Reviewerly/exHarmony huggingface dataset:
Detection of epidemic prevention and control behavior based on the yolov3-spp pruning model

Due to the need for epidemic prevention and control and the performance superiority of the yolov3-spp network, this paper mainly uses yolov3-spp, which is a Yolo-based pruning network to detect epidemic prevention and control behaviors. Specifically, firstly, the epidemic prevention and control dataset suitable for the Yolo algorithm is constructed. Then, on the premise of saving cost and easy deployment, the model compression experiment is carried out. Through comparative experiments, we found that: (1) Yolov3-spp can be applied to the epidemic prevention and control dataset and detect the epidemic prevention and control behavior. (2) The Recall and mAP of the model before and after pruning are close, but the inference speed, model size, and model parameters after pruning are about 2 times, 10.7% and 10.8% respectively of those before pruning. (3) Balancing the requirements of model performance, inference speed, and computing power, our method is more suitable for deployment on edge devices than yolov3-spp, yolov4-tiny, and yolov4. These experimental results show the feasibility, effectiveness, and adaptability of the pruning method described in this paper.
Preservation of Perceived Colorfulness of Down-Mapped, Hybrid Log-Gamma (HLG) to Standard Dynamic Range Content Using a Color Appearance Model (CAM)

Most of today’s high dynamic range (HDR) hybrid log-gamma (HLG) to standard dynamic range (SDR) conversions rely on a mathematical approach for color volume reduction from an International Telecommunication Union-Radioommunication (ITU-R) Recommendation BT.2100 container into an ITU-R BT.709 one, typically through “tone-mapping” applied to a luminance signal. However, this might not be the only plausible option for HLG to SDR conversion. A color appearance model (CAM) has been extended in this work to compute HLG to SDR down-mapping. This alternative down-mapping approach can be carried out through a CAM combined with a chromatic adaptation transform (CAT). The CAT transforms the image into a uniform color space (UCS), and the CAM calculates values of perceived quantities of the viewed media, such as lightness and colorfulness given specific display parameters. Perceptual quantities in the model are, for the most part, conserved to compute the down-mapping. This method has shown great promise in several natural images and colors, while having limitations with engineering test signals. SDR footage derived from an HDR capture using a CAM look-up table (LUT) results in natural pictures that are more visually accurate than those from conventional conversion technologies or SDR capture since many colors in nature exist outside of a BT.709 container. Other examples of down-mapping can be found in the ITU-R report BT.2446. Such down-mapping conversions attempt to maintain color fidelity between the two visual representations and are usually implemented in a 3D LUT. Such approaches, however, cannot take full account of psychovisual factors and may struggle to differentiate between colors such as yellow and brown that are similar in hue but vary in luminance. A CAM-based approach may not deliver mathematically ideal output values for artificial test patterns, so the article will also discuss what changes may need to be made to TV production workflows if the improvements offered by CAM-based down-mapping are to be fully realized.
Renewing an electronics course at university with support of ICT resources after the pandemic context

Consequences of pandemic have severely affected teaching methodologies at every academic level. University programs have adapted to online and blended methodologies while the situation of the sanitary crisis did not permit face-to-face attendance. In this context, we present a case-study based on the renewal of an electronics course taught at the Miguel Hernandez University, in Spain. Lectures still pursue its former goal of achieving active learning and empowering practical skills amongst students. Nonetheless, the experience after two academic courses since the beginning of the pandemic have allowed us to assess the validity and significance of several learning resources (Information and Communication Technology [ICT]-based) for the final outcome and performance of the students. Therefore, now that face-to-face attendance has been reestablished, we have renewed the learning program of this course with a blended methodology sustained by face-to-face lectures and with the inclusion of relevant resources such as: interactive exercises in a Jupyter Notebook of Google, video lessons in a Youtube channel, e-books in Google Books, simulation exercises in Tinkercad. The key aspect lays on these resources, partially used in a flipped lesson manner (mainly for the videos and e-books), whereas in most cases they were combined during the face-to-face lectures, in order to facilitate the theoretic explanations by on site exemplification. The results demonstrate the validity of the approach by means of the assessment of the performance of the students and the success ratios. The acquisition of competences and perception reported by students about this renewed program were also analyzed. Certain comparison statistics are also presented to reinforce the validation.
Improving Consumers Continuous Usage of Chatbots: The Perspective of Multiple Reference Effects

