Reviewerly/exHarmony huggingface dataset:
An Improved Calibration Method for Stereo Fisheye Vision

The fisheye stereo vision system has been widely studied as primary vision sensors in the field of robot vision because of its wide view of scene. However, currently, serious image distortion has delayed its further development. To remedy this, the paper focuses on the calibration and distortion correction for stereo fisheye system. And then an improved, specialized and systematic calibration method named Stereo Fisheye Calibration Method (SFCM) will be proposed to calibrate the parameters and correct the image distortion of the fisheye stereo vision. The SFCM is introduced by the following two aspects. Firstly, a precise mathematical model based on the law of fisheye lens imaging is established, which assumes that the imaging function can be described by a general polynomial approximation. Secondly, a simple five-point calibration algorithm was implemented to find the possible solutions for relative camera pose between two calibrated views. And then a nonlinear refinement based on the maximum likelihood for internal and external parameters was carried out with Levenberg-Marquarat (LM) algorithm. Finally, using real data captured by equipment, we performed experiments covering all the necessary stages to obtain a high-performance stereo fisheye vision system. The correctness and effectiveness of the proposed method are demonstrated with the statistical analyses of the experimental results.
Learning Strategy Training Based on Online Autonomous English Learning Platform

According to previous research, autonomous learning strategy and autonomous learning competence are positively related. In order to improve the students’ autonomous learning competence and solve the problems they encounter in the process of applying the Online Autonomous English Learning Platform, this research sorts out 19 strategies through questionnaire and conducts a training program to the experimental group for a semester. The findings indicate that the experimental group's strategy awareness as well as speaking and listening performance have significantly enhanced compared with the control group, which confirms the positive influence of learning strategy training based on the Online Autonomous English Learning Platform.
CycHash: A Novel Deep Learning to Hash Method for Image Retrieval

We propose a deep learning to hash method named CycHash to improve image retrieval. Its network is an encoder-decoder architecture. Cooperated with full-connected layers, an encoder is used to transform image to hash code. The decoder network is used to restore high-dimensional data from encoded feature maps. We define a restoration loss between the restored data from decoder network and inputted data of decoder network. The restoration loss and central similarity loss are summarized to guide the learning of hash function. Experiments of image retrieval demonstrate the effectiveness of our CycHash method and CycHash outperforms state-of-the-arts.
On the Security of Privacy-Preserving Cross-Domain Authentication and Data Deduplication Protocol

Recently, Yang et al. proposed a privacy-preserving cross-domain data deduplication scheme in cloud (IEEE Transactions on Big Data, DOI 10.1109/TBDATA.2017.2721444) to achieve both privacy-preserving and data availability and to resist brute-force attacks. Their scheme was claimed to resist the brute-force attack from Cloud Server Provider. Unfortunately, in this work, we analyze the security of Yang et al. scheme, analysis indicates that their scheme can be brute-force attack, therefore, their scheme is insecure. After analyzing the reasons to produce such an attack, we suggest an improved method to overcome such an attack.
Outlier Detection On Graduation Data Of Darussalam Gontor University Using One-Class Support Vector Machine

Outlier detection is an important field of study because it is able to detect abnormal data distribution from a set of data. In the student graduation data, there are students with high semester GPA but do not graduate on time but students with low semester GPA can graduate on time. This study aims to detect outlier values ​​in student graduation data for the 2020-2021 class. Factors (attributes) used in this study are Student Academic Support Credit Scores (AKPAM) and Social Studies from semester one to semester six. The dataset used is 1204 graduates. The outlier detection method used is One-Class Support Vector Machine (SVM). One-class SVM is a derivative of SVM method that detects outliers based on data outside the specified class. The results of outlier detection using One-Class SVM method with three types of kernels produce the following reference values: kernel 'rbf' n by 91.4%, kernel 'linear' by 90% and kernel 'poly' by 90%. After normalization using MinMaxScaler the reference value increased by 2% in each kernel. The results of testing the One-Class SVM method get an average 90.3%, thus it can be concluded that the One-Class SVM method is feasible to be used as an outlier detection method.
Efficient Off-policy Adversarial Imitation Learning with Imperfect Demonstrations

