Reviewerly/exHarmony huggingface dataset:
A Pre-Service Teacher Experiences of Creating Vocabulary Quizzes for EFL Adult Learners: the ACTIONS Model

&lt;div&gt;&lt;div&gt;&lt;div class="page" title="Page 1"&gt;&lt;div class="section"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span&gt;IDLE (Informal Digital Learning of English) is a worldwide phenomenon that represents one of the most significant advances in autonomous language learning outside the classroom in recent decades. This study examines the experiences of IDLE activities based on the ACTIONS model (Access, Cost, Teaching and learning, Interactivity and user-friendliness, Organizational issues, Novelty, and Speed) which focused on vocabulary. The results of the study are intended to be a self-reflection on the factors involved in creating English vocabulary quizzes on Instagram as IDLE sources for higher education students. The study aims to use social media, especially Instagram, as a learning tool in a digital context. The researcher uses written narratives that contain her experiences in creating such English vocabulary quizzes. For that reason, the study participant is the researcher herself, a 21-year-old female undergraduate student in the English Education Department. In the study, the researcher uses thematic analysis to analyse the narrative data. This includes three activities: 1) repeated reading of the data; 2) coding and categorising the data extracts; and 3) recognising the thematic headings. The results show that creating IDLE activities based on the ACTIONS model leads to flexibility of access, affordable costs, teaching and learning needs based on followers' feedback and correction, excellent interactivity and user-friendliness, no organisational issues, novelty, and speed. The study offers new insights into how English pre-service teachers' engagement with IDLE serves as a significant factor in their future teaching tasks. &lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
IVTEN: integration of visual-textual entities for temporal activity localization via language in video

This study looks into the stumbling block of temporal activity localization via natural language (TALL) in the untrimmed video. It’s a difficult task since the target temporal activity may be misled by disorder query. Existing approaches used sliding windows, regression, and ranking to handle the query without the usage of grammar-based rules. When a query is out of sequence and cannot be correlated with the relevant activity, these approaches suffer performance deterioration. We introduce the visual, action, object, and connecting words concepts to address the issue of non-sequence queries. Integration of visual-textual entities network (IVTEN) is our proposed architecture, which consists of three submodules: (1) visual graph convolutional network (visual-GCN), (2) textual graph convolutional network (textual-GCN), and (3) compatible method for learning embeddings (CME). Visual nodes detect activity, object, and actor in the same way as textual nodes maintain word sequence using grammar-based rules. (CME) integrates several modalities (activity, query) and trained grammar-based words into the same embedding space. We also include a stochastic latent variable in CME to align and retain the query sequence with the relevant activity. On three typical benchmark datasets, our IVTEN approach outperforms the state-of-the-art Charades-STA, TACoS, and ActivityNet-Captions.
PRNU-based source camera identification on smart video surveillance: a practical approach

Video surveillance is very common nowadays, with systems deployed in conventional networks, as well as in the cloud and IoT domains. While the internet-based video surveillance systems provide ease of operation, at the same time they are prone to cyber attacks. Therefore, video authentication cannot be guaranteed if someone hacks into the system and gets to the video source. In order to identify the video source, a source identification method employing the PRNU (Photo-Response Non-Uniformity) noise as the detecting signal has been devised. PRNU is a kind of sensor pattern noise, which can be found from every digital image captured by a digital camera. It has been proved to be useful in image and video camera source identification. However, the challenges of real-life applications have not been fully addressed, especially on the IoT-based video surveillance. In this paper, we present a practical approach of the PRNU-based source verification scheme incorporated into the smart video surveillance system with limited resources, such as low computation power at the edge of the networks. The performance of the proposed scheme is evaluated through simulation tests on different cameras taking video scenes at different periods in a day. It comes up with the results of an efficient and effective prototype for our method, which can be comparable to the state-of-the-art techniques in related works.
Low-light image enhancement via channel-wise intensity transformation

We propose a low-light image enhancement algorithm that learns channel-wise transformation functions. First, we develop a lightweight network, called the transformation function estimation network (TFE-Net), to predict the channel-wise transformation functions. TFE-Net learns to generate the transformation functions by considering both the global and local characteristics of the input image. Then, we obtain enhanced images by performing channel-wise intensity transformation. Experimental results show that the proposed algorithm provides higher image quality than conventional algorithms.
Optical Character Recognition (OCR) and Medieval Manuscripts: Reconsidering Transcriptions in the Digital Age

