Reviewerly/exHarmony huggingface dataset:
Intelligent Conversational Agents for Ambient Computing

We are in the midst of an AI revolution. Three primary disruptive changes set off this revolution: 1) increase in compute power, mobile internet, and advances in deep learning. The next decade is expected to be about the proliferation of Internet-of-Things (IoT) devices and sensors, which will generate exponentially larger amounts of data to reason over and pave the way for ambient computing. This will also give rise to new forms of interaction patterns with these systems. Users will have to interact with these systems under increasingly richer context and in real-time. Conversational AI has a critical role to play in this revolution, but only if it delivers on its promise of enabling natural, frictionless, and personalized interactions in any context the user is in, while hiding the complexity of these systems through ambient intelligence. However, current commercial conversational AI systems are trained primarily with a supervised learning paradigm, which is difficult, if not impossible, to scale by manually annotating data for increasingly complex sets of contextual conditions. Inherent ambiguity in natural language further complicates the problem. We need to devise new forms of learning paradigms and frameworks that will scale to this complexity. In this talk, we present some early steps we are taking with Alexa, Amazon's Conversational AI system, to move from supervised learning to self-learning methods, where the AI relies on customer interactions for supervision in our journey to ambient intelligence.
Quantum Protocol for Electronic Voting without Election Authorities

Electronic voting is a very useful but challenging internet-based protocol that despite many theoretical approaches and various implementations with different degrees of success, remains a contentious topic due to issues in reliability and security. Here we present a quantum protocol that exploits an untrusted source of multipartite entanglement to carry out an election without relying on election authorities, simultaneous broadcasting or computational assumptions, and whose result is publicly verifiable. The level of security depends directly on the fidelity of the shared multipartite entangled quantum state, and the protocol can be readily implemented for a few voters with state-of-the-art photonic technology.
QUARE: 1st Workshop on Measuring the Quality of Explanations in Recommender Systems

QUARE - measuring the QUality of explAnations in REcommender systems - is the first workshop that aims to promote discussion upon future research and practice directions around evaluation methodologies for explanations in recommender systems. To that end, we bring together researchers and practitioners from academia and industry to facilitate discussions about the main issues and best practices in the respective areas, identify possible synergies, and outline priorities regarding future research directions. Additionally, we want to stimulate reflections around methods to systematically and holistically assess explanation approaches, impact, and goals, at the interplay between organisational and human values. The homepage of the workshop is available at: https://sites.google.com/view/quare-2022/.
SpaceQA

We present SpaceQA, to the best of our knowledge the first open-domain QA system in Space mission design. SpaceQA is part of an initiative by the European Space Agency (ESA) to facilitate the access, sharing and reuse of information about Space mission design within the agency and with the public. We adopt a state-of-the-art architecture consisting of a dense retriever and a neural reader and opt for an approach based on transfer learning rather than fine-tuning due to the lack of domain-specific annotated data. Our evaluation on a test set produced by ESA is largely consistent with the results originally reported by the evaluated retrievers and confirms the need of fine tuning for reading comprehension. As of writing this paper, ESA is piloting SpaceQA internally.
Development of an integrated covid-19 tracking and monitoring system

During the COVID-19 pandemic, several policies were instituted to help mitigate the spread of the virus and contact traceability as the virus is spread through person-to-person and person-to-surface contact. One of these was a mandatory completion of a COVID-19 compliance form that captured the details of users of a premises before being granted access. This project proposes an integrated COVID-19 monitoring and tracking system that ensures that the filing process of the compliance forms is fully automated, thus eliminating person-to-person and person-to-surface contact. The automated system will use machine learning algorithms for facial recognition and Natural Language Processing developed in Python to interact with the client through voice prompts.
Modeling Indonesia’s Inflation Based on E-Money Transaction Volume with Local Polynomial Estimator Approach

Background: The volume of electronic money transactions in Indonesia tends to fluctuate, before finally experiencing a decrease at the beginning of the Covid-19 pandemic and reaching the lowest level in May 2020 which then slowly increased again until September 2021. Bank Indonesia (BI) with the government and relevant authorities cooperated in taking steps to monitor, assess, and mitigate the impact of the spread of the Covid-19 virus. Aim: This study aims to find out how the variable effect of electronic money transaction volume (e-money) influences inflation in Indonesia and how accurate the prediction of the volume of electronic money transactions in the future Method: The method used in this research is a quantitative research method with an associative type of research approach. This study uses a type of secondary data involving a quantitative variable where data in the form of Time series that takes the object of the State of Indonesia as a reference. Data involves a single quantitative predictor variable. Findings: Based on this study, the authors found that there needs to be an increase in the volume of electronic transactions by 484,630,000 to increase inflation to reach the target in 2021.
Assessing the impact of climate change on Ha Tu coal mine, Quang Ninh province

