Reviewerly/exHarmony huggingface dataset:
Intrinsically Typed Sessions with Callbacks (Functional Pearl)

All formalizations of session types rely on linear types for soundness as session-typed communication channels must change their type at every operation. Embedded language implementations of session types follow suit. They either rely on clever typing constructions to guarantee linearity statically, or on run-time checks that approximate linearity. We present a new language-embedded implementation of session types, which is inspired by the inversion-of-control design principle. With our approach, all application programs are intrinsically session-typed and unable to break linearity by construction. Our design relies on a tiny encapsulated library, for which linearity remains a proof obligation that can be discharged once and for all when the library is built. We demonstrate that our proposed design extends to a wide range of features of session type systems: branching, recursion, multichannel and higher-order sessions, as well as context-free sessions. The multichannel extension provides an embedded implementation of session types which guarantees deadlock freedom by construction. The development reported in this paper is fully backed by type-checked Agda code.
Kontruksi Berita Portal asahankab.go.id Tentang Sekda Tutup Turnamen Old Crack PWL Asahan 2023: Analisis Framing Perspektif Zhondang pan – Gerald M Kosicki

The reason for this study is to figure out the outlining investigation (casing or bundling) of political news on the asahankab.go.id media entry with respect to the conclusion of the PWI Asahan 2023 Old Break football competition. This exploration utilizes a constructivist approach, which is an investigation that sees talk because of the development of social reality. This exploration is named library research; information gathered with citing, adjusting, and examining utilizing outlining investigation with delegate writing that has significance to the issue talked about, then, at that point, evaluated and closed. After holding a discussion on the framing analysis of the Regional Secretary Close the PWI Asahan 2023 Old Crack Tournament using the Zhongdang Pan and Gerald method. M. Kosicki. The author finds, in terms of framing devices (syntactic, script, thematic and rhetorical), shows asahankab.go.id in constructing social reality, especially news about The closing of Old Crack PWI Asahan 2023 is quite dominant in alignments the media.
Active Learning for News Article’s Authorship Identification

Over time, the amount of textual data has increased drastically, especially due to the publication of articles. As a consequence, there has been a rise in anonymous content. Research is being conducted to determine alternative methods for identifying unknown text authors. To this end, a system has to be developed to accurately determine the author of unknown texts, given a group of writing samples. Active Learning is utilized in this study because it iteratively selects the most informative samples to include in the training set, which enables a more precise and accurate authorship identification approach with fewer examples. Makes it useful for analyzing the rising amount of anonymous content and identifying unknown text authors. This study proposes a novel approach that utilizes active learning (AL) based machine and deep learning models, namely Logistic Regression (AL-LR), Random Forest (AL-RF), XGboost (AL-XGB), and Multilayer Perceptron (AL-MLP) for authorship identification. The proposed approach extracts valuable characteristics of the writer using the Term Frequency-Inverse Document Frequency (TF-IDF). This study’s selected comprehensive dataset, "All the news," is divided into three subsets: Article 1, Article 2, and Article 3. We have restricted the dataset’s scope and selected the top 50 authors for our experimentation. The experimental outcomes reveal that the proposed AL-XGB model achieves superior performance on Article 1 of the "All the news" dataset. Further, the AL-LR model permed well on Article 2, and the AL-MLP performed well on Article 3. The results suggest using the proposed approach for authorship identification.
Effects of e-learning and progression requirements on the English proficiency of university students:

The purpose of this study was to build a foundation for Evidence-Based Policy Making (EBPM) by evaluating the effects of each educational policy on Global Test of English Communication (GTEC) scores, an English proficiency test, using data from university students. The study introduced two educational measures to improve the English proficiency of university students: implementing an English proficiency test score as a requirement for progression, and introducing an e-learning program, “Microstep Study,” which is based on the findings of implicit memory research that has been shown to enable efficient vocabulary acquisition in a short period of time using educational big data. The relationship between each educational measure and GTEC scores was examined through a multilevel analysis using data from 2,193 first-year university students who took the GTEC in April and December of the 2020 academic year, excluding missing values. The results indicated that establishing GTEC scores as a requirement for progression may have contributed to improvement in students’ English proficiency. Further, the introduction of Microstep Study may improve students’ vocabulary and, ultimately, their English proficiency.
A Data-Driven Maneuvering Target Tracking Method Aided with Partial Models

