Reviewerly/exHarmony huggingface dataset:
Missing data completion method based on KNN and random forest

Data missing is one of the relatively difficult problems in the process of data mining,because the lack of part of the property value will seriously affect the results of data analysis. Most of the absence of missing value interpolation does not take into account the relationship between missing properties and other attributes in the sample. Aiming at this problem, this paper proposes a missing data based on KNN and random forest completion algorithm (RF-KNN) .Algorithm firstly chosen by KNN model of K value, makes it as building one of the parameters of the random forest. Then according to the characteristics of the random forest attribute, attribute classification prediction model was constructed, the missing attribute value for effective completion. Through model test and evaluation on the public data sets, the results show that compared with the traditional KNN algorithm and random forest, RF-KNN in more than one, this method is applicable to a variety of data missing value in the field of completion,which can effectively improve the efficiency of data mining analysis.
Auto-Tracking Camera System for Remote Learning Using Face Detection and Hand Gesture Recognition Based on Convolutional Neural Network

The purpose of this study is to develop an auto-tracking camera used for remote learning with a direct recording classroom video type, which aims to focus on reducing the model's size so that the model can run on devices that have low computational capabilities, such as the use of the Central Processing Unit (CPU). This study used face detection FaceBoxes to detect faces so that auto-tracking can be applied. This study also used hand gesture Unified Gesture and Fingertip Detection, which can be used for doing specific commands towards the camera. The idea to implement this multi-modal is to help the lecturers create a seamless transition during the lecturing process thus the lecturers do not need to worry the their movement that could make the camera pointing to the wrong direction due to the static movement. This study conducted experiments on the number of batch sizes, CNN architecture, and optimizer as well as the IoT protocols. While the model inferences are running on CPU, we used Raspberry Pi for controlling the servo motor to follow the face in the frame by defined protocols. The expected result is that an auto-tracking camera can enhance learning more effectively and study a more accurate model and inference time to do face detection and recognize hand gestures in real-time on CPU devices.
Image Super-resolution Reconstruction Based on Multi-layer Parallel Residual Network

The existing methods can improve the overall visual effect of the image, but the reconstructed image is still lack of high-frequency information, leading to texture blur. To solve these problems, this paper proposes an image super-resolution reconstruction algorithm based on multi-layer parallel convolution and residual network. Taking the multilayer parallel structure as the whole framework, firstly, different convolution combinations are used to enrich the feature information of the parallel structure, and the jump connection is added to further enrich the feature and fuse the output to extract more high-frequency information. Secondly, an adaptive residual network is introduced to supplement information and optimize network performance. Finally, perceptual loss is used to improve the overall quality of the restored image. Experimental results show that the proposed algorithm performs well for the reconstructed image, and the PSNR and structural similarity are significantly improved in the objective evaluation.
Research on Dehazing Algorithm of Single UAV Reconnaissance Image under Different Landforms Based on Retinex

Abstract Aiming at the weakness of the dark channel prior modelin and Retinex algorithm such as slow calculation speed, halo effect, and loss of details in the processing of UAV single image dehazingand, an improved Retinex algorithm is proposed. First, convert the RGB color space to the HIS color space, separate the brightness I, assign values to the three scales of large, medium and small, use Gaussian surround to obtain the three scale brightness images and then weight, and then switch the brightness image to the RGB color space, in order to achieve the information with more details. Finally, the haze images which is distinguished in three different landforms such as city, mountainous area and forestwill be performed simulation experiments in MATLAB, and adjust the image restoration effect by changing the parameters.
Model fit criteria curve behaviour in class enumeration – a diagnostic tool for model (mis)specification in longitudinal mixture modelling

The use of longitudinal finite mixture models (FMMs) to identify latent classes of individuals following similar paths of temporal development is gaining traction in applied research. However, FMM’s users may be unaware of how data features as well as the inappropriate specification of the model’s covariance structure impacts class enumeration. To elucidate this, we investigated model fit-criteria curve behaviour across an array of data conditions and covariance structures. Fit statistic patterns were variable among the fit criteria and across a range of data conditions. This variability was greatly attributable to the level of class separation and the presence/absence of random effects. Our findings support some widely held notions (e.g. BIC outperforms other criteria) while debunking others (adding random effects is not always the solution). Based on the obtained results, we present guidelines on how the behaviour of fit criteria curves can be used as a diagnostic aid during class enumeration.
A rule-based enhancement of a vision guided, collision-free robotic application

