Reviewerly/exHarmony huggingface dataset:
Rendering the Visual History of Transilvania University of Brașov Using Augmented Reality

The 11th International Conference on Information Science and Information Literacy – ICISIL 2021 – will take place in Brașov, Romania, on the 11th and 12th of March, 2021. The conference will be organized by Transilvania University of Brasov, Lucian Blaga University of Sibiu, with the support of The Public Library of Brașov County and MB Think Tank. ICISIL 2021 will focus on: The Role of Information in Services Provided to Innovative Users. ICISIL 2021 will bring together professionals, academic staff, including doctoral students, with a focus on Information Sciences, along with policy-makers and stakeholders. The conference fosters an inter-disciplinary exchange of knowledge. Sessions include contributed papers, keynote presentations and panel discussions. We support PhD students in the field through reduced registration fees. In addition, a PhD workshop will be held in the morning of 12 March 2021, as part of the ICISIL 2021 annual conference, at no extra cost for all registered students. Moreover, ICISIL 2021 will host a workshop on Innovative Technologies, focusing on 3D printing. Please submit your article via Editorial Manager Editorial Manager ABSTRACTING & INDEXING 11th International Conference on Information Science and Information Literacy is covered by the
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners

Author(s): Chuang, Yun-Shiuan; Zhang, Xuezhou; Ma, Yuzhe; Ho, Mark K; Austerweil, Joseph Larry; Zhu, Jerry | Abstract: Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectations people have about a learner with a behavioral experiment: Hu- man teachers were asked to teach a sequential decision-making task to an artificial dog in an online manner using rewards and punishments. The artificial dogs were implemented with either an Action Signaling agent or a Q-learner with different dis- count factors. Our findings are threefold: First, we used ma- chine teaching to prove that the optimal teaching complexity across all the learners is the same, and thus the differences in human performance was solely due to the discrepancy between human teacher’s theory of mind and the actual student model. Second, we found that Q-learners with small discount factors were easier to teach than action signaling agents, challenging the established conclusion from prior work. Third, we showed that the efficiency of teaching was monotonically increasing as the discount factors decreased, suggesting that humans’ theory of mind bias towards myopic learners.
Transductive Polyhedral Conic Classifiers

In this study, transductive (semi-supervised) polyhedral conic classifiers are proposed. In the proposed method, concave-convex procedure is used to solve the optimization problem as in Robust Transductive Support Vector Machines. This procedure decomposes a non-convex optimization problem into concave and convex parts. Unlike RTSVM that uses SVM formulation, our proposed method uses polyhedral conic classifiers (PCC) that provides tight and closed decision boundaries compared to SVM. In this way, effective, fast and better results can be obtained in large-scale datasets.
Robust stabilization of uncertain rectangular singular fractional order T-S fuzzy systems with the fractional order 0 < α < 1

This paper presents a novel method to investigate the robust stabilization problem of uncertain rectangular singular fractional order Takagi-Sugeno (T-S) fuzzy systems with the fractional order 0 < α < 1. Firstly, the uncertain rectangular singular fractional order T-S fuzzy system is transformed into an augmented uncertain square singular fractional order T-S fuzzy system by designing a new T-S fuzzy dynamic compensator. Secondly, a sufficient condition in the form of linear matrix inequalities (LMI) is obtained for the robust stabilization of the uncertain rectangular singular fractional order T-S fuzzy system. Finally, a numerical example is given to verify the effectiveness of the results proposed.
Dynamic Resource Allocation Method Based on Symbiotic Organism Search Algorithm for HPC Application

Cloud computing (CC) merits such as scalability, reliability, and resource pooling have enticed experts to implement their high performance computing (HPC) applications on cloud. Nonetheless, HPC applications can pose critical defies on cloud that could emasculate the acquired merits if appropriate care is not taken. The main objective of this research is to develop a task scheduling algorithm that convalesces HPC application performance on software-defined network (SDN) with abridged cost. Moreover, a novel framework is adapted to improve HPC task scheduling performance on cloud in an SDN-enabled network, which is a fundamental part of CC and used for resource delivery. A proposed algorithm for task scheduling architecture manages scheduling tasks. Also, the proposed algorithm monitors CPU utilization, cost, and provides best optimal results when compared with conventional algorithms such as first come first served, round robin, and optimized time synchronization approach.
Pelatihan Pembuatan Permainan Kuis Interaktif Online yang Keren dan Kekinian Dengan Aplikasi Quizizz

