Reviewerly/exHarmony huggingface dataset:
Self-Regulation for Semantic Segmentation

In this paper, we seek reasons for the two major failure cases in Semantic Segmentation (SS): 1) missing small objects or minor object parts, and 2) mislabeling minor parts of large objects as wrong classes. We have an interesting finding that Failure-1 is due to the underuse of detailed features and Failure-2 is due to the underuse of visual contexts. To help the model learn a better trade-off, we introduce several Self-Regulation (SR) losses for training SS neural networks. By "self", we mean that the losses are from the model per se without using any additional data or supervision. By applying the SR losses, the deep layer features are regulated by the shallow ones to preserve more details; meanwhile, shallow layer classification logits are regulated by the deep ones to capture more semantics. We conduct extensive experiments on both weakly and fully supervised SS tasks, and the results show that our approach consistently surpasses the baselines. We also validate that SR losses are easy to implement in various state-of-the-art SS models, e.g., SPGNet and OCRNet, incurring little computational overhead during training and none for testing.
Development and Training of Quantum Neural Networks, Based on the Principles of Grover's Algorithm

This paper highlights the possibility of creating quantum neural networks that are trained by Grover's Search Algorithm. The purpose of this work is to propose the concept of combining the training process of a neural network, which is performed on the principles of Grover's algorithm, with the functional structure of that neural network, interpreted as a quantum circuit. As a simple example of a neural network, to showcase the concept, a perceptron with one trainable parameter - the weight of a synapse connected to a hidden neuron.
Categorical Vehicle Classification and Tracking using Deep Neural Networks

The classification and tracking of vehicles is a crucial component of modern transportation infrastructure.Transport authorities make significant investments in it since it is one of the most critical transportation facilities for collecting and analyzing traffic data to optimize route utilization, increase transportation safety, and build future transportation plans.Numerous novel traffic evaluation and monitoring systems have been developed as a result of recent improvements in fast computing technologies.However, still the camera-based systems lag in accuracy as mostly the systems are constructed using limited traffic datasets that do not adequately account for weather conditions, camera viewpoints, and highway layouts, forcing the system to make trade-offs in terms of the number of actual detections.This research offers a categorical vehicle classification and tracking system based on deep neural networks to overcome these difficulties.The capabilities of generative adversarial networks framework to compensate for weather variability, Gaussian models to look for roadway configurations, single shot multibox detector for categorical vehicle detections with high precision and boosted efficient binary local image descriptor for tracking multiple vehicle objects are all incorporated into the research.The study also includes the publication of a high-quality traffic dataset with four different perspectives in various environments.The proposed approach has been applied on the published dataset and the performance has been evaluated.The results verify that using the proposed flow of approach one can attain higher detection and tracking accuracy.
응급구조과 대학생의 원격수업 경험 분석

Purpose: The study was a qualitative study to examine the synchronous and asynchronous distanced learning experience of online paramedic students during the COVID-19 pandemic. Methods: The subjects included 10 students enrolled in the department of emergency medical service at J City C University. Written consent was provided by the subjects prior to the study, and focus group interviews were then conducted with sufficient explanation. The interviews were recorded and were directly transcribed immediately after the interview. Research results were then derived through content analysis. Results: A total of 4 domains and 9 categories were derived from the experiences of paramedic students on distanced learning. The 4 domains included “distanced lectures type,” “student’s adaptation and non-adaptation,” “change of evaluation,” and “learning anxiety.” Conclusion: Contents of each domain derived from this study are expected to be used as basic data for the design of the distanced learning in the future.
DeepPSL: End-to-end perception and reasoning with applications to zero shot learning

We introduce DeepPSL a variant of Probabilistic Soft Logic (PSL) to produce an end-to-end trainable system that integrates reasoning and perception. PSL represents first-order logic in terms of a convex graphical model -- Hinge Loss Markov random fields (HL-MRFs). PSL stands out among probabilistic logic frameworks due to its tractability having been applied to systems of more than 1 billion ground rules. The key to our approach is to represent predicates in first-order logic using deep neural networks and then to approximately back-propagate through the HL-MRF and thus train every aspect of the first-order system being represented. We believe that this approach represents an interesting direction for the integration of deep learning and reasoning techniques with applications to knowledge base learning, multi-task learning, and explainability. We evaluate DeepPSL on a zero shot learning problem in image classification. State of the art results demonstrate the utility and flexibility of our approach.
A Literature Review on the Readiness for Providing Cloud Services Locally in Yemen﻿

