Reviewerly/exHarmony huggingface dataset:
Filter Bubbles and Content Diversity? An Agent-Based Modeling Approach

Personalisation algorithms play an important role in catering the information that is relevant to us. The best results are achieved by the algorithms when they monitor the user activity. Most of the algorithms adapt to the users' personal preferences by filtering out the information that is irrelevant to the user. However, one of the criticisms of this process is that it is leading to informational bubbles called the filter bubbles which is a personal space of content familiar to the user, which would reinforce their confirmational biases or create informational blind spots. This phenomena however is highly debated. In this light, we propose an agent based model study, which tries to verify the implications claimed by the filter bubble theorists and also create an hypothetical environment that does not have a filter bubble and test difference in the information dispersion and opinion formation in both the environments.
Library Publishing Coalition

librarypublishing.orgThe Library Publishing Coalition (LPC) began in 2013 to provide a centralized location for scholarly publishing activities. It involved 61 academic libraries in collaboration w...
First Language Interference On English Use

This study aimed to investigate and analyze in depth about first language interference on English use of First Year Students at Senior High School in Bengkulu City. The research method was Qualitative Descriptive. This research is able to capture and provide the description clearly of a language phenomenon that is the object of study. The study was conducted in 4 high schools in Bengkulu city such as SMAN 7, MAN 2, SMKS 11 Serunting 2 and SMA Pembangunan. The data was analyzed totaling 30 data and completed with a questionnaire test. The results of the study were conducted by testing 5 types of interference namely lexical interference, semantic interference, idiomatic interference, collocation interference and cultural interference. From 30 data that investigated and analyzed showing 80% of errors occurred at the level of lexical interference, 5% semantic interference, 10% collocation interference, 5% cultural interference and 0 % idiomatics interference. Lexical interference is more dominant in this study because the mastery of the first language is more dominant in the process of learning foreign language. Key words: Language, Interference
Human Competency Considerations in an Integrated Software Configuration Management Framework

This paper proposes an integrated framework that combines software testing and software quality into standard Software Configuration Management process, whilst highlighting the importance of human competency.Software quality characteristics are embedded in change control procedures whilst software testing approaches provide means for evaluating software artefacts.Existing software configuration management, software quality and software testing standards are referred to in the development of this framework.The importance of competency is highlighted where the minimum competency for implementing each software configuration management process is presented.Advantages over traditional approaches are presented from four different aspects namely People, Process, Tools and Documentation.Major challenges in the realization of this integration are discussed including competency assessment; quality characteristics and metrics; test approach; and automation.
Superresolution Reconstruction of Video Based on Efficient Subpixel Convolutional Neural Network for Urban Computing

Video surveillance is an important data source of urban computing and intelligence. The low resolution of many existing video surveillance devices affects the efficiency of urban computing and intelligence. Therefore, improving the resolution of video surveillance is one of the important tasks of urban computing and intelligence. In this paper, the resolution of video is improved by superresolution reconstruction based on a learning method. Different from the superresolution reconstruction of static images, the superresolution reconstruction of video is characterized by the application of motion information. However, there are few studies in this area so far. Aimed at fully exploring motion information to improve the superresolution of video, this paper proposes a superresolution reconstruction method based on an efficient subpixel convolutional neural network, where the optical flow is introduced in the deep learning network. Fusing the optical flow features between successive frames can compensate for information in frames and generate high-quality superresolution results. In addition, in order to improve the superresolution, a superpixel convolution layer is added after the deep convolution network. Finally, experimental evaluations demonstrate the satisfying performance of our method compared with previous methods and other deep learning networks; our method is more efficient.
Discrete Artificial Dragonflies Algorithm in Agent Based Modelling for Exact Boolean kSatisfiability Problem

The development of metaheuristics and Boolean Satisfiability representation plays an important part in a neural network (NN) and Artificial Intelligence (AI) communities. In this paper, a new hybrid discrete version of the artificial dragonfly algorithm (DADA) applying a minimum objective function in agent-based modelling (ABM) obeying a specified procedure to optimize the states of neurons, for optimal Boolean Exact Satisfiability representation on NETLOGO as a dynamic platform. We combined the artificial dragonfly algorithm for its random searching ability that encourages diverse solutions and formation of static swarm’s mechanism to stimulus computational problems to converge to the best global optimal search space. The global performance of the proposed DADA was compared with genetic algorithm (GA) that are available in the literature based on the global minimum ratio (gM), Local Minimum Ratio (yM), Computational time (CPU) and Hamming distance (HD). The final results showed good agreement between the proposed DADA and discrete version of GA to efficiently optimize the Exact-kSAT problem. It found that DADA-ABM has high potentiality for optimizing or modelling a network that is very hard or often impossible to capture by exact or traditional optimization modelling techniques such as Boolean satisfiability problem is better than existing methods in the literature.
Cómo adaptar una investigación cualitativa a contextos de confinamiento./ [How to adapt qualitative research to confinement contexts].

