Reviewerly/exHarmony huggingface dataset:
Utilizing Blockchain Technology in Social Media Bot Identification

&lt;p&gt;As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.&lt;i&gt;&lt;/i&gt;&lt;/p&gt;
On the Temporality of Priors in Entity Linking

Entity linking is a fundamental task in natural language processing which deals with the lexical ambiguity in texts. An important component in entity linking approaches is the mention-to-entity prior probability. Even though there is a large number of works in entity linking, the existing approaches do not explicitly consider the time aspect, specifically the temporality of an entity's prior probability. We posit that this prior probability is temporal in nature and affects the performance of entity linking systems. In this paper we systematically study the effect of the prior on the entity linking performance over the temporal validity of both texts and KBs.
The principle of invariance in the Donsker form to the partial sum processes of finite order moving averages

We consider the process of partial sums of moving averages of finite order with a regular varying memory function, constructed from a stationary sequence, variance of the sum of which is a regularly varying function. We study the Gaussian approximation of this process of partial sums with the aid of a certain class of Gaussian processes, and obtain sufficient conditions for the $C$-convergence in the invariance principle in the Donsker form
APPLICATION OF SQ3R ON THE SUCCESS OF THE "ROLE PLAY" METHOD IN "ENGLISH FOR SPECIFIC PURPOSE" COURSE, ADMINISTRATIVE STUDY PROGRAM, STIA BANTEN, PANDEGLANG, BANTEN

SQ3R is a method to get knowledge from reading sources. Thus, the contents of a reading source will become effective as a knowledge, in this case the knowledge instructed by the lecturer in the "English for Specific Purpose" Course. The desired knowledge obtained is the knowledge of "ISO" (International Organization for Stadardization) . After getting the contents of a reading about the "ISO", the lecturer will form a group for a conversation simulation in a company meeting by using the "Role Play" method, where students in the group will act as managers in a company. The limitation of this research is to only focus on the role of the SQ3R method on the "Role Play" method as one of the methods for teaching students' Speaking Skills at STIA Banten, especially students of the "English for Specific Purpose" class. In conducting this research, the author used the Action Research method or take certain actions or treatments to see the success of students to be able to communicate. Then, the data used is observation data with recording techniques and also the value of student learning outcomes.&#x0D; The results achieved in this study are that there is a close relationship between SQ3R conducted by students to find out about "ISO" and then practice it in the classroom using the "Role Play" method. From the results obtained by students, there is also visible progress before using the SQ3R method with students' speaking ability after using the SQ3R method.
Design of Rectifying Controller Based on Fuzzy PID Control Strategy

Strip offset can cause registration errors, resulting in a large amount of waste during the printing process. In order to achieve precise control of the running track, this paper designs a correction system controller based on fuzzy PID control strategy. Firstly, the basic principle of the belt transmission is analyzed, and the dynamic model of the belt movement is established. Secondly, The ultrasonic sensor is used as the detection device, SCM is used as the control core and the stepping motor is used as the actuator. Thirdly, a correction system control method based on fuzzy PID control strategy is proposed, and the control system software is developed. Experiments show that the correction controller can realize rectifying control of various types of printing materials and is suitable for a variety of high-end printing equipment.
Fifty Years of Abstracts in Economie et Statistique

[eng] Natural language processing, is nowadays a toolbox routinely used to explore the content of various texts. On the occasion of the 50th anniversary of the journal Economie et Statistique (then Economie et Statistique / Economics and Statistics), we propose in this short article an application to the abstracts of the 2,184 “academic” articles that were published in this journal. Which words are most frequently used? What underlying topics do they suggest and have these topics changed over the years?
Host Static and Dynamic Content on AWS Cloud

This is the modern era where people are trying to increase the accessibility of things. Everything organization is trying to digitalize the process. For example, banking, electricity and water bill, and application submission to the government agencies. By adhering to the digital process, the productivity of the employees increased. Even the entertainment industry is also adopting the process of digital content delivery, digital advertisement, etc. Now, there are few questions that are important to answer i.e. how we can deliver the content to the world? What steps have to be taken in order to deliver the enriched quality content with low latency? How can we scale the web server to deliver the best performance whenever needed? How to reduce the cost of hosting to deliver static and dynamic content? Here, we are discussing how static and dynamic pages can be served in a highly available and scalable environment such as AWS cloud?
Simple Answers to Difficult Issues about Learning and Learners Internationally at pK-12 Levels