Over time, the importance of information and the need to use it are increased. Cloud computing provides data and computing resources to clients as a service. However, cloud computing provides to the end user computing recourses such as software, hardware, and information as a service through the available interface over the Internet to be with more advantages to make the information available and accessible whenever is needed, by using the cloud computing services offers these services. The main purpose of this literature review paper is to develop an understanding of cloud computing adoption. Moreover, this paper forms the basis of our theoretical study. In particular, we review the state of the art of cloud computing and discuss about it aspects and features. In addition, we provide a set of cloud readiness criteria and review cloud migration in some countries that have already shifted to cloud. In this paper, we investigate the readiness to provide cloud services locally, as well as the chances and challenges of combining cloud services with existing infrastructure. Furthermore, the telecom sector infrastructure is suitable to provide cloud services, as the first step to provide the cloud into the network infrastructure of such providers, enabling and enhancing the telecom sector services and infrastructure.
Enhanced Graphical Representation of Data in Web Application (Case Study: Covid-19 in the UK)

This paper describes the analysis, design, and implementation of responsive data representation in the web application that can render data asynchronously to users by making an Application Programming Interface (API) request from a webserver.At the same time, provides high-quality downloadable Scalable Vector Graphics (SVG) images for journals, magazines, and other printed media.For this issue, large-scale data that uses open-source Covid-19 data was used to improve the Covid-19 data visualization and the other improvements that can be done for proper representation of such vital data to the general public.During the development process, qualitative research into data representation with responsive charts and/or Scalable Vector Graphics images file has been conducted in contrast of each other to answer questions like what tools and technologies are often used, what are the alternative tools and technology, when, where, and why developers make use of certain approach to data representation.
Sound design thinking

The practice of sound design involves the thinking and rethinking of the manners in which relations are formed, interactions are stimulated and communication is established. And this thinking is done through working with sound. This chapter explores the epistemic potentiality of sound design considered as a practice of sonic thinking. Sounds in general may themselves already be informative, but the real epistemological potential lies in the manipulations that can be performed on them. The thinking and reflection takes place both via the practice of sound design itself as well as by listening to the end result of this practice. Through the creation of sound designs features of the sounds and their resulting resonances may be brought to light that would have remained obscured otherwise.
Analisis Dan Kajian Teori Media Pembelajaran Elektronik Menggunakan Metode Mcda Framework

Since the COVID-19 pandemic, it has changed the mindset of people in all areas of life. E-Learning is one of the learning media so that the educational process can continue. Various electronic learning media used by the public are in accordance with the features and services contained in the E Learning media. In this article, the author analyzes 5 E-Learning platforms that are widely used in Indonesia, namely: RuangGuru, Zenius, Quipper, Google Classroom, and Rumah Belajar. Each of these E-Learning platforms has different features and services from one another. This causes there to be one or two E-Learning platforms that are in great demand and more widely used compared to other E-Learning platforms. The purpose of writing this article is to analyze according to the discipline of software engineering. The author uses the Multi-Criteria Decision Analysis (MCDA) Framework method in carrying out the analysis accompanied by a literature study. The output of the writing of this scientific article is in the form of a scientific recommendation solution in the form of performance matrix addressed to the developers, actors and policy makers related to the field of E-Learning, both for academic purposes/further awakening and technical in the field.
Generating Summaries for Scientific Paper Review

The review process is essential to ensure the quality of publications. Recently, the increase of submissions for top venues in machine learning and NLP has caused a problem of excessive burden on reviewers and has often caused concerns regarding how this may not only overload reviewers, but also may affect the quality of the reviews. An automatic system for assisting with the reviewing process could be a solution for ameliorating the problem. In this paper, we explore automatic review summary generation for scientific papers. We posit that neural language models have the potential to be valuable candidates for this task. In order to test this hypothesis, we release a new dataset of scientific papers and their reviews, collected from papers published in the NeurIPS conference from 2013 to 2020. We evaluate state of the art neural summarization models, present initial results on the feasibility of automatic review summary generation, and propose directions for the future.
Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy

Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well its context. On the other hand, static word embeddings such as GloVe represent words by relatively low-dimensional, memory- and compute-efficient vectors but are not sensitive to the different senses of the word. We propose Context Derived Embeddings of Senses (CDES), a method that extracts sense related information from contextualised embeddings and injects it into static embeddings to create sense-specific static embeddings. Experimental results on multiple benchmarks for word sense disambiguation and sense discrimination tasks show that CDES can accurately learn sense-specific static embeddings reporting comparable performance to the current state-of-the-art sense embeddings.
Framing, betekenis en pragmatiek

Frames are an important tool for describing the meaning of language. Within the NWO-funded project Framing Situations in the Dutch language, a team of researchers at VU University Amsterdam and the University of Groningen is currently developing a database of linguistic frames in Dutch, as well as language technology for automatically detecting frames in texts.
Palynofacies analysis – principles and application for paleoenvironmental studies: implications from Bulgaria

The use of palynofacies analysis has been steadily increasing during the last decades. This stems from both commercial interests (i.e., hydrocarbon exploration) and academic research of past environments and studies of the global climate changes that took place during the Phanerozoic. In both commercial and academic contexts, there is a need for a better understanding of palaeoenvironmental changes in order to correctly date and interpret the depositional facies recorded in boreholes and outcrops. The present paper introduces this important analysis in the Bulgarian literature with characteristic examples of different palynofacies types documented by the author in Cretaceous sections in Bulgaria and their interpretation. These examples highlight the value of using palynofacies and palynomorph associations in an integrated approach for paleoenvironmental reconstructions in Bulgaria.
A deep learning approach to face swap detection

We propose the method for detecting an incident at face authentication when an imposter falsifies the client's face using a digital technique named Face Swap to cheat the system. The method is based on a convolutional neural network to get facial features and classify them. The proposed method can work with faces obtained with low quality and heavy lighting conditions. It is confirmed by experiments on a big test dataset. Experiments show that the accuracy reaches values over 98% for low-quality images and over 99% for high-quality images. Classification results are congruent to the best results shown by the other known methods tested on the same test dataset. The proposed method can be applied to improve the quality of face authentication systems.
Preliminary Studies on Register Function of Chinese Function Words

Abstract This paper explores the effectiveness of Chinese function words in register studies based on text mining and attempts to determine their register specific functions based on quantitative methods. Following the hypothesis that function words can be used as features to differentiate different registers, our text clustering experiment demonstrates that different registers in Chinese can in fact be classified in terms of distribution of function words. Our correspondence analysis shows that the correlations between function words and different registers vary, hence some function words have register-specific functions. In addition, the correlation coefficients between function words and various registers can determine the register-specific function of function words. Our quantitative model of register specific function of function words makes an important contribution to our understanding of the correlation between function words and registers.
The Effects of Online-based Lifelong Education Program through Key Words Card Production and Class Demonstration on Job Preparation Skills for Workplace of Workers with Developmental Disabilities

The purpose of this study was to verify the effects of the online lifelong education program which is a key words card production and demonstration on the job preparation skills for work for part-time developmental disabilities. The subjects of this study were three developmental disabilities workers working in a restaurant business on a part-time basis, and the experimental environment was composed of the home and subway stations where they reside. The study method used a single subject study, and the baseline, intervention, and maintenance were reflected as the design conditions of the experiment. As a result, the subjects of the study effectively acquired and maintained the job preparation skills for work work through the online lifelong education program for the production and demonstration of key words cards. Through the results of this study, it was possible to discuss and conclude that the significant functional relationship between the independent variables and the dependent variables is valid.
E -Content Development in Higher Education: An Essential Criteria for Post Pandemic Quality Education

Higher education is an essential phase of learning that prepares competent and skillful youth and manpower for country.However due to COVID 19 outbreaks, this system of education get affected negatively in India.Face to face mode of education is not possible due to lock down and closing of educational institutions throughout the country.In this concerns, there is a great need of preparing e contents in various subjects by expert faculties of various colleges and universities in order to maintain the quality of higher education.The present paper tries to explain importance of e content development in higher education system and explore various tool and techniques that can be utilized to develop e content for quality higher education system.
The Culture Difference in English and Korean Address System

The culture difference between English and Korea leads to the distinctness of address system. Language is the way to inherit culture. Address system is not only a part of language, but also a part of culture. By comparison and analysis, this article discusses the address system to explore the culture connotations of these two kinds of language, and to seek the different value tropism. It can prevent misunderstanding in the intercultural communication and improve the quality of communication.
La película como recurso didáctico en la enseñanza y el aprendizaje del inglés