This essay will discuss an ongoing project to train an optical character recognition (OCR) system on medieval manuscripts—specifically, the OCR engine Kraken, which we trained to transcribe early-fifteenth-century Middle English manuscripts. Our current model, trained on Scribe D's handwriting, has a 97 percent training accuracy rate and transcribes unseen manuscripts with a range of accuracy rates between 27 and 86 percent. Our project adds to the growing number of successful experiments in training medieval manuscripts on OCR, a technology that could have an immense impact on medieval studies.
Swin transformer and fusion for underwater image enhancement

In an underwater scene, refraction, absorption, and scattering of light by suspended particles in water degrade the visibility of images, causing low contrast, blurred details and color distortion. Based on the characteristics of underwater image degradation, we proposed a fusion neural network, which builds on the blending of two images that are derived from a white-balanced and color-compensated version of the raw underwater image. The two images are fused through the image enhancement module. In the previous works, convolutional neural networks (CNN) have been widely used in underwater image enhancement tasks. However, the local computational characteristics of convolutional operations limit the effect of the image enhancement. Recently, transformers have shown impressive performance on low-level vision tasks. In this paper, we propose a module SwinMT for image enhancement based on the swin transformer. First, we generate two inputs by respectively applying white balance (WB) and gamma correction (GC) algorithms to an underwater image. Second, the SwinMT module extracts features respectively, which consists of two parts: low-frequency feature extraction module and high-frequency feature to restore high-quality. We conduct experiments on rendered synthetic underwater images. Experiments on underwater images show that our method produces visually pleasing results, and we compare results with state-of-the-art techniques.
A feasibility study of watermark embedding in RNN models

Deep learning models are created using a large amount of time and data, and are therefore very costly. Therefore, attention has been focused on technologies that protect rights by embedding digital watermarks in learning models. In this work, we target recurrent neural networks (RNNs) and embed watermarks during model training. There are few studies that show the possibility of watermark embedding for RNN training models. Therefore, in our previous research, we have shown that watermark embedding is possible for learning models generated by LSTM networks, a type of RNN, and have conducted detection. In this paper, we investigate the effect of watermarking on the model when it is embedded into the training model of RNN. In particular, we will conduct experiments and discuss the results regarding the impact of embedding watermarks on the task and the impact of increasing the number of bits embedded in the watermark.
Pose estimation using object contour and projection distortion for dynamic projection mapping

Projection mapping, a spatial augmented reality technology that changes the appearance of an object by projecting images using a projector, has become widespread. We have achieved projection mapping on moving objects by estimating the position and orientation of the object by mapping the contour of the object acquired with an infrared camera to the contour of a 3D model acquired in advance. However, when the contour of the object is occluded, the estimation accuracy is degraded, which is a problem for precise projection mapping. In this study, we propose robust estimation of position and orientation by using distortion information of the projected image caused by the motion of the target object in addition to the contour. The projection distortion is reproduced in the simulation environment, and the estimated accuracy of the position and orientation is evaluated.
Social Distance Evaluation in Transportation Systems and Other Public Spaces using Deep Learning

Abstract This research put forward an efficacious real-time deep learning-based technique to automate the process of monitoring the social distancing in transportation systems (e.g., bus stops, railway stations, airport terminals, etc.) and other public spaces with the purpose to mitigate the impact of coronavirus pandemic. The proposed technique makes use of the YOLOv3 model to segregate humans from the background of each image of a surveillance video and the linear Kalman filter for tracking the humans’ motion even in case in which another object or person overlaps the trajectory of the person under analysis. The performance of the model in human detection is extremely high as demonstrated by the accuracy of the model that reaches values higher than 95%. The detection algorithm can be applied for alerting people to keep a safe distance from each other when they are in crowded places or in groups.
Holistic Language Teaching Method in College English Vocabulary Teaching under Big Data and Multimedia Environment

With the rapid advancement of informatization on a global scale, human society has entered a new era of information explosion and continuous data updating. In this situation, big data have become the future development trend of the world. In the context of big data, we must not only understand the world but also learn how to obtain information, and traditional teaching models can no longer meet the needs of teaching. Therefore, it is necessary to combine modern information technology, multimedia technology, and other means to assist the development of classroom teaching. At the same time, the holistic language teaching method plays an important role in improving students’ comprehensive ability, comprehension, expression, and thinking innovation ability. Therefore, this article attempts to combine the teaching background of big data and multimedia and use their advantages in teaching to find effective teaching methods to promote students’ vocabulary acquisition. This article uses questionnaire surveys and data analysis methods to understand the difficulties encountered by college students in English vocabulary learning and their views on the application of holistic language teaching methods in English vocabulary teaching. According to the survey results, due to factors such as large English vocabulary and complicated spelling rules, most interviewees think English vocabulary learning is more difficult. In addition, most of the interviewees believe that the application of the whole language teaching method in college English vocabulary teaching is very necessary and will get better teaching results, thereby improving the efficiency of college students in learning English vocabulary.
Robust real-time video face recognition system for unconstrained environments

