Reviewerly/exHarmony huggingface dataset:
The Impacts of Integral Teaching of the Listening and Speaking Skills on EFL Communicative Competence

The listening and speaking skills are connected in real life and complement each other as input and output of language. However, in the Iraqi context, these two skills are taught in isolation; not taught in an integrated manner, thus, the result is poor performance of the students. This study investigated the impacts of integrating the listening and speaking skills in an English class and how it enhances English as a Foreign Language (EFL) students’ communicative competence. A mixed-methods research design was employed to gather the necessary data through a one group pre-posttest and interviews. The participants were 51 university students at Charmo University, North of Iraq. In analyzing the quantitative data, a paired samples t-test was run to find answers to the research questions posed. The results indicated that the participants’ communicative competence had statistically improved as there was a significant difference between the pretest and posttest scores. In addition, the analysis of the semi-structured interviews which were carried out among the participants, revealed that they had positive attitudes towards the semester and felt it was a success. This study is concurrent with the previous literature and highlights the importance of integrating the language skills to teach the English language effectively.&#x0D; &#x0D; &#x0D; Keywords: listening skill, speaking skill, English as a Foreign Language (EFL), communicative competence
Breast cancer detection using machine learning and image processing

Nowadays breast cancer is the frequent type of cancer in women which leads to death. Mammography and ultrasound are the common ways to detect breast cancer. This paper employs Machine Learning for identification of breast cancer using mammography images. Ultrasound and Elastography are the combined imaging techniques used to separate benign and malignant breast lesions. Support vector machine is a classifier which is used for the classification of combined B-mode and Elastography image. This paper helps the physician to detect breast cancer earlier.
A Study on the Computer Aided Diagnosis System for Early Gastric Cancer Lesion Based on EfficientNetV2-L through Data Filtering

Gastric cancer is a common cancer worldwide, especially in Korea. Early diagnosis is very important to increase the full recovery rate. However, early gastric cancer has no special symptoms and is a disease that even experts find difficult to diagnose in gastroscopy. Therefore, in this paper proposed a computer-aided diagnosis(CADx) for early gastric cancer diagnosis using EfficientNetV2-L. Due to the nature of medical data, it is difficult to collect a large amount of data. The data used for training was augmented using Cifar10 policy of the Google
Influence of multiphoton events on the quantum enhanced phase estimation

Quantum metrology can approach measurement precision of Heisenberg Limit using an ideal quantum source, which has attracted a great interest in fundamental physical studies. However, the quantum metrology precision is impressionable to the system noise in experiments. In this paper, we analyze the influence of multiphoton events on the phase estimation precision when using a nondeterministic single photon source. Our results show there are an extra bias and quantum enhanced region restriction due to multiphoton events, which declines the quantum phase estimation precision. A limitation of multiphoton probability is obtained for quantum enhanced phase estimation accuracy under different experimental model. Our results provide beneficial suggestions for improving quantum metrology precision in future experiments.
Taro Kageyama, Peter E. Hook and Prashant Pardeshi (eds.): <i>Verb-verb Complexes in Asian Languages</i>

Article Taro Kageyama, Peter E. Hook and Prashant Pardeshi (eds.): Verb-verb Complexes in Asian Languages was published on November 1, 2022 in the journal Journal of Japanese Linguistics (volume 38, issue 2).
The Axiomatic Approach to Non-Classical Model Theory

Institution theory represents the fully axiomatic approach to model theory in which all components of logical systems are treated fully abstractly by reliance on category theory. Here, we survey some developments over the last decade or so concerning the institution theoretic approach to non-classical aspects of model theory. Our focus will be on many-valued truth and on models with states, which are addressed by the two extensions of ordinary institution theory known as L-institutions and stratified institutions, respectively. The discussion will include relevant concepts, techniques, and results from these two areas.
DAMO-NLP at NLPCC-2022 Task 2: Knowledge Enhanced Robust NER for Speech Entity Linking

Speech Entity Linking aims to recognize and disambiguate named entities in spoken languages. Conventional methods suffer gravely from the unfettered speech styles and the noisy transcripts generated by ASR systems. In this paper, we propose a novel approach called Knowledge Enhanced Named Entity Recognition (KENER), which focuses on improving robustness through painlessly incorporating proper knowledge in the entity recognition stage and thus improving the overall performance of entity linking. KENER first retrieves candidate entities for a sentence without mentions, and then utilizes the entity descriptions as extra information to help recognize mentions. The candidate entities retrieved by a dense retrieval module are especially useful when the input is short or noisy. Moreover, we investigate various data sampling strategies and design effective loss functions, in order to improve the quality of retrieved entities in both recognition and disambiguation stages. Lastly, a linking with filtering module is applied as the final safeguard, making it possible to filter out wrongly-recognized mentions. Our system achieves 1st place in Track 1 and 2nd place in Track 2 of NLPCC-2022 Shared Task 2.
Science Production in The Context of Post Digital Theory: Notes For A Meta Convergence Approach Prioritizing Human

