Reviewerly/exHarmony huggingface dataset:
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems

Transformer models have achieved state-of-the-art results in a wide range of NLP tasks including summarization. Training and inference using large transformer models can be computationally expensive. Previous work has focused on one important bottleneck, the quadratic self-attention mechanism in the encoder. Modified encoder architectures such as LED or LoBART use local attention patterns to address this problem for summarization. In contrast, this work focuses on the transformer’s encoder-decoder attention mechanism. The cost of this attention becomes more significant in inference or training approaches that require model-generated histories. First, we examine the complexity of the encoder-decoder attention. We demonstrate empirically that there is a sparse sentence structure in document summarization that can be exploited by constraining the attention mechanism to a subset of input sentences, whilst maintaining system performance. Second, we propose a modified architecture that selects the subset of sentences to constrain the encoder-decoder attention. Experiments are carried out on abstractive summarization tasks, including CNN/DailyMail, XSum, Spotify Podcast, and arXiv.
Design and Research of Intelligent Bottle Recycling Device

With the rapid development of mechatronics, most mechanical components can be controlled by computer, and PLC system is more mature. The main function of the intelligent bottle recycling device is to scan the “stuffed” bottles, then compare the scanning results with the data in the memory, and then judge whether or not to spit currency and how many coins to spit.
Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations

Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level. However, there has been limited progress in generating useful discourse-level representations. In this work, we propose to use ideas from predictive coding theory to augment BERT-style language models with a mechanism that allows them to learn suitable discourse-level representations. As a result, our proposed approach is able to predict future sentences using explicit top-down connections that operate at the intermediate layers of the network. By experimenting with benchmarks designed to evaluate discourse-related knowledge using pre-trained sentence representations, we demonstrate that our approach improves performance in 6 out of 11 tasks by excelling in discourse relationship detection.
Construction of a family of positive but not completely positive map for the detection of bound entangled states

We construct a family of map which is shown to be positive when imposing certain condition on the parameters. Then, we show that the constructed map can never be completely positive. After tuning the parameters, we found that the map still remains positive, but it is not completely positive. We then use the positive but not completely positive map in the detection of bound entangled state and negative partial transpose entangled states.
Achieve distributed and secure Internet of things structure using multi-chains solution of blockchain technology

The Internet of Things (IoT) is considered a high-impact technology in many major markets. It promises many opportunities, but there are several risks and drawbacks associated with this technology stand in the way of realizing its potential opportunities, such as the IoT devices limited capabilities (restricted resources) as well as the current access control systems based on central structures and central hierarchies. with all IoT devices, the security risks and threats are enormous. Blockchain is defined as tamper-proof, tamper-resistant digital ledgers that are executed in a distributed fashion, i.e., without a central repository and usually without central authority, allowing all parties to track information over an insecure network without the need for third-party verification. This technology could be a solution to the problems facing the Internet of Things, however the integrating between blockchain and Internet of Things system comes with many challenges as such blockchain suffers from high resource consumption, slow response, etc. In this research we will propose a new architecture for how to integrate blockchain with the Internet of Things. This structure consists of the smart home responsible for its own data (own chain) and the service provider which considered indirect manager of the network. The performance of the proposed architecture will be studied in terms of expansion, decentralization, security and permanent availability.
Pelatihan Kewirausahaan Pembuatan Costume Case Character Dalam Meningkatkan Pendapatan Masyarakat Di Kota Pekanbaru

This community service activity aims to provide knowledge to the community regarding the process of making costume case characters that can be used as entrepreneurial opportunities for the community in Pekanbaru City. This activity was held in Pekanbaru City on June 19, 2021. The training participants were housewives and teenagers in Tangkerang Barat Village with a total of 15 trainees. The method of community service activities uses lecture and demonstration methods about making costume case characters. The evaluation design for service activities is the achievement of the target number of training participants, the training objectives are well achieved, the planned material targets are achieved and the participants are able to understand the training material. The result of this service activity is that the participants understand the entrepreneurial opportunities of making costume case characters and training participants can make costume case characters.
Identify Finger Rotation Angles With ArUco Markers and Action Cameras

