Reviewerly/exHarmony huggingface dataset:
Approval Rating of Peruvian Politicians and Policies using Sentiment Analysis on Twitter

Nowadays, using the social network Twitter, a subject can easily access, post, and share information about news, events, and incidents taking place currently in the world.Recently, due to the high number of users and the capability to transfer information instantly, Twitter had attracted the interest of politicians with the goal to interact with their followers and to communicate theirs polices.Fearing the disagreements and disturbances that the application of some policies might cause, usually politicians use surveys to support their actions.However, such studies still use traditional questionnaires to recover information and are costly and time-consuming.Recent advances in automatic natural language processing have allowed the extraction of information from textual data, like tweets.In this work, we present a method to analyze Twitter data related to Peruvian politicians and able to score the latent sentiment polarity of such messages.Our proposal is based on an embedding representation of tweets, which are classified by a convolutional neural network.For evaluation, we collected a new dataset related to the current President of Peru, where the model achieved 91.2% of sensibility and 94.4% of specificity.Furthermore, we evaluated the model in two politic topics, that were totally unknown for the model.In all of them, our approach gives comparable results to renowned Peruvian pollsters.
Analysis of pedagogical activity as a basis for professional growth of teachers of urban and rural schools

One of the modern challenges for the Russian education system is the implementation of continuous professional development of teachers. The development of pedagogical excellence depends on a teacher's ability to self-analyze and selfevaluate his/her activity. The authors consider reflexion as a tool that helps teachers reflect upon their pedagogical reality and determine the future directions of professional development. Practice shows that not all teachers are able and willing to study the state and results of their work and, therefore, need help and support in matters of selfdiagnosis and professional development. For this reason a course for teachers in urban and rural schools in Perm region is being offered at the Center for Continuing Professional Development of Educators, under the additional education program «Designing a Teacher's Individual Educational Path in the Informational and Educational Space of the Region». The basis of this course is self-analysis of pedagogical activity and identification of priority areas for professional development of teachers. The article describes the methodology of evaluating professional competencies «Teacher Self-Assessment» developed by the teachers of the Department of Professional Mastery of this center. The authors of the article explain the procedure for conducting the methodology, which consists of several stages — self-analysis of aspects of professional activity, choosing a resource for change and choosing a priority direction for changes in their professional growth. An analysis of the results of the degree of satisfaction with the organization of the educational process and general aspects of pedagogical activity of teachers from urban and rural schools of Solikamsk urban district, carried out by this methodology, is presented. In addition, the priority areas of their professional growth are described.
IMPACTO DA COVID-19 NA GERAÇÃO E DISTRIBUIÇÃO DA RIQUEZA EM EMPRESAS DO SETOR VAREJISTA LISTADAS NA B3

The present study aims to identify the impact of Covid-19 on the generation and distribution of wealth through the demonstration of added value (DAV) evidenced by companies in the retail sector listed on B3. Initially, an approach is made on the DAV, the stakeholder theory, the theory of disclosure, on the retail sector, and on the Covid-19 epidemic, to present the analysys results then. Twenty-eight companies were analyzed in 2019 and 2020 using the Wilcoxon test. The results showed a significant reduction in the added value generated by companies in 2020 compared to the previous year, contributing to academia and society with empirical evidence of the effect of the Covid-19 health crisis on the retail sector. In addition, it contributes relevant information on the sector’s behavior with its interest groups, with a reduction in the distribution of wealth to shareholders in 2020. The socially responsible behavior of the sector with its personnel is highlighted, which maintained the distribution of wealth for this group and the lack of reduction in distribution to the government, which denotes the lack of effective public policies to face the crisis by the sector.
Applying the Stereotype Content Model to assess disability bias in popular pre-trained NLP models underlying AI-based assistive technologies

Stereotypes are a positive or negative, generalized, and often widely shared belief about the attributes of certain groups of people, such as people with sensory disabilities.If stereotypes manifest in assistive technologies used by deaf or blind people, they can harm the user in a number of ways-especially considering the vulnerable nature of the target population.AI models underlying assistive technologies have been shown to contain biased stereotypes, including racial, gender, and disability biases.We build on this work to present a psychologybased stereotype assessment of the representation of disability, deafness, and blindness in BERT using the Stereotype Content Model.We show that BERT contains disability bias, and that this bias differs along established stereotype dimensions.
Scale parameter recognition of blurred moving image based on edge combination algorithm

