Reviewerly/exHarmony huggingface dataset:
Enhancing Automatic Readability Assessment with Verb Frame Features

Patterns of verb usage, as encoded in verb frame elements, have been shown to correlate with text readability. While automatic readability assessment models have incorporated a wide range of features on lexical, syntactic, semantic and discourse properties of a text, none of these features directly exploit the semantic information provided in verb frames. This paper investigates whether features based on semantic frames can improve the performance of Chinese readability assessment models. We conduct experiments with manually annotated verb frame elements in Mandarin VerbNet, including the use of non-core frame elements, subject omission, metaphoric usage and clause as verb argument. Experimental results on a corpus of Chinese texts, spanning twelve school grades, show that these frame features significantly raise readability assessment accuracy over a baseline model that relies on surface features only.
A Survey of Machine Reading Comprehension Methods

With the gradual maturity of deep learning technol-ogy, machine reading comprehension in natural language processing has become a popular research direction. Its research goal is to use computers to build models that enable computers to read articles, analyze semantics, and answer questions like humans. Due to the explosive growth of current text data, using models to quickly focus relevant information from text can save a lot of costs. Therefore, machine reading comprehension technology that can automatically process text has great research value. This paper summarizes the machine reading comprehension based on neural network in detail: first, the task definition and development process of machine reading comprehension are briefly introduced; secondly, the data sets and evaluation indicators of machine reading comprehension are introduced; then, the neural network of machine reading comprehension is introduced Model, including model architecture and some typical models; finally, the future development trend of machine reading comprehension is prospected.
Text-Level Contrastive Learning for Scene Text Recognition

Scene text recognition (STR) is a task of identifying text from natural scene text images. Recently, based on the advantages of self-supervised contrastive learning, some studies incorporate contrastive learning strategies for STR task. However, these studies mainly focus on data argumentation of images from a visual perspective, ignoring the fact that scene text often contains large noise and has the characteristics of diversity. For addressing this issue, in this paper, we propose a text-level contrastive learning strategy to learn a better representation of text in scene text images to effectively improve the prediction performance of STR task. We perform extensive experiments on several public benchmark datasets and compare with baseline models, and the experimental results demonstrate the effectiveness of our method.
Sight Guidance Enhanced VR Video Transmission

This paper studies the enhancement of the tile-based multicasting transmission for virtual reality (VR) panoramic videos. A sight guidance scheme is proposed to aggregate the viewpoints of users, so as to increase the opportunity of multicasting and reduce the traffic load. Aimed at minimizing the weighted sum of average traffic load and users’ watching preference derivation, we formulate a target guidance viewpoint optimization problem, and propose an approximate gradient descent algorithm to find a solution. Simulation results based on a real VR video dataset demonstrate the benefits brought by sight guidance, and show that compared with some heuristic sight guidance methods, the proposed algorithm has significant advantages in reducing traffic load and maintaining users’ watching preference.
A Video Abnormal Detection Framework based on Appearance-Motion Fuse Memory

Abnormal events are usually considered extraneous event deviation from standard events. Context and Movement mode play a pivotal part in determining abnormal events. Due to the forms being unbounded and ambiguous, it is impossible to straight fitting abnormal. Based on this requirement, following a recent series of methods, we used an object detector, followed by abnormal detection at the object level. This paper proposes a brand new video abnormal detection approach. By occupying Appearance-Motion Fuse Memory (AMFM), the new pattern will utilize the appearance message and motion representation. The Spatio-temporal model uses a two-stream encoder. The two streams of input refer to several preceding video frames and their corresponding optical flow data. In the course of training, the two-stream encoder takes the role of extracting the superficial and moving features by the merely normal entities. We discover that a design like this two-stream encoder has the ability to commit the characteristics of normal samples to memory. Experimental results on the commonly used ShanghaiTech dataset and UCSD PED2 dataset verified that our model achieves competitive performance. While we conduct ablation studies to prove the validity of the components constructed in our model.
A self-supervised CNN for image denoising with self-similarity prior

Recently, a kind of blind-spot based self-supervised learning denoising method has attracted extensive research. The key of this kind of methods is to input Bernoulli-sampled instances and train network to recover the unsampled pixels. Based on the assumption that the image pixel is locally correlated while the noise exhibits statistical independence, the network will only recover the clean signal. Non-local self-similar priors play an important role in traditional image denoising methods, and can provide effective information for the reconstruction of unsampled pixels. We take blind-spot based method one step further by introducing non-local self-similarity prior into network processing flow. Specifically, we take the similar patch group as the processing unit, and design a non-local module in the network architecture to fuse the local and non-local information. Experiments show that the proposed non-local module can significantly improve the denoising performance.
On Applying Gradient Based Thresholding on the Canny Edge Detection Results to Improve the Effectiveness of Fuzzy Hough Transform for Colonoscopy Polyp Detection Purposes