Abstract Measuring the motion of human hand joints is a challenging task due to the high number of DOFs. In this study, we proposed a low-cost hand tracking system built on action cameras and ArUco markers to measure finger joint rotation angles. The lens distortion of each camera was corrected first via intra-calibration and the videos of different cameras were aligned to the reference camera using a dynamic time warping based method. Two methods were proposed and implemented for extracting the rotation angles of finger joints: one is based on the 3D positions of the markers via inter-calibration between cameras, named pos-based method; the other one is based on the relative marker orientation information from individual cameras, named rot-based method. An experiment was conducted to evaluate the effectiveness of the proposed system. The right hand of a volunteer was included in this practical study, where the movement of the fingers was recorded and the finger rotation angles were calculated with the two proposed methods, respectively. The results indicated that although using the rot-based method may collect less data than using the pos-based method, it was more stable and reliable. Therefore, the rot-based method is recommended for measuring finger joint rotation in practical setups.
Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?

In this paper, we investigate what types of stereotypical information are captured by pretrained language models. We present the first dataset comprising stereotypical attributes of a range of social groups and propose a method to elicit stereotypes encoded by pretrained language models in an unsupervised fashion. Moreover, we link the emergent stereotypes to their manifestation as basic emotions as a means to study their emotional effects in a more generalized manner. To demonstrate how our methods can be used to analyze emotion and stereotype shifts due to linguistic experience, we use fine-tuning on news sources as a case study. Our experiments expose how attitudes towards different social groups vary across models and how quickly emotions and stereotypes can shift at the fine-tuning stage.
Covid-Prev Observatory

The Covid-19 pandemic and its countermeasures have brought pressures on social and welfare assistance. Along with this, come the challenges of information being dispersed and complex, susceptible to distortions, and a society with distinct capacities for assimilation and analysis. This paper aims to present the experience of the Covid-Prev Observatory as a tool for transparency and accountability in the public management of social security in times of pandemic. This experience with the social observatory has encompassed the choice of themes and variables to be disseminated and analyzed, the visual planning and dissemination strategy, and the discussion of the observatory's role and potential. Thus, a multidisciplinary team has developed a project aiming to analyze the impact of Covid-19 on the Brazilian Social Security System, from different perspectives, both with the reproduction of data and information, and for the elaboration of analyses: impacts of the pandemic on the labor market and its effects on the evolution of social security revenues and expenses, effects of the pandemic on vulnerable groups, sustainability of pension funds, impacts on the financial market, among others. This information was first disclosed on the Blogger platform, and then on an electronic website whose disclosure was made both on the social networks of the research participants themselves and on the social networks of entities interested in the topic, such as the Brazilian Association of State and Municipal Pension Institutions (ABIPEM). In its short period of existence, the observatory has obtained more than 10 thousand accesses, either from Brazil and other countries. The observatory has presented data and analyses in a simple way, based on scattered information with different levels of complexity. In these analyses, the aim has been to establish relationships between facts correlated to social security and welfare. Thus, it is understood that the observatory has collaborated as a transparency and accountability tool with respect to the theme of social security in Brazil, serving as a discussion of the challenges and potentialities of social observatories.
Digital Skills and Digital Competencies of Higher Education Teachers

Descriptive-survey study was conducted to find out the relationship between Digital Skills (DS) and Digital Competence (DC) among the Faculties of Higher Education Namakkal District. A sample of 750 Faculties from various discipline was selected by convenience sampling technique. To collect the data, Digital Skills Questionnaire (DSQ) and Digital Competence Questionnaire (DCQ) were used which was designed and constructed by the C.Sangeetha and Dr.K.Saileela. DSQ consists of 10 items and DCQ consists of 17 items with dichotomous responses i.e. Yes or No. The data was collected with the help of Google forms. The data were analyzed using the statistical techniques like Percentage analysis, Mean, Standard Deviation, t-test, ANOVA and Pearson’s Product Moment Correlation. In this study we could find that Female Faculties had mean score for Digital Skills greater than Male. But Male Faculties had mean score for Digital Competence greater than Female. There is significant difference in the Digital Skills and Digital Competence of the Faculties in terms of Age. The finding shows that there is a significant relationship between Digital Skills (DS) and Digital Competence (DC) among the Faculties of Higher Education of Namakkal District, Tamilnadu, India
Swarm Flight Control for Multiple Drones Based on Learning Type Flocking Algorithm

