Reviewerly/exHarmony huggingface dataset:
Institutional Teaching Innovation under the Demand of Internet + PE

In order to study the effectiveness of student-based teaching methods, this study uses open-ended and closed-ended questionnaires to collect qualitative results and studies by collecting different types of data, including quantitative data of the body mass index. The results show that the biggest difference from the traditional teaching strategy is that the internet teaching model can help to improve personal communication ability and better develop interpersonal relationship. At the same time, through social interaction in e-learning, learners can cultivate their ability to find and solve problems, collect, analyze, and use information, and learn to share and cooperate. The research of the project can provide some reference ideas and theoretical basis for follow-up research.
A Fusion Framework for Multi-Spectral Pedestrian Detection using EfficientDet

Recently, research related to Advanced Driver Assistance Systems is active. In this paper, EfficientDet Fusion Framework for multi-spectral pedestrian detection is constructed through Sum, Max, and Concatenation at the feature level. In the experiment, it was confirmed that the performance improvement of the convergence multispectral network was quantitatively improved by about 10&#x0025; compared to the single spectral network. In addition, it shows that the shortcomings of a single spectral can be actually compensated through the resulting image. In the future, various fusion studies will be conducted based on the EfficientDet Fusion Framework.
A YOLOv5s-SE model for object detection in X-ray security images

The target detection algorithm YOLOv5 is used in real-time object detection with its faster detection speed, good accuracy and flexibility of application. In this paper, a YOLOv5s-SE algorithm is proposed to solve the problem of complex background of X-ray security image and occlusion and overlap of objects. A custom channel attention module is added to the algorithm, which enables the network to focus on feature channels with a large amount of information and suppress unimportant feature channels. The position and quantity of the modules in the network are determined by the method of comparative experiments, and the GIoU loss of target box regression is replaced by CIoU loss to accelerate the convergence of the model. The object detection experiment on the X-ray security image dataset shows that compared with the YOLOv3 and Faster RCNN algorithms, the proposed algorithm has better detection results for partially occluded targets and the case where the distance between the targets is too close. Compared with YOLOv5s, the precision, recall, and mAP are increased by 1.2%, 2.8%, and 2.5% respectively, the loss convergence is also faster.
CHARM: Collaborative Host and Accelerator Resource Management for GPU Datacenters

Emerging latency-critical (LC) services often have both CPU and GPU stages (e.g. DNN-assisted services) and require short response latency. Co-locating best-effort (BE) applications on the both CPU side and GPU side with the LC service improves resource utilization. However, resource contention often results in the QoS violation of LC services. We therefore present CHARM, a collaborative host-accelerator resource management system. CHARM ensures the required QoS target of DNN-assisted LC services, while maximizing the resource utilization of both the host and accelerator. CHARM is comprised of a BE-aware QoS target allocator, a unified heterogeneous resource manager, and a collaborative accelerator-side QoS compensator. The QoS target allocator determines the time limit of an LC service running on the host side and the accelerator side. The resource manager allocates the shared resources on both host side and accelerator side. The QoS compensator allocates more resources to the LC service to speed up its execution, if it runs slower than expected. Experimental results on an Nvidia GPU RTX 2080Ti show that CHARM improves the resource utilization by 43.2%, while ensuring the required QoS target compared with state-of-the-art solutions.
Construction of Innovation and Entrepreneurship Platform Based on Deep Learning Algorithm

As the national economy has entered a stage of rapid development, the national economy and social development have also ushered in the “14th Five-Year Plan,” and the country has also issued support policies to encourage and guide college students to start their own businesses. Therefore, the establishment of an innovation and entrepreneurship platform has a significant impact on China’s economy. This gives college students great support and help in starting a business. The theory of deep learning algorithms originated from the development of artificial neural networks and is another important field of machine learning. As the computing power of computers has been greatly improved, especially the computing power of GPU can quickly train deep neural networks, deep learning algorithms have become an important research direction. The deep learning algorithm is a nonlinear network structure and a standard modeling method in the field of machine learning. After modeling various templates, they can be identified and implemented. This article uses a combination of theoretical research and empirical research, based on the views and research content of some scholars in recent years, and introduces the basic framework and research content of this article. Then, deep learning algorithms are used to analyze the experimental data. Data analysis is performed, and relevant concepts of deep learning algorithms are combined. This article focuses on exploring the construction of an IAE (innovation and entrepreneurship) education platform and making full use of the role of deep learning algorithms to realize the construction of innovation and entrepreneurship platforms. Traditional methods need to extract features through manual design, then perform feature classification, and finally realize the function of recognition. The deep learning algorithm has strong data image processing capabilities and can quickly process large-scale data. Research data show that 49.5% of college students and 35.2% of undergraduates expressed their interest in entrepreneurship. Entrepreneurship is a good choice to relieve employment pressure.
A Lightweight AES Coprocessor Based on RISC-V Custom Instructions