In order to overcome the problems of the traditional fuzzy motion image scale parameter recognition method with high recognition error and poor denoising effect, the paper proposes a new fuzzy motion image scale parameter recognition method based on edge combination algorithm. A fuzzy moving image denoising method based on fuzzy wavelet threshold denoising is used to remove noise in the blurred moving image. The scale parameter of the blurred moving image after denoising is extracted through the blurred moving image degradation model. According to the scale parameters of the blurred moving image, the edge combination algorithm is used to realise the recognition of the scale parameters of the blurred moving image. Experimental results show that the proposed method has the best quality of the blurred motion image after denoising, the lowest misrecognition rate of the scale parameter of the blurred motion image, and the application performance is remarkable.
Estimating Scale and Scope Economies of Academic Libraries: Using the Flexible Fixed Cost Quadratic Function

This study estimates scale and scope economies of academic libraries. Previous studies using Cobb-Douglas cost function or a linear cost function have limitations in measuring multiple outputs of academic libraries since they used circulation as a single output measure. They also have limitations in estimating long-run cost functions of academic libraries. Some researchers applied the translog cost function to public libraries and recognized the multiple-output nature of libraries, but they mostly failed to incorporate multiple output measures in their analyses empirically. This study develops multiple aggregate output measures and applies the flexible fixed cost quadratic function to academic libraries.
Face Recognition System Design and Implementation using Neural Networks

Face recognition technology is used in biometric security systems to identify a person digitally before granting the access to the system or the data in it.There are many kidnappings or abduction cases happen around us, however, the kidnap suspects will be set free if there is lack of evidence or when the victims are not able to testify in court because they suffer from post-traumatic stress disorder (PTSD).The objectives of this study are, to develop a device that will capture the image of a kidnapper as evidence for future reference and send the captured image to the family of the victim through email, to design a face recognition system to be used in searching kidnap suspects and to determine the best training parameters for the convolution neural network (CNN) layers used by the proposed face recognition system.The accuracy of the proposed system is tested with three different datasets, namely the AT&T database, face database from [23] and a custom face dataset.The results are 87.50%,92.19% and 95.93% respectively.The overall face recognition accuracy of the proposed system is 98.48%.The best training parameters for the proposed CNN model are kernel size of 5x5, 32 and 64 filters for first and second convolutional layers and learning rate of 0.001.
Multi-task Knowledge Graph Representations via Residual Functions

In this paper, we propose MuTATE, a Multi-Task Augmented approach to learn Transferable Embeddings of knowledge graphs. Previous knowledge graph representation techniques either employ task-agnostic geometric hypotheses to learn informative node embeddings or integrate task-specific learning objectives like attribute prediction. In contrast, our framework unifies multiple co-dependent learning objectives with knowledge graph enrichment. We define co-dependence as multiple tasks that extract covariant distributions of entities and their relationships for prediction or regression objectives. We facilitate knowledge transfer in this setting: tasks $$\rightarrow $$ graph, graph $$\rightarrow $$ tasks, and task-1 $$\rightarrow $$ task-2 via task-specific residual functions to specialize the node embeddings for each task, motivated by domain-shift theory. We show 5% relative gains over state-of-the-art knowledge graph embedding baselines on two public multi-task datasets and show significant potential for cross-task learning.
HW-TSC’s Participation in the IWSLT 2022 Isometric Spoken Language Translation

This paper presents our submissions to the IWSLT 2022 Isometric Spoken Language Translation task.We participate in all three language pairs (English-German, English-French, and English-Spanish) under the constrained setting, and submit an English-German result under the unconstrained setting.We use the standard Transformer model as the baseline and obtain the best performance via one of its variants that shares the decoder input and output embedding.We perform detailed pre-processing and filtering on the provided bilingual data.Several strategies are used to train our models, such as Multilingual Translation, Back Translation, Forward Translation, R-Drop, Average Checkpoint, and Ensemble.We experiment on three methods for biasing the output length: i) conditioning the output to a given targetsource length-ratio class; ii) enriching the transformer positional embedding with length information and iii) length control decoding for non-autoregressive translation etc.Our submissions achieve 30.7, 41.6 and 36.7 BLEU respectively on the tst-COMMON test sets for English-German, English-French, English-Spanish tasks and 100% comply with the length requirements.
Doctor Recommendation in Online Health Forums via Expertise Learning

