Reviewerly/exHarmony huggingface dataset:
A Survey of Machine Narrative Reading Comprehension Assessments

As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks. Based on narrative theories, reading comprehension theories, as well as existing machine narrative reading comprehension tasks and datasets, we propose a typology that captures the main similarities and differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension.
Binary Morphological Neural Network

In the last ten years, Convolutional Neural Networks (CNNs) have formed the basis of deep-learning architectures for most computer vision tasks. However, they are not necessarily optimal. For example, mathematical morphology is known to be better suited to deal with binary images. In this work, we create a morphological neural network that handles binary inputs and outputs. We propose their construction inspired by CNNs to formulate layers adapted to such images by replacing convolutions with erosions and dilations. We give explainable theoretical results on whether or not the resulting learned networks are indeed morphological operators. We present promising experimental results designed to learn basic binary operators, and we have made our code publicly available online.
Students' Paraphrased Texts and Their Perceptions of Paraphrasing in Academic Writing

This descriptive qualitative study aims to investigate students’ paraphrased texts based on Keck’s Paraphrase Taxonomy (2006) and on McInnis’ level of paraphrase appropriateness (2009), and to explore their perceptions toward paraphrasing. Fifty nine students from English Language Education Study Program of UNP academic year 2017 were selected to participate in this study and were committed to do a paraphrasing task expecting them to paraphrase 4 separate sentences of one author.
The Effect of Online Discovery Learning in Google Classroom Environment on Students' Scientific Literacy Skills

This research aimed to determine the effect of the discovery learning models using a google classroom to develop the scientific literacy abilities. The sample of this research is tenth grade students of science 2 and science 3 groups which are selected through purposive sampling technique. The data are analyzed statistically with an independent sample t-test. The average results of the N-gain scientific literacy ability of the experimental class (0.45 ± 0.11) with medium category and the control class (0.29 ± 0.14) with low category. The average percentage for student presentations is 83.75%, while the learning implementation observation sheet has a 94.4%. The results showed that there is a significant effect of using online discovery learning in google classroom environment towards the improvement of scientific literacy skills of tenth graders students in ecosystems topics. Keywords: discovery learning, scientific literacy, google classroom. Abstrak: Penelitian ini bertujuan untuk mengetahui pengaruh model online discovery learning menggunakan google classroom terhadap peningkatan kemampuan literasi sains siswa. Sampel penelitian ini adalah siswa kelas X IPA 2 dan X IPA 3 yang dipilih melalui teknik purposive sampling. Data dianalisis secara statistik dengan independent sample t-test. Rata-rata hasil N-gain kemampuan literasi sains kelas eksperimen (0,45 ± 0,11) dengan kategori sedang dan kelas kontrol (0,29 ± 0,14) dengan kategori rendah. Rata-rata persentase presentasi siswa sebesar 83,75%, sedangkan lembar observasi pelaksanaan pembelajaran sebesar 94,4%. Hasil penelitian menunjukkan bahwa terdapat pengaruh signifikan penggunaan pembelajaran discovery online dengan google classroom terhadap pengembangan kemampuan literasi sains siswa kelas X pada materi ekosistem. Kata kunci: pembelajaran discovery, literasi sains, google classroom. DOI: http://dx.doi.org/10.23960/jpmipa/v23i1.pp316-326
Graph Convolutional Networks

In this chapter, we will talk about graph convolutional networks (GCNs), which aim to generalize convolutions to the graph domain. As convolutional neural networks (CNNs) have achieved great success in the area of deep learning, it is intuitive to define the convolution operation on graphs. Advances in this direction are often categorized as spectral approaches and spatial approaches. As there may have vast variants in each direction, we only list several classic models in this chapter.
Research on the Education and Design policy of bio economic and management facts of the intelligent test based on fuzzy particle swarm optimization algorithm