Technological developments produced through physics-based scientific convergences, known as Convergence 1.0 in the twentieth century, took place in the shadow of the driving force of war. Radio, television, telephone, aeroplane, radar, and computer, which have become inseparable parts of human life today, are the products of these convergences. The twenty-first century is the age of biology-based convergences in scientific production, called Convergence 2.0. which includes cancer-fighting nanoparticles, viruses used for energy production, and proteins used in water filtration. Using scientific and technological advances in the name of human dignity and the best interests of human beings is as important as the production of science. While the wars in the world and the dramas caused by migration and environmental disasters continue; scientific and technological developments need to be addressed with a meta convergence approach, including philosophy and social sciences. The post-digital theory offers scientists an important framework with its unique concepts and analyses for such a meta convergence that will prioritise human beings. This research is an effort to think about the possibilities of meta convergence that preserves the best interests of human beings and carries them to future generations within the framework of the post-digital theory.
A System for Generating Student Progress Reports in Cram School

In many cram schools, instructors write reports on students’ progress after each class. The generation of these reports is a heavy burden for instructors, and there is a need to reduce this burden. Therefore, in this paper, we propose a system that automatically generates a student learning status reports. Students’ learning status is often evaluated from several specific items, and rule-based sentence generation can be considered for those items. However, since viewpoints other than the specific items are often incorporated into the report document, a keyword-based sentence generation function is required to incorporate expressions that are difficult to be generated by the rule-base methods. Here we consider two keyword-based methods: the Sequence-to-Sequence-based method, which learns the correspondence between keywords and sentences, and the Information Retrieval-based method, which directly retrieves and reuses past reports. In this paper, we compare and evaluate the two methods and implement the model with better performance into our report generation system. We evaluated the two methods based on actual data of about 200,000 reports written by instructors, and confirmed that the Seq2Seq-based model with Attention had the best performance, and was able to generate more accurate sentences by learning positive and negative expressions separately.
Introduction to graph analytics

Graph is considered to be the collection of the points or vertices or nodes and the lines between the points or the edges. The structural characteristics of a data can be plotted easily using graphical representation. In this chapter, we are going to deal about the introduction of graph analytics and about graph structure data. A detailed discussion about the graph algorithms and graph databases gives an additional advantage to read this chapter. Some of the latest graph analytics tools also explained. Data science plays a major role in today&apos;s computing world. The subdivision of data science that contracts with mining information from graphs by executing analysis on them is known as "Graph Analytics." These graph analytics are mainly supported in data science for handling huge datasets such as Google, Amazon, Facebook, e-Commerce, and Finance. It can also support use cases include cybersecurity, drug interaction, reference engines, contact tracing, social networks, and supply chains.
Overview of Autonomous Vehicle

Autonomous vehicle, or self-piloting automobile, driverless car, computer-driven car, or wheeled mobile robot, is a type of intelligent car autonomously controlled by computer system.
BIT-WOW at NLPCC-2022 Task5 Track1: Hierarchical Multi-label Classification via Label-Aware Graph Convolutional Network

This paper describes the system proposed by the BIT-WOW team for NLPCC2022 shared task in Task5 Track1. The track is about multi-label towards abstracts of academic papers in scientific domain, which includes hierarchical dependencies among 1,530 labels. In order to distinguish semantic information among hierarchical label structures, we propose the Label-aware Graph Convolutional Network (LaGCN), which uses Graph Convolutional Network to capture the label association through context-based label embedding. Besides, curriculum learning is applied for domain adaptation and to mitigate the impact of a large number of categories. The experiments show that: 1) LaGCN effectively models the category information and makes a considerable improvement in dealing with a large number of categories; 2) Curriculum learning is beneficial for a single model in the complex task. Our best results were obtained by an ensemble model. According to the official results, our approach proved the best in this track.
Determining Phishing Emails using URL Domain Features

