Reviewerly/exHarmony huggingface dataset:
Procedural Content Generation for Math Education

Mathematics education plays an essential role in children’s development. In the past few years, online mathematics learning has gained increasing popularity. The online learning platform needs a large variety of textual and visual content to offer children a convenient learning experience and help them practice various mathematical skills. However, manually creating content is hugely time-consuming, expensive, and tedious for the content editors. This project proposes a generic approach for procedural generation of mathematical problems and corresponding textual and visual content. We analyzed and clustered hundreds of primary school curriculum-based math knowledge components, and built flexible templates for generating abstract math problems, including arithmetic, comparison, ordering, mathematical relationships, measurements, and geometry. Then our system realizes the abstract math problems in natural language through the lexicalization of language-independent semantic configurations and syntactically structured templates. Our system generates visual content through text-based image retrieval and visualization of abstract math content, varying in the forms of table, chart, geometry, or picture for counting objects. Human expert evaluations found that our generated contents are understandable, sensible, and achieve well usefulness for primary school students.
Internet of Things (IoT) and 5G Based Smart Algorithm

The (IoT) is a narrative paradigm, which is contemplate to apply to a diversity of promising areas applications to access wireless communication system and artificial information technologies. Meanwhile, the emerging cellular network infrastructure of the fifth generation providesthe opportunity to install big sensorsin IOT & to procedure massive data that threatens communicationstechnology and data mining. A novel model has been proposed for 5G Intelligent Internet of Things, for intelligent management of big data and maximizing channels of communication. First of all, author articulate the 5G and IOT concept and introduce three main 5G and IOT components. Then the author will explore the relationships between these elements and present the main methods & techniques depend on oursuggested model, including the big data mining. Furthermore, an experimental test assesses the efficiency of 5G and IOT and greatly improves the efficient use of channels and QoS. Eventually, there will be a discussion on dif erent systems and transparent problems.
Metode Fuzzy Subtractive Clustering Dalam Pengelompokkan Penggunaan Energi Listrik Rumah Tangga

The use of electricity in household sector has increased, especially during the Covid-19 pandemic. The large number of activities carried out in home such as Work from Home, online schools, and online businesses caused difficulty to monitor the electricity consumption. The absence of electricity usage provisions affects the electricity monitoring process. Hence it takes a real time monitoring application of electricity consumption. Fuzzy subtractive clustering is an unsupervised method to form the number and center of clusters according to data conditions. This method serves to classify the household electricity users with the parameters used, is the amount of usage in rupiah and electric power. The grouping results from this method help users to monitoring electricity consumption in real time. The output describes the level of high, medium and low user electricity consumption. Based on the test results, the best Silhouette Coefficient value is 0.8322535 and three clusters are formed, with an accept ratio is 0.5, a reject ratio of 0.15, a radius of 1.7 and a squash factor of 0.5 hence a high level of use is obtained with an average value of the number of uses in IDR 655,993, power 2757 VA, medium level 240,553, 1071 VA and low level 46,479, 675 VA
Wireless Sensor Network Based Weather Forecasting

Weather forecasting is a complex and challenging science that depends on the efficient interplay of weather observation, data analysis by meteorologist and computers, and rapid communication system.Monitoring, controlling, and gathering environmental/weather conditions or parameters can easily be achieved with the use of wireless sensor networks.This system was built around Arduino Nano Microcontroller.This research simulated and implemented a modern method of collecting and storing environmental data; this method was used because of its accuracy, versatility, and speed.The result shows these parameters: humidity, temperature, atmospheric pressure and wind speed plotted in GUI graph in real time form.
Anti-Theft Box: Arduino Safety Box with IoT Notifications

Theft cases often go unnoticed and are everywhere we are. It can happen in public, at school, at work or even in your own home. Things like this often happen to teens and older adults. Some of them do not have a safe place to store their valuables because of their carelessness. This is because they do not take this seriously. On the other hand, they already have their own safety box which is a great example of keeping items from being stolen or robbed. With the Internet of Things (IoT), this safety box can be improved by adding features that make it even safer to receive notifications on a mobile phone when the safety box is opened. With these improvements, property owners will become more aware of their goods because not everyone around us can be trusted. The use of these notifications is an update that will be created to improve the security of your goods. The methodology used to carry out this project is called Prototyping Method. In this way, prototype can be said to be both a design and an example of an existing system, but not yet fully functional. Prototypes will outline how a system works, and most prototype systems are not yet the perfect system to run.
PIECC: Point Inversion algorithm for Elliptic Curve Cryptology to Secure IoT Data Communication