Target tracking plays a vital role in both civil and military fields. Traditional radar point (maneuvering) target tracking methods always require a prior kinematic model to match the target motion. In fact, it is hard to satisfy this requirement in practice, especially when sudden maneuvering happens for non-cooperative target tracking, which leads to an undesirable tracking peak error. Motivated by this, a new data-driven maneuvering target tracking method aided with partial models is proposed in this paper to suppress this peak error. Here, since the historical target tracks include as many kinds of sudden maneuvers as possible, a data-driven learning-based network is trained to obtain high-precision state estimate when the target makes unpredictable maneuvers. Meanwhile, when the target has weak or no sudden maneuver, state estimate with satisfying accuracy is still obtained via prior kinematic models, where the Kalman filtering gain and model parameters are learned through the designed network to further promote its adaptivity. In particular, the kinematic modeling-based estimation also maintains the tracking robustness in the absence of representative training data. In addition, a discriminant network of evolution models is constructed to decide the dominant model (i.e. data-driven evolution model or one of the kinematic models) online based on radar measurements in the sliding window, which actually outputs the weights to make the final estimate as a weighted combination of the data-driven learning-based estimate and the kinematic modeling-based estimate. The proposed method is discussed in detail from the perspectives of tracking precision comparison, timeliness analysis, and noise analysis. The results show that the algorithm can ensure the timeliness of calculation and has a certain robustness under different noises. And most importantly, it has higher tracking precision when target maneuver happens suddenly.
Efficient Explanatory Math Animation Generating with the Integration of the Step-by-step Solver

Due to the development of digital learning, instructional videos become a popular method for self-study. To reduce the time cost of producing instructional videos, a system for generating instructional videos automatically has been proposed. However, the types of explanatory math animation (EMA) generated from the system are limited, and the animation template design is also time-consuming. In this paper, we propose the technology for generating EMA by using the solution steps analyzed from the step-by-step solver. This framework can greatly reduce the time cost of the animation template design and can generate more various EMAs.
Integrated Vegetable Supply System Based on Smart Contract and LSTM Prediction

The technologies associated with the Internet of Things (IoT) have great potential for application in the domain of agriculture to increase production and reduce energy consumption. This paper builds a integrated vegetable supply system which integrates planting, storage, logistics and consumption through IoT technology. Using blockchain and Web development technology, this system creates traceable smart contract to ensure food safety. Environment data collected by sensors are used to predict the growth requirements of the plants by Long Short-Term Memory (LSTM) neural network. A prototype system is created to prove the feasibility of the system.
Performance Evaluation of Video Classification Method for Learning Data Using Parametric Eigenspace

In this paper, we investigated on whether adding learning data based on parametric eigen-space method that allows highly accurate classification with a small number of video image data, based on a small amount of initial learning data, would enable re-generation. As a result, the proportion of discernible data increased in all experiments. In addition, we conducted experiments based on somewhat inaccurate and correct determination results, and confirmed that it is important to carefully select additional data to be added to the learning data.
System Integration and Optimization of AI Hardware Acceleration Architecture for Object Detection

This paper proposes a system integration and optimized hardware acceleration design for the lightweight YOLOV3 model in the object detection network architecture, including the Convolution Layer, the Maxpooling Layer, the Detection Layer, the Shortcut layer, and the optimized i output layers. In addition, this paper is verified and implemented in hardware on the Xilinx Zynq UltraScale+MPSoc ZCU102FPGA platform. The operating frequency is 180 MHz. The usage of bandwidth for the Convolution and Maxpooling Layer Fusion and Shortcut and Convolution Layer Fusion can be reduced by 85.33% and 45.27%, respectively. While optimizing Maxpooling Layer and Shortcut Layer, the running time is faster than ARM CortaxA53 15 and 26 times, respectively. Furthermore, the realization and the results of the system integration are exhibited through the HDMI monitor.
Scout: An Efficient Federated Learning Client Selection Algorithm Driven by Heterogeneous Data and Resource

Federated Learning is a novel distributed machine learning paradigm that leverages the computing power of numerous decentralized data sources for jointly training machine learning models while ensuring user privacy. In the most commonly used cross-device scenarios, the client cluster typically cover a vast number of heterogeneous end devices. Due to physical limitations such as bandwidth, only a few clients can participate in each round of training. The core issue of the client selection is to determine an appropriate client set for each training round. However, existing selection algorithms, especially the widely adopted random selection, suffer from a number of issues that prevent them from achieving a good balance between training efficiency and speed. Therefore, we propose Scout, which utilizes the heterogeneity features of clients’ data and resources to jointly model the utility function, and enhances the utilization of correlation among clients and the diversity among selected clients to achieve better training efficiency and speed. Furthermore, Scout maintains the scalability and fairness. Our experiments demonstrate that in large-scale heterogeneous clients scenarios, Scout outperforms three baseline algorithms and the state-of-the-art dual-feature dimension algorithm Oort in evaluation metrics.
Research on a Mathematical Modeling Algorithm Based on OLS-STEAM Framework