The ongoing COVID-19 pandemic has had a significant impact on the education section. All educators and students are affected by this pandemic. All learning processes, which are usually done face-to-face, under government recommendations, must be carried out online from home (&#x0D; E-learning&#x0D; ) now.&#x0D; Thus, online-based distance learning (E-learning) is becoming more and more developed with a variety of different techniques and strategies. One form of utilizing E-Learning applications that can be used for current learning activities is Quizizz. Quizizz can be used as a solution to create interactive and fun online interactive quizzes. Quizizzhas many advantages that are very useful in motivating students to learn by using the principle of learning while playing. The purpose of this research is to assist teachers to be able to use and utilize online learning media with the support of the Quizizzapplication to create cool, contemporary, interactive and fun online interactive quizzes. The implementation method is carried out in the form of training activities for making cool and contemporary online interactive quiz games, with the participants from Alqur'an Taqarrub Kindergarten teachers, Medan. Based on these training activities, it was identified that the teachers were very enthusiastic and understood how to make their quizzes for kindergarten students by using the menu provided. Thus, it is clear that the use of the Quizizz E-Learning application can assist teachers as educators in conducting evaluations without being limited by place, an attractive appearance and regulated time settings guide students' concentration.
User Experience Evaluation in a Code Playground (Short Paper)

Learning computer programming is a complex activity and requires a lot of practice. The viral pandemic that we are facing has intensified these difficulties. In this context, programming learning platforms play a crucial role. Most of them are characterized by providing a wide range of exercises with progressive complexity, multi-language support, sophisticated interfaces and automatic evaluation and gamification services. Nevertheless, despite the various features provided, others features, which influence user experience, are not emphasized, such as performance and usability. This article presents an user experience evaluation of the LearnJS playground, a JavaScript learning platform which aims to foster the practice of coding. The evaluation highlights two facets of the code playground: performance and a usability. In the former, lab and field data were collected based on Google Lighthouse and PageSpeed Insights reports. In the later, an inquiry was distributed among students from a Web Technologies course with a set of questions based on flexibility, usability and consistency heuristics. Both evaluation studies have a twofold goal: to improve the learning environment in order to be officially used in the next school year and to foster the awareness of user experience in all phases of the software development life-cycle as a key facet in Web applications engagement and loyalty.
RANCANG BANGUN SISTEM INFORMASI AKADEMIK BERBASIS WEB (Studi kasus Sekolah Tunas Gading Jakarta)

The development of technology in the current era of globalization is progressing very rapidly and the information circulating is more and more complex so that the world of education must be able to keep up with technological developments, especially in the field of computer technology, along with current technological developments. The human need for information at this time became so easily fulfilled by the presence of the internet, which made it possible to transfer information in a very short time.The use of information systems in education is very important because it provides convenience for users or users to access the things they want and facilitate all existing activities. Things that are currently still done manually, for example delivery of information / announcements by staff and teachers, sending files of subject matter to students, procuring quiz / exams that are still inefficient, storing student assessment data that is still stored in their respective It is hoped that each teacher's laptop / computer can be done using the system.
Will Multi-modal Data Improves Few-shot Learning?

Most few-shot learning models utilize only one modality of data. We would like to investigate qualitatively and quantitatively how much will the model improve if we add an extra modality (i.e. text description of the image), and how it affects the learning procedure. To achieve this goal, we propose four types of fusion method to combine the image feature and text feature. To verify the effectiveness of improvement, we test the fusion methods with two classical few-shot learning models - ProtoNet and MAML, with image feature extractors such as ConvNet and ResNet12. The attention-based fusion method works best, which improves the classification accuracy by a large margin around 30% comparing to the baseline result.
A computational model for simulating the future using a memory timeline

Author(s): Tiganj, Zoran; Tang, Wei; Howard, Marc | Abstract: The ability to learn temporal relationships and use that knowledge to simulate future events is among the most remarkable aspects of cognition. Recently introduced behavioral task called Judgment of Imminence (JOI) combined with a well-known Judgment of Recency (JOR) task pointed to a remarkable symmetry between the temporal organization of memory and prediction. The data were consistent with the hypothesis that both memory and prediction can be organized as a compressed mental timeline. This means that the past and future can be remembered or simulated sequentially relative to the present. The compression implies that events closer to the present, regardless of whether they are in the past or in the future, were represented more accurately than those further from the present. Here we used the existing JOR model based on a compressed memory timeline to build an associative representation that can learn the temporal relationships and create a timeline of the future, which mirrors the timeline of the past. We show that this approach can simultaneously account for response times and accuracy in both JOR and JOI. This work provides a time-local neural-level mechanistic account for how the temporal organization of the memory can be used to learn the temporal structure of the world and simulate the future in an efficient manner as a compressed mental timeline.
Trends in building fungible blockchains for data and value exchange

