Reviewerly/exHarmony huggingface dataset:
Learning the chemical elements through an augmented reality application for elementary school children

Traditional learning in the chemistry course has often been seen as a boring course among students due to the use of static, non-dynamic, and interactive learning materials. The implementation of digital media in education has provided multiple advantages, the implementation of technologies such as augmented reality promotes effective learning because it makes it possible for students to interact with virtual elements in our real environment. The present research was delimited in the development of an application with augmented reality for the learning of chemical elements in 5th grade of elementary school students, to provide a support tool for both teachers and students, to help achieve a better understanding of the chemical elements. For the development of the augmented reality application called "Atomik-3D", the Mobile-D methodology was used, which is focused on the development of mobile applications; once the application was developed, functional tests were carried out to analyze surface recognition characteristics and ease of use, to analyze the performance a total of 20 tests were carried out giving us positive results. Through the analysis of the results, it was possible to identify that, for the recognition of flat surfaces, the mobile application has a good performance on illuminated surfaces with more than 30 lux. Likewise, the Marker technology used in this project has a very efficient recognition time of a flat area, being mostly no more than 1.5 seconds. Finally, 85% of the participants surveyed agreed that the application developed was easy and very easy to use.
Quiz Maker: Automatic Quiz Generation from Text Using NLP

In the past few years, there has been great technological advancement in the field of deep learning and natural language processing. One of the applications is automatic generation of quizzes from text. The recent advancement in NLP techniques has shown a lot of promise. The proposed solution uses an NLP pipeline involving Bert and T5 transformers to extract keywords and gain insights from the text input. From the extracted keywords, different types of questions are generated such as fill in the blanks, true or false, Wh-type and multiple choice questions. Latest state-of-the-art models proved to perform better in all stages of our pipeline. The results from these models have shown a lot of promise. Through a survey created for evaluating the model, around 60% questions generated by the model were incorrectly identified as human generated or could not be determined by the survey participants.
Sabre: A Narrative Planner Supporting Intention and Deep Theory of Mind

Sabre is a narrative planner—a centralized, omniscient decision maker that solves a multi-agent storytelling problem. The planner has an author goal it must achieve, but every action taken by an agent must make sense according to that agent's individual intentions and limited, possibly wrong beliefs. This paper describes the implementation of Sabre, which supports a rich action syntax and imposes no arbitrary limit on the depth of theory of mind. We present a search procedure for generating plans that achieve the author goals while ensuring all agent actions are explained, and we report the system's performance on several narrative planning benchmark problems.
Discussion and mediation of social outrage on Twitter: The reaction to the judicial sentence of "La Manada"

In recent years, digital feminism has been prominent in the international public sphere and the massive release of individual testimonies of violence against women has had an important presence on social media. In Spain, the reaction to the judicial sentence of the La Manada case, which only convicted the five men accused of abuse, rather than rape, provoked a public outrage that was negotiated on social networks and was later displayed in mass demonstrations across the country. The aim of this study is to analyze the reaction that took place on Twitter on the day of the ruling of the sentence (26 April 2018) and how the dominant frames were initiated and stabilized on Twitter. With a sample of almost 500.000 tweets, the posts achieving most retweets per hour and the most salient media objects (including hashtags, visual content and links) have been analyzed based on the framing theory. Media objects have been explored through flowcharts allowing the visualization of the evolution of the dominant messages. The results show a hegemonic conversation on Twitter against the ruling of the court with a high presence of feminist demands. Furthermore, the analysis of the evolution of the main frames shows that the different Twitter affordances allow rapidity in the negotiation of the dominant messages. This paper contributes to the understanding of the articulation of framing from a complex perspective including media objects such as images and URLs that broaden the layers of meaning of the Twitter discourse on collective actions.
Audio to Indian and American Sign Language Converter using Machine Translation and NLP Technique