The possibilities of improving the effectiveness of fuzzy Hough transform calculations in the detection of colonoscopy image processing and polyp detection by gradient based preprocessing is studied. For the Hough transforms a black and white image consisting of line segments is necessary, thus most of the times an edge detected image is used as the basis of the transform. Here, the gradient magnitude values corresponding to the Canny edge pixels of the image are used for determining, which are the typical magnitude values for the polyp contours, in order to remove part of the non-contour edges from the image. Three publicly available databases with images and ground truth masks are used for testing, whether a general threshold for the gradients is applicable, but based on the histograms it seems to be not possible to generate a database independent normalized gradient based domain that can be used for sorting out the unnecessary edges.
Trusted Monitor: TEE-Based System Monitoring

As trusted computing becomes increasingly important, Trusted Execution Environments (TEEs) see more widespread use. A particular high demand for security arises in the context of embedded systems in critical infrastructures. We present a novel intrusion detection system called the Trusted Monitor (TM) that protects its integrity even in the presence of a system-level attacker by running inside the ARM TrustZone TEE. The TM constantly monitors the system using hardware performance counters and detects intrusions based on the classification by an application-specific machine learning model. Our evaluation shows that the TM correctly classifies 86% of 183 evaluated workloads, while the performance overhead stays below 2%. In particular, we show that a real-world kernel-level rootkit observably influences the hardware performance counters and, thus, can be detected.
Design and Validation of an Instrument to Measure Digital Skills in University Students of the First Cycles of Health Careers

The objective of this research was to describe the process of validation and reliability of an instrument to measure the information selection, search and processing capabilities of university students in the first cycles of health careers. This need arose because in our environment, there is a gap in the validation and quality of instruments for information management in digital contexts. It is descriptive-quantitative research of instrumental design with psychometric properties. For this, the following steps were followed: 1) review of the literature on digital competencies; 2) construction of the instrument based on the Likert scale; and 3) testing of the content validity and reliability of the instrument by 15 experts. From this evaluation, it was obtained that the content validity index 0,80, based on Lawshe's method; and, by means of Cronbach's Alpha coefficient, the VAR POB 0,997 was determined. Therefore, it is concluded that the instrument allows measuring students' capabilities in information management in the Peruvian university context.
Assessment and Optimization of 1D CNN Model for Human Activity Recognition

Artificial Intelligence (AI) solves complex tasks like human activity and speech recognition. Accuracy-driven AI models introduced new challenges related to their applicability in resource-scarce systems. In Human Activity Recognition (HAR), state-of-the-art presents proposals using complex multi-layer LSTM networks. The literature states that LSTM networks are suitable for treating temporal-series data, a key feature for HAR. Most works in the literature seek the best possible accuracy, with few evaluating the overall computational cost to run the inference phase. In HAR, low-power IoT devices such as wearable sensors are widely used as data-gathering devices, but little effort is made to deploy AI technology in these devices. Most studies suggest an approach using edge devices or cloud computing architectures, where the end-device task is to gather and send data to the edge/cloud device. Most voice assistants, such as Amazon's Alexa and Google, use this architecture. In real-life applications, mainly in the healthcare industry, relying only on edge/cloud devices is not acceptable since these devices are not always available or reachable. The objective of this work is to evaluate the accuracy of convolutional networks with a simpler architecture, using 1D convolution, for HAR. The motivation for using networks with simpler network architectures is the possibility of embedding them in power- and memory-constrained devices.
A comparison in the teaching of programming for development kits with IoT

Education 4.0 promotes programming at all training levels, making block programming a compatible alternative, including developing applications with hardware. This paper compares UIFLOW and Arduino IDE in programming the M5Stack Core2 development kit for education. The designed task allows students to read, locally view, send, and remotely view the inertial sensor data integrated into the device. The programming in UIFLOW application uses block programming; this software is for the M5stack kits. To program in C code using Arduino IDE is need to install the necessary libraries for compatibility with M5Stack. Students of computer science engineering implement the program using both programming languages. The compared data are development times and acceptance of the participants. The results show the coding times of the program and the scores obtained in the acceptance test, determining faster times in the case of block programming and a better acceptance rate that demonstrates the advantages of using this language in programming education.
Building Indonesian Dependency Parser Using Cross-lingual Transfer Learning