In the Internet of Things (IoT), the internet-connected objects send the Collected data and act on the received data.Encryption controls a large number of structured and unstructured data protection during transmission.Inadequate memory and processing capacity of IoT devices demand Elliptic Curve Cryptography (ECC) for simple, secure functionalities.Scalar Multiplication frequently uses Modular Inversions that impact significantly on ECC-based applications with low resource usage with the enhancement of reliable IoT System availability.The Point Inversion algorithm for Elliptic Curve Cryptology (PIECC) enhances security and reduces the Computation time of Modular Point Inversion of Elliptic Curve using High-Speed Split Multiplication and Squaring.The use of limited intermediate registers for Cryptographic functions optimizes the Storage.The proposed algorithm reduces the Computation Time of the Cryptographic operations in terms of Clock cycles using chain Fermat-based Inversion compared with High-Speed multiplication and Product Scanning algorithms with lower Space Complexity.
One Line To Rule Them All: Generating LO-Shot Soft-Label Prototypes

Increasingly large datasets are rapidly driving up the computational costs of machine learning. Prototype generation methods aim to create a small set of synthetic observations that accurately represent a training dataset but greatly reduce the computational cost of learning from it. Assigning soft labels to prototypes can allow increasingly small sets of prototypes to accurately represent the original training dataset. Although foundational work on ‘less than one’ -shot learning has proven the theoretical plausibility of learning with fewer than one observation per class, developing practical algorithms for generating such prototypes remains an unexplored territory. We propose a novel, modular method for generating soft-label prototypical lines that still maintains representational accuracy even when there are fewer prototypes than the number of classes in the data. In addition, we propose the Hierarchical Soft-Label Prototype k-Nearest Neighbor classification algorithm based on these prototypical lines. We show that our method maintains high classification accuracy while greatly reducing the number of prototypes required to represent a dataset, even when working with severely imbalanced and difficult data. Our code is available at https://github.com/ilia10000/SLkNN.
La maestra de inglés; Odio amoroso; Una visita al papa (relatos)

Odio amoroso and La maestrina d'inglese , first published in Vita di Alberto Pisani (Milan, Luigi Perelli editore, 1870), were released nine years later in the collection of short stories Goccie d'inchiostro (Rome, Stabilimento Tipografico Italiano, 1879), in which Una visita al Papa was also published. Spanish translation by Paolino Nappi.
그래픽 노블 번역에 나타난 명시화 전략 고찰 - 『풀』영역본을 중심으로

This study seeks to examine explicitation translation strategies in Keum Suk Gendry-Kim’s graphic novel Grass translated by Janet Hong, drawing on the notion of experiential, interpersonal and textual metafunctions in systemic functional linguistics. The analysis results demonstrate 1) in the experiential metafunction, the situation faced by ‘comfort women’ rendered more explicitly with more details, with related information added in the gutter, 2) in the interpersonal metafunction, emotions and opinions of victims elaborated more clearly and convincingly, and 3) in the textual metafunction, images and texts more closely connected based on onomatopoeia relocated into the next panel or added in the speech bubble given the interplay between verbal and visual elements and some information deleted to give more focus on the image. By shedding light on the examples of explicitation translation strategies illustrated in a graphic novel based on the notion of three metafunctions, this study draws attention to explicitation as a strategic choice to help readers in other cultures fully appreciate the literary work, pointing to the need for further delving into the contribution of explicitation to meaning-making in a wider range of genres.
5 Ways COVID-19 Pushed Digital Services into the Spotlight

From fast-paced development and a focus on equity to GIS work and contact tracing, the pandemic showed that, despite challenges, digital services are crucial to making government work well today.
StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations

This demonstration paper presents StreamSide, an open-source toolkit for annotating multiple kinds of meaning representations. StreamSide supports frame-based annotation schemes e.g., Abstract Meaning Representation (AMR) and frameless annotation schemes e.g., Widely Interpretable Semantic Representation (WISeR). Moreover, it supports both sentence-level and document-level annotation by allowing annotators to create multi-rooted graphs for input text. It can open and automatically convert between several types of input formats including plain text, Penman notation, and its own JSON format enabling richer annotation. It features reference frames for AMR predicate argument structures, and also concept-to-text alignment. StreamSide is released under the Apache 2.0 license, and is completely open-source so that it can be customized to annotate enriched meaning representations in different languages (e.g., Uniform Meaning Representations). All StreamSide resources are publicly distributed through our open source project at: https://github.com/emorynlp/StreamSide.
TAKING THE REDPILL: TALKING ABOUT EXTREMISM