This study shows the results of a pedagogical experience implemented using the film “The boy in striped pajamas” as a didactic resource for the teaching and learning of English of a group of fifth-year students in Secondary Education, in the Municipality of Lobatera, Tachira state. Based on the film, a series of pre, during, and post tasks were developed through mobile learning and using Facebook and WhatsApp as social networks. A questionnaire and a forum were used to assess the experience from the students’ perspective. Results indicate that the use of films and mobile learning improves linguistic skills in pronunciation, vocabulary, and writing. It is also evident that the theme of the movie helped reinforce values about discrimination.
Not all that glitters is gold: political stability and trade in Sub-Saharan Africa

This study examines linkages between political stability and trade openness dynamics in a panel of 44 countries in SSA from 1996 to 2016. The empirical evidence is based on the generalized method of moments. From the findings, the negative relationship between political stability and merchandise trade is not significant while the negative relationship between political stability and trade openness (exports plus imports) is significant. Hence, the findings do not validate the tested hypothesis that political stability/no violence increases trade in the sub-region. The perspective that some forms of political stability can slow down and prevent international trade is consistent with Oslon in Rise and Decline of Nations (RADON) and recent contributions to the economic development literature which have shown that not all forms of political stability are development friendly because much depends on the extent to which stability translates into, inter alia, good governance. The principal policy implication is that standards of political governance need to be boosted in order to improve the anticipated effects of political stability on trade, especially in the light of the ambitious African Continental Free Trade Area (AfCFTA). Other policy implications are discussed.
SPatchGAN: A Statistical Feature Based Discriminator for Unsupervised Image-to-Image Translation

For unsupervised image-to-image translation, we propose a discriminator architecture which focuses on the statistical features instead of individual patches. The network is stabilized by distribution matching of key statistical features at multiple scales. Unlike the existing methods which impose more and more constraints on the generator, our method facilitates the shape deformation and enhances the fine details with a greatly simplified framework. We show that the proposed method outperforms the existing state-of-the-art models in various challenging applications including selfie-to-anime, male-to-female and glasses removal.
Synthetic Data Generation for Fraud Detection using GANs

Detecting money laundering in gambling is becoming increasingly challenging for the gambling industry as consumers migrate to online channels. Whilst increasingly stringent regulations have been applied over the years to prevent money laundering in gambling, despite this, online gambling is still a channel for criminals to spend proceeds from crime. Complementing online gambling's growth more concerns are raised to its effects compared with gambling in traditional, physical formats, as it might introduce higher levels of problem gambling or fraudulent behaviour due to its nature of immediate interaction with online gambling experience. However, in most cases the main issue when organisations try to tackle those areas is the absence of high quality data. Since fraud detection related issues face the significant problem of the class imbalance, in this paper we propose a novel system based on Generative Adversarial Networks (GANs) for generating synthetic data in order to train a supervised classifier. Our framework Synthetic Data Generation GAN (SDG-GAN), manages to outperformed density based over-sampling methods and improve the classification performance of benchmarks datasets and the real world gambling fraud dataset.
Using of remote sensing and aeromagnetic data for predicting potential areas of hydrothermal mineral deposits in the Central Eastern Desert of Egypt

This article explored mineral resources and their relation to structural settings in the Central Eastern Desert (CED) of Egypt. Integration of remote sensing (RS) with aeromagnetic (AMG) data was conducted to generate a mineral predictive map. Several image transformation and enhancement techniques were performed to Landsat Operational Land Imager (OLI) and Shuttle Radar Topography Mission (SRTM) data. Using band ratios and oriented principal component analysis (PCA) on OLI data allowed delineating hydrothermal alteration zones (HAZs) and highlighted structural discontinuity. Moreover, processing of the AMG using Standard Euler deconvolution and residual magnetic anomalies successfully revealed the subsurface structural features. Zones of hydrothermal alteration and surface/subsurface geologic structural density maps were combined through GIS technique. The results showed a mineral predictive map that ranked from very low to very high probability. Field validation allowed verifying the prepared map and revealed several mineralized sites including talc, talc-schist, gold mines and quartz veins associated with hematite. Overall, integration of RS and AMG data is a powerful technique in revealing areas of potential mineralization involved with hydrothermal processes.