People with hearing loss use Sign Language as their mother tongue communication. Unlike acoustic hearing, sign language is a visual language that uses body language and physical communication to communicate effectively thoughts. It usually consists of hand gestures and facial expressions. Generally, communicating with different disabled people seems very difficult. This is because it takes a long time to learn the language, not just the language, people who are not disabled but also people who are. Establishment to communicate in such cases, both parties need to know sign language, or they use a human translator to make communication possible. Information Technologies with their own modern methods such as artificial intelligence, cloud computing has an amazing role to play in improving communication people with speech impairments and ordinary people. Sign language recognition can be done in two ways, glove-based or vision-based recognition. The solution proposed in this paper will produce software that takes over input in the form of speech and indicates appropriate Sign Language. The software is developed in Python platform to convert speech to Indian and American sign languages (ISL and ASL) which provides hearing impairment assistant. This software can be useful in many areas, such as in educational institutes, hospitals, police stations, and for general everyday life conversation.
Requirements decision-making as a process of Argumentation: A Google Maps Case Study with Goal Model

In social media platforms, crowd-users extensively interact and contribute information related to software applications. Usually, crowd-users discuss software features or hot issues and record their opinions about the software applications under discussion either in textual form or via end-user votes. Such requirements-related information is considered a pivotal alternative source for requirements engineers to the already existing in-house stakeholders in order to illustrate decision-making. Also, requirements decision-making for Crowd requirements engineering is a difficult task, as it is always based on incomplete knowledge and requires trade-offs from multi-perspectives. However, existing requirements models and associated tools are still lacking, which enable requirements engineers to make informed decision-making and capture conflicting requirements knowledge. This paper elaborates the interaction among the crowd-users about the Google Map mobile application in the Reddit forum to recover conflicting requirements-related information using the goal modeling approach. For this purpose, we extracted critical arguments from a crowd-users conversation in user forums regarding a given design; built a graphical argumentation model based on the extracted information; aligned types of arguments with goal-oriented modeling constructs in the non-functional requirements framework; conducted exiting goal-model analysis to the requirements model to reach consensus based on argumentation and reasoning, such as supporting, attacking, undefined, and conflicting. The proposal is described with illustrative example models and the associated evaluation processes of design decision-making situation for Google Map interface design.
Explainable bilevel optimization: An application to the Helsinki deblur challenge

In this paper we present a bilevel optimization scheme for the solution of a general image deblurring problem, in which a parametric variational-like approach is encapsulated within a machine learning scheme to provide a high quality reconstructed image with automatically learned parameters. The ingredients of the variational lower level and the machine learning upper one are specifically chosen for the Helsinki Deblur Challenge 2021, in which sequences of letters are asked to be recovered from out-of-focus photographs with increasing levels of blur. Our proposed procedure for the reconstructed image consists in a fixed number of FISTA iterations applied to the minimization of an edge preserving and binarization enforcing regularized least-squares functional. The parameters defining the variational model and the optimization steps, which, unlike most deep learning approaches, all have a precise and interpretable meaning, are learned via either a similarity index or a support vector machine strategy. Numerical experiments on the test images provided by the challenge authors show significant gains with respect to a standard variational approach and performances comparable with those of some of the proposed deep learning based algorithms which require the optimization of millions of parameters.
Penerapan Metode PIECES Framework Pada Tingkat Kepuasan Sistem Informasi Layanan Aplikasi Myindihome

The development of information systems and information technology, of course, can also change people's behavior in carrying out their activities, which were originally done manually, now they can be done digitally so that they are more effective and efficient. Customer satisfaction is a person's level of feeling after being able to compare the perceived performance or results compared to their expectations. Indihome is one of the products of PT. Telkom as a provider of Indihome service products which has services in the form of mobile application-based services, namely Myindihome. The PIECES framework is a framework that has six categories in classification and problem solving, namely Performance, Information and Data, Economics, Control and Security, Efficiency, and Service. This study uses the PIECES Framework method in analyzing data, the use of the PIECES framework method is very suitable in analyzing each system variable being studied because it can provide real results on the effectiveness of the system used by the company. Customer satisfaction with mobile application-based services is measured by the PIECES framework to determine the level of customer satisfaction with services. In this study, measurements were carried out using the PIECES Framework, resulting in an average score of 4.26 with a Likert scale. So service users are categorized as satisfied with mobile-based information system services.
Techno-Economic Analysis of a 12-kW Photovoltaic System Using an Efficient Multiple Linear Regression Model Prediction