There is great public concern about far-right radicalization online, the process by which individuals are exposed to internet content and then adopt extremist or hateful ideas. However, this concept has two major problems. First, the idea of “radicalization” and “extremism” assumes that to study the radical is to study the other, yet white supremacy and racism are hardly new phenomenon in America. Second, while the internet clearly contributes to spreading fringe and far-right beliefs, "online radicalization" furthers simplistic narratives of media effects that ignore political, economic, and emotional complexities. Thus, to understand the adoption of fringe and far-right beliefs outside the problematic frame of “radicalization”, this paper takes up narratives of “redpilling,” slang for coming to believe something counterfactual to mainstream consensus. Drawing from qualitative and ethnographic research on far-right online subcultures and critical terrorism studies, we ask how members of far-right and fringe communiteis understand and perform their own community enrollment. We conduct critical discourse theory and qualitative data analysis on a broad corpus of in situ discussions of redpilling drawn from internet spaces devoted to far-right and fringe discussions, including 12 different subreddits, Gab.ai, Discord, Parler, Telegram, and 4chan. Our preliminary findings suggest that “online radicalization” is an ongoing process in which people come to believe extremist viewpoints by consuming far-right content, participating in far-right internet spaces, viewing interpersonal interactions through an ideological lens, and interacting with friends and family with similar views.
mGNN: Generalizing the Graph Neural Networks to the Multilayer Case

Networks are a powerful tool to model complex systems, and the definition of many Graph Neural Networks (GNN), Deep Learning algorithms that can handle networks, has opened a new way to approach many real-world problems that would be hardly or even untractable. In this paper, we propose mGNN, a framework meant to generalize GNNs to the case of multi-layer networks, i.e., networks that can model multiple kinds of interactions and relations between nodes. Our approach is general (i.e., not task specific) and has the advantage of extending any type of GNN without any computational overhead. We test the framework into three different tasks (node and network classification, link prediction) to validate it.
The initial process of creating a guide to evaluate the usability in Virtual Learning Environments

The usability evaluation includes a method set to analyze the system use quality in different development life cycle stages, for which there are a wide variety of evaluation methods (EM), each method uses certain ways and techniques to measure different aspects. Its choice does not only depend on which answer you are looking for but on multiple factors, the problem arises when looking for which of the existing EM are appropriate to assess the Virtual Learning Environments (VLE) usability, considering that there aren´t a combination methods or specific evaluation methods for this type of software to obtained a complete, consistent evaluation and considering factors such as effectiveness, efficiency, satisfaction, reasonable times, among others, this is why the following question arises: Which existing usability EMs are appropriate to apply on VLEs? This paper focuses on showing the initial process before the construction of the guide obtained from the EM combination that allows the usability evaluation in VLE. Process that was carried out, initially, with a bibliographic analysis about the usability EM of interactive systems existing and a comparison among them, where the first combination version was obtained, with this version, a metrics set was defined that will be applied to the VLE object of study and will allow selecting the useful methods to assess usability in this context. The result of the application of these metrics will be the combination of usability EM to form the VLE evaluation guide in upcoming research.
Two Way Traceability using Smart Tags QR DL Tags

The main goal of this project is to provide proof of goods from the start and their path through the supply chain along with preventing duplication and low-cost exploitation of goods. The network between the company and its suppliers can be described as a supply chain to manufacture and distribute a specific product to the final customers. This network encompasses numerous operations, persons, organizations, knowledge and resources. So, in order to helps the costumers and manufactures by bringing the transparency and avoiding the duplication. The Quick Response code (QR) is generated at the final stage of the production by combing the data from the various blocks of data such as from the raw materials to the dealers. This is a web-based application where every entity gets registered. Every raw material, manufactured sub product is to be given unique id and the data regarding that has to be stored in block of data. Everyone is provided with login and an automated unique id are generated at every stage so that all can be combined to obtain a final Quick Response code (QR) which the customer scans by Android application to get the details about the product.
An Enhanced Round Robin Virtual Machine Load Balancer for Cloud Infrastructure

With continuous increase in volume of data and its computational capacity in cloud, support for users request in cloud cannot be ignored. Considering problem of load imbalance resulting in high throughput and response time, there is the need therefore to develop a better model that will improve resource utility and performance of distributed systems so as to reduce response and processing time while providing better Virtual Machine (VM) efficiency. This paper analysed Round Robin load balancing algorithm and the deficiencies of this algorithm served as a basis for improvement in the proposed load balancer. The proposed algorithm has three phases; VM categorization phase that re-arrange VMs in the increasing order of their processing speed with 80% of total threshold value of each VM computed. Allocation phase helps find suitable VM for execution of cloudlet and the reliability assessment phase ensures that each VM performs at it optimal level before its consideration for allocation of cloudlets. An extensive simulation was carried out to evaluate the proposed algorithm using cloud analyst simulator. Results obtained from simulation shows that the proposed algorithm yields better response time, datacentre processing time and a lower turnaround time than the existing round robin load balancing algorithm.
A Study on Stress and Alcohol Consumption of College Students