This paper aims to propose a mathematical modeling research based on the OLS-STEAM coupling algorithm, in which STEAM contains five factors to construct the coupling algorithm based on OLS, and uses case data for empirical deduction. The results show that: (1) the algorithm constructed in this paper can effectively solve the related problems in the mathematical modeling algorithm (2) the OLS-STEAM proposed in this paper has good accuracy and robustness.
Influence of Alcohol Advertising on Alcohol Consumption among School Students

Alcohol advertising refers to the advertising of alcoholic beverages by alcohol manufacturers through various media. Exposure to alcohol advertising is the most important leading factor related to alcohol consumption among adolescents and youth. This study is focused on influences of alcohol advertising on alcohol consumption among school students. This study was based on online cross-sectional survey design. All of the secondary school students studying at the schools of Banke district were the population of the study. The researcher included the total 156 secondary school students as a study population by using a multistage sampling method. The data were collected from July 12, 2021 to July 29, 2021 through Google from. The collected data were analyzed by using SPSS version 20, including frequency, percentage, median, mode and chi-square test. This study found that 88.6 percent of the respondents had seen alcohol advertising. Likewise, 31.4 percent of the respondents agreed and 32.7 percent of the respondents disagreed that they pay attention to alcohol advertising on social media. Similarly 85.9 percent of the respondents reported that alcohol advertising increases the number of drunks. Furthermore one fifth of the respondents indicated that alcohol advertising attracts them to taste alcohol. Lastly, this research revealed that there is a weak positive association between respondents who see alcohol advertising and attract them to taste alcohol. It is learned that alcohol advertising influences the purchasing and drinking behaviour of school students. School age is not a legal age for purchasing and drinking alcohol so it is necessary to conduct awareness programs through formal and informal institutions.
Improving neural network’s robustness on tabular data with D-layers

Abstract Artificial neural networks ( $${{{\texttt {ANN}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ANN</mml:mi> </mml:math> ) are widely used machine learning models. Their widespread use has attracted a lot of interest in their robustness. Many studies show that ’s performance can be highly vulnerable to input manipulation such as adversarial attacks and covariate drift. Therefore, various techniques that focus on improving $${{{\texttt {ANN}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ANN</mml:mi> </mml:math> ’s robustness have been proposed in the last few years. However, most of these works have mostly focused on image data. In this paper, we investigate the role of discretization in improving $${{{\texttt {ANN}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ANN</mml:mi> </mml:math> ’s robustness on tabular datasets. Two custom $${{{\texttt {ANN}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ANN</mml:mi> </mml:math> layers– and (collectively called ) are proposed. The two layers integrate discretization during the training phase to improve $${{{\texttt {ANN}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ANN</mml:mi> </mml:math> ’s ability to defend against adversarial attacks. Additionally, integrates dynamic discretization during testing phase as well, to provide a unified strategy to handle adversarial attacks and covariate drift. The experimental results on 24 publicly available datasets show that our proposed add much-needed robustness to $${{{\texttt {ANN}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ANN</mml:mi> </mml:math> for tabular datasets.
RECEIVER OPERATING CHARACTERISTIC OPTIMIZATION BASED ON CONVEX HULL AND EVOLUTIONARY ALGORITHMS

Classification is basically a Multi-objective problem. The efficiency of classification vastly depends on performance of a classifier which can be evaluated on the basis of Receiver Operating Characteristic (ROC) graph, Area under Curve (AUC), and selection of different threshold values are generally used as a tool. In machine learning, generally, 2-D classifiers are available that deal with bi-objective problems where overlapping of class may occur i.e. sensitivity and specificity may overlap. Recently, multi-class classification in which classes are mutually exclusive is in research trends along with the evolutionary algorithm. The application of ROC graph is extended to evaluate multi-dimensional classification as it is cost sensitive learning. The goal of this paper is to gather recent achievements in the field at one place to analyze the classifier performance for multi-dimensional classification problems using convex hull and evolutionary algorithms. In this paper, we tried to cover all the existing recent advance techniques in maximization of ROC and proposed a convex hull and evolutionary algorithm based new model for ROC maximization.
Pivan: A Web-platform for Document Annotation