Chatbots have been widely employed in e-commerce activities, and are replacing manual customer service, which shows strong market potential. However, compared with its rapid development, consumers&#x0027; usage rate of chatbots still has room for growth. Therefore, this study aims to elucidate the factors that determine consumers&#x0027; continuous usage of chatbots. Grounded on the multiple reference effects model, this study examines the role of the focal-object referents, other-object referents, and self-based referents in the judgment process, and uses mood as a moderating variable. Focal-object referents stand for consumers&#x0027; evaluation results of the chatbot service performance with their expectations. Other-object referents are the performance of the alternative options that can be assessed by consumers compared with the focal object. Self-based referents indicate the self-image of consumers compared with the image of the focal object. The potential implications of this study were further discussed.
EnclaveSim:A Micro-architectural Simulator with Enclave Support

Intel SGX preserves the confidentiality and integrity aspects of data and code through enclaves (that reside in the trusted part of the memory) and protects it from different layers of the malicious system software, including the OS. Micro-architecture research in the presence of SGX is an interesting theme to explore as SGX does not mitigate timing side-channel attacks at various levels of a memory hierarchy and causes significant performance slowdown. The research community extensively uses existing benchmark suites like SPEC CPU 2017 for evaluating new proposals on the various aspects of micro-architecture research. As there is no benchmark suite available for micro-architecture research with SGX, state-of-the-art micro-architecture research in the presence of SGX assumes an entire SPEC benchmark is running inside an enclave. In reality, Intel SGX assumes that a major portion of the application's code and data do not require security, and only a tiny fraction of it needs security via an enclave. To the best of our knowledge, there are no open-source micro-architectural simulators that can simulate Intel SGX fairly, for micro-architecture research. In this regard, we propose EnclaveSim, a detailed yet flexible, trace-based micro-architectural simulator that simulates trusted code execution through enclaves.
Design of wireless force measuring instrument for sports based on STM32

Nowadays, the data acquisition system is applied in all aspects of production and life, and as an important indicator, the research on the pressure acquisition system is particularly important. This paper proposes a pressure acquisition system that can collect four pressure signals, the sampling rate of a single channel can reach 150Hz, and the range is 30 to 1000 kg. The pressure acquisition system adopts a full bridge sensor, which has the characteristics of good linearity and high sensitivity. The pressure acquisition system can be sampled by the host computer through Bluetooth control. Experiments show that the comprehensive accuracy of the system is lower than that of the current common systems, and the standard deviation of the continuous sampling of the system is less than 0.39, which meets the design requirements.
Sentiment Analysis for Twitter Tweets: A Framework to Detect Sentiment Using Naïve Bayes Algorithm

In today’s society, people communicate through social media. Digital life has expanded dramatically. Twitter is a social media platform where users can post tweets. The researchers used sentiment analysis, a popular research topic in recent years. Correctly identifying sentiments is critical since it opens up a world of possibilities. The researchers used the Twitter API and the tweepy python module to extract real-time tweets from Twitter. To imprive the rigor of this study, we used a Kaggle dataset to create a model that using Naïve Bayes Algorithm to recognize sentiments better. The researchers also compared different algorithms to identify the best algorithm for the data. In addition, the researchers collected and obtained information from credible sources to assess the trained model’s correctness and reliability. The analysis of the result highlights the implication of our studies.
Image Recognition in Autonomous Driving Based on Improved Swin Transformer

Traffic image recognition is one of the most important phases in the field of autonomous driving, including the classification of real-time periods and the detection of pedestrians, vehicles, etc. on the road. In this paper, we proposed an end-to-end classifcation and object detection method based on Swin Transformer with improved cascade RoI heads. Our method focuses on the scale problem from language to vision field in traditional Transformer model and the mismatch problem of bounding box regression in previous object detection methods (e.g. Faster R-CNN). A modified Swin Transformer architecture with multiple RoI heads is adopted in the proposed model to perform classification and object detection, meanwhile improved optimization strategies are used. We applied the model to SODAIOM, an autonomous driving dataset released by Huawei, and finally attained a classification accuracy of 95.3% and a detection mAP of 91.9%, both achieving state-of-the-art.
Generating Smooth Near Time-Optimal Trajectories for Steering Drones

In this paper, we address a minimum-time steering problem for a drone modeled as point mass with bounded acceleration, across a set of desired waypoints in the presence of gravity. We first present a method to calculate the minimum-time control input to steer the drone between two waypoints based on a continuous-time problem formulation that is solved using Pontryagin's Minimum Principle. Subsequently, we use this two-point solution to find a minimum-time trajectory across multiple waypoints. We solve for the time-optimal trajectory across a given set of waypoints by discretizing in the time domain and formulating the minimum-time problem as a non-linear program (NLP). The velocities at each waypoint obtained from solving the NLP are then used as boundary conditions to extend our two-point solution across those multiple waypoints. We apply this planning methodology to execute a surveying task that minimizes the time taken to completely explore a target area or volume. Numerical simulations and theoretical analyses of this new planning methodology are presented. The results from our approach are also compared to traditional polynomial trajectories like minimum snap planning.
Research on Text Summarization Generation and Classification Method Based on Deep Learning