Social engineering is the process of getting a person to provide a service or complete a task that may give away private or confidential information. Phishing is the most common type of social engineering. In phishing, an attacker poses as a trustworthy source in an attempt to have the victim release personal or private information. This research aims to identify phishing emails by examining the domain section of URLs. What makes this research novel is the extraction of concise and high quality features used to identify phishing emails in high accuracy as well as provide users with features to identify phishing emails. Through our experiments, we discovered that all of our phishing detection models performed similarly; however, the multinomial naïve bayes model may be best suited for phishing detection via URL domain. This model produced an accuracy of 97.22% with a precision of 0.974 and recall of 0.9776. We also discovered that the most significant features in determining whether a URL is phishing or legitimate were the age of the domain, the length, and the presence of a dash "-".
LargeNetVis: Visual Exploration of Large Temporal Networks Based On Community Taxonomies

Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose LargeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map - TAM) shows the community- and node-level activity under a temporal perspective. We demonstrate the usefulness and effectiveness of LargeNetVis through two usage scenarios and a user study with 14 participants.
Learning-based Denoising Algorithm for the Reconstructed Image using Electromagnetic Emanations from the Display Device

This paper proposes a learning-based denoising algorithm that improves the signal-to-noise ratio (SNR) of the information signal emitted from the display device. The information signal is easily degraded by noise and interference on the channel and has various SNR. In this situation, an algorithm that enhances the model's robustness is required to improve the degraded information signal into a learning-based denoising model. Therefore, this paper proposes a normalization method to enhance the robustness of the model.
DeltaExplainer: A Software Debugging Approach to Generating Counterfactual Explanations

The profound black-box nature of Machine Learning (ML) based Artificial Intelligence (AI) systems leads to the problem of interpretability. Explainable Artificial Intelligence (XAI) tries to provide explanations to human users to understand the decisions made by ML-based systems. In this paper, we propose a software debugging-based approach called DeltaExplainer for generating counterfactual explanations for predictions made by ML models. The key insight of our approach is that the problem of XAI is similar to the problem of software debugging. We evaluate DeltaExplainer on eight ML models trained using real-world datasets. We compare DeltaExplainer to two state-of-the-art counterfactual explanation tools, i.e., DiCE and GeCo. Our experimental results suggest that the proposed approach can successfully generate counterfactual explanations and, in most cases, generate better explanations than DiCE and GeCo.
ANALISIS TINGKAT PELAYANAN PROSES BELAJAR MENGAJAR SECARA ONLINE TERHADAP KEPUASAN MAHASISWA DI FAKULTAS EKONOMI UNIVERSITAS LANCANG KUNING

Efforts to fulfill student desires are the key to success in winning the competition and the best action that needs to be taken by higher education institutions is to use feedback from students to make changes to services. Every student wants maximum satisfaction fromevery service available in educational institutions, with maximum satisfaction obtained by students will be able to improve the quality of educational services. The purpose of this study was to analyze the level of student satisfaction in receiving the online teaching and learning process, to analyze the quality of services provided by education providers to students online. To analyze the effect of the level of service provided by education providers on student satisfaction at the Faculty of Economics, Lancang Kuning University. From the results of the study, it showed that the level of student satisfaction in receiving the teaching and learning process, on average, could be said to be satisfied in accepting online learning. While services for online infrastructure that can be used by students are still not supportive, namely the campus wifi problem. The results of the online teaching and learning process services on student satisfaction which are carried out at the Faculty of Economics have a significant influence on student satisfaction, but there are two variables that are not significant. and has a negative value, namely the responsiveness variable and tangible variable. This is due to the lack of communication between students and the academic community and the lack of equipment available for online learning.
Detecting Inconsistencies in Public Bids: An Automated and Data-based Approach

One application for using government data is the detection of irregularities that may indicate fraud in the public sector. This paper presents an approach that analyzes public bidding data available on the Web to detect bidder inconsistencies. Specifically, we propose a hierarchical decision approach from public bidding data, where each bidder is classified as Valid, Doubtful, or Invalid, based on the compatibility between the bidding items and the divisions of the CNAE codes (National Classification of Economic activities). The results reveal that combining commonly available data on bidders and extracting the description of bid items can help in fraud detection. Furthermore, the proposed approach can reduce the number of bids a specialist must analyze to detect fraud, making it easier to identify inconsistencies.
Generate Beaufort Cipher Key Based on Blum-Blum Shub For Secure Digital Image