The confinement of the population, researchers, shows the need to adapt the qualitative methodology, techniques and tools, to the current context generated by COVID-19. The Internet and the social media allow the collection of textual data, sequences, images or narratives about a limited reality for access to it in person. We enter the era of e-research, nuancing and reorienting the observation, conversation, collection and analysis of information. Currently, we are working on techniques and models of good practice in this area. This note includes data collection tools used in qualitative research in health sciences, a selection of techniques for qualitative online research is made, and 10 rules for the design of an e-research of this type are provided.
Leap Motion-Enhanced Augmented Reality Video Games to Encourage Intergenerational Engagement and Wrist Exercise

For years, the health industry has been urging people to live a healthy life either by exercising or eating healthily.Furthermore, often, retired seniors would like to play with their family but there is not much common interest.In addition, one of the acute health problems at the workplace is carpal tunnel syndrome.Hence, inspired by Kinect and Wii, this project aims to: a) encourage more intergenerational family-senior engagement and b) encourage exercise at the workplace whenever they need a break.A video game gives a sense of achievement and progress is visible.Thus, we have developed Leap-Motion-enhanced games such as rolling ball, trivia, and maze video games of different levels to cater to different abilities.We also hope that these games would add to the activities in retirement homes/villages.The theme and objects in the games can be easily changed.Thus, the system can be easily personalized for different socio-cognitive-affective engagement.Feedback from the Leap Motion-enhanced developer community is positive and constructive with areas for improvement.
Current Situation and Analysis of College English Teaching Resource Database in English Teaching

&lt;p&gt;College English, as a compulsory course in college, requires students to put a lot of energy into it. While providing students with high-quality teachers, colleges and universities should also create a good English learning environment for students. According to each student's learning habits and English foundation, an English learning platform should be built for students to meet the needs of different students as much as possible, that is, college English teaching resource database. The establishment of English teaching resource base should be based on information and network technology and popularized through campus network. At present, online English teaching resources on campus are insufficient, relevant equipment is idle, and teaching resources are seriously wasted, which cannot stimulate students' interest in learning English and cultivate their self-learning ability. This paper discusses the application and improvement of the existing college English resource base.&lt;/p&gt;
DIGITAL LITERACY OF UNIVERSITY STUDENTS: CHALLENGES AND OPPORTUNITIES

Transformations in modern society seriously affect the landscape of modern labour market that requires reviewing and developing clusters of competencies attributed to young professionals. With further advancement of information technologies and changes in society lifestyles, higher education institutions encounter new challenges to modernize their educational process. Interdisciplinarity, integration and synergy of modern science are seen as the reality of modern education. Digitalization and digital literacy are penetrating each area of education and social life. To meet these challenges language education tends to review and transform syllabi and foreign language courses by introducing digital educational modules and active and interactive methods that will provide the synergy of core competencies and develop student digital literacy. The authors analyze theoretical foundation and approaches to foreign language teaching and present research results that are based on their experience of designing and implementing new educational module of student net communications in foreign language programmes in Udmurt State University and Izhevsk State Agricultural Academy. The research rests on the principles of competency-based and communicative approaches and is underpinned by active and interactive methods of foreign language teaching as well as experiential learning.
Detecting acoustic backdoor transmission of inaudible messages using deep learning

The novel secret inaudible acoustic communication channel [11], referred to as the BackDoor channel, is a method of embedding inaudible signals in acoustic data that is likely to be processed by a trained deep neural net. In this paper we perform preliminary studies of the detectability of such a communication channel by deep learning algorithms that are trained on the original acoustic data used for such a secret exploit. The BackDoor channel embeds inaudible messages by modulating them with a sinewave of 40kHz and transmitting using ultrasonic speakers. The received composite signal is used to generate the Backdoor dataset for evaluation of our neural net. The audible samples are played back and recorded as a baseline dataset for training. The Backdoor dataset is used to evaluate the impact that the BackDoor channel has on the classification of the acoustic data, and we show that the accuracy of the classifier is degraded. The degradation depends on the type of deep classifier and it appears to impact less the classifiers that are trained using autoencoders. We also propose statistics that can be used to detect the out-of-distribution samples created as a result of the BackDoor channel, such as the log likelihood of the variational autoencoder used to pre-train the classifier or the empirical entropy of the classifier's output layer. The preliminary results presented in this paper indicate that the use of deep learning classifiers as detectors of the BackDoor secret channel merits further research.
An Empirical Study on Stack Overflow Security Vulnerability in Well-known Open Source Software Systems