with the passage of time, the era of technology is coming, and the huge demand of this modern world is the best education of the people in order to survive in the fast-developing era. Education system of any country holds great importance in the development of the country economy. Therefore, many developing and under developing countries has been focusing on the development of the education system in their country to improve their country’s economy as well as meet the requirements of the modern world. Apart from this, this allows organizations exceeds several universal standards, particularly in terms of educational fulfilment and higher education system exposition. moreover, there seems significant problems that policies makers must address, such as a low retention rate in blog academic achievement, universities and school improvement, a significant achievement gap between poor class, education funding, social classes, etc., this research focus on the management of the educational policies and intelligent test scheduling system based on fuzzy swarm particle optimized algorithm involving the business sector and educational program changes. Moreover, for the progress of the education system there is essential need to reduce errors rate in the system. this study explains the research on the design and management of the intelligent test scheduling system. Aiming at the problem of high error rate in the traditional intelligent test scheduling system of higher education institutions, an intelligent test scheduling system of higher education institutions based on fuzzy particle swarm optimization algorithm is designed. In terms of hardware, x86 server is designed. In terms of software, standardize the business process of intelligent arrangement in colleges. The fitness function is established based on fuzzy particle swarm optimization algorithm, and the intelligent test scheduling time selection operator is dynamically planned. This paper analyzes the data flow of intelligent test scheduling in colleges, constructs a database, and comprehensively considers the conflict factors of test scheduling. In the case of no missing candidates, the intelligent test scheduling of higher education institutions is realized, and the system design is completed. For this purpose, we collected sample data from various educational institutions of China and investigated collected data by utilizing optimized particle swarm algorithm. The experimental results show that the test scheduling error rate of the designed system is significantly lower than that of the traditional test scheduling systems 1, 2 and 3, which can solve the problem of high-test scheduling error rate of the traditional intelligent test scheduling system in colleges.
Remote Learning with Virtual Reality Technologies: Student Mastery, Community and Agency

The affordances of virtual reality (VR), including a strong sense of spatial and social presence, could make it a useful tool to improve learning and social connection in remote learning, yet little research has examined its effectiveness in authentic learning environments. This mixed-methods case study of a remote Master’s in Education course asked how using VR repeatedly and with varying implementations impacted students’ learning, and how synchronized or shared experiences in VR impacted their sense of community. Students completed weekly surveys of their sense of presence in VR applications, sense of class community, and learning experience, and also participated in a post-course interview. Surveys revealed students’ sense of presence remained high over time, indicating little evidence of a “novelty effect,” but that it varied based on features of the VR applications. Interview results pointed to students’ increased mastery of the technology and its affordances for learning, and highlight how connecting with classmates via videoconference while using VR can create a shared experience. Students’ sense of agency in VR emerged as an important consideration for learning design. These findings suggest fruitful ways to use VR to increase educators’ mastery of immersive technology and provide remote students a sense of shared experience and raise questions about the impact of VR implementation and instructional design in authentic learning environments to promote exploration and community.
Generative Multi-Stream Architecture For American Sign Language Recognition

With advancements in deep model architectures, tasks in computer vision can reach optimal convergence provided proper data preprocessing and model parameter initialization. However, training on datasets with low feature-richness for complex applications limit and detriment optimal convergence below human performance. In past works, researchers have provided external sources of complementary data at the cost of supplementary hardware, which are fed in streams to counteract this limitation and boost performance. We propose a generative multi-stream architecture, eliminating the need for additional hardware with the intent to improve feature richness without risking impracticability. We also introduce the compact spatiotemporal residual block to the standard 3-dimensional convolutional model, C3D. Our rC3D model performs comparatively to the top C3D residual variant architecture, the pseudo-3D model, on the FASL-RGB dataset. Our methods have achieved 95.62% validation accuracy with a variance of 1.42% from training, outperforming past models by 0.45% in validation accuracy and 5.53% in variance.
Improved multiple-image authentication based on optical interference by wavelength multiplexing

In this paper, an improved multiple-image authentication based on optical interference by wavelength multiplexing is proposed, which has high security and easy optical implementation. The Fresnel spectra of original images are diffracted from the same axial position but by different wavelengths, which makes the optical implementation easy and stable without any mechanical translation. Then, the Fresnel spectra are sparsely sampled by predesigned binary amplitude masks and diffracted again, and all spectra are multiplexed into one synthetized spectrum. Finally, the synthetized spectrum is analytically decomposed into one phase-only mask and one amplitude-only mask by an improved interference-based encryption (IBE) scheme. Benefiting from the wavelength multiplexing, the encryption capacity is enlarged, and the optical implementation for decryption becomes easy. With the aid of the sparse sampling, every decrypted image could be entirely unrecognizable but authenticated by nonlinear correlation. Moreover, instead of a conventional IBE, an improved IBE is used in this scheme, which can attenuate the information leakage and further enhance the security. Various numerical simulation results are presented to demonstrate the feasibility and effectiveness of this scheme.
Offset-decoupled deformable convolution for efficient crowd counting