Data security is not only due to the complexity of the algorithms used, but one of the most important factors is the security of the keys used. One of the most popular data that needs to be backed up today is digital images. However, these days, the algorithms used to back up data are often attacked. This is because the structure of the key formations used is still weak. One of the data security algorithms that form a simple key is the Beaufort cipher. It relies on the duplication of key characters to form the key for each protected element. Key generators based on blum-blum shub technology can generate very random and difficult-to-guess keys, so the strength of the keys you use can enhance the resilience of protected data. Keys generated based on blum-blum shub technology will later be used in the process of encrypting and decrypting pixel values ​​in digital images.
Automatons, Androids, and Stepford Wives

Fictional imaginings about artificial women date back to antiquity with tales like Ovid’s <italic>Pygmalion</italic>. Here, Pygmalion carves his ideal partner out of ivory, before calling on the divine power of the goddess Venus to turn his sculpture into a living being. Later, following industrialisation, tales about men making artificial women as superior versions of their fleshly counterparts emerged as a trope with stories such as E.T.A Hoffman’s <italic>Der Sandmann</italic> (1816) and Auguste Villiers de l’Isle-Adams’ <italic>L’Eve Future</italic> (1886). This chapter situates <italic>The Stepford Wives</italic> in the context of fictions from the 1800s forward, including <italic>L’Eve Future</italic>. It establishes the importance of critical debates about the nature of life, initiated by experiments in galvanic resurrection, to the idea of life-like automations.
THE IMPLEMENTATION OF TEACHING WRITING THROUGH A GENRE-BASED APPROACH

This study reports on the effectiveness of teaching writing through a genre-based approach. This study employed a qualitative research design, embracing the characteristics of a case study. The data were obtained from classroom observation. The study involved students at a public high school in Bandung. The result shows that the teaching program of Genre Based Approach which includes Building knowledge of the field (BKOF), Modeling of the text (MOT), Joint construction of the text (JCOT), and Independent construction of the text (ICOT) helped the students to express the idea in writing. It also helped students to understand the language features and structures of the text. Based on the analysis, it is recommended that the teaching program can be applied in various text genres to improve the teaching of English and enhance the students’ writing ability.
A Low-Resolution Vehicle Detection Solution by Using Super-Resolution Recovery

For vehicle detection tasks, the accuracy of detection model trained by high-resolution images will be decreased in low-resolution images. To solve this problem, a new object detection network is proposed which cascades super-resolution and target detection networks. SwinIR restores the image from low-resolution to high-resolution which integrates the advantages of both CNN and Transformer. YOLOv5 is improved by replacing its loss function with EIOU to enhance convergence speed and localization accuracy. The experiments are conducted on UA-DETRAC dataset, and the effectiveness of the detection network is verified.
Graph Convolutional Networks Using Node Addition and Edge Reweighting

AbstractGraph convolutional networks (GCNs) provide a promising way to explore datasets that have graph structures in nature. The presence of corrupted or incomplete graphs, however, dramatically decreases the performance of GCNs. To improve the performance, recent works on GCNs reweighted edges or added missing edges on the given graphs. On top of that, this paper further explores the domain of node addition. This paper presents a simple but effective extension of GCNs by combining node addition and edge reweighting. Node addition adds new nodes and edges as communication centers to the original graphs. By doing so, nodes can share information together for efficient inference and noise reduction. Moreover, edge reweighting re-distributes the weights of edges, and even removes noisy edges considering local structures of graphs for performance improvement. Based on four publicly available datasets, the experimental results demonstrate that the proposed approach can achieve better performance than four state-of-the-art approaches.KeywordsGraph convolutional networkSemi-supervised learningNode additionEdge reweighting
InSciC—Knowledge-Aware Crawler for Indian Sciences

The National Education Policy aims to provide a high-quality education to make India a global knowledge power by rooting the education system with Indian culture and ethics. While there is a pool of resources available, we need an organized approach to structure this pool of resources into a knowledge base. Through this work, we propose a model to design and develop a system that recommends the web content relevant to a term in the context of Ancient Indian Sciences. We aim to build a crawler with a knowledge-aware module to emphasize the significance of Ancient Indian sciences. It learns from its own crawling experiences and improves the crawling process in future crawls. The project's key focus is a methodology that deploys a design to overcome the challenges of maintaining visited pages and finding a relation between the crawled pages after having them in the knowledge base, which helps the crawler preserve focus in the domain of sciences using similarity measures. We discuss the data structure design, annotations, and the knowledge base in this work. The structure and results promise to provide an initial organization system for the knowledge hub.