Renewable energy sources are expected to replace traditional energy sources such as oil and gas in the future. It goes without saying that solar energy has been demonstrated to be a key source of green energy. Solar energy is used because it is abundant, pollution-free, and easily available. However, the power utility market requires highly exact solar energy forecasts. These challenges need the creation of a device that can precisely predict solar energy output via processing the location's weather data, which is accomplished through the use of machine learning and multiple linear regression (MLR). Some elements, such as the number of cloudy days, humidity, temperature, wind condition, and precipitation, should be addressed while simulating solar power output. In this paper, a 12-kW photovoltaic (PV) system on the rooftop of a house in Isfahan was studied using the System Advisor Model (SAM). The most significant research contribution of the proposed paper is to predict the output power of a solar system with the lowest possible error. According to the simulation results, by using the MLR model, the predicted power has an error of 6 % with the actual power, which is a very good estimation. In addition, this system meets each household's energy needs plus an additional 8430 kWh per year, resulting in being paid by utility companies, a fewer number of outages, and lower air pollution levels.
The Role of Artificial Intelligence in Banking for Leveraging Customer Experience

Purpose: In light of digital advancements, banks need to create customer experiences that strengthen loyalty and trust.For establishing a strong digital banking base, it is crucial for banks to make their processes efficient and fast.The purpose of the paper is to analyze the efficacy of banking functions on implementing Artificial Intelligence for enhancing customer engagement and improving customer satisfaction.It targets banks in metropolitan cities of India having tech-savvy customers, leading a fast-paced life who desire personalization and expect faultless and seamless services. Methodology:The study focusses on front, middle and back-office banking processes.The data for middle and back-office processes is collected through 10 interviews of senior officials and head of IT team in major banks.Literature review and theoretical research is carried out for various international and Indian banks with respect to the integration of AI to improve customer interactions and internal banking processes.For understanding the front-office user experience with AI-Banking, data has been gathered through a survey regarding usage of Chatbots on online banking platforms.A quantitative analysis using the Relative Importance Index reveals major use-cases ranked by customers.Spearman correlation is applied to find the relationship between the two most popular use-cases. Findings:The research paper reveals banking features integrated with AI.Chatbot use-cases on banking platforms are ranked based on customer experience.It is proved that there is a positive correlation (0.247) between the two most popular use-cases.The paper proposes IT Architecture and best practices for the digital banking sector.Practical/Theoretical implications: Based on the complete picture of AI integration with banking operations, evolving Indian banks could focus on the most popular use-cases to attract customers.A comparison with the features developed for various banks may provide a way for growth in the digital banking sector.The correlation between Chatbot use-cases may benefit the established Indian banks to further expand business.Originality/value: Implementation of AI in banking is identified for Indian Banks.It is proved that if a person uses Chatbot for assistance in customer service, they are likely to use Chatbot for recommendation regarding offers and discounts.
Stochastic learning control of adiabatic speedup in a non-Markovian open qutrit system

Precise and efficient control of quantum systems is essential to perform quantum information-processing tasks. In terms of adiabatic speedup via leakage elimination operator approach, for a closed system, the ideal pulse control conditions have been theoretically derived by P-Q partitioning technique. However, it is a challenge to design the corresponding control pulses for an open system, which requires noisy environments to be addressed. In this paper, we apply the stochastic search procedures to an open qutrit system and successfully obtain the optimal control pulses for significant adiabatic speedup. The calculation results show that these optimal pulses allow us to acquire higher fidelities than the ideal pulses. The improvement of fidelity is large for relatively strong system-bath coupling strength and high bath temperature. For certain coupling strength and bath temperature, the maximal improvement can be achieved for a critical characteristic frequency which represents the memory time of the environment. Our investigation indicates that the stochastic search procedures are powerful tools to design control pulses for combating the detrimental effects of the environment.
Calibrating the Classical Hardness of the Quantum Approximate Optimization Algorithm