Generative Adversarial Imitation learning (GAIL) enables the agent to learn a desired policy from the demonstrations provided by human beings. For complex continuous control tasks, such as robotic tasks, demonstrations are usually be obtained from multiple demonstrators to save time. However, the differences in the abilities of multiple demonstrators lead to the drift of demonstration distribution, which results in learning a suboptimal policy in the adversarial training process. In this paper, we proposes a new imitation learning (IL) algorithm, IM-GAIL, to enable the agent to learn effectively from imperfect demonstrations of multiple demonstrators. The proposed method rebuild the objective function in the adversarial training, making the policy match the optimal policy. Meanwhile, to improve the sample efficiency, off-policy training framework is adopted and a curious experience replay mechanism is designed for the training. The experiments are conducted on robotic simulation tasks, and results show that our method can efficiently learn from the given demonstrations and it achieves better performance than other state-of-the-art methods.
B-SYSTEMS AND C-SYSTEMS ARE EQUIVALENT

Abstract C-systems were defined by Cartmell as models of generalized algebraic theories. B-systems were defined by Voevodsky in his quest to formulate and prove an initiality conjecture for type theories. They play a crucial role in Voevodsky’s construction of a syntactic C-system from a term monad. In this work, we construct an equivalence between the category of C-systems and the category of B-systems, thus proving a conjecture by Voevodsky.
POI recommendation model based on Attention-based Gated Recurrent Unit Network

Point-of-Interest (POI) recommendation is one of the main functions in Location-based Social Networks (LBSNs). At present, some POI recommendation models couldn’t effectively capture users' complex information and their recommendation performances are not good enough. Motivated by this, we propose a kind of POI recommendation model which is based on Attention-based Gated Recurrent Unit Network (Att-GRU). Firstly, we adopt the Gated Recurrent Unit (GRU) network to learn the complex sequential transition patterns from users’ check-in behavior. Secondly, we extract user’s preferences by using the target-guided attention mechanism. Finally, we conduct extensive experiments on a real-world LBSN dataset, and the experimental results demonstrate that our proposed Att-GRU based POI recommendation model has better performance than those of other mainstream ones in some evaluation criteria.
Design and FPGA implementation of folded SHA-256 using 4-2 adder compressor

The Secure Hash Algorithm is a cryptographically secure hash function that enables high security in electronic systems. SHA-256 refers to the output length of 256 bits. The folded SHA-256 algorithm is a one-way functional method that is used to represent data in a compact manner. It is utilized in a variety of authentication techniques including digital signal creation, password storage, computer vision and database access. SHA is a four step process involving pre-processing, message scheduling, digest calculation and digest update. In the partial product accumulation stage, the architecture of 4-2 adder compressor in    SHA-256 ensures an improvement in computing time by lowering the number of adders. Architectural folding is used for maximum resource sharing between the stages. The delay encountered for the entire system is 2.594 ns and power consumption is 177mW. The proposed design successfully attained the throughput efficiency by reducing the critical path delay.
The auto-monitoring of geo-technical centrifuge operating state based on weighted data fusion

Abstract There are many parameters which could reflect the operating state of geotechnical centrifuge. However, only one parameter is detected generally ; this is insuficient and unsafe for the running of the geotechnical centrifuge. This paper put forward an auto-running sate monitoring method which based on the multi-parameters’ weighted data fusion. The way by multi-sensor acquirring the running state data of the geotechnical centrifuge, then processing the data with weighted data fusion could produce the comprehensive running state parameter, which feed forward to the control system to keep the equipment running in a safe manner. The method in this paper could be implemented automatically and the result for safety monitroing is sufficient, the effect is much more efficient.
Remote sensing analysis of oilfield IoT based on density clustering

In this paper, a density-based neighborhood search clustering method is proposed. A decision graph is obtained through density calculation, this method only needs to select density centers, and clustering results can be achieved automatically without irregular and frequent parameter adjustment. The algorithm is suitable for industrial big data clustering. It not only explains the clustering results vividly, but also increases or decreases the amount of clustering rapidly according to the need. Experimental results show that this method performs well on artificial data sets in contrast to the traditional methods and is successfully applied to a real oilfield engineering case.
RETRACTED: Artificial Neural Network Based shafts surface pressures Analysis