This paper presents a rule-based method to solve a collision issue for an industrial robot manipulator. The approach applies a set of rules over the information received from a vision system to consider an extended set of constraints for end-of-arm tooling. Collision-free, finding objects that are safe for gripping or the order of operation are based on a graph dependency mechanism. The interaction among a robot, a vision system, and the developed module is obtained through an IoT system extension. Validated on a real manufacturing system, our method can simplify and improve several robotized applications.
Password Strength Signaling: A Counter-Intuitive Defense Against Password Cracking

We introduce password strength signaling as a potential defense against password cracking. Recent breaches have exposed billions of user passwords to the dangerous threat of offline password cracking attacks. An offline attacker can quickly check millions (or sometimes billions/trillions) of password guesses by comparing a candidate password’s hash value with a stolen hash from a breached authentication server. The attacker is limited only by the resources he is willing to invest. We explore the feasibility of applying ideas from Bayesian Persuasion to password authentication. Our key idea is to have the authentication server store a (noisy) signal about the strength of each user password for an offline attacker to find. Surprisingly, we show that the noise distribution for the signal can often be tuned so that a rational (profit-maximizing) attacker will crack fewer passwords. The signaling scheme exploits the fact that password cracking is not a zero-sum game i.e., it is possible for an attacker to increase their profit in a way that also reduces the number of cracked passwords. Thus, a well-defined signaling strategy will encourage the attacker to reduce his guessing costs by cracking fewer passwords. We use an evolutionary algorithm to compute the optimal signaling scheme for the defender. We evaluate our mechanism on several password datasets and show that it can reduce the total number of cracked passwords by up to \(12\%\) (resp. \(5\%\)) of all users in defending against offline (resp. online) attacks. While the results of our empirical analysis are positive we stress that we view the current solution as a proof-of-concept as there are important societal concerns that would need to be considered before adopting our password strength signaling solution.
Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion

Although recent learning-based calibration methods can predict extrinsic and intrinsic camera parameters from a single image, the accuracy of these methods is degraded in fisheye images. This degradation is caused by mismatching between the actual projection and expected projection. To address this problem, we propose a generic camera model that has the potential to address various types of distortion. Our generic camera model is utilized for learning-based methods through a closed-form numerical calculation of the camera projection. Simultaneously to recover rotation and fisheye distortion, we propose a learning-based calibration method that uses the camera model. Furthermore, we propose a loss function that alleviates the bias of the magnitude of errors for four extrinsic and intrinsic camera parameters. Extensive experiments demonstrated that our proposed method outperformed conventional methods on two large-scale datasets and images captured by off-the-shelf fisheye cameras. Moreover, we are the first researchers to analyze the performance of learning-based methods using various types of projection for off-the-shelf cameras.
HSRec: Hierarchical self-attention incorporating knowledge graph for sequential recommendation

Modeling user’s fine-grained preferences and dynamic preference evolution from their chronological behaviors are challenging and crucial for sequential recommendation. In this paper, we develop a Hierarchical Self-Attention Incorporating Knowledge Graph for Sequential Recommendation (HSRec). HSRec models not only the user’s intrinsic preferences but also the user’s external potential interests to capture the user’s fine-grained preferences. Specifically, the intrinsic interest module and potential interest module are designed to capture these two preferences respectively. In the intrinsic interest module, user’s sequential patterns are characterized from their behaviors via the self-attention mechanism. As for the potential interest module, high-order paths can be generated with the help of the knowledge graph. Therefore, a hierarchical self-attention mechanism is designed to aggregate the semantic information of user interaction from these paths. Specifically, an entity-level self-attention mechanism is applied to capture the sequential patterns contained in the high-order paths while an interaction-level self-attention mechanism is designed to further capture the semantic information from user interactions. Moreover, according to the high-order semantic relevance, HSRec can explore the user’s dynamic preferences at each time, thus describing the user’s dynamic preference evolution. Finally, experiments conducted on three real world datasets demonstrate the state-of-the-art performance of the HSRec.
Organizational and methodological problems of training future teachers in a distance learning format