Industries applaud blockchain technology because of its nature of removing intermediaries involved in assets and value exchange. The first and second-generation blockchain operates in silos and res...
A One-class Classification Approach Based on SVDD for Imbalanced and Overlapping Classes

Imbalanced data classification is a challenge especially when there is overlapping between two classes. The overlap makes it almost impossible to create differences in the two classes and isolate them. In the real world, many of the datasets are imbalanced and overlapped. This paper identifies the overlapping regions optimally by comparing the results of a single-class support vector data description (SVDD) algorithm performed on each class. Then, the method uses the nearest-neighbour algorithm to classify the data in the overlapping region. The result of the evaluation on the datasets with a high imbalanced rate shows better performance than other approaches.
An ASP-based Solution to the Chemotherapy Treatment Scheduling problem

The problem of scheduling chemotherapy treatments in oncology clinics is a complex problem, given that the solution has to satisfy (as much as possible) several requirements such as the cyclic nature of chemotherapy treatment plans, maintaining a constant number of patients, and the availability of resources, e.g., treatment time, nurses, and drugs. At the same time, realizing a satisfying schedule is of upmost importance for obtaining the best health outcomes. In this paper we first consider a specific instance of the problem which is employed in the San Martino Hospital in Genova, Italy, and present a solution to the problem based on Answer Set Programming (ASP). Then, we enrich the problem and the related ASP encoding considering further features often employed in other hospitals, desirable also in S. Martino, and/or considered in related papers. Results of an experimental analysis, conducted on the real data provided by the San Martino Hospital, show that ASP is an effective solving methodology also for this important scheduling problem. Under consideration for acceptance in TPLP.
THE TEACHER AND STUDENTS’ TALKS IN ENGLISH FOR YOUNG LEARNERS’ CLASSROOM SITUATION

The objective of study is to identify and describe the types of talks practiced by a teacher and her students in an English class. This is a descriptive qualitative study that observed a classroom instructional practices involving a teacher and 30 students at a private Elementary school in Indonesia. The data were obtained through instructional practices recorded and posted in a Youtube Channel with introducing animals as the topic of the meeting. The obtained data were then analyzed by using Flender’s Interaction Analysis Categories (FIAC) that focuses on types of talks between a teacher and students. The steps like transcribing, coding, and analyzing the findings were interpreted in descriptive form. The finding shows that every type of talks was practiced by the teacher and the students. On the types of the talks, it was revealed that asking question, giving direction, and praises or encourages are the most frequently-used types by the teacher and on the types of students’ talks, giving response, silence or confuse were dominant. These types of talks conclude good interactions between the teacher and students particularly on the types of talks that are they used. Through this study, the writer wish that can useful in giving a contribution in English classroom interaction.
Managing information risks: threats, vulnerabilities and responses

This book is an extremely detailed and meticulously researched exploration of risks affecting information through its creation, storage, retention, disclosure, and beyond. It aims to be a practical...
UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial Attacks.

Scene understanding is crucial for autonomous systems which intend to operate in the real world. Single task vision networks extract information only based on some aspects of the scene. In multi-task learning (MTL), on the other hand, these single tasks are jointly learned, thereby providing an opportunity for tasks to share information and obtain a more comprehensive understanding. To this end, we develop UniNet, a unified scene understanding network that accurately and efficiently infers vital vision tasks including object detection, semantic segmentation, instance segmentation, monocular depth estimation, and monocular instance depth prediction. As these tasks look at different semantic and geometric information, they can either complement or conflict with each other. Therefore, understanding inter-task relationships can provide useful cues to enable complementary information sharing. We evaluate the task relationships in UniNet through the lens of adversarial attacks based on the notion that they can exploit learned biases and task interactions in the neural network. Extensive experiments on the Cityscapes dataset, using untargeted and targeted attacks reveal that semantic tasks strongly interact amongst themselves, and the same holds for geometric tasks. Additionally, we show that the relationship between semantic and geometric tasks is asymmetric and their interaction becomes weaker as we move towards higher-level representations.
Residual Tree Aggregation of Layers for Neural Machine Translation