In recent years, the utilization of drone for pesticide spraying, load carriage, infrastructure inspection, and remote sensing has been proceeded because it is able to improve the work efficiency and the safety. In particular the utilization of multiple drones in the field of agriculture will enable to reduce the total time of pesticide spraying. On the other hand there are some examples flying multiple drones as the application to art and entertainment. Thus the opportunity of swarm flight control for multiple drones is expected to increase gradually. Therefore, this research aims at the realization of swarm flight control which is useful in the field of agriculture and amusement and so on. The swarm flight control simulation according to human operation using the flocking algorithm Boids well-known for the group behavior simulation of birds and fish was performed in this research. Boids is a kind of computer algorithm expressing swarm flight of birds based on three basic rules. In this research the parameters of three basic rules were tuned by genetic algorithm (GA). Moreover the experiment of evaluation for the operationability was performed after an operator controls one or multiple drones.
REST Architecture Optimization in Cloud Computing Ecosystem to Support E Learning Platform

This study will present an application design process in the style of Representational State Transfer (REST) architecture to support the E-Learning platform in the cloud computing ecosystem. An application optimization process will be presented to provide E-Learning applications for schools, faculties or universities that in most cases need manual deployment and require more time for server provisioning.This process is optimized by providing application solutions that can provide speed of provisioning.The core system used Kubernetes containerization technology to provide scalability of growing E-Learning tenants. Evaluation of the core system architecture uses the Architecture Trade-off Analysis Method (ATAM) to evaluate aspect of performance and scalability as quality attributes. From the experimentalresults, the process of making new tenants for schools requires an average time of around 173.4 seconds. This meets the expectations of the set time limit of 5 minutes. The results of stress tests for 250 concurrent users show that the system has availability above 98%.Thus,education stakeholders such as schools and universities, no longer need to provide expensive e-learning infrastructure in the form of hardware or manpower to deploy the e-learning application on premise. In the future, this solution will provide a scalable E-Learning system that can spread at scale on the cloud computing ecosystem and support a Software as a Service solution in educational technology.
Construction of Side Channel Attacks Resistant S-boxes using Genetic Algorithms based on Coordinate Functions.

Background and Objectives: Substitution-box (S-Box) is one of the essential components creating confusion and nonlinear properties in cryptography. To strengthen a cipher against various attacks, including side channel attacks, these boxes need to have numerous security properties. In this paper, a novel S-Box construction method is introduced aimed at improving the resistance of S-Boxes against power analysis attacks.Methods: In the preprocessing phase of this approach, a suitable initial S-Box with some basic security properties was generated by adopting a fast algorithm. Then, in the main stage, using the initial S-Box, we generate new S-Boxes which not only have the properties of the initial S-Box but also have significantly improved under another set of security properties. To do this, new S-Boxes were generated using a genetic algorithm on a particular subset of the linear combination set of coordinate functions of the initial S-Box.Results: The performed experiments demonstrated that the values of all security properties of these new S-Boxes, especially the measures of transparency order, signal-to-noise ratio, confusion coefficient, bijection property, fixed point, and opposite fixed points, have been substantially improved. For example, our experiments indicate that 70, 220, 2071, 43, and 406 S-Boxes are found better than the initial S-Box, respectively, in the dimensions of 4×4 through 8×8Conclusion: In this paper, a new S-Box construction method is introduced where the properties related to side channel attacks are improved, without destroying other security features. Besides, some results obtained from generated S-Boxes in the dimensions of 4×4 through 8×8 demonstrated that the generated S-Boxes are not only improved relative to the initial S-Box, but also in certain cases, considerably better than some well-known S-Boxes.
Soft Calibration Objectives for Neural Networks

Optimal decision making requires that classifiers produce uncertainty estimates consistent with their empirical accuracy. However, deep neural networks are often under- or over-confident in their predictions. Consequently, methods have been developed to improve the calibration of their predictive uncertainty both during training and post-hoc. In this work, we propose differentiable losses to improve calibration based on a soft (continuous) version of the binning operation underlying popular calibration-error estimators. When incorporated into training, these soft calibration losses achieve state-of-the-art single-model ECE across multiple datasets with less than 1% decrease in accuracy. For instance, we observe an 82% reduction in ECE (70% relative to the post-hoc rescaled ECE) in exchange for a 0.7% relative decrease in accuracy relative to the cross entropy baseline on CIFAR-100. When incorporated post-training, the soft-binning-based calibration error objective improves upon temperature scaling, a popular recalibration method. Overall, experiments across losses and datasets demonstrate that using calibration-sensitive procedures yield better uncertainty estimates under dataset shift than the standard practice of using a cross entropy loss and post-hoc recalibration methods.
INTEGRATION OF TRADITIONAL AND DISTANCE LEARNING METHODS IN HIGH SCHOOL