The article describes the problems (organizational, technical, methodological) faced by university teachers when switching to a distance learning format. On the example of the Mordovian State Pedagogical University named after M. E. Evsevjev, the solution of some problems associated with the organization of distance learning and the peculiarities of building the educational process in new conditions is demonstrated. The platforms that served as the technological basis for launching distance learning at MSPU, in particular LMS Moodle, are described. The examples of preparation of future bachelors of pedagogical education, studying informatics, for professional activity in conditions when training becomes mixed, that is, part of the classes can be conducted in person, and part — in a distance format are given. The methodology for conducting an elective course “Development technology and methodology for conducting elective courses in informatics” using distance technologies are described. The structure of the course is given, highlighting the modules and the content that should be learned during the course. Recommendations for conducting classes in this discipline are given. The conclusion is made about the positive and negative results of distance learning and its further prospects.
Eligibility Analysis of Different Chaotic Systems Derived from Logistic Map for Design of Cryptographic Components

One of the topics that have successful applications in engineering technologies and computer science is chaos theory. The remarkable area among these successful applications has been especially the subject of chaos-based cryptology. Many practical applications have been proposed in a wide spectrum from image encryption algorithms to random number generators, from block encryption algorithms to hash functions based on chaotic systems. Logistics map is one of the chaotic systems that has been the focus of attention of researchers in these applications. Since, Logistic map can be shown as the most widely used chaotic system in chaos-based cryptology studies due to its simple mathematical structure and its characterization as a strong entropy source. However, in some studies, researchers stated that the behavior displayed in relation to the dynamics of the Logistic map may pose a problem for cryptology applications. For this reason, alternative studies have been carried out using different chaotic systems. In this study, it has been investigated which one is more suitable for cryptographic applications for five different derivatives of the Logistic map. In the study, a substitution box generator program has been implemented using the Logistic map and its five different derivatives. The generated outputs have been tested for five basic substitution box design criteria. Analysis results showed that the proposals for maps derived from Logistic map have a more robust structure than many studies in the literature.
Universal Dependencies for Mandarin Chinese

This article presents a Universal Dependency (UD) annotation scheme for Mandarin Chinese, as well as the current UD Chinese HK treebank. Our focus is mainly on parts-of-speech tags and syntactic relations, with a quite large array of phenomena investigated. The main goal is to make transparent the linguistic consideration behind our annotation choices, and show how we articulated these choices with the criteria of Universal Dependencies. This scheme has been developed with reference to two other dependency schemes for this language, i.e. the Chinese Stanford Dependencies (Chang et al., 2009) and the Chinese Dependency Treebank (HIT-SCIR, 2010). We provide mappings between our scheme and the two others. The content of the UD Chinese HK treebank is discussed in relation to the other UD treebanks for Chinese, and the inter-annotator agreement on POS and dependency annotation is reported. Our proposed scheme is motivated by reasoned linguistic analysis, is suitable for cross-linguistic comparison, and produced a high level of agreement between annotators.
Issues, Challenges and Techniques for Resource Provisioning in Computing Environment

Cloud computing is an emerging topic in day-to-day life among users, starting from individuals at home to the organizations. It refers to many different types of services and applications being delivered in the internet. The use of hybrid clouds is a challenging topic in cloud computing. The main challenges in cloud computing environment are resource provisioning, security provisioning and allocating the resources. Maximizing the performance in less amount of time and maximizing data transfer rate with minimum cost are the main aim of resource provisioning. When the Quality of Service (QoS) of the present application is degraded and also the Service level Agreement (SLA) is violated, the workload of the services is supposed to increase rapidly. In this paper, we have conducted a review on various resource provisioning techniques in cloud computing environment.
RTR AR Photo Booth: The Real-Time Rendering Augmented Reality Photo Booth

The use of Photo Booth at several events has become a means of documentation such as selfies and wefies, However, in some applications, the technology has not been utilized properly. This study aims to describe how the application of Augmented Reality technology to be applied to the form of photography services in the form of a photo booth with real-time rendering techniques from Spark AR and the use of the cloud. We propose a creation schema to implement augmented reality in photobooths for marketing purposes in exhibitions or events. The implementation description will include how augmented reality technology is used, development methods, and references on how to use it until it is ready for use. The performance measurement was also carried out using Frame Rate Per Second (FPS) on two different device configurations in several experiments. The results show that the proposed photobooth can run up to more than 60 FPS or at above standard performance.
The effect of digital books based on kvisoft flipbook maker on student learning outcomes

