Reviewerly/exHarmony huggingface dataset:
IRDP Ride System: A Privacy Preservation System for Online Ride-Hailing

Online ride-hailing and navigation (ORHN) is a significant application in location-based services (LBSs). With this application, users can get convenient services in daily travel. However, during the process of ORHN, the user has to confront the violation of personal privacy (such as the initial point, the destination, and the moving path) from the taxi driver or the ride-hailing sharer. Therefore, in order to cope with the problem of privacy violation, in this article, an initial, route, and destination preservation ride system (short for IRDP Ride system) is proposed. In the IRDP Ride system, the initial point, the destination, and the route can be preserved during the process of ORHN, no matter whether the adversary (such as the un-trusted driver or sharer) utilizes the analysis method in the same car (such as the range attack or the segment direction attack). In this article, before elaborating on the IRDP Ride system, two types of methods used for analyzing the initial point and destination were given. Then based on the characteristics of Voronoi and the navigation deviation, three algorithms used in this system for providing privacy preservation service are proposed. In the last part of this article, the IRDP Ride system’s security and stability are discussed from the theoretical analysis. At the same time, this system is also verified with several groups of experiments in the real environment to determine the optimization parameters, and then this system is also compared with other similar systems to demonstrate its superiority.
Evaluation method of substation equipment health status based on data unified semantic representation

Aiming at the problem of maintenance data quality of substation equipment in the power grid industry, an evaluation method of substation equipment health status based on unified semantic representation was proposed. Firstly, the current health data of substation equipment and historical data of integrated equipment archives are analyzed. Multi-source heterogeneous substation equipment data is processed by data governance and structured equipment health record database are constructed. Secondly, the selection and construction of data features are designed by observing the data scale of archives. Then, a unified semantic distributed representation model is constructed, and the word bag vector compressor is used to reconstruct the structured text data of the station into a standard state vector. Finally, a state evaluation method based on neural network is proposed to evaluate the state vector. Experiment result shows that the method proposed in this paper is feasible in the health status evaluation of substation equipment, and the accuracy rate is up to 80%.
Multi-source Data Hiding in Neural Networks

This paper proposes a multi-source data hiding scheme for neural networks, in which multiple senders can simultaneously transmit different secret data to a receiver using the same neural network. In our scheme, multiple senders execute data embedding in the overlapping position of a network, so that the existence of other senders can be concealed. Each sender uses a unique embedding key to scramble the parameters for embedding, preventing an attacker or other senders from pretending him. In addition, data embedding is achieved during the training process of the neural network instead of modifying the neural network after training. As a result, the operation of data embedding has a tiny impact on the original neural network. On the receiver side, the corresponding embedding key is used to extract the secret data, while additional decoding networks are unnecessary. Experiments verified the effectiveness and security of our scheme, including embedding capacity and undetectability.
Dual-Resolution Local Attention Unfolding Network for Optical Remote Sensing Image Super-Resolution

Single image super-resolution technology based on deep learning is widely applied in remote sensing. In recent years, the deep unfolding super-resolution strategy has been proposed, which combines the neural networks with traditional optimization-based algorithms, making the neural networks interpretable and achieving high performance. However, the typical deep unfolding algorithms usually treat different kinds of blurring kernels in the same way, so the algorithms cannot take advantage of the properties of blurring kernels, limiting the algorithm’s performance. To design a super-resolution network that can fully use the properties of Gaussian blurring kernels, a dual-resolution local attention unfolding network (DLANet) is proposed. Based on the Gaussian blurring functions, a low-resolution (LR) space branch is designed to supplement the high-resolution (HR) space branch. Specifically, for Gaussian blurring kernels, the closer the pixel is to the center, the greater the weight is. It means that the pixel points retained after downsampling will contain more information about the original corresponding pixel points, and it could be easier to estimate their original pixel values. So we design two branches. The HR branch completes the estimation of the whole image, and the LR branch only estimates the points retained after downsampling. To better complete the feature fusion of the two branches, we propose a row-column decoupling local attention module. This module can retain more information when fuse features and the row-column decoupling strategy can reduce computational complexity. Comprehensive experiments demonstrate the superiority of our method over the current state-of-the-art on remote sensing datasets.
Student Workload Computational Optimization: A Case of Business Administration Program