Although attention-based Neural Machine Translation has achieved remarkable progress in recent layers, it still suffers from issue of making insufficient use of the output of each layer. In transformer, it only uses the top layer of encoder and decoder in the subsequent process, which makes it impossible to take advantage of the useful information in other layers. To address this issue, we propose a residual tree aggregation of layers for Transformer(RTAL), which helps to fuse information across layers. Specifically, we try to fuse the information across layers by constructing a post-order binary tree. In additional to the last node, we add the residual connection to the process of generating child nodes. Our model is based on the Neural Machine Translation model Transformer and we conduct our experiments on WMT14 English-to-German and WMT17 English-to-France translation tasks. Experimental results across language pairs show that the proposed approach outperforms the strong baseline model significantly
Adaptive cubature quadrature filter for nonlinear state estimation

A new filtering algorithm has been proposed for nonlinear state estimation where the measurement vector is a nonlinear function of system states and measurement noise. The proposed adaptive cubature quadrature filter demonstrably presents improved estimation performance in the situation where the measurement noise covariance remains unknown to the designer. The filter has been designed based on Bayesian filtering framework with cubature quadrature rule for approximation of Gaussian integral and also incorporates adaptation algorithm designed for auto-tuning of unknown measurement noise covariance. The adaptation algorithm, theoretically developed following maximum likelihood estimation (MLE) for non-additive noise, is numerically stable as it secures the positive definiteness of adapted measurement noise covariance. The superiority of the proposed filter has been demonstrated in simulation over its non-adaptive counterpart and the competing algorithms of adaptive nonlinear filters with the help of some non-trivial case studies. Additionally, suitability of the proposed algorithm is also validated for non-stationary measurement noise.
Exploring the mel scale features using supervised learning classifiers for emotion classification

Human emotions are inherently ambiguous and impure but emotions are important while considering the human uttered speech. The role of human speech is intensified by the aspect of the emotion it conveys. There are several characteristics of speech that differentiates it among different utterances. Various prosodic features like pitch, timbre, loudness and vocal tone categorise speech into several emotions and other domains. The sample speech is changed when it is subjected to various emotional environments. Researches support various experimental analyses for phonetics and prosodic parameters that quantify the quality of speech. Identification of different emotional states of an actor (speaker) can also be done on the basis of mel scale. MFCC is one such variant to study the emotional aspects of the utterances by the speaker. The paper implements a model to identify several emotional states from MFCC for two datasets. The work classifies emotions for two datasets on the basis of MFCC features and gives the comparison of both. This work implements a classification model based on dataset minimisation that is done by taking the mean of features for the improvement of classification accuracy on different machine learning algorithms.
The Effectiveness of Online Learning Assisted by Edmodo Application on Mastery of Science Concepts and Digital Literacy for Elementary Students

The world is currently faced with a covid-19 pandemic situation that affects the entire order of life. In the field of education, it requires alternative learning that can be implemented at home, through distance learning, or online learning. Edmodo is an alternative that can facilitate the learning process online. This study aims to determine the effectiveness of online learning assisted by Edmodo on the mastery of science concepts and the digital literacy of elementary students. The method used in this research is pre-experimental with One Group Pre-test & Post-test design. The research subjects were students of class V SDN Jelegong 02 with a non-probability sample sampling technique from a purposive sample type. The research instrument used was a test of mastery of science concepts and a digital literacy questionnaire. The data analysis technique is by calculating the N-Gain score of each student and then categorizing it into high, medium, and low criteria and determining the effectiveness of learning based on the percentage of students who met the high criteria according to predetermined standards. The results showed that online learning assisted by the Edmodo application was effective for mastery of science concepts with low effectiveness criteria. The results of research on students' digital literacy in terms of individual competence, technical skill aspects, are at the advanced level with moderate effectiveness criteria. In the aspect of critical understanding, it is at an advanced level with moderate effectiveness criteria. Meanwhile, the communicative abilities aspect was at the medium level with low effectiveness criteria.
Schimbări și convenții lingvistice în netspeak

This paper presents a synthetic view on the linguistic changes that Romanian undergoes in netspeak and sheds light on the virtual environment conventions that help build meaning in cyberspace. The study goes through all linguistic layers, starting from the use of punctuation, graphical and phonetic alterations to morphosyntactic adaptations, as well as lexical and semantic trends. The pragmatics of the environment is also tackled, by presenting the most important netspeak conventions that combine text and image to generate meaning: emojis, gifs, and memes, and exploring their possible pragmatic functions, as resulted from the current state of the research in the field. The examples presented in the paper are taken from instant messaging applications.