Abstract This study aimed at determining the learning outcomes of students who use digital books based on the kvisoft flipbook maker compared to the students without using digital books based on kvisoft flipbooks. It is because students learning outcomes were low so we use digital books based on kvisoft flipbook maker. This type of research was a quasi-experimental design with Post-test-Only Control Group Design. The data collection method used was the description test method to measure student learning outcomes. The learning outcome data were then analyzed using one-way ANOVA with prerequisite tests, namely the normality test and the homogeneity test. The results showed that there were several differences in the learning outcomes of students who used kvisoft flipbook-based digital books and students without using kvisoft flipbook-based digital books. The results of the analysis showed with a significance of 0.000. If the significance level is set = 0.05, and the significance value is smaller than α so that F is significant. This means that H0 was rejected and H1 was accepted, which states that there are differences in learning outcomes between students who took lessons using Kvisoft Flipbook Maker based digital books compared to the students who took lessons without using Kvisoft Flipbook Maker based digital books. The mean of experiment group was 84.56 and mean of control group was 70.32.
Application of the Covering Method for Trajectories Design for Car-like Robot

The article discusses an algorithm for constructing simple motion maneuvers for a system describing the movement of a car-like robot by the covering method. The flatness of the system is proved, and the construction of the corresponding covering is given. Computational experiments demonstrate the effectiveness of the proposed approach.
RETRACTED ARTICLE: Evaluation of air pollution and business English vocabulary translation in coastal cities based on distributed storage

There are many ways to produce environmental pollutants; not only the local pollutants, but also the pollutants in the surrounding areas, would reach the research city. Therefore, it is very important to study the impact of environmental pollution on the surrounding areas in the research city. In order to control environmental pollution and improve environmental quality, this paper analyzes the environmental pollution of coastal cities and evaluates the translation effect of English business words on the basis of distributed memory. The motivation for learning English has become a world of learning, with good research and business. Therefore, numerous non-English-speaking nations, including Arab nations, are endeavoring to improve their instructive framework in teaching English as a language, and the English jargon improves understudies’ English-speaking capability. It is an important factor. Also, in various countries, there is a lack of motivation for English learners to influence the learning process of students in foreign languages. Understudies in the exploratory gathering altogether improved their inspiration level after a portable learning intercession. Our findings show that apps are useful for underperforming students who are learning English first and improving their confidence. The portable application game was effectively evolved by inspired nearby understudies to learn English as a subsequent language.
Facial Biometric Identification in The Masked Face

The COVID-19 pandemic has brought challenges in the field of biometrics to be able to carry out biometric identification on masked faces. Various studies on biometric identification on masked faces have been carried out and some have obtained promising results. This study aims to obtain a biometric identification method for masked faces using the JAFFE database dataset which has been manipulated into masked face images. The proposed method in this study can produce an accuracy value of 96%, which is promising enough to be applied in the biometric industry. The proposed method uses the face area segmentation technique and extraction of local binary pattern and histogram of oriented gradient features with the Support Vector Machine classification method.
Research on Photovoltaic power prediction technology Based on Machine Learning

Abstract In recent years, the photovoltaic power generation has obvious intermittent, randomness and volatility, and high permeability photovoltaic will have a huge impact on the safety and stability of the grid. The prediction of photovoltaic power generation is to improve the quality of photovoltaic grid, optimize grid scheduling, and ensure the basic technology of power grid safety and stability. In order to improve the prediction accuracy of photovoltaic power generation, this article will comprehensively carding and compare from 3 dimensions: photovoltaic power generation and meteorological factor correlation analysis, similar day selection, prediction method based on machine learning, and summarize the advantages and disadvantages of various methods. Further research has been put forward accordingly.
Creating Meaningful Connections Through COVID-19 Data Manifestation

This paper proposes that the physical manifestation of data can help people to meaningfully connect with the COVID-19 pandemic. Four data objects are presented and analyzed, created by third-year graphic design students at OCAD University. The projects are developed through the design practice of data manifestation; the communication of quantitative information through objects, installations, and sensory experiences. Designed in the autumn of 2020, these projects stop time, offering people an opportunity to reflect on what has happened (and is happening). They illustrate the potential of data manifestation to connect people to the shared experiences of the pandemic, as well as its disproportionate impacts. They further demonstrate the capacity of data manifestation to connect people to incomprehensible magnitudes of loss. Concern by Binhwa Cho explores the fear of infection; Yellow Mask by Lynn Liang takes on anti-East Asian discrimination; A Blinding Truth by Michael Zhang examines COVID-19 case numbers in the United States; Mourning Globe by Minah Lee translates global deaths into a contemplative object.