When implementing a system in which students can freely choose to study, it is very important for a New Normal student to correctly calculate the time or workload to spend on learning. One way to improve the management of higher education is to manage the learning process based on optimizion of calculation time of a student on course-related activities. Curricula with too much content and high emphasis on each content lead to high student workload. When the workload is too much, the student to get the grade performs the tasks with a lot of memorization without understanding the meaning, copying without thinking, so they learn passively. The property-determined workload is one of the important factors affecting student learning, so it is necessary to optimize calculation student workload.
A Hybrid TDNN-HMM Automatic Speech Recognizer for Filipino Children's Speech

Previous studies presented in the literature in the recent years have shown the feasibility of developing an automatic speech recognition (ASR) system for Filipino-speaking children. However, most of these studies are solely based on the Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM). In this paper, we present the development of a hybrid ASR system using both HMM and Time Delay Neural Network (TDNN). The Filipino Children's Speech Corpus (FCSC), which is purely composed of read speech, was used to train and test all the models. We performed several sets of experiments on various phoneme sets, various numbers of HMM states, and various enhanced models that employed vocal tract length normalization (VTLN), linear discriminant analysis (LDA), and speaker adaptive training (SAT). Our experiments show that a basic TDNN-HMM model could consistently outperform an HMM-GMM model regardless of how many HMM states are present. We also present that VTLN slightly enhances the performance of the model. The best performing model is the 4-state TDNN-HMM hybrid that obtained the lowest word error rate (WER) of 0.97%.
Attacks and Security Schemes in Cloud Computing: A Survey

Cloud computing provides the virtual environment for users to access the hardware resources and services. The resources such as storage, applications, services, networks and servers that quickly prerequisite and remain unhindered with insignificant management effort. Security is one of the major factors in cloud computing, and many users are paid for on-demand cloud resources and services. Some of the users are not authorized to access, misappropriate or change data that are called as attackers. The cloud is committed to preserving the integrity and dependability of the data, it contains by the cloud service provider. The attackers are degrading the performance of services as well as make the legitimate users unable to receive response on time. This paper presents an overview on cloud computing, attacks on cloud and their possible security solutions with merits and demerits. There are many security schemes proposed previously, but each of them had some constrained related to configuration, computing and storage. The researchers are continuously giving the contribution in the field of cloud security to improve communication and the performance of the network. It is very typical for the security scheme to identify the normal or a malicious user on the network. Hence, there is demand of strong security schemes that can easily identify the malicious users as well as can handle the adverse effect of attackers in cloud.
Hierarchical Information Fusion Graph Neural Networks for Chinese Implicit Rhetorical Questions Recognition

The rhetorical question is a commonly used rhetorical technique in modern Chinese. It can be divided into explicit rhetorical questions and implicit rhetorical questions according to whether it contains rhetorical cues. The implicit rhetorical questions express more emotion and are more complex in form, which has caught the attention of researchers. Most previous works relied too much on manually designed features or massive labeled data, failing to address the integration of multi-level information in sentences. In this paper, we propose a novel implicit rhetorical questions recognition framework named Hierarchical Information Fusion Graph Neural Networks (HIFGN). In particular, in order to improve the robustness of the encoder, we design a contrastive adversarial representation learning approach to map samples into the representation space. Moreover, different subgraphs of the heterogeneous graph are constructed to help the model extract profound sentence information from diverse perspectives. The experimental results on the Chinese rhetorical questions corpus show that the proposed HIFGN model achieves state-of-the-art performance on the task of implicit rhetorical questions recognition.
Construction of a Gateway Boundary Security Protection Platform Based on the Internet of Things and Cloud Computing

In view of the problems of long warning time and poor protection effect of traditional gateway boundary security protection platform, a gateway boundary security protection platform based on Internet of things and cloud computing is designed. Net FPGA chip is used for the verification and development of network communication equipment, connecting the ATA serial port connection line port of multiple boards. Combined with the register host computer, the read-write operation of the registers inside each module in the hardware is completed through PCI bus, and the hardware design of the gateway boundary security protection platform is completed. Establish the gateway border security protection module and complete the software design of the gateway border security protection platform. Based on the Internet of things and cloud computing technology, match the network security link, so as to realize the security protection of the network boundary. The experimental results show that the security protection effect of the platform constructed in this paper is better, and can effectively shorten the security early warning time.
AIRE 2022: 9th International Workshop on Artificial Intelligence and Requirements Engineering

