Reviewerly/exHarmony huggingface dataset:
Application of Adaptive Neural Network Algorithm Model in English Text Analysis

Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model’s ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers’ composition correction.
A grammar for interpreting geo-analytical questions as concept transformations

Geographic Question Answering (GeoQA) systems can automatically answer questions phrased in natural language. Potentially this may enable data analysts to make use of geographic information without requiring any GIS skills. However, going beyond the retrieval of existing geographic facts on particular places remains a challenge. Current systems usually cannot handle geo-analytical questions that require GIS analysis procedures to arrive at answers. To enable geo-analytical QA, GeoQA systems need to interpret questions in terms of a transformation that can be implemented in a GIS workflow. To this end, we propose a novel approach to question parsing that interprets questions in terms of core concepts of spatial information and their functional roles in context-free grammar. The core concepts help model spatial information in questions independently from implementation formats, and their functional roles indicate how concepts are transformed and used in a workflow. Using our parser, geo-analytical questions can be converted into expressions of concept transformations corresponding to abstract GIS workflows. We developed our approach on a corpus of 309 GIS-related questions and tested it on an independent source of 134 test questions including workflows. The evaluation results show high precision and recall on a gold standard of concept transformations.
Study on the Design and Optimization of Learning Environment Based on Artificial Intelligence and Virtual Reality Technology

More than half of the universities have recently added advanced technical courses for their students. The specialised courses would be the backbone to keep the students’ knowledge updated. In that case, artificial intelligence (AI) and virtual reality (VR) have been lightened up recently. This paper introduces the basic concepts of AI and VR and the changes that can be made when they are implemented in real-time applications. Students will be more focused on the courses utilising virtual reality. This VR makes the visualisation of any challenging course easier. Many students have been working on virtual reality in their colleges, and their involvement shows the actual output in the upcoming years. The research was carried on the Virtual Reality Driving Simulator Dataset to implement the virtual reality method. The results are compared to the real world and show that the proposed model is 98.67% more accurate than the real world.
Development of e-Book with Flip PDF Professional Based on Scientific Literacy

Technological developments can be used to support learning. One example of technological development is replacing printed books with nonprinted books (e-Books). E-books can be used as interactive learning resources because they can be integrated with images, animations, videos, and flash to make them more exciting and look natural. This paper is one of the results of developing an e-Book based on scientific literacy on temperature and heat concepts. This study aims to produce a good e-Book used to support learning. This research uses ADDIE model development research (Analysis-Design-Development- Implementation-Evaluation) with descriptivequantitative data analysis data collection techniques using validation sheets to determine the validity of ebooks. The results show that the scientific literacybased e-Book that has been developed is very valid to be used as a learning resource. The percentage value of validity from the media aspect was 92.98%, while the material aspect was 94.73%. Finally, the percentage value of the validity of the language aspect was 94.60%.
Comparative Assessment of Completeness of CDISC Controlled Terminology

The CDISC Controlled Terminology (CT) defines the terms that may be used to represent clinical trial data in the CDISC standards. Despite its unique importance, there has been limited systematic examination of the coverage of this terminology. In this work, we performed an assessment of the completeness of CDISC CT's coverage by comparing clinical outcomes for multiple sclerosis (MS) available in CDISC CT with two independent high-fidelity benchmarks: (1) 71 expert-selected outcomes catalogued by the National Institute of Neurological Disorders and Stroke (NINDS), and, (2) 66 common outcomes used in MS trials registered on ClinicalTrials.gov (CTG). We employed a semi-automated search and term-mapping process to identify possible CDISC equivalents to the benchmarks' measures. We found that 55% of the NINDS outcomes and 52% of the CTG outcomes are absent from the CDISC Terminology, indicating a need for expanding the terminology to take into account other established standards and real-world practice.
Robust age estimation model using group‐aware contrastive learning

