Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Intelligent Chatbot in a Digital Campus

With the rapid development of artificial intelligence technology, all walks of life have started the process of intelligent construction. The intelligent Q&A system in the field of artificial intelligence has the characteristics of all-weather and intelligent, and exploring the deep integration of intelligent Q&A system and campus business in the process of smart campus construction has practical significance for campus service model innovation. Based on the shortcomings of the traditional information service model, this paper elaborates from the overall framework and core modules of the campus intelligent question and answer system, and summarizes and analyzes its application advantages and subsequent development directions in smart campus services, so as to explore its more application potential and practical value in the field of campus intelligent services. This paper presents the implementation of a digital campus intelligent chatbot using two distinct technologies. The chatbot is made up of three main modules: the chat module, the campus information module, and the virtual teaching assistant module. The Encoder-Decoder framework is used in the chat module, while the campus information module and virtual teaching assistant module prioritize accuracy and employ a retrieval mode. Through the integration of these two modules, employing different technical approaches, the overall user experience for students is significantly enhanced.
A Revised Spatial CNN Steganalysis Scheme Based on Yedroudj-Net

Steganography hides secret data into a cover image so that it cannot be detected by unintended recipients, whereas steganalysis attempts to discover hidden data in suspected images. In recent years, much progress has been made in developing steganalysis because of deep learning. The performance of steganalysis has been improved using the Convolution Neural Network (CNN). Yedroudj et al. proposed the Yedroudj-Net CNN that composed of 30 pre-processing filters block, 5 convolutional blocks, and a fully connected block. Yedroudj-Net is an efficient CNN and achieves performance improvement. In this paper, we proposed a revision method which selects 10 out of the 30 filters in the pre-processing block. The proposed method not only achieves close performance, but also saves half of the training time. The performance is evaluated against WOW and S-UNIWARD embedding algorithms.
Design and Application of Intelligent Language Translation Software

At present, most of the English translation software on the market has some problems and cannot meet people’s translation needs. Based on this, this paper designs an intelligent English translation software. The software can help users realize Chinese-English translation and provide English-Chinese translation and other functions to meet the user’s needs for translation in various languages. This paper elaborates on the design concept and functional application of the software in detail, and then verifies the accuracy rate and translation time of the intelligent translation software and traditional translation software through experiments. The results show that in terms of accuracy, the intelligent translation software is much higher than the traditional translation software, and the average accuracy rate of many experiments is 82.66%. In terms of translation time, the overall average translation time of intelligent software is shorter than that of traditional software, with a longer time of 8.9s and a shorter time of only 2.3s. The overall performance of intelligent software has been greatly improved.
The Improvements for the Hands Gesture Recognition Based on the Mediapipe

With the development of machine vision, people are more and more inclined to a simple operation method, which can be done by raising a hand. Therefore, gesture command recognition has become one of the hottest directions at the moment. This paper mainly studies whether the simple gesture recognition model based on mediapipe can recognize the same or small error value under the condition of the same gesture but different presentation angles. The author simplifies the skeleton model of the author's own hand by using the Python3.9 and pycharm editing environment to call mediapipe, thus achieving the established goal. Through the research process of this paper, the author found that it is not enough to model and recognize the palm itself. In order to recognize the corresponding gesture command regardless of the position and angle of the camera in the recognition area, it should be added, a ranging function assists in deriving the actual values of those reference parameters.
Event-based Colored-Noise Kalman Filtering for Improved Resource Effiency

In modern automated systems, the number of agents and sensors is rapidly increasing and with them the energy consumption and the burden on communications networks. One way to increase the energy and spectral efficiency in sensor networks is to replace periodic transmissions between nodes with event-based transmissions and to use matching estimation techniques at the receiving nodes. Since multi-sensor systems and smart event-trigger designs often lead to correlations between measurements due to correlated process and measurement noise, estimators that can handle such correlations are required to guarantee good performance. In this paper, an event-based colored-noise Kalman filter was developed and specifically designed for an finite impulse response-based stochastic trigger. Nevertheless, the concept is suitable for a wide class of correlated input data.
Addressing Weight Competition in Long Short-Term Memory (LSTM) Networks for Improving Temperature Prediction

