Reviewerly/exHarmony huggingface dataset:
The Literature Review of Writing Test in Past Decade in China

Writing reflects not only learners' ability to use language, but also their character as a person. The study of writing test has important implications for both Second Language Acquisition and teaching process. This paper reviews the research findings on English writing test in China over the past decade, including assessment, validity and backwash effect. The results demonstrate that researches on assessment of writing test includes scoring dimensions, scoring approaches, development of scoring scales and so on. Moreover, in terms of the validity, cognitive validity, content validity, and context validity have all been researched. However, compared with the validity of different task types of writing test, there are relatively few studies on these three dimensions. However, structure validity has been rarely mentioned in the validity studies of the last decade. As for backwash effect, both positive and negative effect have been studied, but there are still no practical measures to effectively attenuate or circumvent negative backwash effect. In a word, the researches related to writing test in China have become more and more systematic in the last decade.
Chinese Higher Education Entering the Age of Artificial Intelligence: Opportunities, Challenges and Prospects

Currently, artificial intelligence is in a period of vigorous development, with various new technologies and applications continuously emerging, which has brought many changes to our lives. Similarly, artificial intelligence has also led to disruptive changes in the field of higher education in China. Therefore, this article analyzes the opportunities and challenges posed by artificial intelligence to China's higher education, and puts forward a pioneering outlook.
The Use of Canva Application on Material of Advertising Text

Students' understanding of Indonesian language learning, especially in advertising texts, was low. They had difficulty in understanding and processing the information in the advertising text using only limited learning media. Therefore, a solution is needed to optimise the effectiveness of advertising text learning. The Canva application can be used to solve this problem. The purpose of this research was to help teachers and students explore and facilitate the learning of advertising text material by helping students understand advertising text through visual representations made with The Canva application. The subjects of this study were 8th grade students. This research used descriptive qualitative methods with data collection techniques through observation, interviews, and questionnaires, as well as data reduction and data analysis techniques. The result of this study was that the use of Canva application media in learning Indonesian advertising text was considered effective and interesting by 100% of students who were used as research subjects. Students easily understand advertising text learning material after using Canva, which increases student involvement in the learning process.
Scratch-Based Interactive Games to Increase Interest in Learning Mathematics for the Second Grade Elementary School Students

Technology has developed rapidly and supports various aspects of life, including education. Many schools, especially in developed countries, often use technology in the learning process. This study aims to increase the interest in learning mathematics of second-grade elementary school students through scratch-based interactive games. Second-grade teachers expressed the need for learning media that is interesting for students. The researcher developed scratch-based interactive games validated by experts and found to meet the appropriate criteria. The test results using the paired t-test showed a significant mean difference between the pretest and posttest of interest in learning mathematics, indicating that using interactive games positively increased students' interest in learning. In addition, the use of interactive games media also has a good effect in improving students' interest in learning mathematics, as shown by the results of the N-Gain test. Therefore, this study recommends using interactive games as a practical learning media to increase interest in learning mathematics in various other classes and schools. It can even be developed for additional lessons to increase students' interest in learning.
“Condensation-Expansion” and “AB Double Texts” Based on PAD Class Teaching Model and Practices—The Case of English Writing

Addressing the major problems in current English Writing course in China, the study puts forward “condensation-expansion” and “AB double Texts” based on PAD Class Teaching Model, which revolutionized the traditional English writing teaching. The study decodes model essays by splitting, locating keywords and abstracting; and encodes the writing by brainstorming, lexical clustering and extended writing after analyzing the title and directions. Text A is used to demonstrate by teachers and Text B is used to practice by learners. The teaching practices from 2019 till now show that the “condensation-expansion” and “AB Double Texts” based on PAD Class teaching model can effectively improve the learners’ writing ability, cultivate learners’ critical thinking, expressive and cooperative ability.
The Ilocutionary Act of Joe Biden’s Speech Between Rusia and Ukraine

This study discusses about the Illocutionary Act of Joe Biden Speech War between Rusia and Ukraine. The purposed of this study were to explain the types of illocutionary applied by Joe Biden in his speech. This study used a pragmatic approach from Leech (1989), Illocutionary Act from Searle (1999),and Leech (1993). The method used qualitative descriptive method to analyzed the data, the researcher used vidio and the script to collect the data, source of data was take from Joe Biden speech video. The findings of this study showed that (1) Joe Biden used five kinds of illocutionary act , they are Declarative, Directive, Expressive, Representative, and Commisive. Then, (2) there four function of illocutionary Act used by Joe Biden speech war, they are, Competitive, Collaborative, Convivial, and Conflictive
‘I’m not a virus’: Asian hate in Donald Trump’s rhetoric