Trading fidelity for scale enables approximate classical simulators such as matrix product states (MPS) to run quantum circuits beyond exact methods. A control parameter, the so-called bond dimension $\chi$ for MPS, governs the allocated computational resources and the output fidelity. Here, we characterize the fidelity for the quantum approximate optimization algorithm by the expectation value of the cost function it seeks to minimize and find that it follows a scaling law $F\bigl(\ln\chi\bigr/N\bigr)$ with $N$ the number of qubits. With $\ln\chi$ amounting to the entanglement that an MPS can encode, we show that the relevant variable for investigating the fidelity is the entanglement per qubit. Importantly, our results calibrate the classical computational power required to achieve the desired fidelity and benchmark the performance of quantum hardware in a realistic setup. For instance, we quantify the hardness of performing better classically than a noisy superconducting quantum processor by readily matching its output to the scaling function. Moreover, we relate the global fidelity to that of individual operations and establish its relationship with $\chi$ and $N$. We sharpen the requirements for noisy quantum computers to outperform classical techniques at running a quantum optimization algorithm in speed, size, and fidelity.
Federated Learning for Condition Monitoring of Industrial Processes: A Review on Fault Diagnosis Methods, Challenges, and Prospects

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set

The recent work ``Combinatorial Optimization with Physics-Inspired Graph Neural Networks'' [Nat Mach Intell 4 (2022) 367] introduces a physics-inspired unsupervised Graph Neural Network (GNN) to solve combinatorial optimization problems on sparse graphs. To test the performances of these GNNs, the authors of the work show numerical results for two fundamental problems: maximum cut and maximum independent set (MIS). They conclude that "the graph neural network optimizer performs on par or outperforms existing solvers, with the ability to scale beyond the state of the art to problems with millions of variables." In this comment, we show that a simple greedy algorithm, running in almost linear time, can find solutions for the MIS problem of much better quality than the GNN. The greedy algorithm is faster by a factor of $10^4$ with respect to the GNN for problems with a million variables. We do not see any good reason for solving the MIS with these GNN, as well as for using a sledgehammer to crack nuts. In general, many claims of superiority of neural networks in solving combinatorial problems are at risk of being not solid enough, since we lack standard benchmarks based on really hard problems. We propose one of such hard benchmarks, and we hope to see future neural network optimizers tested on these problems before any claim of superiority is made.
Drone Detection Method Based on MobileViT and CA-PANet

Aiming at the problems of the large amount of model parameters and false and missing detections of multi-scale drone targets, we present a novel drone detection method, YOLOv4-MCA, based on the lightweight MobileViT and Coordinate Attention. The proposed approach is improved according to the framework of YOLOv4. Firstly, we use an improved lightweight MobileViT as the feature extraction backbone network, which can fully extract the local and global feature representations of the object and reduce the model’s complexity. Secondly, we adopt Coordinate Attention to improve PANet and to obtain a multi-scale attention called CA-PANet, which can obtain more positional information and promote the fusion of information with low- and high-dimensional features. Thirdly, we utilize the improved K-means++ method to optimize the object anchor box and improve the detection efficiency. At last, we construct a drone dataset and conduct a performance experiment based on the Mosaic data augmentation method. The experimental results show that the mAP of the proposed approach reaches 92.81%, the FPS reaches 40 f/s, and the number of parameters is only 13.47 M, which is better than mainstream algorithms and achieves a high detection accuracy for multi-scale drone targets using a low number of parameters.
Qualitative reasoning in a two-layered framework

The reasoning with qualitative uncertainty measures involves comparative statements about events in terms of their likeliness without necessarily assigning an exact numerical value to these events. The paper is divided into two parts. In the first part, we formalise reasoning with the qualitative counterparts of capacities, belief functions, and probabilities, within the framework of two-layered logics. Namely, we provide two-layered logics built over the classical propositional logic using a unary belief modality B that connects the inner layer to the outer one where the reasoning is formalised by means of Gödel logic. We design their Hilbert-style axiomatisations and prove their completeness. In the second part, we discuss the paraconsistent generalisations of the logics for qualitative uncertainty that take into account the case of the available information being contradictory or inconclusive.