This conference paper presents a study on the application of Long Short-Term Memory (LSTM) networks to predict temperature changes in the upcoming week. During our research, we encountered the issue of weight competition within the LSTM architecture, which can lead to suboptimal predictions. To overcome this challenge, three novel methods including Independent Time Series (ITS) Model, Arithmetic Segmented Time Series (ASTS) Model and Arithmetic Segmented Rolling Time Series (ASRTS) Model, respectively, were used to address weight competition and enhance the accuracy of temperature change forecasts. The effectiveness of these methods were evaluated through extensive experiments and compared against standard LSTM models. The results demonstrate the significance of mitigating weight competition to achieve more reliable and accurate temperature predictions.
Research on Multi-Objective Optimization of Low Carbon Environment and Key Technologies of Data Security Based on Blockchain

China strives to reach the peak of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. This article explores the future transformation of China’s energy consumption structure to promote the sustainable development of China’s economy, energy, and environment under multi-objective constraints. In the context of carbon peaking and carbon neutrality, energy consumption in the production process of enterprises is an important carbon emission channel. Measuring the energy consumption of enterprises and ensuring the safety of measurement data is one of the important work contents in low-carbon environmental management. How to ensure the energy consumption data security is an important issue. This article designs a data sharing system based on blockchain that can achieve data sharing while ensuring data security.
Information Management System for Online English Learning Platform Based on Computer Technology

With the rapid development of Internet technology, E-learning has become more and more popular. Moreover, due to the impact of the 2019 epidemic, E-learning has been carried out all over the world. For example, Tencent Conference, Learning Link, Nailing and other software can all carry out E-learning. English is crucial not only in the college entrance examination but also in university, and learning English is often limited by time and geography. People may encounter problems such as forgetting their progress. To address these issues, this article used computer technology and MySQL database to build an online English learning platform based on B/S structure. This system had two modules in total, namely the user module and the administrator module. Users achieved functions such as memorizing vocabulary, listening exercises, reading books and video teaching, and activating VIP. The administrator mainly managed all information in the backend (including user information, audio information, video information, and book information). The test results showed that this system could effectively solve the geographical and time constraints on people’s learning, and could fully utilize online educational resources. Saving learning progress in the system was not easy to forget and could effectively improve people’s learning efficiency.
Debiasing Recommender Systems: Applying DANCER to Neural Collaborative Filtering Models

In recent years, the demand for improving the performance of recommender systems (RSs) has become increasingly important due to the exponential growth of big data. Many prediction models have been developed and further optimized, but improvements based on several traditional models are still needed because dynamic user preferences and selection bias are seldom considered. In this paper, we use a semi-synthetic dataset based on MovieLens-Latest-small and apply a deep-learning model — Neural Collaborative Filtering (NCF) — to the DANCER debiasing method. We then evaluate the performance of NCF models with different propensities and neural architecture. Although preliminary experimental results don’t exceed those obtained by the DANCER-TMF model, we focus on the sensitivity analysis of NCF and provide guidance for further tuning to investigate how well NCF pairs with DANCER.
The periodic product recommendation system based on deep reinforcement learning and the multi-objective framework

The primary objective of the recommendation system is to suggest suitable products to users. The need for a personalized recommendation system has become essential with the continuous growth in the number of users and the increasing diversity of products. In this paper, we proposed a novel approach that combines a reinforcement learning algorithm with user clustering to address the recommendation challenge for periodic products. Additionally, we extend our framework to accommodate multi-objective scenarios. Our contributions encompass the development of meaningful representation vectors for actions and states, along with the definition of reward vector aligning with users’ long-term preferences and optimizing profits. We apply our proposed method to the problem of recommending mobile packages in Vietnam. Notably, our approach yields favorable recommendation results and exhibits superior performance compared to alternative methods.
The Effects of Virtual Escape Cooperative Learning into Fifth Grade Mathematics on Learning Motivation and Mindset: Taking the Factors and Multiples Unit as An Example

Prior research in the field of mindset has predominantly focused on growth mindset interventions, often overlooking the impact of teaching styles on mindset. As an experiential approach, cooperative learning allows students to surpass the prescribed curriculum by exchanging ideas and leveraging one another's strengths. By integrating cooperative learning into the framework of an escape room game, students' motivation and effectiveness can be enhanced. This study aims to foster a growth mindset among students by implementing a cooperative learning model utilizing virtual escape rooms. A Factors and Multiples course was chosen to validate the proposed approach. The experimental group (n=76) received instruction via virtual escape room cooperative learning, while the control group (n=59) followed traditional instructional methods. Both groups achieved similar learning outcomes; however, qualitative interview data indicated the experimental group had a preference for the instructional material used with them. Furthermore, students in the experimental group were inclined towards a growth mindset, as evidenced by their increased willingness to tackle challenges and their reliance on peers for problem-solving. Regression analysis revealed that pre-course mindset significantly influenced post-course performance, suggesting the potential for sustained improvements resulting from the intervention.
Design and Implementation of Image Recognition System Based on AI Intelligent Video Technology