In recent years, cross-lingual transfer learning has been gaining positive trends across NLP tasks. This research aims to develop a dependency parser for Indonesian using cross-lingual transfer learning. The dependency parser uses a Transformer as the encoder layer and a deep biaffine attention decoder as the decoder layer. The model is trained using a transfer learning approach from a source language to our target language with fine-tuning. We choose four languages as the source domain for comparison: French, Italian, Slovenian, and English. Our proposed approach is able to improve the performance of the dependency parser model for Indonesian as the target domain on both same-domain and cross-domain testing. Compared to the baseline model, our best model increases UAS up to 4.31% and LAS up to 4.46%. Among the chosen source languages of dependency treebanks, French and Italian that are selected based on LangRank output perform better than other languages selected based on other criteria. French, which has the highest rank from LangRank, performs the best on cross-lingual transfer learning for the dependency parser model.
CCASinGAN: Cascaded Channel Attention Guided Single-Image GANs

Training a generative model through a single image is essential in areas where it is impossible to obtain large datasets. However, the current methods, such as SinGAN and ConSinGAN, are challenging to capture the global information of the image and process complex images. This paper proposes a cascaded channel attention guided single-image GANs model (CCASinGAN). Specifically, channel attention uses only average pooling and maximum pooling to aggregate global features. The global features are easily affected by particular points (e.g., the maximum value may appear at the isolated region, or the outliers may increase the average value), leading to unpredictable performance. To eliminate the influence of these points and improve the quality of the generated image, we propose a cascaded channel attention mechanism, which cascades the input feature maps to the weighted feature maps to increase the robustness of the attention module. We have carried out experiments on different image processing tasks. Compared with the state-of-the-art models, CCASinGAN achieves superior performance, especially when processing images with complex structures.
Feature Re-Attention and Multi-Layer Feature Fusion for Fine-Grained Visual Classification

Fine-grained visual classification (FGVC) distinguishes sub-categories under a large category, with significant differences within classes and minor differences between classes. Many current methods propose to apply the attention mechanism to mine the most salient parts of objects to obtain more refined feature representations. However, using the attention mechanism will bring two limitations: One is that the attention mechanism usually pays attention to the most salient parts of the object and ignores the insignificant but discriminative parts. The other is that the separate use of features of specific object part ignores the connections between different parts. To address the first limitation, we propose a feature re-attention module (FRAM) to obtain feature representations of multiple enhanced specific object parts. At the same time, the multi-layer feature fusion module (MLFFM) is used to fuse the feature representations of multiple enhanced object-specific parts to learn semantically complementary information from each other. The proposed method can be trained end-to-end and does not require additional human annotation information. Extensive experiments on three fine-grained public datasets demonstrate that our method achieves state-of-the-art performance.
Self-supervised Pre-training with Learnable Tokenizers for Person Re-Identification in Railway Stations

Railway station is the place of complex and densely-crowded, once events that threats public safety occurs, managers are in charge of keeping eyes on relevant passengers and their trajectories among crucial areas. In order to improve the efficiency of retrieving with centralized monitoring, a self-supervised pretraining with learnable tokenizers is proposed in this paper for person refine feature representation. In our framework, oriented token prior is introduced for person-specific mask image modeling, knowledge distillation based tokenizer encoder is applied for universal person and body parts feature understanding. Under the paradigm of self-supervision and contrastive learning, our model is pre-trained on a larger LUPerson dataset, that could tell the feature distinction between complex background as well as regions that contain complete body semantics. Extensive experiments are carried out to verified the proposed method with baselines of supervised ReID and contrastive learning networks, the proposed method yield the state-of-the-art performance in mainstream ReID datasets. Moreover, we show the advantages in few-shot learning and unsupervised learning, which makes further evidence of the robustness of our model.
Averting Mode Collapse for Generative Zero-Shot Learning

When there is no labeled data for certain classes, Zero-shot learning is a promising approach for classification. Indeed, in zero-shot learning, no labeled sample from some classes is used in the training phase, yet the model successfully classifies data of all classes. Some recent studies have demonstrated that conditional generative adversarial networks can be used to create synthetic data for classes that require labeled data. As part of this research, we provide a novel architecture for generative adversarial networks to increase the quality of data produced from classes with no labeled sample. In addition to the changes made in the training phase of the generative adversarial networks, a regulator has also been utilized to ensure the diversity of generated data. This regulator also prevents mode collapse. The proposed architecture is assessed on the Animals With Attributes (AWA) dataset. Classification accuracy in ZSL and GZSL settings improved by 1.7% and 2.3% in comparison to the state-of-the-art results.
Soccer Video Event Detection Using Metric Learning