The information extraction and classification of text is of great significance in many aspects such as text retrieval, news statistics, and literary works analysis. However, the exponential increase of texts generated in the information age poses a huge challenge to the sort texts. Based on deep learning technology, this paper generates abstracts for long texts, and then classifies texts based on the content of the abstracts. Since the abstract contained main information, the primary information of the document is extracted and stored in the abstract through automatic summarization, and then the classification may have a better effect. This paper adopts the improved model Pointer-generator network based on the Sequence-to-sequence attentional model to generate generative summaries, and then builds a TextCNN model to classify the generated summaries at the character level. Those not only generate the abstract of the document but also classify the document. The fusion of the two can more effectively reduce the search pressure. The experiment shows that the proposed method has achieved the desired results and could be utilized in many aspects in the future.
A Self-Attention Based Joint Sequence Labeling Model

Many sequence tasks can be effectively treated as Sequence labeling (SL) problems in Natural Language Processing. A lot of the existing studies solve these tasks as independent sequence labeling problems, or use multiple auxiliary tasks to increase the performance of specific target tasks. These studies ignore the potential relationship and influence between multi-tasks in natural language processing. We propose an innovative and well-designed self-attention based joint sequence model (SA-JSL) in a neural network frame, which fully utilize the possible interactions and influences between many sequence labeling tasks, so as to promote and improve the performance of each sequence labeling task simultaneously. Specifically, by fusing the self-attention and joint tag mechanism, this framework model converts multiple sequence tasks into a unified sequence labeling task for processing, and effectively utilizes the potential relationship between these tasks, so as to realize the mutual promotion and improvement of multi-tasks. The joint model combines with the self-attention mechanism to obtain richer context information and improve the performance of the model. Experiments were carried out on seven popular benchmark datasets to verify the superiority of the model.
Fuzzy Logic-based N-gram Graph Technique for Evaluating Textual Documents Indexes

Fuzzy logic is a term that is used more and more often by researchers specialized in information retrieval and indexing. It is supposed to fill the gaps of the current domain by adding meaning to the information. In this article, the main goal is introducing the fuzzy logic approach in the field of the representation of a corpus of documents, more precisely in the proposal of a new method based on fuzzy functions that allow the evaluation of the generated indexes of a specific corpus.The evaluation is based on calculating the degrees of relevance of a term in relation to another whether it is in the same document of the corpus or in two or more completely different documents of the same corpus.
Computer-Aided Polyp Image Segmentation Using A Residual Attention Convolutional Neural Network

With a promising application in Computer-Aided Diagnosis (CAD) pipelines, medical image segmentation is the most commonly studied subject in the field of deep learning-based medical imaging. Yet existing methods failed to achieve satisfactory performance in pixel-level segmentation, thanks in part to the class imbalance between foreground and background regions. In addition, the robustness and generalizability of segmentation models lacked due attention in previous research. In this paper, we presented a residual attention convolutional network for polyp segmentation, which took advantage of residual learning, multi-level feature fusion, and channel-wise and spatial attention for adaptive feature calibration. For evaluation of the competitiveness of the proposed algorithm, we performed extensive experiments using five publicly accessible polyp datasets. Experimental results showed that our method enjoyed consistent improvement over state-of-the-art algorithms. For the assessment of segmentation models’ robustness and generalizability, we further performed cross-dataset and challenging-scenario evaluations. Experiments verified that our network can well generalize across different datasets and was exceptionally robust against challenging images.
A receding-horizon estimation and control framework for the content sequencing problem

We propose a full receding-horizon approach to solve the problem of adaptively selecting the contents of a test while the test is being taken by a learner, while accounting for the past performance and the knowledge uptake needs of the learner itself. We thus present an approach that embeds a moving-horizon estimator to robustly infer the current knowledge levels of the learner, together with a model predictive controller to promote the robust selection of which items should be included in the test as it is being taken by the learner. Both receding-horizon algorithms are built on top of a model of the learners' knowledge uptake dynamics that accounts for Zone of Proximal Development effects, i.e., the hypothesis for which there exists an optimal difficulty level for the questions to maximize the learning at each step. We show that the proposed closed-loop approach outperforms the current policies that build tests either by randomly selecting questions within a database, or by making the difficulty of the questions nondecreasing as the test progresses.
An Improved Convolutional Neural Network with Reservoir Computing for Boiler Four Tube