Stack overflow is one of the most common security vulnerabilities in software systems.It occurs when a program tries to load more data in a buffer than its allocated limit.It may result in serious security issue when a program having the vulnerability is run with administrator privileges.Attackers can inject malicious code into the running program through overflowing its stack.When the malicious code is executed, it allows the attackers to take control of the program.So, this security vulnerability is considered as one of the easiest and reliable techniques to gain unauthorized access to a computer system.In this article, it has been shown that how stack overflow occurs in a software system.Besides, a survey has been conducted on three popular open source projects -Linux, Git and PHP.The survey results show that the projects contain such code portions in which it is possible to overflow the stacks and inject malicious script to harm the normal execution of processes.In addition, this article raises a concern to avoid writing such codes which are potentially sources for the security attack.
Effects of kernels and the proportion of training data on the accuracy of SVM sentiment analysis in lecturer evaluation

Support vector machine (SVM) is a known method for supervised learning in sentiment analysis and there are many studies about the use of SVM in classifying the sentiments in lecturer evaluation. SVM has various parameters that can be tuned and kernels that can be chosen to improve the classifier accuracy. However, not all options have been explored. Therefore, in this study we compared the four SVM kernels: radial, linear, polynomial, and sigmoid, to discover how each kernel influences the accuracy of the classifier. To make a proper assessment, we used our labeled dataset of students’ evaluations toward the lecturer. The dataset was split, one for training the classifier, and another one for testing the model. As an addition, we also used several different ratios of the training:testing dataset. The split ratios are 0.5 to 0.95, with the increment factor of 0.05. The dataset was split randomly, hence the splitting-training-testing processes were repeated 1,000 times for each kernel and splitting ratio. Therefore, at the end of the experiment, we got 40,000 accuracy data. Later, we applied statistical methods to see whether the differences are significant. Based on the statistical test, we found that in this particular case, the linear kernel significantly has higher accuracy compared to the other kernels. However, there is a tradeoff, where the results are getting more varied with a higher proportion of data used for training.
UNVEILING THE LAYERS OF THE L.O.L. SURPRISE DOLL: A LINGUISTIC ANALYSIS THROUGH THE GRAMMAR OF VISUAL DESIGN

The L.O.L. Surprise doll was created in 2016 by MGA Entertainment inspired by the YouTube trend of “unboxing” videos, in which children and adults open toys packages, or other objects, and show to the viewers how it works or what it does. Being a collectible toy with the possibility of more than a hundred surprise dolls, L.O.L. Surprise, which is a best seller, explores playing from the unwrapping of the several layers involved in the unboxing of the doll’s package. The marketing of L.O.L. Surprise does not involve TV advertisements and all its strategy is focused on YouTube videos showing kids unboxing the toy. It is possible to perceive that opening the package, and consequently finding the doll, is part of the experience/play of L.O.L. Surprise. Since the rise of the North American doll L.O.L. is a trend not only in Brazil but all around the world (FROMM, 2019), it is a rising successful trend in last years (GREEN, 2018), this study aims at describing and analyzing the multimodal features of the layers of the doll’s package, perceiving how the surprise element is constructed through it. Therefore, to analyze the layers of the package, we will draw on Kress &amp; Van Leeuwen’s Grammar of Visual Design (2006 [1996]) and its visual metafunctions, which derive from Halliday’s Systemic Functional Linguistics (1978). The description and analysis reveal a stimulus to children’s curiosity, enhanced by the interactive aspect perceived through the verbal and visual language, as well as a tendency towards consumerism and social media engagement.
Comparing Methods to Retrieve Tweets: a Sentiment Approach