RE researchers have employed AI techniques to tackle different notions of requirements quality, have applied the techniques to different case studies and domains, and have used different metrics to assess the performance of their techniques. Given the pervasiveness of AI-based systems in our daily life, recent years have also seen an increasing need for RE techniques to support sound and structured development of AI system, with particular interest in explainability of system behaviour. The primary purpose of the AIRE workshop is to explore synergies between AI and RE in order to identify complex RE problems that could benefit from the application of AI techniques and the other way round, thus addressing RE for AI challenges. The edition of the workshop in 2022 received 6 submissions, which were independently reviewed by at least three program committee members. In the end, 5 papers were accepted. All the conflicts of interest were treated seriously and independently. The workshop takes place virtually on August 16, 2022. We hope that you enjoy the AIRE’22 workshop and its proceedings. We consider that in the days when AI is gaining prominence in our daily lives, the RE community cannot neglect the benefit that AI techniques can deliver to the practice of requirements engineering. The workshop will feature also two keynotes, from Fabiano Dalpiaz, from the University of Utrecht, the Netherlands, on Requirements Conversations: A New Frontier in AI-for-RE, and from Jennifer Horkoff, on Requirements Engineering for Machine Learning: Non-functional Requirements as Core Functions. We look forward to seeing you all at this workshop and the future editions. We are very grateful to the Program Committee members and authors of the submissions for their hard work and dedication in putting together this program. We would like to thank you all for your participation in AIRE’22.
Design of Remote Monitoring System for Ski Resorts Based on CPS

In recent years, ski resorts have gradually become the first choice for people to travel and vacation in winter. But the current monitoring system of ski resorts is not mature and perfect. Therefore, this paper proposes a remote real-time monitoring system for ski resorts based on the CPS (information physical system) three-layer (physical layer, cyber layer and decision layer) theoretical model, combined with the B/S (browser/server) mode of computer technology. The physical layer uses embedded devices and sensors to gather information about the ski resorts. In the network layer, a web cloud server is built based on the ASP.NET Core framework to complete the data transmission. The decision-making layer uses the front-end development language to build a web human-computer interaction page for data display. This system can realize remote real-time monitoring of the ski resort meteorological data (temperature and humidity, wind speed and direction) and image information, which is the intelligent information of the ski resort monitoring system. Transformation provides actionable solutions.
On Depth Error from Spherical Camera Calibration within Omnidirectional Stereo Vision

As a depth sensing approach, whilst stereo vision provides a good compromise between accuracy and cost, a key limitation is the limited field of view of the conventional cameras that are used within most stereo configurations. By contrast, the use of spherical cameras within a stereo configuration offers omnidirectional stereo sensing. However, despite the presence of significant image distortion in spherical camera images, only very limited attempts have been made to study and quantify omnidirectional stereo depth accuracy.In this paper we construct such an omnidirectional stereo system that is capable of real-time 360° disparity map reconstruction as the basis for such a study. We first investigate the accuracy of using a standard spherical camera model for calibration combined with a longitude-latitude projection for omnidirectional stereo, and show that the depth error increases significantly as the angle from the camera optical axis approaches the limits of the camera field of view.In contrast, we then consider an alternative calibration approach via the use of perspective undistortion with a conventional pinhole camera model allowing omnidirectional cameras to be mapped to a conventional rectilinear stereo formulation. We find that conversely this proposed approach exhibits improved depth accuracy at large angles from the camera optical axis when compared to omnidirectional stereo depth based on a spherical camera model calibration.
Improving the Robustness of Low-Resource Neural Machine Translation with Adversarial Examples