Since the start of Covid-19, anti-Asian sentiment spiked. From March 2020 to June 2021, there were a total of 9,081 self-reported incidents of hate across the United States (Stop AAPI Hate. (2021). https://stopaapihate.org/stop-aapi-hate-national-report-2/). As Covid-19 spread into the U.S., President Trump immediately blamed China by referring to the virus as the ‘Chinese Virus’ and used the hashtag #ChineseVirus on Twitter (Weise, E. 2021). Anti-Asian hashtags soared after Donald Trump first tied COVID-19 to China on Twitter. (USA Today. https://www.usatoday.com). Anti-Asian rhetoric expressed on Twitter grew after Trump’s tweet about the ‘Chinese virus,’ and the number of Chinese and other Asian hate crimes grew exponentially. This study explores the rhetorical strategies that Trump utilized to create a sense of fear against the dangerous ‘Other.’ We use a rhetorical thematic analysis to analyze Trump’s tweets that contain language such as ‘Chinese virus’ or ‘Kung Flu.’ Themes such as scapegoating, fear of the other, China bashing, and populist appeals were prevalent. Describing Chinese and other Asian bodies as ‘spreaders’ of diseases, reinforces the Yellow Peril and perpetual foreigner stereotypes. The study shows the importance of presidential rhetoric in influencing public opinion in the context of COVID-19 and Asian hate.
Filter Pruning with Convolutional Approximation Small Model Framework

Convolutional neural networks (CNNs) are extensively utilized in computer vision; however, they pose challenges in terms of computational time and storage requirements. To address this issue, one well-known approach is filter pruning. However, fine-tuning pruned models necessitates substantial computing power and a large retraining dataset. To restore model performance after pruning each layer, we propose the Convolutional Approximation Small Model (CASM) framework. CASM involves training a compact model with the remaining kernels and optimizing their weights to restore feature maps that resemble the original kernels. This method requires less complexity and fewer training samples compared to basic fine-tuning. We evaluate the performance of CASM on the CIFAR-10 and ImageNet datasets using VGG-16 and ResNet-50 models. The experimental results demonstrate that CASM surpasses the basic fine-tuning framework in terms of time acceleration (3.3× faster), requiring a smaller dataset for performance recovery after pruning, and achieving enhanced accuracy.
Efficient Distributed Mapping-Based Computation for Convolutional Neural Networks in Multi-Core Embedded Parallel Environment

Embedded systems are the best solution to achieve high-performance edge terminal computing tasks. With the rapid increase in the amount of data generated by edge devices, it is imperative to implement intelligent algorithms with large amounts of data and computation on embedded terminal systems. In this paper, a novel multi-core ARM-based embedded hardware platform with a three-dimensional mesh structure was first established to support the decentralized algorithms. To deploy deep convolutional neural networks (CNNs) in this embedded parallel environment, a distributed mapping mechanism was proposed to efficiently decentralize computation tasks in the form of a multi-branch assembly line. In addition, a dimensionality reduction initialization method was also utilized to successfully resolve the conflict between the storage requirement of computation tasks and the limited physical memories. LeNet-5 networks with different sizes were optimized and implemented in the embedded platform to verify the performance of our proposed strategies. The results showed that memory usage can be controlled within the usable range through dimensionality reduction. The down-sampling layer as the base point of the mapping for the inter-layer segmentation achieved the optimal operation in lateral dispersion with a reduction of around 10% in the running time compared with the other layers. Further, the computing speed for a network with an input size of 105 × 105 in the multi-core parallel environment is nearly 20 times faster than that in a single-core system. This paper provided a feasible strategy for edge deployments of artificial intelligent algorithms on multi-core embedded devices.
A Bi-Directional Two-Dimensional Deep Subspace Learning Network with Sparse Representation for Object Recognition