With the increasing popularity of the Internet of Things (IoT), the issue of its information security has drawn more and more attention. To overcome the resource constraint barrier for secure and reliable data transmission on the widely used IoT devices such as wireless sensor network (WSN) nodes, many researcher studies consider hardware acceleration of traditional cryptographic algorithms as one of the effective methods. Meanwhile, as one of the current research topics in the reduced instruction set computer (RISC), RISC-V provides a solid foundation for implementing domain-specific architecture (DSA). To this end, we propose an extended instruction scheme for the advanced encryption standard (AES) based on RISC-V custom instructions and present a coprocessor designed on the open-source core Hummingbird E203. The AES coprocessor uses direct memory access channels to achieve parallel data access and processing, which provides flexibility in memory space allocation and improves the efficiency of cryptographic components. Applications with embedded AES custom instructions running on an experimental prototype of the field-programmable gate array (FPGA) platform demonstrated a 25.3% to 37.9% improvement in running time over previous similar works when processing no less than 80 bytes of data. In addition, the application-specific integrated circuit (ASIC) experiments show that in most cases, the coprocessor only consumes up to 20% more power than the necessary AES operations.
Multi-view partial label machine

In partial label learning (PLL), each instance is associated with a set of candidate labels, among which there is only ground-truth label. PLL aims to identify the ground-truth label out of these candidate labels. Most of the existing PLL approaches are proposed to handle the single-view PLL problem, and the multi-view PLL problem has not been addressed. In this paper, we propose a novel multi-view paunknown.
Deep Adaptive Few Example Learning for Microscopy Image Cell Counting

Deep learning networks have demonstrated robust performance for cell counting in medical images. However, obtaining promising results requires large amount of annotated data for supervised training, which is labor-intensive. To address this problem, we propose a novel adaptive few example counting network that aims at localizing cells in microscopy images with only a few annotated examples. We incorporate a few-shot learning phase to transfer information of features from general training images to novel medical images. We also present a training strategy that takes both novel images with their few instances as the input to predict the final results. Furthermore, we introduce an adaptation stage during test time which takes a query medical image as well as its corresponding few object instances to boost the network performance. As the task is firstly established, we further create three few-shot cell counting datasets by adding cell exemplars on top of the public medical images and set them as the baseline. Extensive experiments on the three microscopy datasets show that our framework outperforms the other baseline approaches.
Amazon Product Reviews: Sentiment Analysis Using Supervised Learning Algorithms

E-commerce is gaining attraction in today's digital environment by making products closer to customers without forcing them to leave their homes. A customer has to go through hundreds of reviews before making a purchase. The number of reviews for a single product can easily approach millions and becomes difficult to understand the overall client feedback. However, machine learning techniques can understand and learn from big data. Consequently, sentiment analysis is a new study area combining natural language processing and text analytic to extract information from sources and classify the polarity of express sentiments. We have employed Support Vector Machine, Naive Bays, Decision Tree, Random Forest, Logistic Regression, and Multi-Layer Perceptron Classifiers for large-scale supervised learning on the Amazon Electronic products reviews data set and obtained satisfactory results. We have compared three different preprocessing techniques: TF-IDF, Bag of Words, and Word2Vec for representing the words in the review. MLP classifier produced the best results with Bag of Words preprocessing showing 92% accuracy.
Benefits of Adaptive Learning Transfer From Typing-Based Learning to Speech-Based Learning

