Reviewerly/exHarmony huggingface dataset:
Enregistered spellings in interaction

Abstract Against the backdrop of the societal differentiation of literacy, the paper investigates spelling variation in digital written communication beyond the binary paradigm of standard and nonstandard. To this end, the paper proposes a formal classification of digital spelling variants and then focuses on the socio-communicative functions of these variants in usage. Theoretically grounded in the notions of register and social indexicality , the paper discusses how spelling variants are metapragmatically ordered by social actors and deployed in text-messaging interactions in order to indicate interpretive context. To investigate these phenomena holistically, the paper furthermore presents a tripartite research framework that addresses digital writing regarding its I) structural variants, II) communicative practice, and III) reflexive awareness. Afterwards, this methodological approach is applied empirically. This is done based on a data set that includes samples of everyday literacy by 23 German adolescents: informal WhatsApp texting, on the one hand, formal school essays on the other. The exemplary analyses focus on phonostylistic spellings (e. g. elisions such as &lt;ich hab&gt; instead of &lt;ich habe&gt;) and graphostylistic spellings (e. g. graphemic substitutions such as &lt;daß&gt; instead of &lt;dass&gt;) in these WhatsApp interactions, reconstructing the metapragmatic status of standard orthography in digital writing. By combining structure-oriented, interactional, and ethnographic perspectives, the paper seeks a disciplinary dialogue by relating concepts of sociolinguistics and linguistic anthropology not only to research fields of media linguistics but also to research on writing systems.
Superresolution Reconstruction of Remote Sensing Image Based on Middle-Level Supervised Convolutional Neural Network

Image has become one of the important carriers of visual information because of its large amount of information, easy to spread and store, and strong sense of sense. At the same time, the quality of image is also related to the completeness and accuracy of information transmission. This research mainly discusses the superresolution reconstruction of remote sensing images based on the middle layer supervised convolutional neural network. This paper designs a convolutional neural network with middle layer supervision. There are 16 layers in total, and the seventh layer is designed as an intermediate supervision layer. At present, there are many researches on traditional superresolution reconstruction algorithms and convolutional neural networks, but there are few researches that combine the two together. Convolutional neural network can obtain the high-frequency features of the image and strengthen the detailed information; so, it is necessary to study its application in image reconstruction. This article will separately describe the current research status of image superresolution reconstruction and convolutional neural networks. The middle supervision layer defines the error function of the supervision layer, which is used to optimize the error back propagation mechanism of the convolutional neural network to improve the disappearance of the gradient of the deep convolutional neural network. The algorithm training is mainly divided into four stages: the original remote sensing image preprocessing, the remote sensing image temporal feature extraction stage, the remote sensing image spatial feature extraction stage, and the remote sensing image reconstruction output layer. The last layer of the network draws on the single-frame remote sensing image SRCNN algorithm. The output layer overlaps and adds the remote sensing images of the previous layer, averages the overlapped blocks, eliminates the block effect, and finally obtains high-resolution remote sensing images, which is also equivalent to filter operation. In order to allow users to compare the superresolution effect of remote sensing images more clearly, this paper uses the Qt5 interface library to implement the user interface of the remote sensing image superresolution software platform and uses the intermediate layer convolutional neural network and the remote sensing image superresolution reconstruction algorithm proposed in this paper. When the training epoch reaches 35 times, the network has converged. At this time, the loss function converges to 0.017, and the cumulative time is about 8 hours. This research helps to improve the visual effects of remote sensing images.
Blockchain Using Proof-of-Interaction

Proof-of-Work is originally a client-side puzzle proposed to prevent spam or denial of service attacks. In 2008, Satoshi Nakamoto used it as an election mechanism (or equivalently, to replace a centralized time server) in the first Blockchain: Bitcoin. In the same year, another spam prevention algorithm was proposed, based on a guided-tour puzzle, but received only little attention.The main motivation of our work is to see if a Blockchain protocol can use the guided-tour puzzle like Bitcoin uses Proof-of-work.In this paper we extend the guided tour puzzle to a new Puzzle called Proof-of-Interaction and we show how it can replace, in the Bitcoin protocol, the Proof-of-Work algorithm. We show that it uses a negligible amount of computational power compared to Bitcoin, and scales very well in term of number of messages. We analyze the security of our protocol and show that it is not subject to selfish mining. However, our protocol currently works only when the nodes in the network are known, but we discuss how this assumption could be weakened in future work.
Continual Few-Shot Learning for Text Classification