Weak robustness and noise adaptability are major issues for Low-Resource Neural Machine Translation (NMT) models. That is, once some tiny perturbs are added to the input sentence, the model will produce completely different translation with high confidence. Adversarial example is currently a major tool to improve model robustness and how to generate an adversarial examples that can degrade the performance of the model and ensure semantic consistency is a challenging task. In this paper, we adopt reinforcement learning to generate adversarial example for low-resource NMT. Specifically, utilizing the actor-critic algorithm to modify the source sentence, the discriminator and translation model in the environment are used to determine whether the generated adversarial examples maintain semantic consistency and the overall deterioration of the model. Furthermore, we also install a language model reward to measure the fluency of adversarial examples. Experimental results on low-resource translation tasks show that our method highly aggressive to the model while maintaining semantic constraints greatly. Moreover, the model performance is significantly improved after fine-tuning with adversarial examples.
Entanglement Assisted Bistatic Radars Outperforming Coherent States-based Quantum Radars

We propose an entanglement assisted bistatic radar employing the optical phase conjugation on transmitter side and classical coherent detection on receiver side, which significantly outperforms corresponding classical and coherent states-based quantum radars.
Signal Location Method Based on GRU Regression Model for Sagnac Optical Fiber Perimeter Sensing

A location method based on gated regression unit (GRU) regression model is proposed for optical fiber perimeter sensing systems. The positions of three types of disturbance sensing signals are predicted. The simulation results show that this method has the advantages of simple data processing, high location accuracy and less influence on the location performance by the disturbance type.
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts

Traditional model-based reinforcement learning (RL) methods generate forward rollout traces using the learnt dynamics model to reduce interactions with the real environment. The recent model-based RL method considers the way to learn a backward model that specifies the conditional probability of the previous state given the previous action and the current state to additionally generate backward rollout trajectories. However, in this type of model-based method, the samples derived from backward rollouts and those from forward rollouts are simply aggregated together to optimize the policy via the model-free RL algorithm, which may decrease both the sample efficiency and the convergence rate. This is because such an approach ignores the fact that backward rollout traces are often generated starting from some high-value states and are certainly more instructive for the agent to improve the behavior. In this paper, we propose the backward imitation and forward reinforcement learning (BIFRL) framework where the agent treats backward rollout traces as expert demonstrations for the imitation of excellent behaviors, and then collects forward rollout transitions for policy reinforcement. Consequently, BIFRL empowers the agent to both reach to and explore from high-value states in a more efficient manner, and further reduces the real interactions, making it potentially more suitable for real-robot learning. Moreover, a value-regularized generative adversarial network is introduced to augment the valuable states which are infrequently received by the agent. Theoretically, we provide the condition where BIFRL is superior to the baseline methods. Experimentally, we demonstrate that BIFRL acquires the better sample efficiency and produces the competitive asymptotic performance on various MuJoCo locomotion tasks compared against state-of-the-art model-based methods.
The Transformation Evaluation System of Scientific Study Achievements in Colleges based on Entropy Method

The high-quality development of the national economy requires attention to the transformation of technological achievements. The transformation of scientific study achievements in colleges is an important part of the transformation of technological achievements. This paper takes the scientific study achievements of colleges in Liaoning Province as the research object, and evaluates the transformation ability of scientific study achievements of colleges in Liaoning Province according to the established evaluation index system of scientific study achievements in colleges. Through the evaluation, this paper analyzes the trend of input and output changes in the transformation of scientific study achievements in colleges in Liaoning Province, so as to find out the deficiencies in the transformation of scientific study achievements in colleges in Liaoning Province, and find out the reasons for the problems. Based on the current situation, this paper puts forward suggestions for the transformation and development of study achievements in colleges in Liaoning Province.
How to Generate the Examination Paper: A Novel Hierarchical Backtracking-Based Algorithm

The random function method, genetic algorithm, and backtracking search algorithmare widely used to generate paper. However, typical paper-generating algorithms are inefficient to the point that they cannot ensure generate high-quality paper . In this study, we focus on this problem and devise a novel hierarchical backtracking paper-generational gorithm (HBPA). The HBPA optimizes the traditional paper-generating algorithm by using a hierarchical structure for pruning to solve the problems of ineffectiveness and inefficiency in the paper generation process.In addition, to measure the efficiency of the algorithm, an algorithm analytical model is established. Evaluation results reveal the effectiveness of the algorithm’s execution, whereby the quality of paper generation improves significantly.In addition, the number of test questions is increased.