Although great efforts have been devoted to developing lightweight models for age estimation in recent works, the robustness is still unsatisfactory in unconstrained environments. This paper proposes a Group-aware Contrastive Network (GACN), a robust lightweight model, which extracts discriminative features by leveraging contrastive learning rather than increasing model parameters. Specifically, with a carefully designed contrastive loss function, GACN minimizes intra-class distances and maximizes inter-class distances between different age groups in feature space. Thus, faces belonging to the same age group are pulled together, while clusters of faces from different age groups are pushed apart. Unlike existing contrastive learning methods, which are separated from the downstream tasks, GACN integrates contrastive learning into age regression and jointly optimizes them for age representation learning. This allows to achieve robust age estimation using a lightweight network that is 1/662 of the model size of VGGNet. Extensive experiments on IMDB-WIKI, Morph II, and FG-NET demonstrate that the proposed method has a significant improvement over the baseline model and performs comparably to existing compact and bulky methods.
Analyses of Spatial Distribution and Temporal Trends of Temperature and Its Extremes over Nigeria using Climate Indices

Climate change and its associated extreme climate and extreme events are threat to man and his environment. Over the years, climatic parameters have been used as climate change indicators to study climate change. In order to access extreme climate due to climate change in Nigeria, this paper attempt to evaluate spatial distribution and temporal trends of temperature and its extremes over Nigeria for 35 years (1979 - 2013). Daily temperature data used were obtained from the Nigeria Meteorological Agency (NIMET), Lagos. The data was subjected to quality checked and homogenization. Climate extreme indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was adopted in this study. The data were analysed employing Sen’s slope estimator and Mann-Kendall test. Results revealed that spatial distribution of temperature and its extremes over Nigeria varied across the country, increasing/decreasing mostly towards the northern or the coastal regions. Significant increase was observed at p &lt; 0.05 in the temporal trends of averages of yearly maximum and minimum temperature and hot temperature indices in all the regions in line with the global warming trend. The increase in warm days and warm nights doubled the rate at which cold days and cold nights decreased in most of the study areas. The annual and decadal changes in temperature and its extremes as well as the period under investigation (35 years) were determined. Our findings depict that the country is trending towards a drier and warmer climate which are clear evidence of climate change. The results further suggest that the Atlantic Ocean and the Sahara Desert which control the atmospheric condition of the country may be promoter of the observed extreme events caused by climate change in Nigeria. This study discussed contemporary issues in our society which are useful to the general populace, researchers, and policy makers among others.&#x0D; HIGHLIGHTS&#x0D; &#x0D; Spatial distribution of temperature and its extremes over Nigeria varied across the country from one region to another&#x0D; Maximum and minimum temperature and hot temperature indices increased significantly (p &lt; 0.05) in all the regions in line with the global warming trend&#x0D; The increase in warm days/nights doubled the rate at which cold days/cold nights decreased in most of the regions&#x0D; Our findings suggest that Atlantic Ocean and Sahara Desert which control the atmospheric conditions of Nigeria may promote extreme events caused by climate change in Nigeria&#x0D; &#x0D; GRAPHICAL ABSTRACT
Design and Implementation of Intelligent Analysis Technology in Sports Video Target and Trajectory Tracking Algorithm

The object tracking is an important task to future generations to find accurate information from frame (sports or any cctv). The available models like RFO, DT, Xboosting, Gradient boosting, and CNN are outdated for sports video tracking using video. The target and trajectory finding are very important for accurate tracking. Therefore, for crossover, the above limitation’s intelligence analysis is performed with ANN. The video tracking can be very simple and efficient with proposed model. As a result, this ANN offers a sports camera target detection system based on an upgraded grey neural network. This research uses the automatic threshold technique to divide the target object of sports camera and derive the designated target of sports video based on the pixel grey difference of the target image in sports video. The moving objects are tracked by the object tracking algorithm, and features are extracted to obtain the results. The performance measures like accuracy of 95.23%, sensitivity of 94.45%, and recall of 93.34% had been attained.
Pengaruh Penggunaan Media Audio Visual Terhadap Hasil Belajar Kosa Kata Bahasa Inggris Siswa Kelas II MI Dwi Dasa Warsa