The traditional educational model was designed in the industrial age of the 20th century. That model is outdated for various reasons. This paper is introducing a case study of a project, "The S-Cool Days Program," which applies new routines to reframe those aspects. The introduction of the Program does not require significant changes in the curriculum, can be realized in Art and Music classes, or as a homeroom activity in advisory class. The Program was implemented in Europe, in two countries, in 3 different school types, one American School, a School Start-up and a traditional public school. The activities are introduced by creative art and craft sessions, are engaging enough to invite students to present their passion projects in the classroom, to take the first steps towards purpose-driven learning, learn how to focus on monitoring and expressing of emotions, among other things. The goal is to have some school habits reframed, that results in mindset change. New values are introduced and through them, the class is transformed into a comfort zone for students to envision and follow their dreams. The expected changes can be realized by easy-to-do new routines, which maintain the newly introduced behaviors. The Program supports the 4C's; the 21st-century skills education is supposed to focus on: the creativity, communication, collaboration, and critical thinking. As visualized on social maps, the research realized before and after the Project shows changes in the social relations of the participating classes after the new routines were introduced. New mutual common choices were emerging in all participating grades, classes. The paper shares insights from participating teachers, how they felt in and after the Program, what changes did they identify in the classroom.
Experiential Learning in Higher Education: Tribulations in Execution

Experiential learning often yield good results and help to improve the teaching learning process. There is dire need to use such type of learning at higher level so that whatever students learn remains fixed in their mind for longer time. Teacher must provide opportunity to students to learn by doing. The major purpose of this research has been to analyze the main tribulations teacher faced during execution of experiential learning in actual classroom. The study was conducted in Ranbir Singh Pura Tehsil of Jammu District. The study was qualitative in nature. The sample comprised of 50 teachers of higher education institutions. Interview schedule was prepared to collect the data and percentage was used to analyze the data. It has been found that experiential learning is useful to make teaching learning process more effective but teachers faced problems to implement experience based learning in the classroom due to overloaded curriculum, lack of knowledge of new skills, more emphasized on theoretical work, lack of budget, limited class time, limited access to resources and like that. It has been suggested by the investigator to modify the curriculum and equal weightage should be given to both theoretical and practical courses. Teachers (both pre- service and in -service) must provide new skills, practical work and the way to conduct experiential learning in an interesting and effective way.
Artificial Realities in Library Services: A Study regarding Perspectives of Library Professionals

This study is meant to analyse the familiarity of artificial reality services among the library professionals and to analyse the usage of these advanced services among them, if any. Technology is developing at a vast rate and it is essential to make use of these services within the libraries and information centres for being up-to-date with the latest technology. The study has analysed the awareness of these services among library professionals and their interest in implementing these services in libraries. It also examines the various perspectives of library professionals regarding artificial reality services and various obstacles they need to face while implementing these services. This study shows that the majority of the respondents are aware about virtual reality services, but are not aware about augmented and mixed reality services and the majority of them are not using these services. This study shows that the library professionals opined that these services can help the library users for quick access to information. Also, majority of the respondents agreed with the statement that library services in India need to be developed more using the most modern technologies.
Optimal speed algorithm for calculating high and low thresholds to be used in FPGA based Canny focused on maximum performance

The paper presents an optimal speed algorithm for accurately calculating high and low thresholds. It is to be used in FPGA based Canny targeting maximum performance. Scrutinized is the proposed algorithm’s computational mechanism. Its functionality is set forth in detail. Proved is the algorithm’s practicality in terms of guaranteeing the calculation of the most appropriate threshold values in a dynamic fashion. On a comparative basis, explored amd analyzed are the proposed algorithm’s capabilities to ensure reliable results at optimal speed under all test conditions.
The Use of Neural Network Technologies in the Electronic Information and Educational Environment in the Formation of Digital Skills