Climate change has been affecting the socio-economic life of Quang Ninh province in general, and the coal mining activities of many companies, including Ha Tu Coal Joint Stock Company in particular. By the methods of collection, synthesis, statistics, field survey, and public consultation, this study has evaluated the climate condition of the study region and the impact of climate change on the coal mines in the past. The environmental incident of landslides caused by heavy rain in 2015 has filled rivers and streams and destroyed infrastructure. According to the scenario of climate change for Quang Ninh province by 2100 (B2), the regional temperature will increase by 2.50C, and the rainfall will increase by 7.3% compared to the period 1980÷1999. The study predicted the potential impacts of climate change on mining operations in the future such as the increased risk of landslides, floods, geological hazards, the drawdown of groundwater levels, and subsidence of the ground surface at the mining site. By the forecast results, the larger the mining depth is, the higher the radius affected by the lowering of the groundwater level will become, which can be up to 2.4 km compared to the mining area. In addition, the increase in temperature will be one of the factors that cause difficulties for production activities, affect the health of workers, slow down the process of reclamation and restoration of the mine environment, and increase electricity and water costs, etc. The study also proposed several technical and management solutions to reduce the risk and respond to environmental incidents before the impact of climate change on the Ha Tu coal mine towards sustainable development.
Review and Applications of Machine Learning and Artificial Intelligence in Engineering

This chapter presents a summary review of the development of artificial intelligence (AI). The chapter provides definitions of AI, together with its basic features, and illustrates the development process of AI and machine learning. Further, the authors outline the developments of applications from the past to today and the use of AI in different categories. Finally, they describe prediction applications using artificial neural networks for engineering applications. The outcomes of this review show that the usage of AI methods to predict optimum results is the current trend and will be more important in the future.
Compositional Generalization in Grounded Language Learning via Induced Model Sparsity

We provide a study of how induced model sparsity can help achieve compositional generalization and better sample efficiency in grounded language learning problems.We consider simple language-conditioned navigation problems in a grid world environment with disentangled observations.We show that standard neural architectures do not always yield compositional generalization.To address this, we design an agent that contains a goal identification module that encourages sparse correlations between words in the instruction and attributes of objects, composing them together to find the goal. 1 The output of the goal identification module is the input to a value iteration network planner.Our agent maintains a high level of performance on goals containing novel combinations of properties even when learning from a handful of demonstrations.We examine the internal representations of our agent and find the correct correspondences between words in its dictionary and attributes in the environment.
Correction: Cryptanalysis and improvement of an elliptic curve based signcryption scheme for firewalls

[This corrects the article DOI: 10.1371/journal.pone.0208857.].
SBRidAPI – DETECT DECIMATE AND PREVENT SOCIALBOT IN OSN USING DEEP LEARNING TECHNIQUES

WEB 2.0-BASED Online Social Networks (OSNs) are one of the high impacting human innovations of the 21st century that facilitate their users to express views and thoughts on current affairs and personal life, connect with friends and celebrities, and get updated with the breaking news. OSNs facilitate their users in terms of connectivity, information sharing, knowledge acquisition, and entertainment, but these are not without any repercussions. The real-time message broadcasting and anonymity have exposed. The malicious and anti-social elements generally perform such activities using fake profiles in the form of bots, human-assisted cyborgs, Sybil, and compromised accounts. Recently, OSN platforms have witnessed emerging threats, having serious repercussions that are much more sophisticated in comparison to the classical cyber threats like spamming, DDoS attack, and identity theft. Among OSN-specific threats, automated profiles (aka social bots) are one of the major enablers of advanced illicit activities like political astroturfing. Social bots are very deceptive; they mimic human behavior to gain trust in an OSN and then exploit it for illicit activities. As a result, researchers are analyzing different malicious aspects of social bots. This project presents SBRidAPI, to profile users for detecting social bots on OSNs. To the best of our knowledge, this is the first deep learning-based approach that jointly models a comprehensive set of profile, temporal, activity, and content information for user behavior representation, which is fed to a two-layer stacked BiLSTM, whereas content information is fed to a deep CNN.
Instagram como herramienta motivacional docente: Estudio de caso

