the limited dataset and overlapping classes is considered. Such limitations happen quite often in real-world tasks. The lack of long training datasets during solving real tasks in the problem of medical diagnostics causes not being able to use the mathematical apparatus of deep learning. Additionally, considering other factors, such as in a dataset, classes can be overlapped in the feature space; also data can be specified in various scales: in the numerical interval, numerical ratios, ordinal (rank), nominal and binary, which does not allow the use of known neural networks. In order to overcome arising restrictions and problems, a hybrid neuro-fuzzy system based on a probabilistic neural network and adaptive neuro-fuzzy interference system that allows solving the task in these situations is proposed. Methods: Computational intelligence, artificial neural networks, neuro-fuzzy systems compared to conventional artificial neural networks, the proposed system requires significantly less training time, and in comparison with neuro-fuzzy systems, it contains significantly fewer membership functions in the fuzzification layer. The hybrid learning algorithm for the system under consideration based on self-learning according to the principle “Winner takes all” and lazy learning according to the principle “Neurons at data points” has been introduced. Results: The proposed system solves the problem of classification in conditions of overlapping classes with the calculation of the membership levels of the formed diagnosis to various possible classes. Conclusion: The proposed system is quite simple in its numerical implementation, characterized by a high speed of information processing, both in the learning process and in the decision-making process; it easily adapts to situations when the number of diagnostics features changes during the system's functioning.
Task Ordering Matters for Incremental Learning

Current incremental learning is confronted with the problem of catastrophic forgetting. Existing work usually addressed this problem by enlarging the sample database. But few people pay attention to the order-sensitive problem in incremental learning. In this article, we propose a task sorting method with the feature similarity between the consecutive tasks. Experimental results on CIFAR-100 and CORe50 datasets demonstrate that the learning sorting matters for incremental learning. The task sorted with the highest feature similarity will get a better performance than that of random sorting.
Machine Learning-based Currency Information Retrieval for Aiding the Visually Impaired People

Paper currency is one of the most in-demand and long-established payment modes across the globe. People suffering from visual disabilities often face difficulties while handling paper currencies. Over the years, assisting technology has been rekindling itself to serve the aged and disabled person more aptly. Image processing methods and other sophisticated technologies, like Artificial Intelligence, Deep Learning, etc., can be employed to identify banknotes and fetch other valuable pieces of information from them. This paper proposes a framework that focuses on an integrated approach to retrieving data from the paper currency’s uploaded image. The current version of the framework focuses on identifying the authenticity of the paper currency and classifying it according to its value. This work is an initiative to help visually impaired people to use paper currencies without assistance from other individuals and support them in living independently.
GeoSneakPique: Visual Autocompletion for Geospatial Queries

How many crimes occurred in the city center? And exactly which part of town is the “city center”? While location is at the heart of many data questions, geographic location can be difficult to specify in natural language (NL) queries. This is especially true when working with fuzzy cognitive regions or regions that may be defined based on data distributions instead of absolute administrative location (e.g., state, country). GeoSneakPique presents a novel method for using a mapping widget to support the NL query process, allowing users to specify location via direct manipulation with data-driven guidance on spatial distributions to help select the area of interest. Users receive feedback to help them evaluate and refine their spatial selection interactively and can save spatial definitions for re-use in subsequent queries. We conduct a qualitative evaluation of the GeoSneakPique that indicates the usefulness of the interface as well as opportunities for better supporting geospatial workflows in visual analysis tasks employing cognitive regions.
Multiparty Reach and Frequency Histogram: Private, Secure, and Practical

Abstract Consider the setting where multiple parties each hold a multiset of users and the task is to estimate the reach (i.e., the number of distinct users appearing across all parties) and the frequency histogram (i.e., fraction of users appearing a given number of times across all parties). In this work we introduce a new sketch for this task, based on an exponentially distributed counting Bloom filter. We combine this sketch with a communication-efficient multi-party protocol to solve the task in the multi-worker setting. Our protocol exhibits both differential privacy and security guarantees in the honest-but-curious model and in the presence of large subsets of colluding workers; furthermore, its reach and frequency histogram estimates have a provably small error. Finally, we show the practicality of the protocol by evaluating it on internet-scale audiences.