In everyday life, different biometric applications such as iris recognition and fingerprint recognition are used to identify individuals. The face is an important identifier for humans that can be used for surveillance, security purposes, access control, automated attendance, and so on. In this paper, a fully functional end-to-end facial recognition system is presented that can be applied in the real world. A simple face frontalness measure is proposed to filter out non-frontal faces that may result in false recognitions. To further reduce false positives, aggregation of prediction results across multiple frames is applied to form a single decision. Our model achieved a False Acceptance Rate (FAR) of 4.13% and False Rejection Rate (FRR) of 9.3% at the confidence threshold of 0.8 on the test dataset. Finally, the recognition result is displayed by a Web application. The system also records daily punch-in/punch-out times of employees and presents their monthly timesheet reports in the Web application. Our method achieves considerably lower false positive rates and runs at 18- 20 FPS in our testbed machine for a single camera.
Optimizing coded patterns with various lengths

The performance of coded exposure photography-based image deblurring highly depends on its coded pattern to use. Conventionally, the length of the coded pattern has been optimized under the assumption that its length is equal to the length of motion blur. However, coded patterns of different lengths from the same motion blur may have better invertibility than the conventional patterns. In this paper, we investigate a method to optimize the coded pattern within an extended range of length candidates. We demonstrate the effectiveness of the proposed method using a real dataset.
Measuring speaking time from privacy-preserving videos

The ongoing pandemic caused by the COVID-19 virus is challenging many aspects of daily life such as restricting the conversation time. A vision-based face analyzing system is considerable for measuring and managing the person-wise speaking time, however, pointing a camera to people directly would be offensive and intrusive. In addition, privacy contents such as the identifiable face of the speakers should not be recorded during measuring. In this paper, we adopt a deep multimodal clustering method, called DMC, to perform unsupervised audiovisual learning for matching preprocessed audio with corresponding locations at videos. We set the camera above the speakers, and by feeding a pair of captured audio and visual data to a pre-trained DMC, a series of heatmaps that identify the location of the speaking people can be generated. Eventually, the speaking time measurement of each speaker can be achieved by accumulating the lasting speaking time of the corresponding heatmap.
PSO-Based ANN for Studying on Carrying Capacity of Iron Ore Resources-Economy-Environment in Southern Shaanxi, China

The southern Shaanxi Province, China, has abundant iron ore resources due to local ideal metallogenic conditions. The development and utilization of iron ore resources and other related industries serve as important pillars of the regional economy. In this paper, principals and methodology of system dynamics are adopted to develop a system model of interactions of development and utilization of mineral resources, social economy, and ecological environment, using the iron ore reserves in the southern Shaanxi region as a resource base, the regional GDP as a benchmark of regional economic development, and the environmental treatment expenditure as the measurement of the regional environmental protection efforts. The model is performed based on an optimized neural network by the particle swarm optimization algorithm. Through setting variables and parameters for the system and model simulation as well as comparative research, this paper analyzes the interconnectivity and interaction of the three subsystems and explores associated relationships among mineral resources, social economy, and environmental carrying capacity in the southern Shaanxi region. In addition, the present study also proposes advice on the development and utilization of iron ore resources, ecological protection, and high-quality development of the economy in the southern Shaanxi region based on the research results.
Performance analysis of generated predictive frames using PredNet bi-directionally

For generating motion-compensated predictive frames, which is one of the video coding processes, there has been a lot of studies on using DNN without using motion vectors. Conventional methods of generating motion-compensated predictive frames using DNN use only the source frames for prediction in the forward direction. However, in the ever-standardized video coding schemes, it has been confirmed that the bi-directional prediction, e.g., B-picture, improves coding efficiency. Thus, for generating motion-compensated predictive frames to be used in video coding, we propose to apply PredNet bidirectionally, that is a future frame generation model using DNN based on the prediction process of visual input stimuli in brain. In this paper, the performance of the predictive frames generated by the proposed method is evaluated by using MSE and SSIM compared with the prediction accuracy applying PredNet only to the forward direction. In addition, we also investigate whether the prediction accuracy of the predicted frames can be improved by increasing the amount of training frames in videos chosen from YouTube-8M. The results show the effectiveness of the proposed method in terms of less prediction error compared with the forward-only PredNet, as well as the performance increasing by more training data.
Scale adaptive structure tensor based rolling trilateral filter