In the article the researchers are exploring the integration of traditional and distance learning methods identified as a result of quarantine measures during the coronavirus in high school. The main complex contradictions are revealed. Methods and decisions of a complex problem are developed. The tendencies of the development of distance education are researched. Scientific assumptions about an educology methodology are formed. Main stages of modern educational technologies in universities are defined. The study of integration processes between traditional and distance learning is carried out. The relations between subjects and objects of the university department are learned. It was revealed that the structure of the educational process is complicated due to the use of mobile means of communication and distance forms of educational communications in it, which leads to a change in teaching methods and forms. The features of the learning processes of modern students have specific features now. The necessity of using mobile devices and new software is analyzed. Attention is drawn to the fact that not all teachers are well versed in modern means of online group communications. The basic means of group communications such as: Moodle, Zoom, Skype, Teams and others, providing remote communication of students and teachers are analyzed. The features and specificity of these means of communication are considered. Their possibilities are being studied. Results of reengineering studies at the department are presented. The results of testing student training based on modeling methods are presented too. A digital platform of knowledge models, which consists of two components is created and used. The first component is an interconnected set of models of academic disciplines developed by teachers, and the second component is the knowledge models of these disciplines that students create during their studies. Teaching methods based on integrated intelligence during pandemic in universities are made. Ways to improve learning technologies based on the use of integrated intelligence are proposed in high school.
Quantum Gates and Circuits

Quantum gates and circuits are a natural extension of both classical gates and circuits. They are also another way of thinking about the mathematics that describes sending qubits from Alice to Bob.
Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study

Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We successfully reproduce state-of-the-art cross-topic stance detection work (Reimers et. al, 2019), and systematically analyze its reproducibility. Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context. We ask: To what extent is stance detection topic-independent and generalizable across topics? We compare the model’s performance on various unseen topics, and find topic (e.g. abortion, cloning), class (e.g. pro, con), and their interaction affecting the model’s performance. We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection. References Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, and Iryna Gurevych. 2019. Classification and Clustering of Arguments with Contextualized Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 567–578, Florence, Italy. Association for Computational Linguistics.
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification

Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validating these adversarial inputs against certain criteria (e.g., the preservation of semantics and grammaticality). Enforcing constraints to uphold such criteria may render attacks unsuccessful, raising the question of whether valid attacks are actually feasible. In this work, we investigate this through the lens of human language ability. We report on crowdsourcing studies in which we task humans with iteratively modifying words in an input text, while receiving immediate model feedback, with the aim of causing a sentiment classification model to misclassify the example. Our findings suggest that humans are capable of generating a substantial amount of adversarial examples using semantics-preserving word substitutions. We analyze how human-generated adversarial examples compare to the recently proposed TextFooler, Genetic, BAE and SememePSO attack algorithms on the dimensions naturalness, preservation of sentiment, grammaticality and substitution rate. Our findings suggest that human-generated adversarial examples are not more able than the best algorithms to generate natural-reading, sentiment-preserving examples, though they do so by being much more computationally efficient.
Named Entity Recognition in Turkish Bank Documents

Named Entity Recognition (NER) is the process of automatically recognizing entity names such as person, organization, and date in a document. In this study, we focus on bank documents written in Turkish and propose a Conditional Random Fields (CRF) model to extract named entities. The main contribution of this study is twofold: (i) we propose domain-specific features to extract entity names such as law, regulation, and reference which frequently appear in bank documents; and (ii) we contribute to NER research in Turkish document which is not as mature as other languages such as English and German. Experimental results based on 10-fold cross validation conducted on 551 real-life, anonymized bank documents show the proposed CRF-NER model achieves 0.962 micro average F1 score. More specifically, F1 score for the identification of law names is 0.979, regulation name is 0.850, and article no is 0.850.