The problem in this research is the lack of mastery of students ‘English vocabulary, the teacher’s lack of creativity in using learning media, and students’ assumption that English lessons are difficult and ultimately ignored. Formulation of the problem “how the influence of the use of audio-visual media on learning outcomes of English vocabulary students in grade II MI Dwi Dasa Warsa. The study aims to determine the effect of audio-visual media on learning outcomes of English vocabulary material Pets and Wild Animals in Class II MI Dwi Dasa Warsa This study is quasi-experimental design research with one group pre-test design research. The population in this study was all students in MI Dwi Dasa Warsa with a total of 273 students, while the sample were all students of class II MI Dwi Dasa Warsa amounting to 37 students. Data were analyzed using the Mann-Whitney test formula. From the hypothesis testing, it can be concluded that there is a significant influence of the use of audiovisual media on the learning outcomes of English vocabulary material Pets and Wild Animals.
Fast MILP Models for Division Property

Nowadays, MILP is a very popular tool to help cryptographers search for various distinguishers, in particular for integral distinguishers based on the division property. However, cryptographers tend to use MILP in a rather naive way, modeling problems in an exact manner and feeding them to a MILP solver. In this paper, we show that a proper use of some features of MILP solvers such as lazy constraints, along with using simpler but less accurate base models, can achieve much better solving times, while maintaining the precision of exact models. In particular, we describe several new modelization techniques for division property related models as well as a new variant of the Quine-McCluskey algorithm for this specific setting. Moreover, we positively answer a problem raised in [DF20] about handling the large sets of constraints describing valid transitions through Super S-boxes into a MILP model. As a result, we greatly improve the solving times to recover the distinguishers from several previous works ([DF20], [HWW20], [SWW17], [Udo21], [EY21]) and we were able to search for integral distinguishers on 5-round ARIA which was out of reach of previous modeling techniques.
Dynamically Refined Regularization for Improving Cross-corpora Hate Speech Detection

Warning: this paper contains content that may be offensive and distressing.Hate speech classifiers exhibit substantial performance degradation when evaluated on datasets different from the source.This is due to learning spurious correlations between words that are not necessarily relevant to hateful language, and hate speech labels from the training corpus.Previous work has attempted to mitigate this problem by regularizing specific terms from pre-defined static dictionaries.While this has been demonstrated to improve the generalizability of classifiers, the coverage of such methods is limited and the dictionaries require regular manual updates from human experts.In this paper, we propose to automatically identify and reduce spurious correlations using attribution methods with dynamic refinement of the list of terms that need to be regularized during training.Our approach is flexible and improves the cross-corpora performance over previous work independently and in combination with pre-defined dictionaries. 1
COMPARISON OF CONVOLUTIONAL NEURAL NETWORKS FOR CLOUDY OPTICAL IMAGES RECONSTRUCTION FROM SINGLE OR MULTITEMPORAL JOINT SAR AND OPTICAL IMAGES

Abstract. With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images that are impacted by clouds. In this paper, we focus on the evaluation of convolutional neural networks that use jointly SAR and optical images to retrieve the missing contents in one single polluted optical image. We propose a simple framework that ease the creation of datasets for the training of deep nets targeting optical image reconstruction, and for the validation of machine learning based or deterministic approaches. These methods are quite different in terms of input images constraints, and comparing them is a problematic task not addressed in the literature. We show how space partitioning data structures help to query samples in terms of cloud coverage, relative acquisition date, pixel validity and relative proximity between SAR and optical images. We generate several datasets to compare the reconstructed images from networks that use a single pair of SAR and optical image, versus networks that use multiple pairs, and a traditional deterministic approach performing interpolation in temporal domain.
Long Jump Action Recognition Based on Deep Convolutional Neural Network

Long jump is a test item of national student physical health monitoring, which can reflect the quality of students’ lower limb strength. Long jump is a highly technical activity, which includes four basic movements: running aid, jumping, vacating, and landing. Many students have problems with the technical aspects, resulting in test scores that do not objectively reflect the true physical condition of the students, which affects the accuracy of the test results. From the perspective of rapid diagnostic feedback of students’ long jump movements, we design and develop a long jump movement recognition method based on deep convolutional neural network. In this paper, we firstly summarize the traditional visual action recognition algorithm, then apply 3D convolution to extract the spatiotemporal features of long jump action from three directions of the video block, and fuse the spatiotemporal features of the three directions in different ways to achieve feature complementation; finally, using the multimodality of long jump action data, we use 3D convolutional neural network to train the RGB images and then train the depth. This joint training method can accelerate the convergence speed and improve the accuracy of the network on both depth and edge images. The experiments compared the recognition effects of the tandem fusion of features, the maximum fusion, and the multiplicative fusion in the scoring layer, and the highest accuracy of 82.3% was achieved by the tandem fusion of features with the fusion of three modalities.
Sketch Based Image Retrieval for Architecture Images with Siamese Swin Transformer