Abstract Crowd counting is considered a challenging issue in computer vision. One of the most critical challenges in crowd counting is considering the impact of scale variations. Compared with other methods, better performance is achieved with CNN-based methods. However, given the limit of fixed geometric structures, the head-scale features are not completely obtained. Deformable convolution with additional offsets is widely used in the fields of image classification and pattern recognition, as it can successfully exploit the potential of spatial information. However, owing to the randomly generated parameters of offsets in network initialization, the sampling points of the deformable convolution are disorderly stacked, weakening the effectiveness of feature extraction. To handle the invalid learning of offsets and the inefficient utilization of deformable convolution, an offset-decoupled deformable convolution (ODConv) is proposed in this paper. It can completely obtain information within the effective region of sampling points, leading to better performance. In extensive experiments, average MAE of 62.3, 8.3, 91.9, and 159.3 are achieved using our method on the ShanghaiTech A, ShanghaiTech B, UCF-QNRF, and UCF_CC_50 datasets, respectively, outperforming the state-of-the-art methods and validating the effectiveness of the proposed ODConv.
GA-auto-PU

Positive-Unlabeled (PU) learning is a growing field of machine learning that now consists of numerous algorithms; the number is now so large that considering an extensive manual search to select the best algorithm for a given task is impractical. As such, the area of PU learning could benefit from an Automated Machine Learning (Auto-ML) system, which selects the best algorithm for a given input dataset, among a pre-defined set of candidate algorithms. This work proposes such with GA-Auto-PU, a Genetic Algorithm-based Auto-ML system that can generate PU learning algorithms. Experiments with 20 real-world datasets show that GA-Auto-PU significantly outperformed a state-of-the-art PU learning method.
ELSA: A Keyword-based Searchable Encryption for Cloud-edge assisted Industrial Internet of Things

The Industrial Internet of Things (IIoT) plays a powerful role in smart manufacturing by performing real-time analysis for large volumes of data. In addition, IIoT systems can monitor several factors, such as data accuracy, network bandwidth and operations latency. To perform these operations securely and in a privacy-preserving manner, one solution is to use cryptographic primitives. However, most cryptographic solutions add performance overhead causing latency. In this paper, we propose an Edge Lightweight Searchable Attribute-based encryption system (ELSA). ELSA leverages the cloud-edge architecture to improve search time beyond the state-of-the-art. The main contributions of this paper are as follows. First, we present an untrusted cloud/trusted edge architecture, which optimises the efficiency of data processing and decision making in the IIoT context. Second, we enhance search performance over current state-of-the-art (LSABE-MA) by an order of magnitude. We achieve this by improving the organisation of the data to provide better than linear search performance. We leverage the edge server to cluster data indices by keyword and introduce a query optimiser. The query optimiser uses k-means clustering to improve the efficiency of range queries, removing the need for linear search. In addition, we achieve this without sacrificing accuracy over the results.
A Recursive Algorithm for Mining Association Rules

Mining frequent itemsets and association rules are an essential task within data mining and data analysis. In this paper, we introduce PrefRec, a recursive algorithm for finding frequent itemsets and association rules. Its main advantage is its recursiveness with respect to the items. It is particularly efficient for updating the mining process when new items are added to the database or when some are excluded. We present in a complete way the logic of the algorithm, and give some of its applications. After that, we carry out an experimental study on the effectiveness of PrefRec. We first compare the execution times with some very popular frequent itemset mining algorithms. Then, we do experiments to test the updating capabilities of our algorithm.
Improved Convolutional Neural Networks for Course Teaching Quality Assessment

The cultivation of innovative talents is closely related to the quality of course teaching, and there is a correlation between facial expressions and the effectiveness of classroom teaching. In this paper, a separate long-term recursive convolutional network (SLRCN) microexpression recognition algorithm is proposed using deep learning technology for building a course teaching effectiveness evaluation model. Firstly, facial image sequences are extracted from microexpression data sets, and the transfer learning method is introduced to extract spatial features of facial expression frames through pretrained convolutional neural network model to reduce the risk of overfitting in network training. The extracted features of video sequences were input into long short-team memory (LSTM) to process temporal features. Experimental results show that SLRCN algorithm has the best performance in training set and test set. It has the best performance in ROC curve. This effectively distinguishes between seven different expressions in the database. The model proposed in this paper can obtain the changes of students’ facial expressions in class and evaluate students’ learning status, thus promoting the improvement of teaching quality. It provides a new method of course teaching quality evaluation.
Research on Data Analysis and Visualization of Recruitment Positions Based on Text Mining