Natural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances.For example, a Natural Language Inference (NLI) system has to recognize sentiment, handle numbers, perform coreference, etc.Our solutions to complex problems are still far from perfect, so it is important to create systems that can learn to correct mistakes quickly, incrementally, and with little training data.In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples.To this end, we first create benchmarks based on previously annotated data: two NLI (ANLI and SNLI) and one sentiment analysis (IMDB) datasets.Next, we present various baselines from diverse paradigms (e.g., memory-aware synapses and Prototypical networks) and compare them on few-shot learning and continual few-shot learning setups.Our contributions are in creating a benchmark suite 1 and evaluation protocol for continual few-shot learning on the text classification tasks, and making several interesting observations on the behavior of similarity-based methods.We hope that our work serves as a useful starting point for future work on this important topic.
A Deep Learning Based System for the Detection of Human Violence in Video Data

The number of security cameras positioned within the surrounding area has expanded, increasing the demand for automatic activity recognition systems. In addition to offline assessment and the issuance of an ongoing alarm in the case of aberrant behaviour, automatic activity detection systems can be employed in conjunction with human operators. In the proposed research framework, an ensemble of Mask Region-based Convolutional Neural Networks for key-point detection scheme, and LSTM based Recurrent Neural Network is used to create a deep neural network model (Mask RCNN) for recognizing violent activities (i.e. kicking, punching, etc.) of a single person. First of all, the key-points locations and ground-truth masks of humans in an image are selected using the selected region; the temporal information is extracted. Experimental results show that the ensemble model outperforms individual models. The proposed technique has a reasonable accuracy rate of 77.4 percent, 95.7 percent, and 88.2 percent, respectively, on the Weizmann, KTH, and our custom datasets. As the proposed effort applies to industry and in terms of security, it is beneficial to society.
Pengembangan Media Zliqubo Untuk Meningkatkan Kecerdasan Visual Spasial Anak TK

Development of Zliqubo Media (Quiet Book Puzzle) to Improve Visual Spatial Intelligence of Group B Kindergarten Children in Kwadungan District, Ngawi Regency. This study aims to: 1) Describe the effectiveness of Zliqubo (Puzzle Quiet Book) to improve the visual spatial intelligence of kindergarten children in group B in Kwadungan District, Ngawi Regency. 2) Describe the efficiency of Zliqubo (Quiet Book Puzzle) to improve the spatial visual intelligence of Group B Kindergarten children in Kwadungan District, Ngawi Regency. 3) Describe the attractiveness of Zliqubo (Puzzle Quiet Book) to improve the visual spatial intelligence of Group B Kindergarten children in Kwadungan District, Ngawi Regency. The research used was Borg &amp; Gall Research and Development. The steps taken include analysis of potential and problems, data collection, product design, product validation, design revisions, product trials, product revisions. The product was tested on students through two stages of validation testing, namely a small group trial of 5 children and a large group trial of 15 children. The subject of the trial was the children of group B Kindergarten Dharma Wanita Dinden which consisted of a small group trial of 5 students and a large group trial of 13 students. Product viability is based on the results of the assessment of material experts, media experts and learning design experts. Data collection techniques using observation, interviews, and documentation. Data analysis in the form of quantitative descriptive. The results showed that the Zliqubo (Puzzle Quiet Book) which had been developed through a series of trials and expert validation was declared feasible. This is supported by expert material assessment with an average score of 3.42 included in the feasible category, assessment of experts with an average score of 3.7 including the feasible category, learning design expert assessment with an average score of 3.3 including eligible category. product trials get a percentage score (1) 100% of children can do activity I; (2) 93.75% of children can do activity II; (3) 87.5% of children can do activity III; (4) 89.7% of children easily carry out activities; (5) 94.86% of children are safe in playing activities with Zliqubo (Quiet Book Puzzle); (6) 92.30% of children feel happy when playing with Zliqubo (Puzzle Quiet Book). Based on the results of the research that has been done, it can be concluded that the Zliqubo (Puzzle Quiet Book) for the learning of Group B Kindergarten students in Kwadungan Subdistrict, Ngawi Regency is feasible to use.
Utilization of E-Learning as Media in Indonesian Language Courses in Higher Education Post COVID-19 Pandemic