Abstract Sketch-based image retrieval (SBIR) is an image retrieval task that takes a sketch as input and outputs colour images matching the sketch. Most recent SBIR methods utilise deep learning methods with complicated network designs, which are resource-intensive for practical use. This paper proposes a novel compact framework that takes the siamese network with image view angle information, targeting the SBIR task for architecture images. In particular, the proposed siamese network engages a compact SwinTiny transformer as the backbone encoder. View angle information of the architecture image is fed to the model to further improve search accuracy. To cope with the insufficient sketches issue, simulated building sketches are used in training, which are generated by a pre-trained edge extractor. Experiments show that our model achieves 0.859 top-one accuracy exceeding many baseline models for an architecture retrieval task.
A Survey of Deep Fake Detection for Trial Courts

Recently, image manipulation has achieved rapid growth due to the advancement of sophisticated image editing tools. A recent surge of generated fake imagery and videos using neural networks is DeepFake. DeepFake algorithms can create fake images and videos that humans cannot distinguish from authentic ones. (GANs) have been extensively used for creating realistic images without accessing the original images. Therefore, it is become essential to detect fake videos to avoid spreading false information. This paper presents a survey of methods used to detect DeepFakes and datasets available for detecting DeepFakes in the literature to date. We present extensive discussions and research trends related to DeepFake technologies.
Ultrasound image augmentation by tumor margin appending for robust deep learning based breast lesion classification

The difficulty of obtaining sufficient number of appropriately labelled samples is a major obstacle to learning class discriminating features by Machine Learning (ML) algorithms for tumor diagnostics from Ultrasound (US) images. This is often mitigated by sample augmentation, whereby new samples are generated from existing samples by rotation and flipping operations, Singular Value Decomposition (SVD) or generating synthetic image by Generative Adversarial Networks (GANs). The first approach does not generate new genuine samples, SVD generates images may not be easy to recognize as US tumor scans, and while GANs generate images are visually convincing their use for diagnostics may lead to overfitting and subject to adversarial attacks. We propose an innovative sample augmentation approach that utilizes our recently developed Tumor Margin Appending (TMA) scheme. The TMA scheme constructs the Convex Hull (CH) of the tumor region using a small set of radiologist marked tumor boundary points and crops the image at different radial expansion ratios of the CH onto surrounding tissue. Various ML algorithms, handcrafted features and Convolutional Neural Network (CNN), trained with TMA images at different ratios achieved acceptable diagnostic accuracies. In this paper, our sample augmentation scheme expands the ML training datasets by including TMA samples at several expansion ratios. Results of experiments on training CNN tumor diagnostic schemes for breast tumors yield improved classification performance with additional benefits, including robustness against different inadvertently practiced cropping at different hospitals, serves as a regularizer to reduce model overfitting when tested on unseen datasets obtained using unknown tumor segmentation and cropping procedure.
Extending PICO with Observation Normalization for Evidence Computing

While the PICO framework is widely used by clinicians for clinical question formulation when querying the medical literature, it does not have the expressiveness to explicitly capture medical findings based on any standard. In addition, findings extracted from the literature are represented as free-text, which is not amenable to computation. This research extends the PICO framework with Observation elements, which capture the observed effect that an Intervention has on an Outcome, forming Intervention-Observation-Outcome triplets. In addition, we present a framework to normalize Observation elements with respect to their significance and the direction of the effect, as well as a rule-based approach to perform the normalization of these attributes. Our method achieves macro-averaged F1 scores of 0.82 and 0.73 for identifying the significance and direction attributes, respectively.