We presented a novel video-based event detection method in soccer videos. Our proposed method is based on convolutional neural networks(CNNs) and gated recurrent units(GRUs) networks and uses contrastive loss for network training. We also collected a dataset called SoccerEvent to perform various experiments. To achieve the best choice of architecture for CNN and recurrent neural networks(RNNs), Various experiments were performed on different architectures on the SoccerEvent dataset. Then we selected the best architecture for the proposed method. Experiments conducted on two different datasets, SoccerNet and SoccerEvent, show that the proposed method has achieved higher accuracy than other methods. The proposed method has reached 64.9% accuracy in the SoccerNet dataset and 93.76% in the SoccerEvent dataset, which is about 1% better than other state-of-the-art methods.
Foveated Neural Computation

The classic computational scheme of convolutional layers leverages filter banks that are shared over all the spatial coordinates of the input, independently on external information on what is specifically under observation and without any distinctions between what is closer to the observed area and what is peripheral. In this paper we propose to go beyond such a scheme, introducing the notion of Foveated Convolutional Layer (FCL), that formalizes the idea of location-dependent convolutions with foveated processing, i.e., fine-grained processing in a given-focused area and coarser processing in the peripheral regions. We show how the idea of foveated computations can be exploited not only as a filtering mechanism, but also as a mean to speed-up inference with respect to classic convolutional layers, allowing the user to select the appropriate trade-off between level of detail and computational burden. FCLs can be stacked into neural architectures and we evaluate them in several tasks, showing how they efficiently handle the information in the peripheral regions, eventually avoiding the development of misleading biases. When integrated with a model of human attention, FCL-based networks naturally implement a foveated visual system that guides the attention toward the locations of interest, as we experimentally analyze on a stream of visual stimuli.
Structure Evolution on Manifold for Graph Learning

Graph has been widely used in various applications, while how to optimize the graph is still an open question. In this paper, we propose a framework to optimize the graph structure via structure evolution on graph manifold. We first define the graph manifold and search the best graph structure on this manifold. Concretely, associated with the data features and the prediction results of a given task, we define a graph energy to measure how the graph fits the graph manifold from an initial graph structure. The graph structure then evolves by minimizing the graph energy. In this process, the graph structure can be evolved on the graph manifold corresponding to the update of the prediction results. Alternatively iterating these two processes, both the graph structure and the prediction results can be updated until converge. It achieves the suitable structure for graph learning without searching all hyperparameters. To evaluate the performance of the proposed method, we have conducted experiments on eight datasets and compared with the recent state-of-the-art methods. Experiment results demonstrate that our method outperforms the state-of-the-art methods in both transductive and inductive settings.
Real-time 3D measurement system based on infrared light projection

Optical three-dimensional(3D) shape measurement technology has been widely used in industrial manufacturing, defect detection, reverse engineering, human modeling, pattern recognition and other fields. As industrial standards continue to advance, we are demanding more and more functionality and performance from our imaging systems. At present, although a real-time imaging system based on visible light from fringe projection can image well and achieve real-time imaging speed, it is still not applicable to face scanning, shaded object imaging, etc. However, most of the 3D imaging based on the infrared projector cannot achieve the effect of real-time imaging due to the slow scanning speed. In this paper, we combine the near-infrared structured light illumination system with the stereo phase unwrapping method with multi-camera calibration to realize high-precision real-time 3D imaging. The MEMS near-infrared fringe projection device is used as a structured light illumination source, which can reduce the damage of visible structured light to human eyes and animals. Experiments are carried out on static and dynamic scenes, and it is verified that the designed system can achieve high-speed and high precision 3D reconstruction at the speed of 100 frames per second, and the measurement accuracy is about 100µm.
Data science applied to environmental sciences

Abstract In recent years, immense amounts of data have been generated, from sensors to purchase transaction records, mobile GPS signals, digital satellite images, and social media. The raise of data collection has brought the need for quantitative minded professionals able to transform that data into information and decision making. In this opinion piece, we will share some of our views and experiences about the general role that data science plays nowadays, with a special interest in the field of environmetrics. We will include a limited number of examples that highlight the usefulness of data science in environmetrics, and a specific illustration of the behavior of the wildfires in Brazil between January and December of 2021.