Huge volumes of patient queries are daily generated on online health forums, rendering manual doctor allocation a labor-intensive task.To better help patients, this paper studies a novel task of doctor recommendation to enable automatic pairing of a patient to a doctor with relevant expertise.While most prior work in recommendation focuses on modeling target users from their past behavior, we can only rely on limited words in a query to infer a patient's needs for privacy reasons.For doctor modeling, we study the joint effects of their profiles and previous dialogues with other patients and explore their interactions via selflearning.The learned doctor embeddings are further employed to estimate their capabilities of handling a patient query with a multihead attention mechanism.For experiments, a large-scale dataset is collected from Chunyu Yisheng, a Chinese online health forum, where our model exhibits state-of-the-art results, outperforming baselines only considering profiles and past dialogues to characterize a doctor. 1
Increase the application of Information Technology in Innovative Teaching Methods of the Theory Section at Thanh Hoa University of Culture, Sports and Tourism

Information technology is applied in almost all fields of life, including education. ICT influences and transforms teaching and learning through its potential as a source of knowledge, a medium of content delivery, and a medium of interaction and dialogue. IT changes the elements of the curriculum and the type of program that prepares the essential foundation for learners to meet the requirements of the digital world, the information technology, and the technology era. The article focuses on analyzing the roles, and current situation and proposes solutions to enhance the application of information technology in teaching method innovation of theoretical modules at the Thanh Hoa University of Culture, Sports and Tourism.
Media Fusion Visual Threshold Under the Network and New Media Professional Talent Training Path Analysis

Digital technology and network information technology are developing rapidly, and the barriers between various media no longer exist, resulting in new media.The emergence and development of new media has greatly promoted the deep integration of traditional media and new media.In the development of media integration, on the one hand, it has brought unprecedented changes to the new network media industry, on the other hand, it also urges colleges and universities to train new media talents more actively.Based on this, this paper analyzes in detail the network and new media professional training path under the media fusion visual threshold for reference.
Distributionally Robust Finetuning BERT for Covariate Drift in Spoken Language Understanding

In this study, we investigate robustness against covariate drift in spoken language understanding (SLU).Covariate drift can occur in SLU when there is a drift between training and testing regarding what users request or how they request it.To study this we propose a method that exploits natural variations in data to create a covariate drift in SLU datasets.Experiments show that a state-of-the-art BERT-based model suffers performance loss under this drift.To mitigate the performance loss, we investigate distributionally robust optimization (DRO) for finetuning BERT-based models.We discuss some recent DRO methods, propose two new variants and empirically show that DRO improves robustness under drift.
AbductionRules: Training Transformers to Explain Unexpected Inputs

Transformers have recently been shown to be capable of reliably performing logical reasoning over facts and rules expressed in natural language, but abductive reasoning -inference to the best explanation of an unexpected observation -has been underexplored despite significant applications to scientific discovery, common-sense reasoning, and model interpretability.We present AbductionRules, a group of natural language datasets designed to train and test generalisable abduction over natural-language knowledge bases.We use these datasets to finetune pretrained Transformers and discuss their performance, finding that our models learned generalisable abductive techniques but also learned to exploit the structure of our data.Finally, we discuss the viability of this approach to abductive reasoning and ways in which it may be improved in future work.
FirstPiano: A New Egocentric Hand Action Dataset Oriented Towards Augmented Reality Applications

Research on hand action recognition has achieved very interesting performance in recent years, notably thanks to deep learning methods. With those improvements, we can see new visions towards real applications of new Human-Machine interfaces (HMI) using this recognition. Such new interactions and interfaces need data to develop the best user experience iteratively. However, current datasets for hand action recognition in an egocentric view, even if perfectly useful for these problems of recognition, they generally lack of a limited but coherent context for the proposed actions. Indeed, these datasets tend to provide a wide range of actions, more or less in relation to each other, which does not help to create an interesting context for HMI application purposes. Thereby, we present in this paper a new dataset, FirstPiano, for hand action recognition in an egocentric view, in the context of piano training. FirstPiano provides a total of 672 video sequences directly extracted from the sensors of the Microsoft HoloLens Augmented Reality device. Each sequence is provided in depth, infrared and grayscale data, with 4 different points of view for the last one, for a total of 6 streams for each video. We also present the first benchmark of experiments using a Capsule Network over different classification problems and different stream combinations. Our dataset and experiments can therefore be interesting for research communities of action recognition and human-machine interface.
New Directions in Quantum Technologies

Quantum technology is based on multiple research fields that apply the properties of quantum mechanics. There is a vast literature, however, new directions of technologies in quantum science that have a high potential of development are hardly known. This study confronts these problems here by developing a statistical analysis based on data of publications and patents to explain the evolution of emerging research and technological trajectories in quantum science. The scientific exploration reveals that the evolution of new quantum technologies is driven by scientific advances in quantum information, quantum communication, quantum optics, quantum entanglement and quantum cryptography. These findings can clarify current scientific and technological change in these research fields for decision support of R&D investments by stakeholders towards new technological trajectories having a high scientific and technological acceleration for supporting a main economic, industrial and corporate change in the next future.
Is Web Browsing Secure?