Teaching involves constant training in and adaptation to new technologies and tools for learning. The use of social media in teaching-learning processes is an increasingly widespread practice, although its application in terms of motivation and communication, as a tool to promote greater engagement with students, is still pending. The present proposal contextualizes the use of Instagram as a useful tool that allows the establishment of bonds of trust amongst the members of the training process, allowing the acquisition of knowledge, as well as the transversal competences associated with the degree of Higher Level Training Cycles in Audiovisual Projects, taught at the European University of Madrid. For this purpose, the publications made in an educational account managed by the teacher between 2017-2021 are analyzed in order to validate their use and impact. This analysis shows that students demand this type of initiatives, being especially useful in the context of practical work, in relation to information and communication of activities to be developed, as well as in relation to the visibility of their projects and the strengthening of motivation towards the contents of the degree course.
Verification of Sigmoidal Artificial Neural Networks using iSAT

This paper presents an approach for verifying the behaviour of nonlinear Artificial Neural Networks (ANNs) found in cyber-physical safety-critical systems.We implement a dedicated interval constraint propagator for the sigmoid function into the SMT solver iSAT and compare this approach with a compositional approach encoding the sigmoid function by basic arithmetic features available in iSAT and an approximating approach.Our experimental results show that the dedicated and the compositional approach clearly outperform the approximating approach.Throughout all our benchmarks, the dedicated approach showed an equal or better performance compared to the compositional approach.
Application Prototype Attendance System Garuda Indonesia's Premium Service Assistant Employees Use The Waterfall Model

In the era of information technology which has developed rapidly, it becomes a challenge for users of information technology and encourages every activity of the organization to be able to use it as a support for work activities so as to produce fast and accurate information. PT Garuda Indonesia Tbk as a company engaged in commercial air transportation services that requires good management in employee attendance, special data collection needed to record attendance that can be well organized. The employee attendance process still uses the manual method even though it has been assisted by existing technology, namely using WhatsApp group media every day with a predetermined format and making chat results that have been carried out without a report recap for one month which risks losing attendance data and failures that have occurred. can harm the company. Based on these problems, an attendance system application prototype was made in order to minimize errors. The research method used includes observation, interviews, and literature study. While the system development method using the Waterfall model includes analysis, design, manufacture, testing and system maintenance. The prototype system application that will be developed is expected to be able to build this attendance system more quickly and precisely.
Classical and non-classical drama structures

The article attempts to apply a structural approach to the existence and development of the dramatic genre. The au-thor proceeds from the position of two types of structures – classical (order structure) and non-classical (chaos struc-ture). The constant transformation of one structure into another throughout history demonstrates a certain stable mechanism of change, when the centripetal movements of the classical structure give way to centrifugal ones, which causes the classical structure to collapse. In the future, it opens, expands and transforms. Then this mechanism goes in reverse order. Thus, artistic culture, in particular, dramatic (as well as social, historical) processes of compression and expansion, closeness and openness take place over two large cycles. This is a constant «drawing» of the movement of cycles from the classical structure (order) to the non-classical (chaos) and becomes the key to unraveling the existence of the dramatic genre for large periods, perhaps for the period of the entire civilization, showing the «curve» of the course and transformations of the dramatic genre, which does not fit into the linear paradigm. At the same time, this «curve» also testifies to the transformations of social and historical structures. In connection with all this, it is possible to put forward some hypotheses that can be confirmed in related sciences – quantum physics, philosophy, etc. In gen-eral, this indicates the existence of some kind of supra-historical law or meta-law that operates in the universe. In order to understand the movement of genres, in this case, the dramatic, it is not enough to use only a historical descriptive approach. A kind of permanent, universal law of transformations of the existence and movement of the genre gives a structural approach
LibNDT: Towards a Formal Library on Spreadable Properties over Linked Nested Datatypes

Nested datatypes have been widely studied in the past 25 years, both theoretically using category theory, and practically in programming languages such as HASKELL.They consist in recursive polymorphic datatypes where the type parameter changes throughout the recursion.They have a variety of applications such as modelling memory or modelling constraints over regular datatypes without relying on dependent types.In this work, we focus on a specific subset of nested datatypes which we call Linked Nested DataTypes (LNDT).We show that some usual datatypes such has List and Maybe, as well as some well-known nested datatypes such as Nest and even Bush can be built as various instances of LNDT.We proceed by presenting LIBNDT, a library, developed both in AGDA and COQ, which focuses on the set of constructs that can be spread directly from the parameter on which a LNDT is built, to the LNDT itself.These spreadable elements are of two kinds, functions, such as folds and map, and properties, such as the congruence of map or the satisfaction of a given predicate for at least one, or all, elements of the structure.We make use of the dependent type system of both COQ and AGDA to model the latter.This paper ends with a discussion about various interesting topics that were raised throughout our development such as the issue of termination, the comparison of our tools and the proof effort required to extend LIBNDT with additional elements.
“I Speak Not”: Accounts of English Language Learners With Glossophobia