The rapid development of technology has increased the demand for information technology-based learning media, which is becoming increasingly necessary. E-Learning, also known as information technology-based learning media, is changing how education is delivered from a traditional to a digital format. At this time, the world is being affected by the COVID-19 pandemic, which is causing restrictions on activities and gatherings and causing difficulties in lectures. Electronic learning, the most widely used learning medium by all groups, is seen as a solution to keep the learning going in higher education, from primary to post-secondary levels of education. The purpose of this study was to investigate the use of E-Learning as a learning medium in the Indonesian language education department of universities following the outbreak of the COVID-19 pandemic. This study uses a qualitative approach with descriptive analysis to implement Indonesian language education learning by taking samples from lecturers who teach Indonesian courses at the Manado State Islamic Institute and Manado State Christian Institute. According to the study's findings, using information technology as a medium of learning in Indonesian language education courses through e-learning is beneficial, particularly in light of the country's ban on face-to-face instruction. Then, the university should continue to develop e-learning to improve the ease and quality of the teaching and learning process, increase student learning effectiveness, and broaden its reach.
The developing potential of the educational environment of rural schools in the Krasnoyarsk region

The article discusses the results of the sociological research of the educational environment of schools in the Krasnoyarsk region and its impact on the development of the student's personality The theoretical basis of the article is the concept «social development situation» as well as the vector model of the educational environment. It is shown that the social situation of rural schoolchildren compared to urban characteristics are characterized by lower socio-educational status of parents, lower quality of education, limited contacts, poverty of cultural life. However, subjectively these differences are not perceived by the participants of the educational process, and their assessments of the educational environment are practically indistinguishable from the assessments of urban respondents. Features of the Krasnoyarsk region from other regions that participated in the study – the diversity of natural and geographical conditions, a fairly high index of socio-economic development, and the predominance of modern industries. As a result, there are marked differences in the assessment of the role and capabilities of the school, its functions, compared to representatives of other regions. The greatest differences in the assessment of different parameters of the environment are found in the assessments of different participants in the educational process – children, parents and teachers. It is noted that parents are extremely negative about many innovations in education, especially with regard to the introduction of the Single State Examination and the unification of rural schools, their actual destruction in the villages. In general, all participants in the educational process highly appreciate the tension of the educational environment of schools; and there were no significant differences in the assessment of this parameter between rural and urban schools. According to the participants of the educational process, the school creates the necessary conditions for the development of different abilities of students, development of life skills. Based on the analysis of the educational environment by the vector model, it was found that the educational environment of urban schools is shifted towards «career» and rural – «dogmatic» educational environment.
Impact of Personal Branding on the Development of Professional Careers of Managers

Purpose:The purpose of the paper is to present the importance of personal branding in the process of career development of managers, both from theoretical and empirical perspectives.Design/Methodology/Approach: The research method was diagnostic survey, with a research questionnaire.The study was conducted between August 2018 and February 2019 on a group of 1842 respondents (managers).The main hypothesis was "personal branding has a positive impact on managers' professional development".Findings: Personal branding, i.e., creating a personal brand, appeared in theory and practice relatively recently.Managers have always been engaged in building a personal image, although they did not do so in a proper, systematic or fully conscious manner.It is the emergence of social media that made online positioning a key factor determining career.The population surveyed most often perceived personal branding as abilities to build reputation and image that are translated into trust and credibility.The respondents indicated that on today's job market professional development is best facilitated by social competencies.Practical Implications: Managers should pay more attention to personal branding that an increased level of satisfaction can contribute to an increased work efficiency, bringing measurable results.Originality/Value: Although there are many studies dedicated to employer branding, a relatively limited number of such researches are focused on personal branding for managers of organizations in their work careers.The article is, hence, an attempt to fill the aforementioned gap.Conducting the research is sufficient enough to learn about the personal branding and and managers' satisfaction of the work.Furthermore, studies concerning personal branding may be an important source of information which employers and managers can apply to determine areas of further development.
A graph-based representation of knowledge for managing land administration data from distributed agencies – A case study of Colombia

Multiple efforts have been performed worldwide around diverse aspects of land administration. However, land administration data and systems' notorious heterogeneity remains a longstanding challenge to develop a harmonized vision. In this sense, the traditional Spatial Data Infrastructures adoption is not enough to overcome this challenge since data sources' heterogeneity implies needs related to harmonization interoperability, sharing, and integration in land administration development. This paper proposes a graph-based representation of knowledge for integrating multiple and heterogeneous data sources (tables, shapefiles, geodatabases, and WFS services) belonging to two Colombian agencies within a decentralized land administration scenario. These knowledge graphs are developed on an ontology-based knowledge representation using national and international standards for land administration. Our approach aims to prevent data isolation, enable cross-datasets integration, accomplish machine-processable data, and facilitate the reuse and exploitation of multi-jurisdictional datasets in a single approach. A real case study demonstrates the applicability of the land administration data cycle deployed.
You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models

Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources. In this paper, we propose an adversarial sample detection method based on pruned models and evaluate four different pruning methods. We find that pruned neural network models are sensitive to adversarial samples, i.e., the pruned models tend to output labels different from the original model when given adversarial samples. Moreover, the pruned model has an extremely small model size and computational cost. Based on the detection result, we further propose a simple but effective defense approach to identify the true label of the adversarial sample. Experiments show that, on average, four different pruning methods outperform the SOTA multi-model based detection method (64.15% and 73.70%) by 28.65% and 18.73% on CIFAR10 and SVHN, respectively, with significantly fewer models used. The FLOPs of our structured pruned model are only 49.41% and 25.62% of the original model. Our defense approach achieves 68.60% and 72.03% average classification accuracy on CIFAR10 and SVHN, exceeding other advanced defense methods.
Relating Neural Text Degeneration to Exposure Bias

This work focuses on relating two mysteries in neural-based text generation: exposure bias, and text degeneration.Despite the long time since exposure bias was mentioned and the numerous studies for its remedy, to our knowledge, its impact on text generation has not yet been verified.Text degeneration is a problem that the widely-used pre-trained language model GPT-2 was recently found to suffer from (Holtzman et al., 2020).Motivated by the unknown causation of the text degeneration, in this paper we attempt to relate these two mysteries.Specifically, we first qualitatively and quantitatively identify mistakes made before text degeneration occurs.Then we investigate the significance of the mistakes by inspecting the hidden states in GPT-2.Our results show that text degeneration is likely to be partly caused by exposure bias.We also study the self-reinforcing mechanism of text degeneration, explaining why the mistakes amplify.In sum, our study provides a more concrete foundation for further investigation on exposure bias and text degeneration problems.
Vision-based vanishing point detection of autonomous navigation of mobile robot for outdoor applications

The vision-based approach to mobile robot navigation is considered superior due to its affordability. This paper aims to design and construct an autonomous mobile robot with a vision-based system for outdoor navigation. This robot receives inputs from camera and ultrasonic sensor. The camera is used to detect vanishing points and obstacles from the road. The vanishing point is used to detect the heading of the road. Lines are extracted from the environment using a canny edge detector and Houghline Transforms from OpenCV to navigate the system. Then, removed lines are processed to locate the vanishing point and the road angle. A low pass filter is then applied to detect a vanishing point better. The robot is tested to run in several outdoor conditions such as asphalt roads and pedestrian roads to follow the detected vanishing point. By implementing a Simple Blob Detector from OpenCV and ultrasonic sensor module, the obstacle's position in front of the robot is detected. The test results show that the robot can avoid obstacles while following the heading of the road in outdoor environments. Vision-based vanishing point detection is successfully applied for outdoor applications of autonomous mobile robot navigation.
CAGAN: Text-To-Image Generation with Combined Attention Generative Adversarial Networks

Generating images according to natural language descriptions is a challenging task. Prior research has mainly focused to enhance the quality of generation by investigating the use of spatial attention and/or textual attention thereby neglecting the relationship between channels. In this work, we propose the Combined Attention Generative Adversarial Network (CAGAN) to generate photo-realistic images according to textual descriptions. The proposed CAGAN utilises two attention models: word attention to draw different sub-regions conditioned on related words; and squeeze-and-excitation attention to capture non-linear interaction among channels. With spectral normalisation to stabilise training, our proposed CAGAN achieves state-of-the-art FID and comparative IS scores on the CUB dataset and on the more challenging COCO dataset. Furthermore, we demonstrate that judging a model by a single evaluation metric can be misleading by developing an additional model adding local self-attention which scores a higher IS than our other model, but generates unrealistic images through feature repetition.
Learning Evolution: a Survey

Learning is the process of gaining knowledge and implementing this knowledge on behavior. The concept of learning is not strict to just human being, it expanded to include machine also. Now the machines can behave based on the gained knowledge learned from the environment. The learning process is evolving in both human and machine, to keep up with the technology in the world, the human learning evolved into micro-learning and the machine learning evolved to deep learning. In this paper, the evolution of learning is discussed as a formal survey accomplished with the foundation of machine learning and its evolved version of learning which is deep learning and micro-learning as a new learning technology can be implemented on human and machine learning. A procedural comparison is achieved to declare the purpose of this survey, also a related discussion integrates the aim of this study. Finally a concluded points are illustrated as outcome which summarized the practical evolution intervals of the machine learning different concepts.