A principal component analysis network (PCANet), as one of the representative deep subspace learning networks, utilizes principal component analysis (PCA) to learn filters that represent the dominant structural features of objects. However, the filters used in PCANet are linear combinations of all the original variables and contain complex and redundant principal components, which hinders the interpretability of the results. To address this problem, we introduce sparse constraints into a subspace learning network and propose three sparse bi-directional two-dimensional PCANet algorithms, including sparse row 2D2PCANet (SR2D2PCANet), sparse column 2D2PCANet (SC2D2PCANet), and sparse row–column 2D2PCANet (SRC2D2PCANet). These algorithms perform sparse operations on the projection matrices in the row, column, and row–column direction, respectively. Sparsity is achieved by utilizing the elastic net to shrink the loads of the non-primary elements in the principal components to zero and to reduce the redundancy in the projection matrices, thus improving the learning efficiency of the networks. Finally, a variety of experimental results on ORL, COIL-100, NEC, and AR datasets demonstrate that the proposed algorithms learn filters with more discriminative information and outperform other subspace learning networks and traditional deep learning networks in terms of classification and run-time performance, especially for less sample learning.
Sparse Adversarial Attacks against DL-Based Automatic Modulation Classification

Automatic modulation recognition (AMR) serves as a crucial component in domains such as cognitive radio and electromagnetic countermeasures, acting as a significant prerequisite for the efficient signal processing of receivers. Deep neural networks (DNNs), despite their effectiveness, are known to be vulnerable to adversarial attacks. This vulnerability has inspired the introduction of subtle interference to wireless communication signals—interference so minuscule that it is difficult for the human eye to discern. Such interference can mislead eavesdroppers into erroneous modulation pattern recognition when using DNNs, thereby camouflaging communication signal modulation patterns. Nonetheless, the majority of current camouflage methods used for electromagnetic signal modulation recognition rely on a global perturbation of the signal. They fail to consider the local agility of signal disturbance and the concealment requirements for bait signals that are intercepted by the interceptor. This paper presents a generator framework designed to produce perturbations with sparse properties. Furthermore, we introduce a method to reduce spectral loss, which minimizes the spectral difference between adversarial perturbation and the original signal. This method makes perturbation more challenging to monitor, thereby deceiving enemy electromagnetic signal modulation recognition systems. The experimental results validated that the proposed method significantly outperformed existing methods in terms of generation time. Moreover, it can generate adversarial signals characterized by high deceivability and transferability even under extremely sparse conditions.
Forging ahead for fault-tolerant quantum computing

Qubit active reset method showcases speed and efficiency.
Melody

Data visualisation and storytelling techniques help experts highlight relations between data and share complex information with a broad audience. However, existing solutions targeted to Linked Open Data visualisation have several restrictions and lack the narrative element. In this article we present MELODY, a web interface for authoring data stories based on Linked Open Data. MELODY has been designed using a novel methodology that harmonises existing Ontology Design and User Experience methodologies (eXtreme Design and Design Thinking), and provides reusable User Interface components to create and publish web-ready article-alike documents based on data retrievable from any SPARQL endpoint. We evaluate the software by comparing it with existing solutions, and we show its potential impact in projects where data dissemination is crucial.
BUA: a blockchain-based unlinkable authentication scheme for mobile IoT

ABSTRACTMobile IoT, a pioneering IoT facet, enhances data acquisition. Mutual authentication ensures device validity in mobile IoT. Unlinkable authentication needs for mobile IoT data nodes during device validation. Current methods have issues: Certificate Authority (CA) faces computational load, and blockchain deals with storage and throughput. In this paper, BUA, a blockchain-based unlinkable authentication scheme, applies vector commitment for CA reduction and blockchain data compression. A binary tree auxiliary index, minimizes redundancy in vector commitment verification. This scheme fulfills security goals and boosts performance by 13.24%, surpassing similar proposols.KEYWORDS: Mutual authenticationvector commitmentauxiliary proof calculation indexmobile IoT AcknowledgmentsThis work was supported by the National Natural Science Foundation of China (Nos. 62072092 and 62072093); the Key Research and Development Project of Hebei Province (No. 20310702D); the Natural Science Foundation of Hebei Province (No. F2020501013) and VC Research (VCR 00000172). 2023 Hebei Provincial doctoral candidate Innovation Ability training funding project (No. CXZZBS2023168).Disclosure statementNo potential conflict of interest was reported by the author(s).Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThe work was supported by the National Natural Science Foundation of China [62072093]; 2023 Hebei Provincial doctoral candidate Innovation Ability training funding project [CXZZBS2023168]; VC Research [VCR 00000172]; the Natural Science Foundation of Hebei Province [F2020501013]; the Key Research and Development Project of Hebei Province [20310702D]; the National Natural Science Foundation of China [62072092].
Viki LibraRy

We present Viki LibraRy, a virtual-reality-based system for generating and exploring online information as a spatial hypertext. It creates a virtual library based on Wikipedia in which Rooms are used to make data available via a RESTful backend. In these Rooms, users can browse through all articles of the corresponding Wikipedia category in the form of Books. In addition, users can access different Rooms, through virtual portals. Beyond that, the explorations can be done alone or collaboratively, using Ubiq.
Effects of the spiral of silence on minority groups in recommender systems