Abstract This paper exhibits a development of an Artificial Neural Network (ANN) as an instrument for investigation of various parameters of a framework. ANN comprises of various layers of straightforward handling components called as neurons. The neuron performs two capacities, to be specific, assortment of sources of info and age of a yield. Utilization of ANN gives diagram of the hypothesis, learning rules, and uses of the most significant neural system models, definitions and style of Computation. The scientific model of system illuminates the idea of sources of info, loads, adding capacity, actuation work and yields. At that point ANN chooses the sort of learning for modification of loads with change in parameters. At long last the examination of a framework is finished by ANN execution and ANN preparing and forecast quality.
Oral Practice Language Learning Companion Implementation on Voice Assistant Platform

Oral practice for language learning usually needs to find partners or teachers to accompany, no matter how patient they are, it’s not easy to stay for a long-time conversation practice. This study tries to implement an energetic oral practice language learning companion on voice assistant platform, it can always be with you to practice English conversation. It uses natural language processing and voice recognition technology to tell if you are repeating the same sentence, help students can practice conversation by themselves.
Evaluation and Prioritization of Information Security Controls of ISO/IEC 27002:2013 for SMEs Through Fuzzy TOPSIS

Managing a large number of Information Security controls with slight impact may increase the extra effort and time in the shape of implementation and mitigation of risk. Therefore, Information Security Controls need to be prioritized. The main goals of this paper are to an in-depth study of ISO/IEC 27002:2013 that consists of 114 information security controls with 35 security domains and to rank/prioritize these controls. In this study, a questioner was designed and distributed it among Information Security Experts having experience of Information Security deployment in Small Medium Enterprises (SMEs). The study initially studied different methodologies for prioritization of Information Security Controls, developed criteria including effectiveness, implementation time, mitigation time, risk and budgetary constraints to evaluate ISO/IEC 27002:2013 control. The study applies a Fuzzy Technique for Order of Preference by Similarity to Ideal Solution TOPSIS technique to evaluate and rank the information security controls. A fuzzy TOPSIS methodology comprising linguistics data is used to get unclear conditions and, therefore, fuzzy TOPSIS is used as a tool to allow a more precise calculation of inaccurate parameters than old-style methods. We contend that evaluating of ISO/IEC 27002:2013 using fuzzy TOPSIS leads to a great accurate assessment and, therefore, supports an effective selection/ranking/ prioritization of information security controls in SMEs.
A Street View Image Retrieval Method Based on Fusion of Multiple Features

In view of the difficulty in extracting the key area features of the image and the over-complex training in current street view image retrieval methods, this paper proposes a retrieval method that extracts and integrates multiple global features. First, the Convolutional Neural Network is used to extract the features, and then the new multi-scale pooling layer is used to output multiple global features, and the feature loss is reduced by outputting fixed-dimensional features. Finally, the final feature obtained by concatenating multiple global features is used for retrieval. Experimental results show that this method can effectively extract image features, reduce the complexity of training, and improve the accuracy of retrieval.
Evaluation System of Tourism Psychology Teaching Quality Based on Convolutional Neural Network

How to improve the teaching quality (TQ) of tourism psychology in higher education has become the focus of current higher education. Teaching evaluation is a key measure to improve TQ. Therefore, it is particularly important to formulate a scientific and reasonable ES for college classroom TQ. As a new technology, neural network (NN) has been widely used in various evaluation problems by virtue of its non-linear processing, adaptive learning, high fault tolerance and other characteristics. The purpose of this article is to study the ES of tourism psychology TQ based on convolutional NN. This article takes the school's internal talent training quality ES as the research object, and introduces the optimized convolutional NN theory into the college tourism psychology TQ evaluation. The mathematical model is established using the NN model structure, and the convolutional NN algorithm is introduced in detail. The realization of the optimized system is divided into two parts, one is to collect the scoring data of students, teachers and experts through the online ES. The second is to process the collected data through the ES to achieve sample maintenance, optimized NN training and NN evaluation functions to obtain the teacher's final evaluation results. Experimental results show that not only the accuracy of training and prediction is completely within the acceptable range, but the error of the test sample is more than 90% similar to the error of the test sample. It can be seen that the student evaluation model based on NN proposed in this paper is a reasonable and feasible evaluation model.
Towards Energy-Efficient and Real-Time Cloud Computing