With the rapid development of the Internet and the impact of COVID-19, online recruitment has gradually become the mainstream form of recruitment. However, existing online recruitment platforms fail to fully combine the job seekers’ demands for salary, region, benefits, and other aspects, which cloud not display the information related to recruitment positions in a multidimensional way. To solve this problem, this paper firstly uses a web crawler to collect job information from recruitment websites based on keywords retrieved by users, then extracts job information using regular expressions, and cleans and processes the extracted job information using third-party libraries such as Pandas and NumPy. Finally, through the probabilistic theme model of text mining, the topic model of job description content in the recruitment information is modeled. Combining with the django development framework and related visualization technology, the relationship among education requirement, experience requirement, job location, salary, and other aspects in the recruitment information is visually displayed in a multidimensional way. At the same time, the GM model is used to realize the gray prediction of the number of employment personnel in related industries, which provides employment reference for the majority of job seekers and enterprises.
A Blockchain-Based Financial Instrument for the Decarbonization of Irrigated Agriculture

Farming and food production sustainability increasingly depends on the availability of a clean energy model for irrigated agriculture. This can be achieved by massively introducing photovoltaic irrigation systems (PVI) with sufficient quality and reliability. Nevertheless, such PVI projects require high upfront investment and long payback times, so access to long-term, low-cost capital is essential to ensure their competitiveness. In this regard, decentralized financial (DeFi) solutions based on blockchain (BC) technology present a number of features that can be applied to produce financial instruments (FI) well suited to attract investors to PVI projects and to reduce the cost of clean energy for irrigators. In order to assess such a possibility, a DeFi FI tailored for PVI has been produced and implemented in BC. We demonstrate that a single smart contract executed in a distributed ledger can execute the different tasks related to the securitization of PVI assets. The impact on the cost of capital for PVI projects is significant, leading to an estimated reduction in the cost of clean energy for irrigators of 22%. Nevertheless, decentralization also introduces a number of specific risks that must be considered and mitigated.
Endowing third-party libraries recommender systems with explicit user feedback mechanisms

During their daily routine, developers often deal with a plethora of resources, attempting to search for relevant artifacts that can be added to the project under development. This kind of information overload may render developers overwhelmed, thus undermining their productivity and efficiency. Recommender systems are an effective means of easing such a burden, providing relevant items for the current programming contexts, e.g., third-party libraries (TPLs), API calls, or code snippets. By focusing on TPLs, there has been no work to allow for the integration of tailored feedback mechanisms with which users can conveniently accept or discard libraries. In this paper, we propose an approach to handle explicit user feedback, including positive, negative, and additive. Thus, further than accepting or discarding the recommended TPLs, users can also endorse libraries that, in their opinion, are relevant for the current context, even though they are not included in the provided recommendations. As a proof of concept, we demonstrate how user feedback generated by the proposed mechanism can change the outcome of a real TPLs recommender system. The results show that our proposed approach helps the considered system retrieve relevant items, under different configurations.
A Two-Phase Optimal Mass Transportation Technique for 3D Brain Tumor Detection and Segmentation

The goal of optimal mass transportation (OMT) is to transform any irregular 3D object (i.e., a brain image) into a cube without creating significant distortion, which is utilized to preprocess irregular brain samples to facilitate the tensor form of the input format of the U-net algorithm. The BraTS 2021 database newly provides a challenging platform for the detection and segmentation of brain tumors, namely, the whole tumor (WT), the tumor core (TC) and the enhanced tumor (ET), by AI techniques. We propose a two-phase OMT algorithm with density estimates for 3D brain tumor segmentation. In the first phase, we construct a volume-mass-preserving OMT via the density determined by the FLAIR grayscale of the scanned modality for the U-net and predict the possible tumor regions. Then, in the second phase, we increase the density on the region of interest and construct a new OMT to enlarge the target region of tumors for the U-net so that the U-net has a better chance to learn how to mark the correct segmentation labels. The application of this preprocessing OMT technique is a new and trending method for CNN training and validation.
PlacemakingAI : Participatory Urban Design with Generative Adversarial Networks

Machine Learning (ML) is increasingly present within the architectural discipline, expanding the current possibilities of procedural computer-aided design processes.Practical 2D design applications used within concept design stages are however limited by the thresholds of entry, output image fidelity, and designer agency.This research proposes to challenge these limitations within the context of urban planning and make the design processes accessible and collaborative for all urban stakeholders.We present PlacemakingAI, a design tool made to envision sustainable urban spaces.By converging supervised and unsupervised Generative Adversarial Networks (GANs) with a real-time user interface, the decision-making process of planning future urban spaces can be facilitated.Several metrics of walkability can be extracted from curated Google Street View (GSV) datasets when overlayed on existing street images.The contribution of this framework is a shift away from traditional design and visualization processes, towards a model where multiple design solutions can be rapidly visualized as synthetic images and iteratively manipulated by users.In this paper, we discuss the convergence of both a generative image methodology and this real-time urban prototyping and visualization tool, ultimately fostering engagement within the urban design process for citizens, designers, and stakeholders alike.