One of tasks of image filter is to preserve strong edge structure and smooth textures in the given image. Recently, many approaches have been proposed to accomplish this challenging task. In this paper, we propose a scale adaptive structure tensor based rolling trilateral filter to smooth detailed small textures while preserving prominent structures. The proposed method first estimates scale at each pixel using a structure measure; then computes the eigenvalues and eigenvectors of structure tensor constructed from gradient at each pixel. The trilateral filter includes anisotropic weight in spatial space, the Gaussian weight in range space and the Gaussian weight of inner product of eigenvectors in gradient space. Results of experiments conducted on many natural images demonstrate that the proposed filter performs well.
Noise Suppression for Improved Few-Shot Learning

Few-shot learning (FSL) aims to generalize from few labeled samples. Recently, metric-based methods have achieved surprising classification performance on many FSL benchmarks. However, those methods ignore the impact of noise, making the few-shot learning still tricky. In this work, we identify that noise suppression is important to improve the performance of FSL algorithms. Hence, we proposed a novel attention-based contrastive learning model with discrete cosine transform input (ACL-DCT), which can suppress the noise in input images, image labels, and learned features, respectively. ACL-DCT takes the transformed frequency domain representations by DCT as input and removes the high-frequency part to suppress the input noise. Besides, an attention-based alignment of the feature maps and a supervised contrastive loss are used to mitigate the feature and label noise. We evaluate our ACL-DCT by comparing previous methods on two widely used datasets for few-shot classification (i.e., miniImageNet and CUB). The results indicate that our proposed method outperforms the state-of-the-art methods.
Multiple HD Screen-Based Virtual Studio System with Learned Mask-Free Portrait Harmonization

Virtual studio technology allows producers to combine live-action footage and computer-generated imagery and has promoted the development of film and television industry. However, the existing green screen-based virtual studio limits the visual effects just in postproduction. In this paper, we propose a new virtual studio system based on the recent emerging technology using multiple high-definition (HD) screens. Besides using traditional computer graphics like other similar systems, our system enables to capture panoramic video from the real world as the background and project it onto multiple HD screens, so as to recreate the scene in the studio. In addition, we propose a mask-free portrait harmonization network to make sure that the appearances of the foreground region and the background are consistent. Our portrait harmonization network does not need any auxiliary foreground mask and works in an end-to-end manner, which meets the postprocessing requirements for our virtual studio. Experimental result on real composite images shows that our network is superior to the state-of-the-art image harmonization method under the task of portrait harmonization. We also demonstrate our system with a CAVE system.
Oral Cone Beam Computed Tomography Images Segmentation Based On Multi-view Fusion

In computer-aided orthodontic treatment, it is necessary to establish a 3D model of the teeth and alveolar bone. Segmenting the teeth and alveolar bone from cone-beam computed tomography (CBCT) images is the primary step of reconstructing the model. However, previous studies mainly focused on the segmentation and reconstruction of teeth, and the research on alveolar bone segmentation is very rare. Different viewpoints of oral CBCT images provide complementary information. This paper proposes a multi-view information fusion method to realize the segmentation of teeth and alveolar bone in oral CBCT images. Firstly, for each of the three axes of the 3D data, an individual 2D deep network is trained to achieve coarse segmentation results. Secondly, in order to make full use of the information obtained from different viewpoints of oral CBCT images, a convolution network is used for multi-view fusion. To better fuse the information of different viewpoints, the channel-spatial attention mechanism is applied. Final segmentation is obtained by stacking 2D slices predictions, and the contextual information is added in a post-processing manner through fusing three-viewpoints results. According to the experimental results, our proposed method achieves significant improvements over the baseline.
Artificial Intelligence Methods for Face Covering Detections

With the global spreading of the COVID-19 pandemic, the face-covering regulation has become a common disease control policy issued by the public authorities nationwide in the United States and the rest of the world. Many public and private service providers require people to wear an appropriate mask correctly; however, it is still challenging to monitor whether individuals practice such a policy or not. In the artificial intelligence era, deep learning models with computer vision technologies can be applied as an efficient solution for this challenge in the context of the COVID-19 pandemic. In this chapter, a deep transfer learning-based face mask detection model has been proposed with the testing procedure for multiple purposes, such as identifying whether individuals wear face masks or not, recognizing which type of the face-covering a person wears, and detecting whether individuals wear masks correctly or not, with a real-time human-computer interaction representation.