The development and growth of the digital economy necessitates the acquisition and development of digital skills in the majority of the population.During the study, digital skills were divided into 4 levels: "digital literacy", "digital culture", "digital competence" and "digital professionalism".Considering the possible volumes of training and the contingent of students, the formation of such skills should be carried out in an electronic information and educational environment in a distance format.The aim of the study is to identify opportunities for the use of neural network technologies in the formation of digital skills in the remote format.The classification of neural network technologies is described, and a functional analysis is carried out.It was concluded that according to the results of questioning and input testing, neural network technologies automatically classify users according to levels: digital literacy, digital culture, digital competencies, or digital professionalism.Neural network clustering allows you to differentiate learning by identifying a learner in a particular cluster with a specific pace and content of learning.Neural network regression technologies reveal the most successful and most difficult areas of educational content and based on the identification of dependencies, individualized task pools are proposed.Neural network forecasting technologies allow us to design an individual trajectory of both training and further professional development, and based on the analysis of the Internet content of HR-services offers, possible vacancies are searched for and offered to those trained when profiles match.
Reinforcement Learning via Reasoning from Demonstration

Demonstration is an appealing way for humans to provide assistance to reinforcement-learning agents. Most approaches in this area view demonstrations primarily as sources of behavioral bias. But in sparse-reward tasks, humans seem to treat demonstrations more as sources of causal knowledge. This paper proposes a framework for agents that benefit from demonstration in this human-inspired way. In this framework, agents develop causal models through observation, and reason from this knowledge to decompose tasks for effective reinforcement learning. Experimental results show that a basic implementation of Reasoning from Demonstration (RfD) is effective in a range of sparse-reward tasks.
Structural Reweight Spares Subspace Clustering under Unknown Noise

Sparse subspace clustering (SSC) aims at achieving state-of-the-art clustering performance via solving a l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> minimization problem. The l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm is used to instead of l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm for finding a sparse matrix C. Reweight sparse subspace clustering (RSSC) proposed an iterative reweighting l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization algorithm for an accurate neighbor identification. This paper is inspired by RSSC and SSC. By reweighting the sparse subspace clustering algorithm, the proposed method can obtain a more appropriate representation of data. The proposed algorithm also generalizes a modified two-step l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> minimization algorithm for solving clustering noise datasets problem. Even if we don't know the type or any parameter of the noise, the algorithm can perform better. Experiments on synthetic data demonstrate the effect of our algorithm. Especially when there are noise, the proposed algorithm has excellent clustering effect. Experiments on Extended Yale B and ORL show that our algorithm can have state-of-the-art performance on real datasets.
Graph-Based Entity-Oriented Search: A Unified Framework in Information Retrieval

Modern search engines have evolved beyond document retrieval. Nowadays, the information needs of the users can be directly satisfied through entity-oriented search, by taking into account the entities that better relate to the query, as opposed to relying exclusively on the best matching terms. Evolving from keyword-based to entity-oriented search poses several challenges, not only regarding the understanding of natural language queries, which are more familiar to the end-user, but also regarding the integration of unstructured documents and structured information sources such as knowledge bases. One opportunity that remains open is the research of unified frameworks for the representation and retrieval of heterogeneous information sources. The doctoral work we present here proposes graph-based models to promote the cooperation between different units of information, in order to maximize the amount of available leads that help the user satisfy an information need.
Analysis of materials in virtual media space as a method for forming critical thinking of internet users

The relevance of this paper is due to the fact that in conditions of an overabundance of diverse information, there is a problem of its adequate perception. New forms of communication – the Internet and social networks – give not only new opportunities for communication and expression of the opinion, but they also create many vulnerabilities, ways of manipulating public opinion and consciousness. These questions are especially relevant for educators working with young people, whose worldview is in the process of formation, whose opinions, points of view are unstable, are influenced from outside, and mainly from Internet resources. The purpose of this article is to analyze and summarize the experience of working with students in the context of the development of critical thinking by the example of the study of comments on video messages on the YouTube channel. The description of the practical stages of the teacher-students interaction is given in accordance with the technology of teaching critical thinking. The role of the teacher and students in this process is determined. It is shown that the materials uploaded on popular Internet resources can and should be used not only for a purely practical, objective purpose, but also at a general educational, developing, worldview level, with the goal of forming critical thinking of Internet users.
LibGuides: Continuity of Learning Services at Sawyer library: Home