Memorising vocabulary is an important aspect of formal foreign-language learning. Advances in cognitive psychology have led to the development of adaptive learning systems that make vocabulary learning more efficient. One way these computer-based systems optimize learning is by measuring learning performance in real time to create optimal repetition schedules for individual learners. While such adaptive learning systems have been successfully applied to word learning using keyboard-based input, they have thus far seen little application in word learning where spoken instead of typed input is used. Here we present a framework for speech-based word learning using an adaptive model that was developed for and tested with typing-based word learning. We show that typing- and speech-based learning result in similar behavioral patterns that can be used to reliably estimate individual memory processes. We extend earlier findings demonstrating that a response-time based adaptive learning approach outperforms an accuracy-based, Leitner flashcard approach in learning efficiency (demonstrated by higher average accuracy and lower response times after a learning session). In short, we show that adaptive learning benefits transfer from typing-based learning, to speech based learning. Our work provides a basis for the development of language learning applications that use real-time pronunciation assessment software to score the accuracy of the learner's pronunciations. We discuss the implications for our approach for the development of educationally relevant, adaptive speech-based learning applications.
Research on Methods of English Text Detection and Recognition Based on Neural Network Detection Model

With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information. This article focuses on the inability of effective detection and recognition of English text content to conduct research, which is useful for improving the application of intelligent analysis significance. This paper studies how to improve the neural network model to improve the efficiency of image text detection and recognition under complex background. The main research work is as follows: (1) An improved CTPN multidirectional text detection algorithm is proposed, and the algorithm is applied to the multidirectional text detection and recognition system. It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text. This algorithm solves the problem that the CTPN network can only detect the text in the approximate horizontal direction. (2) An improved CRNN text recognition algorithm is proposed. The algorithm is based on CRNN and combines traditional text features and depth features at the same time, making it possible to recognize occluded text. The algorithm was tested on the IC13 and SVT data sets. Compared with the CRNN algorithm, the recognition accuracy has been improved, and the detection and recognition accuracy has increased by 0.065. This paper verifies the effectiveness of the improved algorithm model on multiple data sets, which can effectively detect various English texts, and greatly improves the detection and recognition performance of the original algorithm.
A Blockchain, Smart Contract and Data Mining Based Approach toward the Betterment of E-Commerce

E-commerce platforms have made our life easier and bought plenty of advantages too. However, due to fraud and scams, trust is a concern while buying products online. In this study, we proposed a blockchain-based architecture for the e-commerce sector where data mining technology is used to detect fraudulent users by generating precise and effective rules, and smart contracts are used for enforcement and functionality management within the blockchain network. Our data mining approach yielded a competitive accuracy, precision, recall, and f1-measure of over 99% compared to the state-of-the-art. High performing rules are further tested for ten simulated cycles under completely unseen data to test their rigidity in a real-time scenario. Details analysis of delineation (if any) in rule antecedents has also been analyzed for these cycles. Tested and selected rules are stored in smart contracts deployed within blockchain network for security and immutability. For ensuring strict order criteria maintenance, better return policy, authentic review and scam avoidance, two smart contracts have been written which implement seller reputation mechanism and authentic review maintenance while safeguarding the seller from intentional defaming too. Altogether, the goal of this study is to establish a balance between all the parties within an e-commerce platform so that everyone's right is protected, money is safe and resources are not exploited.
On streaming disaster damage assessment in social sensing: A crowd-driven dynamic neural architecture searching approach

Motivated by the recent advances in Internet and communication techniques and the proliferation of online social media, social sensing has emerged as a new sensing paradigm to obtain timely observations of the physical world from “human sensors”. In this study, we focus on an emerging application in social sensing – streaming disaster damage assessment (DDA) , which aims to automatically assess the damage severity of affected areas in a disaster event on the fly by leveraging the streaming imagery data about the disaster on social media. In particular, we study a dynamic optimal neural architecture searching (NAS) problem in streaming DDA applications. Our goal is to dynamically determine the optimal neural network architecture that accurately estimates the damage severity for each newly arrived image in the stream by leveraging human intelligence from the crowdsourcing systems. The present study is motivated by the observation that the neural network architectures in current DDA solutions are mainly designed by artificial intelligence (AI) experts, which often leads to non-negligible costs and errors given the dynamic nature of the streaming DDA applications and the lack of real-time annotations of the massive social media data inputs. Two critical technical challenges exist in solving our problem: (i) it is non-trivial to dynamically identify the optimal neural network architecture for each image on the fly without knowing its ground-truth label a priori ; (ii) it is challenging to effectively leverage the imperfect crowd intelligence to correctly identify the optimal neural network architecture for each image. To address the above challenges, we developed CD-NAS, a dynamic crowd-AI collaborative NAS framework that carefully explores the human intelligence from crowdsourcing systems to solve the dynamic optimal NAS problem and optimize the performance of streaming DDA applications. The evaluation results from a real-world streaming DDA application show that CD-NAS consistently outperforms the state-of-the-art AI and NAS baselines by achieving the highest disaster damage assessment accuracy while maintaining the lowest computational cost.
Autonomous Exploration Based on Multi-Criteria Decision-Making and Using D* Lite Algorithm