In modern cloud computing environments, there is a tremendous growth of data to be stored and managed in data centers. Large-scale data centers demand high utilization of computing and storage resources, which lead to expensive operational cost for energy usage. Evidence shows that consolidating virtual machines (VMs) can conserve energy consumption in clouds through VM migrations. VM-consolidation techniques, however, inevitably induce a burden on performance. To address this issue, we propose a holistic solution - EGRET - to boost energy efficiency of cloud computing platforms by seamlessly integrating the DVFS scheme with the VM-consolidation technique. EGRET dynamically determines the most energy-efficient strategy by issuing a command to either scale CPU frequencies on a VM or marking the VM as underutilized. We conduct extensive experiments to evaluate the performance of EGRET. The experimental results show that EGRET substantially improves the energy efficiency of cloud computing platforms.
School-Level Based Classification of Indonesian Teaching Materials using KNN

The selection of teaching materials should be adjusted to the student’s ability at each level. It is intended that learning can be done well. However, along with the increasing of existing Indonesian language literature, it is possible that there are differences in the contents that are not well targeted. The school should be more selective choosing the teaching materials that are adjusted the students' abilities at all levels. Therefore an automated system is required that can classify teaching material documents based on the school level. The data used for classification is a text story in the teaching materials. One method of document classification is K-Nearest Neighbor. The K-Nearest Neighbor method is chosen because it has high accuracy and respite on which it is independent. From the research results can be seen that the highest accuracy, precision and recall value is 92.5%, 93.2% and 92%. All three results are achieved at k=5, so in this research used k value k=5 because it is more optimal than other k values.
Adaptive Control Method of UAV Intelligent Rudder Based on Hybrid Genetic Algorithm

This paper proposes a control strategy based on fuzzy GA algorithm for Unmanned Aerial Vehicle (UAV) course control. The control process is very complicated because of the strong nonlinearity and randomness of system dynamics. Therefore, this paper applies different control strategies to solve these problems. Different from previous studies, this paper visualizes the effect of UAV course control through track changes. Through the proportional model to achieve the design of the control scheme, the trajectory tracking of UAV is simulated. Then the advantages and disadvantages of different schemes are analyzed. In addition, the feasibility of hybrid genetic control algorithm is verified. This research experiment shows that the combination of intelligent technology and conventional controller is advantageous to deal with a complex system. The traditional control theory system is perfect, but its performance is poor. The limitation of traditional control theory is improved by technology cooperation. GA based on the traditional controller gain scheduling control to make adaptive adjustment.
A Case for Archival Episodic Content as Technical Evaluative Material

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Within an hour-long TV episode, viewers will typically experience somewhere between 600 and 900 edit points. This means that viewers will experience a unique composition featuring a unique combination of spatial phenomena 600 to 900 times. On top of this, these shots must lead into one another, allowing for changes from the start of a shot to the end of the shot and confronting the “temporal contrast” that comes from having edit points at all. During any season of a show’s thousands of shots, production encounters specific technical imaging and perceptual failures and finds ways to work around them to get the image “good enough” for a general audience. However, these images remain valuable even after production is complete, and in fact the archive of ten episodes per season or more represents a treasure trove of naturally occurring perceptual phenomena. This paper presents guidelines and examples for harvesting challenging images from real production and ways in which these images can be analyzed to further a better end-user experience</i> .
Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems

Adversarial attacks, e.g., adversarial perturbations of the input and adversarial samples, pose significant challenges to machine learning and deep learning techniques, including interactive recommendation systems. The latent embedding space of those techniques makes adversarial attacks challenging to detect at an early stage. Recent advance in causality shows that counterfactual can also be considered one of the ways to generate the adversarial samples drawn from different distribution as the training samples. We propose to explore adversarial examples and attack agnostic detection on reinforcement learning (RL)-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our white-box detector trained with one crafting method has the generalization ability over several other crafting methods.
Exploration of Online and Offline Hybrid Teaching Mode based on “Internet +”: “Network Community and Interactive Cultural Creativity” Course as an Example

In the context of the “Internet +” era, more and more schools are beginning to pay attention to online teaching models, and the country has specific requirements for the promotion of online and offline hybrid teaching models. After the outbreak of COVID-19, online teaching has been promoted. The “Internet Community and Interactive Cultural Creativity” course uses digitalization and informatization as the main teaching methods in teaching. A hybrid teaching model combining online teaching using Wisdom Tree platform and offline traditional face-to-face teaching has been established. Through the construction of a teaching situation integrating context, interaction, experience, evaluation and feedback, problem-oriented to improve students' awareness of active learning, cultivate the spirit of innovation, and obtain significant teaching results.