This qualitative research employed the phenomenological method of study to determine the experiences of English language learners in public speaking and their coping mechanisms to redress such experiences. The study utilized in-depth interviews to elicit responses from the participants. Colaizzi’s data analysis method was used to analyze the data gathered. This study's participants were the ten selected and assessed Grade 12 students of a tertiary school located in Midsayap, North Cotabato. Results revealed that English language learners’ public speaking anxiety stemmed from the fear of perception or negative evaluation, the influence of unpleasant experiences, and pressure from competitiveness. The participants used four coping mechanisms to cope with the anxiety and improve themselves in the process – remediation, positive thinking, teacher’s positive attitude, and peer seeking. The researchers concluded that public speaking anxiety stems from fear of being judged, negative experiences, and pressure from the different environmental factors. However, certain anxious persons can use the abovementioned coping mechanisms to reduce or improve their speaking skills.
Primary vocational education in the digital space of secondary schools in Canada and China

The article deals with peculiarities of vocational programs implemented in Canada and China in the context of digitalization having their own specifics due to their national culture, traditions and social and economic conditions.
Determinants of American Adults Experiencing and Viewing Online Harassment Differently

With the development of the Internet, online harassment becomes a significant problem nowadays. 41% of U.S. adults have personally experienced some forms of online harassment. Two questions are examined in the paper: First, what kinds of characteristics of adults make them regard offensive content online as a big problem? Second, what kind of characteristics of adults make them prone to online harassment? The data from the American Trends Panel Wave 74 September Survey are used. Logistic regression is applied to these two questions. For the first question, choosing a closer view whether offensive content online is too often excused as not a big deal or many people take offensive content online too serious is the outcome variable with several independent variables: gender variable, age group variable, education level variable, race variable and region variable. For the second question, whether the survey respondents have experienced any kind of online harassment is the outcome variable, with gender variable, age group variable, education level variable, race variable and region variable as independent variables. The results of the models show that gender, education level, and age are all important factors for both questions.
Implementasi Pembelajaran Menyenangkan dengan Model Teams Games Tournament (TGT) untuk Memudahkan Pembelajaran IPA Terhadap Peserta Didik

The current learning pattern is in principle a process to make understanding of a material easier to understand, through this process it is hoped that students can and easily understand each subject. Educators need to create a pleasant learning atmosphere so that students do not feel burdened in carrying out learning activities. By using the right media in the learning process it will be more interesting and fun and also help students more easily understand the material being taught. Implementing fun learning is needed in all lessons that are considered difficult for students to understand. The learning process with the game system used in the Teams Games Tournament learning is hoped that students receive material that is easier to understand, relax, and can also foster a sense of responsibility and togetherness in teaching and learning.&#x0D;
LA-ICP-MS analysis of trace and rare-earth element distribution in calcite fracture fillings from Forsmark, Simpevarp and Laxemar (Sweden)

Abstract Concentrations and spatial distribution of trace elements in secondary minerals provide valuable information about mobility controlling processes in natural fractures. Important examples include rare-earth element contents that act as analogues for the retention of trivalent actinides such as Am/Cm or Pu(III). The secondary phases (carbonates) investigated in this study originate from exploration drilling bore cores of the Swedish Nuclear Fuel and Waste Management Company SKB (Forsmark, Simpevarp and Laxemar, Sweden). Here, high-resolution element analysis (Micro-X-ray Fluorescence-Spectrometry (µXRF) and Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS)) is applied to scan for Na and the trace elements Mn, Fe, Sr, Pb, Th and U as well as the rare-earth elements Y, La, Ce and Yb associated with carbonate fracture fillings. High resolution element maps highlight growth zones and microstructures within the samples, which are not detected by the usual point and line measurements. Evidence of phase-dependent partitioning is observed. The partition coefficients, D, determined from formation water and carbonate data were compared to experimentally generated coefficients and values derived from a 17-year precipitation experiment carried out at the Äspö Hard Rock Laboratory (HRL). Distribution coefficients of the light rare-earth elements La and Ce have been found to be relatively high in the studied samples, whereas the coefficients of distribution of Sr and U are remarkably low. Overall, the results of this work show that the secondary calcite formed in deep granitic fractures coprecipitated periodically with significant amounts of radionuclide analogues (i.e., rare-earth elements).