With the ever-growing use of the Internet in our daily lives, an increase of potentially sensitive data being shared and accessed online is observed. Web apps and browsers are the main tools used by Internet users, so being able to guarantee secure browsing is a key point. In this context, this paper explores key aspects of web security and privacy in two of the most widely used browsers worldwide - Google Chrome and Microsoft Edge, running in different operating systems - Windows and Linux. Whilst emulating a browsing routine of a common web user for navigating through heterogeneous services, the aim is to verify if the content being shared online is properly secure. For this purpose, a comparative analysis based on collected web-browsing traffic, protocols in use, cookies and related policies is carried out, being the main browsers’ vulnerabilities identified and discussed.
Cue-bot: A Conversational Agent for Assistive Technology

Intelligent conversational assistants have become an integral part of our lives for performing simple tasks.However, such agents, for example, Google bots, Alexa and others are yet to have any social impact on minority population, for example, for people with neurological disorders and people with speech, language and social communication disorders, sometimes with locked-in states where speaking or typing is a challenge.Language model technologies can be very powerful tools in enabling these users to carry out daily communication and social interactions.In this work, we present a system that users with varied levels of disabilties can use to interact with the world, supported by eye-tracking, mouse controls and an intelligent agent Cue-bot, that can represent the user in a conversation.The agent provides relevant controllable 'cues' to generate desirable responses quickly for an ongoing dialog context.In the context of usage of such systems for people with degenerative disorders, we present automatic and human evaluation of our cue/keyword predictor and the controllable dialog system and show that our models perform significantly better than models without control and can also reduce user effort (fewer keystrokes) and speed up communication (typing time) significantly.
The ASEME methodology

In this paper, we present a complete view of an agent-oriented software engineering methodology called agent systems engineering methodology (ASEME). Several parts of the methodology concerning different aspects of the whole development process have already been published in the past in several papers. However, our goal in this paper is to provide a global view on the methodology by providing information about the agent (and multi-agent systems) development process along with recent works concerning the tools that we have developed in order to facilitate the use of ASEME by agent systems developers. We also provide some information on the different practical applications that we have developed using ASEME and which prove that ASEME is very well suited for the development of real world applications.
A Novel Region Growing Algorithm using Wavelet Coefficient Feature Combination of Image Dynamics

Moving object detection has versatile and potential applications in video surveillance, traffic monitoring, human motion capture etc., where detecting object(s) in a complex scene is vital.In the existing background subtraction method based on frame differencing, the false positive and misclassification rate increases as the background becomes more complex and also with the presence of multiple moving objects in the scene.In this piece of work, an approach has been made to enhance the detection performance of the background subtraction method by exploiting the dynamism available in the scene.The resultant differencing frame so obtained by the spatial background subtraction method is subjected to wavelet transformation.By extracting and combining wavelet features from the dynamics of the scene, a novel method of region growing technique has been further utilized to detect the moving object(s) in the scene.Simulation of various video sequences from CDnet, SBMnet, AGVS, I2R and Urban Tracker database has been applied and the method provides satisfactory detection of the moving object in a complex scene.The quantitative measure like Recall, Precision, F1-measure, and specificity computed for the algorithms, have indicated the algorithms can be a suitable candidate for surveillance applications.
Bayesian Network Modelling for Improved Knowledge Management of the Expert Model in the Intelligent Tutoring System

The expert module is an essential part of the intelligent tutoring system.This module uses only declarative knowledge, excluding other types of domain knowledge: procedural and conditional.This elimination makes the expert module very delicate.To solve this issue, the authors propose to embed knowledge processing into the expert model.The contribution aims to empower the expert model via the fragmentation of the knowledge process into four categories: Analyzation, Application, Conceptualization, and Experimentation using the Bayesian Network method as an instrument for modelling expert systems in uncertain areas.According to the management of the expert system through a list of criteria, the expert module can suggest the correct type of knowledge and their following status.