Recommender systems play a critical role in today's data-rich landscape, where the abundance of information necessitates their ability to present the most relevant choices for individual users. However, it has been noted that recommender systems often fail to offer the most suitable options for minority groups. Therefore, this paper examines one of the potential reasons behind the underrepresentation of minority opinions within recommender systems through a literature review. Specifically, we explore the connection and importance between the spiral of silence concept and the dearth of suitable recommendations for minorities.
SPORE

The paper presents SPORE, a Spatial Recommender System. As we enter a period of unprecedented collaboration between authors and computers, where artificial intelligence in particular seems likely to act increasingly in a co-authoring capacity, SPORE offers a different approach to collaboration. More organic and exploratory than other automated or procedural systems, SPORE aims to mimic the process of storybreaking that already exists in the creative industries.
Pi-Score: An Estimation Strategy of the Class Prior in Positive-Unlabeled Learning for Electrical Insulator Defect Detection with Incomplete Annotations

Insulators in high-voltage power systems serve as brackets for overhead lines and prevent these wires from becoming grounded. Due to long-term exposure to a harsh environment, it is indispensable to apply periodic inspection for defective insulators, facilitating the timely overhaul of insulators. In the field of object detection, convolutional neural networks (CNNs) have been introduced and have achieved good performance. Therefore, various CNN-based detectors are applied to the insulator detection task. Because collecting ideal annotations is time-consuming and labor-intensive, research with imperfect annotations has drawn more attention. However, fewer insulator defect detection approaches account for this imperfect annotation problem. This paper focuses on a novel and challenging insulator defect detection scenario: a part of the insulators in datasets is unannotated and viewed as the background. We introduce positive-unlabeled (PU) learning to solve the problem of incomplete annotation for insulator defect detection. To further improve PU learning, a Pi-Score algorithm is proposed to estimate class prior, a crucial parameter in PU loss. Our designed framework is built on Faster R-CNN and incorporates the improved PU learning in the region proposal network. Experimental results on the Insulator Defect Image Dataset (IDID) demonstrate that the proposed framework achieved an average precision (AP) metric that is approximately 1%–2% higher than the positive–negative mainstream detectors with varying degrees of missing annotation. Meanwhile, the proposed framework obtained 0.33 and 0.47 higher AP metrics than the mainstream PU detectors with complete and half of IDID’s annotations.
Persona Analytics

Personas represent distinct user or customer groups in design, human-computer interaction, and marketing. Persona analytics refers to creating data-driven personas, often employing data science and machine learning algorithms, to address customer analytics use cases. Through persona analytics, various customer segments can become more salient, observable, and approachable for managers and other decision managers without losing the objectivity of quantitative data. In this chapter, we discuss the definition of persona analytics, reasons for its existence, potential use cases for persona analytics, and future directions for employing personas in web and social media analytics.
OASIS'23

Online Social Networks (OSNs) became part of everyday life for many people around the world. They are one of the main channels through which information can spread at lightning speed. Thanks to this fact, people use them for the most disparate reasons, such as sources of information in place of newspapers, to receive emotional or technical support, or to share their ideas and opinions to satisfy their need for sociality.
Anatomy of Hate Speech Datasets

Manifestations of hate speech in different scenarios are increasingly frequent on social platforms. In this context, there is a large number of works that propose solutions for identifying this type of content in these environments. Most efforts to automatically detect hate speech follow the same process of supervised learning, using annotators to label a predefined set of messages, which are, in turn, used to train classifiers. However, annotators can create labels for different classification tasks, with divergent definitions of hate speech, binary or multi-label schemes, and various methodologies for collecting data. In this context, we examine the principal publicly available datasets for hate speech research. We investigate the types of hate speech (e.g., ethnicity, religion, sexual orientation) present in their composition, explore their content beyond the labels, and use cross-dataset classification to examine the use of the labeled data beyond its original work. Our results reveal interesting insights toward a better understanding of the hate speech phenomenon and improving its detection on social platforms.
Data Collection Methods

This chapter provides an overview of the following data-gathering methods: online surveys, crowdsourcing, eye tracking, mouse tracking, search logs, triangulation, and social media APIs. This information is essential for anyone interested in understanding the methods and best practices for gathering data in web and social media analytics. With this information, you should better understand each method’s usefulness and be able to make informed decisions about the best method for your business strategy needs.