Based on the research of AI (Artificial Intelligence) technology and image recognition technology, this article designed a monitoring and recognition system around the principle of universality. Image data collection is achieved by using external devices such as scanners to digitize and store data; Image preprocessing is the enhancement processing of scanned images to achieve a certain degree of visual change to meet certain requirements. The main work includes: image gray correction (histogram equalization, linear and nonlinear transformation of histogram), image noise reduction, and image tilt correction; the image segmentation and recognition part can achieve dynamic cutting of images, and extract images of specific regions, thus analyzing and processing them to obtain accurate information. In image recognition rate monitoring, the recognition rate of personnel leaving the monitoring range without permission was 94%. The system used in this article met the predetermined requirements of the design.
Optimal Design of Container in Submarine Data Center Based on Heat Conduction and TOPSIS Index System

This paper mainly studies the heat dissipation process of the container in the submarine data center, calculates the number of servers by establishing a one-dimensional heat conduction equation, and establishes an optimization model to optimize the shell structure and material requirements of the container. Firstly, based on the Fourier heat conduction equation and Newton’s cooling law, a one-dimensional heat conduction equation is established. After setting the initial value and constraint conditions, the finite interpolation method is used to solve the equation group, and the distribution function in the space-time dimension is obtained, and then the maximum number of servers is obtained according to the working conditions of the server. Secondly, three kinds of common fin structures are selected, and the equations are established using the hydrodynamics control equation and the law of conservation of energy. The three kinds of fin structures are compared, and the heat transfer coefficient is considered. Finally, the corrugated fin is selected as the shell design of the container, and its material is optimized, and it is calculated that the most suitable metal material is maraging 300 steel. Finally, the influence of temperature change and tide change is analyzed, and the variation law of tide and temperature with time is explored by establishing a time series diagram, and the influence of tide and temperature on container heat dissipation is analyzed. It is concluded that tide and temperature will fluctuate with time, which should be predicted in advance to prevent server damage.
Optical Flow Revisited: how good is dense deep learning based optical flow?

Accurate localization is a part of most autonomous systems. GNSS is today the go to solution for localization but is unreliable due to jamming and is not available indoors. Inertial navigation aided by visual measurements, e.g., optical flow, offers an alternative. Traditional feature-based optical flow is limited to scenes with good features, current development of deep neural network derived dense optical flow is an interesting alternative. This paper proposes a method to evaluate the result of dense optical flow on real image sequences using traditional feature-based optical flow and uses this to compare six different dense optical flow methods. The results of the dense methods are promising, and no clear winner amongst the methods can be determined. The results are discussed in the context of how they can be used to support localization.
Research on Mathematical Modeling of Financial Statistics Based on Computer Big Data and Cloud Computing

The development of financial investment industry is conducive to increasing the income level of residents and fostering new economic growth points. Based on this research background, the article uses mathematical statistics, operations research and other theories and methods and computer mathematical experiment technology to establish an optimization model of financial investment income and risk, and uses Maple software to obtain the optimal solution of the model under different conditions. For any investment amount, this model can be used to find the asset allocation plan when the maximum expected return is obtained. The research results show that, in order to obtain higher expected returns under the conditions of a market economy, funds must be invested in several financial assets with different returns and risks instead of risk-free bank deposits in the same period. This will provide a certain theoretical basis and feasible investment decision-making plan for investors to choose investment plans.
Computer-aided Translation in the Context of Artificial Intelligence

With the rapid development of modern science and technology, especially computer technology, artificial intelligence and many traditional scientific domains have gradually realized integration. Computer-aided translation technology will greatly improve the translation efficiency and reduce the cost of translation, so it is gradually favored by all walks of life. Based on this, the paper reviews the development trend of computer-aided translation, introduces the process of computer-aided translation in detail, and summarizes its advantages and disadvantages aiming to provide some references for the study of computer-aided translation. In addition, the study of computer-aided translation in the Context of artificial intelligence also has a certain enlightening effect on interdisciplinary research.
Single-Frame Radar Odometry Incorporating Bearing Uncertainty