Considering the learning advantage of reservoir computing, the echo state network is introduced to replace the full connection layer of the traditional convolutional neural network, and thus, an improved convolutional neural network is proposed for failure detection classification of boiler four tube in this paper. For the failure detection of boiler four tube, due to the difficulty of manual classification, it is necessary to classify the collected pictures by using an improved intelligent classification method. According to the image information processing ability of the convolutional neural network, the image feature information of boiler four tube can be extracted. Because the extracted image feature information is introduced into the reservoir, the failure detection type of boiler four tube can be classified more accurately by using the learning ability of echo state network. In order to reduce the influence of the reservoir parameters on the classification accuracy of the proposed method, a parameter optimization method is given for optimizing the image classification model. Through using the collected images, the simulation results show that the classification accuracy of the proposed classification method reaches 90.1%.
Image Recognition based on Multi-scale Feature Fusion Transformer

Aiming at the problem that image recognition based on transformers has low image recognition rate due to ignoring local information of image blocks, an image recognition framework based on multi-scale Feature Fusion Transformer (FFT) is proposed, where the FFT block is designed to fuse feature information of different scales, and the residual attention module is introduced to emphasize feature channels and feature regions of interest. The FFT framework not only avoids the problem of vision transformer internal structure and local information loss of image feature blocks but also captures richer detailed features, which effectively improves the image recognition rate. A large number of experiments are performed on common image recognition datasets Tiny-ImageNet, CIFAR-10 and CIFAR-100, and the recognition accuracy can reach 57.81%, 82.04% and 56.98%, respectively, which are significantly higher than the mainstream image recognition algorithms.
The application of interpretable machine learning model based on comparative learning and NARMAX in epidemic research

Most of the machine learning models are black box models. However, in practical applications, such as in many medical and health fields, it is very necessary to clearly understand the internal composition, combination or interaction of the model, study the system and predict the system behavior. Therefore, interpretable machine learning models have attracted more and more attention, especially when predicting based on models, the driving factors leading to prediction behavior are deeply studied. This paper proposes an interpretable machine learning model based on comparative learning and NARMAX. Because the input-output relationship of the model and the interaction relationship between input variables are clear, the model can not only be used for prediction, but also explain the relevant “reasons” of prediction behavior. The novel coronavirus pneumonia epidemic data and influenza epidemic data were used to compare the model proposed in this paper. The experimental results show that the model is effective and reliable, and establish a dynamic model for the two diseases’ spreads, and analyze the relationship between disease transmission factors.
Research on anomaly detection of gas-insulated switchgear based on transfer learning

Anomaly detection of gas-insulated switchgear is very important to ensure the reliability of power systems. We propose an anomaly detection algorithm for gas-insulated switchgear based on transfer learning. This algorithm includes the feature extraction and one-class classifier training stages. Firstly, we design a descriptiveness loss function based on cross entropy and a compactness loss function based on variance. they formed a composite loss function to optimize the feature extraction network. The feature extraction network is initialized by a pre-trained model and fine-tuned in batches using reference and target data. The target data is further processed into feature data using a fine-tuned feature extraction network, and input to the one-class classifier for training and testing. The experimental results on the gas-insulated switchgear anomaly detection dataset show that the proposed anomaly detection algorithm has a significant improvement over the current popular anomaly detection algorithms.
Clustered Network Adaptation Methodology for the Resource Constrained Platform

Recently, deep neural networks (DNNs) are widely used for many artificial intelligence (AI) applications, including robotics, self driving vehicles etc. Although DNNs deliver state-of-the-art accuracy on many AI tasks, but are both computationally and memory intensive. This hinders the deployment of DNNs on mobile and IoT edge devices with limited hardware resources and power budgets. Traditional DNN compression is applicable during training to obtain an efficient inference engine. When the inference engine runs on the hardware platform, constrained by battery backup it brings additional challenge in terms of reducing the complexity (like memory requirement, area on hardware etc.). To reduce the memory complexity, we are proposing a new low complex methodology named as "Clustering Algorithm" to eliminate the redundancies present within the filter coefficients (i.e. weights). This algorithm is a three stage pipeline: quantization, coefficient clustering and code-assignment, that work together to reduce the memory storage of neural networks on the cost of low run-time memory requirement. To show the efficacy of the proposed "Clustering Algorithm", we executed it on the network obtained after applying NetAdapt Algorithm, a popular inference engine on pre-trained AlexNet for CIFAR-10 dataset. We observe that our "Clustering Algorithm" reduces the storage required by five layers of already pruned pre-trained AlexNet by 3x (approximately 65%) on FPGA, and 2x (approximately 51%) on CPU. This allows the model to fit into on-chip SRAM cache rather than off-chip DRAM memory.