In current times Internet and social media have become almost unavoidabletools to support research and decision making processes in various fields.Nevertheless, the collection and use of data retrieved from these types ofsources pose different challenges. In a previous paper we compared theefficiency of three alternative methods used to retrieve geolocated tweets overan entire country (United Kingdom). One method resulted as the bestcompromise in terms of both the effort needed to set it and quantity/quality ofdata collected. In this work we further check, in term of content, whether thethree compared methods are able to produce “similar information”. Inparticular, we aim at checking whether there are differences in the level ofsentiment estimated using tweets coming from the three methods. In doing so,we take into account both a cross-section and a longitudinal perspective. Ourresults confirm that our current best option does not show any significantdifference in the sentiment, producing globally scores in between the scoresobtained using the two alternative methods. Thus, such a flexible and reliablemethod can be implemented in the data collection of geolocated tweets in othercountries and for other studies based on the sentiment analysis.
A Comparative Study between Graph-QL&amp; Restful Services in API Management of Stateless Architectures

A stateless architecture design is a web architecture design that typically does not persist data in any database and such applications also does not require any kind of backup storage.Data that flows through a stateless service is data in transition and such data is never stored in any data store.The processing requests that arrive to such architecture does not rely on information gathered or persisted from any previous session.API (Application programming interface) which consists of subroutines, definitions & procedures that can access data on the applications are the communication points between applications and management of API endpoints using stateless architecture is less complex as there is no server side retention of the client session and each client sends requisite information in each request to the server.GraphQL and RESTful services are means of designing such API architecture.This paper discusses and explains in detail both GraphQL and REST API architecture design and management methods and does an analysis of the potential benefits of GraphQL over REST in Stateless architectural API designs.
Biostratigraphy of the Gurpi Formation based on Calcareous Nannofossil in the Kouh-e Sefid Anticline Section, Izeh Structural Zone

Biostratigraphy of the Gurpi Formation based on Calcareous Nannofossil in the Kouh-e Sefid Anticline Section, Izeh Structural Zone
Management of English Language Learning Process for Children and Adolescent through Distance Learning Program at Homeschooling

The research aimed to illustrate how distance learning programs can be organized or administered at "Primagama Course Place" in Makassar. This study combined qualitative development with the design of the case study. For this study, the writer has used several measures to collect data: interviews, observations, and documents. With the credibility, transferability, reliability, and confirming ability the validity of the information was checked. The final results of the study indicated that the students were planning the online classes, learning sources, information technology, and developing the curriculum. The most recent study findings showed that online preparation was developed, sources were studied, information technology and curriculums design were developed; students were able to understand the online programs and other books through their IT; distance learning framework was introduced by the day-to-day newspaper and parent control; programs evaluated.
Pirouette Vertical Ground Reaction Force of Ballet Dancers and Non-Dancers

BACKGROUND: Different amounts of force are needed to produce an effective turn for the pirouette, especially vertical force.&#x0D; AIM: To examine the vertical force produced by the supporting leg during the execution of a pirouette en dehors of ballet dancer and non-dancer participants.&#x0D; METHOD: The participants included five ballet dancers who composed the ballet dancer group and eight girls without previous experience of dance training who composed the non-dancer group. The participants were invited to execute the pirouette en dehors on a force platform with each leg as the supporting leg. Two-way analyses of variance were used to test vertical reaction forces between the two groups over the preferred and non-preferred leg.&#x0D; RESULTS: Among the three vertical forces measured in the present study, the maximum vertical peak for the initial impulse was significantly higher for the ballet dancers compared to the non-dancer girls. The minimum vertical force and maximum vertical peak for the final impulse were similar between both groups.&#x0D; CONCLUSION: The results suggest that the initial vertical force may be critical to the pirouette en dehors, determining proficient execution of this movement in ballet dancers.
Analysis of UDP Traffic Norms through Packet Sniffing on Peer-to-Peer Networks

A significant obstacle with current peer-to-peer (p2p) traffic monitoring and analysis involves the large number of newly emerging p2p applications possessing more intricate communication structures and traffic patterns than the traditional client-server applications. The volume of traffic generated by these applications, such as uTorrent, BitTorrent, eMule, Ares, etc., is stated to be well over half of the total internet traffic. The dynamic use of port numbers, multiple sessions, and other smart features of these applications complicate the characterization of current p2p traffic. Transport-level traffic identification is a preliminary but required step towards traffic characterization, which this article mainly addresses. The traditional traffic identification methods are mostly based on well-known port numbers. These methods are not appropriate for the identification of emerging P2P applications. This paper proposes using a packet sniffer to identify the current p2p traffic. We categorized the most current used p2p network application based on their user datagram protocol (UDP) information to analyze the traffic patterns. Using this categorization, we found that UDP provided the most features that guarantee data integrity.