RADAR sensors come with the benefit that velocity information about moving objects in the environment is directly available from the sensor readings. Hence, RADAR sensors enable single-frame odometry, where the velocity information contained in a single RADAR scan is utilized to determine the sensor’s own velocity. Existing approaches for single-frame RADAR odometry rely on approximations which ignore the bearing uncertainty of a RADAR detection. For this reason, we propose a modification of the common measurement equation for RADAR odometry that incorporates the bearing uncertainties systematically. We investigate the benefits of this approach and how they can be utilized. An evaluation based on simulated and real data from a short-range RADAR is conducted.
Research on PHOTOSHOP Automatic Image Retouching System Based on Computer-Aided Post-Photography

As a documentary method, photography can only play the role of recording and cannot exclude the flaws in the picture. As a means of remedial and beautification, photography satisfies the pursuit of human beings for video images. As users have higher and higher requirements for image images, retouching has gradually become a necessary technology for photography. From the beginning, this technology could only play a decorative role. Today, this technology has achieved a specific position in various fields and has developed well. The meaning behind this technology has also undergone qualitative changes. This paper starts with the automatic image retouching system in the later stage of photography and studies PHOTOSHOP.
Research on Air Passenger Service Data Standards and Data Model

The work summaries the standardizations of air passenger service data. It systematically analyzes the research progress and standard development in the fields of service quality, data interaction, data security, platform technology, data model. Furthermore, it puts forward standard planning suggestions in the field of passenger service. This work assists in the construction of passenger service data resource system and efficient data interconnection.
Improved model for image tampering monitoring based on fast-RCNN

Image tampering detection is a method to detect if an image has been edited or moved. The most common tampering methods are copy, paste and splicing. The current techniques are divided into two main categories: traditional feature extraction-based tampering detection technology and convolutional neural network-based tampering detection technology. In this paper, we train images based on a dual-stream faster R-CNN network to detect the operation images given end-to-end in the tempered area. We replace VGG16 in the convolutional layer model with VGG19, smaller convolutional kernels, smaller pooling kernels and deeper layers are used to make wider feature mapping, learn the appropriate features from a large group of samples more efficiently. And in that case, we avoid unnecessary complex feature extraction process. We also demonstrate our approach through a set of experiments, and the experimental results have shown the effectiveness of using VGG19 instead of VGG 16.
Intrinsic Plasticity Deep Echo State Networks for Industrial Soft Sensor Modeling

In industrial process, the acquisition of crucial quality variables is of great significance for industrial process control. However, due to problems such as harsh environments and equipment failures, obtaining crucial variables in a timely manner poses a significant challenge. In this paper, a soft sensor modeling method based on the intrinsic plasticity deep echo state network(IPDESN) is proposed. A hierarchical dynamic reservoirs structure is used to obtain the high-dimensional feature representation of the inputs, and the intrinsic plasticity (IP) mechanism is used to maximize the information transmitted by the reservoir neurons adaptively. The nonlinear support vector regression(NLSVR) is introduced for output training. The proposed method is applied to estimate the acid gas including hydrogen sulfide(H2S) and sulfur dioxide(SO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) concentration in a sulfur processing unit(SRU) to verify its performance. In comparison to other soft sensor methods, the experimental results show that the proposed IPDESN-NLSVR method demonstrates superior performance, achieving significantly higher modeling accuracy in industrial process.
Attack Tree Analysis for Adversarial Evasion Attacks

Recently, the evolution of deep learning has promoted the application of machine learning (ML) to various systems. However, there are ML systems, such as autonomous vehicles, that cause critical damage when they misclassify. Conversely, there are ML-specific attacks called adversarial attacks based on the characteristics of ML systems. For example, one type of adversarial attack is an evasion attack, which uses minute perturbations called "adversarial examples" to intentionally misclassify classifiers. Therefore, it is necessary to analyze the risk of ML-specific attacks in introducing ML base systems. Unfortunately, there are few methods to analyze evasion attacks. In this study, we propose a quantitative evaluation method for analyzing the risk of evasion attacks using attack trees. The proposed method consists of the extension of the conventional attack tree to analyze evasion attacks and the systematic construction method of the extension. Finally, we conducted experiments on three ML image recognition systems to demonstrate the versatility and effectiveness of our proposed method.