This guide provides information about the Library's online services for faculty, staff and students during the Covid-19 disruptions.
Corrigendum: Reimagining New Socio-Technical Economics Through the Application of Distributed Ledger Technologies

CORRECTION article Front. Blockchain, 07 April 2020Sec. Blockchain for Good Volume 3 - 2020 | https://doi.org/10.3389/fbloc.2020.00014
Algorithm Selection in Multimodal Medical Image Registration

Over the past few decades, fast&lt;strong&gt;-&lt;/strong&gt;growth has occurred in the area of medical image acquisition devices, and physicians now rely on the utilization of medical images for the diagnosis, treatment plans, and surgical guidance. Researchers have classified medical images according to two structures: anatomical and functional structures. Due to this classification, the data obtained from two or more images of the same object frequently provide complementary and more abundant information through a process known as multimodal medical model registration. Image registration is spatially mapping the coordinate system of the two images obtained from a different viewpoint and utilizing various sensors. Several automatic multimodal medical image registration algorithms have been introduced based on types of medical images and their applications to increase the reliability, robustness, and accuracy. Due to the diversity in imaging and the different demands&lt;strong&gt; &lt;/strong&gt;for applications, there is no single registration algorithm that can do that. This paper introduces a novel method for developing a multimodal medical image registration system that can select the most accepted registration algorithm from a group of registration algorithms for a variety of input datasets. The method described here is based on a machine learning technique that selects the most promising candidate. Several experiments have been conducted, and the results reveal that the novel approach leads to considerably faster reliability, accuracy, and more robustness registration algorithm selection.
Design of Big Data Analytics using Unified Data Modelling Systems in Mobile Cellular Networks

Mobile cellular networks have become both the generators and carriers of massive data.Big data analytics can improve the performance of mobile cellular networks and maximize the revenue of operators.In this paper, we introduce a unified data model based on the random matrix theory and machine learning.Then, we present an architectural framework for applying the big data analytics in the mobile cellular networks.Moreover, we describe several illustrative examples, including big signalling data, big traffic data, big location data, big radio waveforms data, and big heterogeneous data, in mobile cellular networks.Finally, we discuss a number of open research challenges of the big data analytics in the mobile cellular networks.
Quantum enhanced optical phase estimation with a squeezed thermal state

Quantum phase estimation protocols can provide a measuring method of phase shift with precision superior to standard quantum limit (SQL) due to the application of a nonclassical state of light. A squeezed vacuum state, whose variance in one quadrature is lower than the corresponding SQL, has been pointed out a sensitive resource for quantum phase estimation and the estimation accuracy is directly influenced by the properties of the squeezed state. Here we detailedly analyze the influence of the purity and squeezing level of the squeezed state on the accuracy of quantum phase estimation. The maximum precision that can be achieved for a squeezed thermal state is evaluated, and the experimental results are in agreement with the theoretical analyses. It is also found that the width of the phase estimation interval $\Delta \theta $ beyond SQL is correlated with the purity of the squeezed state.
Assessment of various initialization strategies for the Expectation-Maximization algorithm for Hidden Semi-Markov Models with multiple categorical sequences

In this study, we propose a method called sequence breaking framework to search high local maximum of the likelihood by providing starting values based on the observations for the Expectation-Maximization algorithm, for Hidden semi-Markov model parameter estimation. The method is shown to be efficient on several datasets with multiple categorical sequences.
Cheetah: Accelerating Database Queries with Switch Pruning

Modern database systems are growing increasingly distributed and struggle to reduce query completion time with a large volume of data. In this paper, we leverage programmable switches in the network to partially offload query computation to the switch. While switches provide high performance, they have resource and programming constraints that make implementing diverse queries difficult. To fit in these constraints, we introduce the concept of data \emph{pruning} -- filtering out entries that are guaranteed not to affect output. The database system then runs the same query but on the pruned data, which significantly reduces processing time. We propose pruning algorithms for a variety of queries. We implement our system, Cheetah, on a Barefoot Tofino switch and Spark. Our evaluation on multiple workloads shows $40 - 200\%$ improvement in the query completion time compared to Spark.