An autonomous robot is often in a situation to perform tasks or missions in an initially unknown environment. A logical approach to doing this implies discovering the environment by the incremental principle defined by the applied exploration strategy. A large number of exploration strategies apply the technique of selecting the next robot position between candidate locations on the frontier between the unknown and the known parts of the environment using the function that combines different criteria. The exploration strategies based on Multi-Criteria Decision-Making (MCDM) using the standard SAW, COPRAS and TOPSIS methods are presented in the paper. Their performances are evaluated in terms of the analysis and comparison of the influence that each one of them has on the efficiency of exploration in environments with a different risk level of a “bad choice” in the selection of the next robot position. The simulation results show that, due to its characteristics related to the intention to minimize risk, the application of TOPSIS can provide a good exploration strategy in environments with a high level of considered risk. No significant difference is found in the application of the analyzed MCDM methods in the exploration of environments with a low level of considered risk. Also, the results confirm that MCDM-based exploration strategies achieve better results than strategies when only one criterion is used, regardless of the characteristics of the environment. The famous D* Lite algorithm is used for path planning.
From Textbook to Teacher: an Adaptive Intelligent Tutoring System Based on BCI

In this work, we propose FT3, an adaptive intelligent tutoring system based on Brain Computer Interface(BCI). It can automatically generate different difficulty levels of lecturing video with teachers from textbook adapting to student engagement measured by BCI. Most current studies employ animated images to create pedagogical agents in such adaptive learning environments. However, evidence suggests that human teacher video brings a better learning experience than animated images. We design a virtual teacher generation engine consisting of text-to-speech (TTS) and lip synthesis method, being able to generate high-quality adaptive lecturing clips of talking teachers with accurate lip sync merely based on a textbook and teacher's photo. We propose a BCI to measure engagement, serving as an indicator for adaptively generating appropriate lecturing videos. We conduct a preliminary study to build and evaluate FT3. Results verify that FT3 can generate synced lecturing videos, and provide proper levels of learning content with an accuracy of 73.33%.
Efficient semantic place categorization by a robot through active line-of-sight selection

In this paper, we present an attention mechanism for mobile robots to face the problem of place categorization. Our approach, which is based on active perception, aims to capture images with characteristic or distinctive details of the environment that can be exploited to improve the efficiency (quickness and accuracy) of the place categorization. To do so, at each time moment, our proposal selects the most informative view by controlling the line-of-sight of the robot’s camera through a pan-only unit. We root our proposal on an information maximization scheme, formalized as a next-best-view problem through a Markov Decision Process (MDP) model. The latter exploits the short-time estimated navigation path of the robot to anticipate the next robot’s movements and make consistent decisions. We demonstrate over two datasets, with simulated and real data, that our proposal generalizes well for the two main paradigms of place categorization (object-based and image-based), outperforming typical camera-configurations (fixed and continuously-rotating) and a pure-exploratory approach, both in quickness and accuracy.
Text Mining in Chat Room of Online Learning for Detection Emotion using Artificial Intelligence

Now, in the Pandemic era, all people use online technology for all-purpose such as messenger application for all their activities. Text message is very fast to give respond but it is difficult to understand about felling or emotion people because in messenger cannot show their face. In online learning, teachers can send a text using a chat room with their students for learning but teachers cannot understand their student condition such as feelings or emotions. The problem that if students are bad emotion, they are very difficult to give them learning, especially in messenger's application, for example, chat room in online learning. The purpose of this research solves this problem by building a system that text documents in the chat rooms will process using an Artificial intelligence algorithm to know about the emotions of people in the chat room. The system uses 3 algorithms: naïve Bayes, fuzzy logic, and NPC. The system was built in existing online learning and embedded in the chat rooms in online learning. The result of testing of the system was 70% accuracy to determine the emotion-based in a text document in the chat room of online learning.