Stress and depression are the major problems at present in the minds of college students.This study was conducted to know the level of stress and alcohol consumption of college students due to the closure of colleges and online teaching in Kanyakumari District.Hundred respondents opined about their stress and the consumption of alcohol.Due to the pandemic situation around the world schools and colleges were closed during the month of March 2020.Colleges started running their activities through digital mode.It leads to stress among teachers, parents, and students.Teachers were forced to run behind a new digital mode of teaching.Due to a lack of awareness about new technologies teachers suffered a lot during the beginning of this pandemic situation.Even now the situation is worse in our district due to the lack of proper net connections in remote areas.The recent statistics of NIAAA estimate that about 1,519 college students age between 18 to 24 die from alcohol-related unintentional injuries.97,000 students age between 18 to 24 experiencing alcohol-related sexual assault.Continuous usage of alcohol leads to permanent brain damage.Madras high court on April 22 2018 stated that the students will not enter into college in inebriated conditions.The judge also expressed "The student phase should be a knowledge gaining phase and the knowledge gained in the process would empower the person to lead a better life by getting appropriate posting and thereby leading a decent life".
Class-Distribution-Aware Calibration for Long-Tailed Visual Recognition

Despite impressive accuracy, deep neural networks are often miscalibrated and tend to overly confident predictions. Recent techniques like temperature scaling (TS) and label smoothing (LS) show effectiveness in obtaining a well-calibrated model by smoothing logits and hard labels with scalar factors, respectively. However, the use of uniform TS or LS factor may not be optimal for calibrating models trained on a long-tailed dataset where the model produces overly confident probabilities for high-frequency classes. In this study, we propose class-distribution-aware TS (CDA-TS) and LS (CDA-LS) by incorporating class frequency information in model calibration in the context of long-tailed distribution. In CDA-TS, the scalar temperature value is replaced with the CDA temperature vector encoded with class frequency to compensate for the over-confidence. Similarly, CDA-LS uses a vector smoothing factor and flattens the hard labels according to their corresponding class distribution. We also integrate CDA optimal temperature vector with distillation loss, which reduces miscalibration in self-distillation (SD). We empirically show that class-distribution-aware TS and LS can accommodate the imbalanced data distribution yielding superior performance in both calibration error and predictive accuracy. We also observe that SD with an extremely imbalanced dataset is less effective in terms of calibration performance. Code is available in https://github.com/mobarakol/Class-Distribution-Aware-TS-LS.
DCUR: Data Curriculum for Teaching via Samples with Reinforcement Learning

Deep reinforcement learning (RL) has shown great empirical successes, but suffers from brittleness and sample inefficiency. A potential remedy is to use a previously-trained policy as a source of supervision. In this work, we refer to these policies as teachers and study how to transfer their expertise to new student policies by focusing on data usage. We propose a framework, Data CUrriculum for Reinforcement learning (DCUR), which first trains teachers using online deep RL, and stores the logged environment interaction history. Then, students learn by running either offline RL or by using teacher data in combination with a small amount of self-generated data. DCUR's central idea involves defining a class of data curricula which, as a function of training time, limits the student to sampling from a fixed subset of the full teacher data. We test teachers and students using state-of-the-art deep RL algorithms across a variety of data curricula. Results suggest that the choice of data curricula significantly impacts student learning, and that it is beneficial to limit the data during early training stages while gradually letting the data availability grow over time. We identify when the student can learn offline and match teacher performance without relying on specialized offline RL algorithms. Furthermore, we show that collecting a small fraction of online data provides complementary benefits with the data curriculum. Supplementary material is available at https://tinyurl.com/teach-dcur.
SCORE-IT: A Machine Learning-based Tool for Automatic Standardization of EEG Reports

Machine learning (ML)-based analysis of electroencephalograms (EEGs) is playing an important role in advancing neurological care. However, the difficulties in automatically extracting useful metadata from clinical records hinder the development of large-scale EEG-based ML models. EEG reports, which are the primary sources of metadata for EEG studies, suffer from lack of standardization. Here we propose a machine learning-based system that automatically extracts components from the SCORE specification from unstructured, natural-language EEG reports. Specifically, our system identifies (1) the type of seizure that was observed in the recording, per physician impression; (2) whether the session recording was normal or abnormal according to physician impression; (3) whether the patient was diagnosed with epilepsy or not. We performed an evaluation of our system using the publicly available TUH EEG corpus and report F1 scores of 0.92, 0.82, and 0.97 for the respective tasks.