The Pivan web platform is an open-source tool for managing different stages of automatic document processing, such as layout analysis, transcription, and named entity recognition. It allows for the visualization of document segmentation, transcription at the line or paragraph level, and annotation of named entities. Pivan's web-based nature makes it perfectly suited for collaborative annotation and offers a smooth experience, even for small machines or connections. It is based on up-to-date web technologies, it includes a comprehensive API, and it can be easily deployed via Docker.
TEACHERS’ ACADEMIC QUALIFICATIONS AND THEIR LEVELS OF PROFICIENCY ON THE ESSENTIAL TEACHING SKILLS: A SURVEY STUDY AMONG TEACHERS OF EARLY CHILDHOOD EDUCATION PROGRAMS

This paper reports on a study which aims at describing the levels of teaching proficiency among the ECCE (Early Childhood Center Education) teachers based on their essential teaching skills in preparing lesson plans, carrying out teaching and learning activities, and assessing learning. This study involved ECCE teachers having a linear educational background (i.e., a bachelor’s degree in early childhood education) and those having a non-linear educational background (i.e., a non a bachelor’s degree in early childhood education). This study is descriptive quantitative in nature employing a survey design. There were 170 ECCE teachers in Sawahan, Nganjuk, East Java participated in this study. Of these participants, 120 had a linear educational background and 50 had a non-linear educational background. The research data were collected using a questionnaire with 30 questions. The results of the study showed that among the teachers involved in the study, 85.88% could be said to be very proficient, 2.94% could be said to be proficient, 8.82% teachers could be said to be less proficient and 2.36% of the teachers could be said to be not proficient. There is no difference in the levels of teaching proficiency between teachers who have linear and non-linear educational background because teachers who do not have linear educational background have got competency enrichment opportunities through teacher trainings, seminars, sharing knowledges with colleagues, and practices in ECCE teacher organizations
Coupled-Tensor Generated Word Embeddings and Their Composition

Contemporary methods of computing vector-space embeddings of words are able to accurately capture both their semantic and syntactic properties. Methods for computing n-gram embeddings do, however, come with downsides. They either require high resources during training or estimation, or come with other disadvantages, such as loss of information about individual positions of words in phrases. We propose two novel approaches to training word vectors enabling a composition of word embeddings into n-gram embeddings. Both methods are based on coupled CP decomposition of tensors that are generated by a sequence of time-shifted word embeddings. We compare our methods with SGNS and show that they provide superior performance on word-analogy tasks.
Building a Recommendation System for E-Commerce Using Machine Learning and Big Data Technologies

The popularization of today’s e-commerce sites made a big impact on IT technologies. Since e-commerce companies want the best experience for their customers, many technologies are involved to create that experience. One way to gain loyal customers is to build recommendations based on the other opinions. Since the best recommendations are based on the customers opinions and experience we can use that knowledge to build a recommendation system. In this research paper we have combined big data technologies with machine learning in order to create the best model for recommending products in e-commerce sites. Using the Apache Spark technologies with the Python language and PySpark module which connected Apache Spark and Python program and Amazon reviews dataset, we have managed to create a model with the Root-mean-square deviation of 0.823. We achieved such a result by utilizing two different approaches for normalization and performing cross validation on them.
Real-time operating system for autonomous drone control

Drones have become a transformative force across industries in the quickly changing world of autonomous technologies. Their applications are expanding, from precision agriculture to emergency response, thanks to developments in real-time operating systems. A key development is a "Real-time operating system for autonomous drone control" that reshapes the potential of unmanned aerial vehicles through the convergence of high-speed data processing, low-latency responsiveness, and complex control algorithms.
Revision for Concision: A Constrained Paraphrase Generation Task

Academic writing should be concise as concise sentences better keep the readers’ attention and convey meaning clearly. Writing concisely is challenging, for writers often struggle to revise their drafts. We introduce and formulate revising for concision as a natural language processing task at the sentence level. Revising for concision requires algorithms to use only necessary words to rewrite a sentence while preserving its meaning. The revised sentence should be evaluated according to its word choice, sentence structure, and organization. The revised sentence also needs to fulfil semantic retention and syntactic soundness. To aide these efforts, we curate and make available a benchmark parallel dataset that can depict revising for concision. The dataset contains 536 pairs of sentences before and after revising, and all pairs are collected from college writing centres. We also present and evaluate the approaches to this problem, which may assist researchers in this area.