Reviewerly/exHarmony huggingface dataset:
Boosting up Source Scanner Identification Using Wavelets and Convolutional Neural Networks

In this paper, we present a conceptually innovative method for source scanner identification (SSI), that is to say, identifying the scanner at the origin of a scanned document. Solutions from literature can distinguish between scanners of different brands and models but fail to differentiate between scanners of the same models. To overcome this issue, the approach we propose takes advantage of a convolutional neural network (CNN) to automatically extract intrinsic scanner features from the distribution of the coefficients of the diagonal high-frequency (HH) sub-band of the discrete stationary wavelet transform (SWT) of scanned images. Such information serves as a reliable characteristic to classify scanners of different/same brands and models. Experiments conducted on a set of 8 scanners yielded a model with an accuracy of 99.31% at the block level and 100% at the full image level, showcasing the potential of using deep learning for SSI and outperforming existing schemes from literature. The influence of the model’s parameters such as the input size, the training data size, the number of layers, and the number of nodes in the fully connected layer as well as the effect of the pre-processing step were investigated.
Penerapan Metode Forward Chaining untuk Mendiagnosa Penyakit Tanaman Sawi

Mustard greens is one of the plants that is very susceptible to disease. There is no type of mustard greens plant that is strong against disease or pests. In order to overcome or treat these diseases and pests, mustard greens farmers need to know the type of disease or pest that is attacking. But unfortunately, farmers' knowledge regarding mustard plant diseases is limited to the visible symptoms or the condition of the affected plant. The lack of knowledge of mustard greens farmer practitioners is due to the lack of counseling so that the presence of experts to help farmers is needed. Unfortunately, the experts are not always able to help if the condition requires fast handling because the number of experts are not many. In this study, this problem was resolved by developing a system equivalent to expert knowledge in detecting diseases in mustard plants. The system developed was a web-based system to detecting types of mustard disease automatically with the forward chaining method which can be used as an alternative to manual activities carried out by experts using 9 pests/diseases and 18 symptoms. The test results showed that the level of accuracy with expert expertise reaches 88.8%
A Model to Predict Users’ Intentions to Adopt Contact-Tracing Apps for Prevention from COVID-19

Abstract Technological advances are increasingly progressing and have brought unprecedented solutions for real-world problems for various domains, particularly, when it comes to a health-related domain. This study aims to examine the predictors of users’ intentions to adopt contact-tracing apps for prevention from COVID-19. Based on the extended unified theory of acceptance and use of technology (UTAUT2), our research model incorporates the following eight constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived privacy, perceived value, safety and accuracy. The empirical results were obtained from a sample of 93 questionnaires (currently still in course). We used the partial least squares approach to test our hypotheses. The results reveal that performance expectancy has the strongest impact on the intentions to use contact-tracing apps. The accuracy, effort expectancy and social influence are also important, followed by perceived value, safety and perceived privacy. Facilitating condition is listed as much less important. The theoretical and managerial implications of these results are discussed.
Pre-service English teachers' experience with various CALL applications: Hindrances and reflection

Computer assisted language learning (CALL) is one of the alternative ways used by teachers in teaching English. This research aims to find out various CALL applications used by the pre-service English teachers, the difficult skills faced by students, hindrances, and pre-service teachers’ reflection. A mixed-method approach with questionnaires, interviews guideline, and observation checklist were employed by the researchers. The sample consisted of 30 pre-service English teachers and 750 students. The data analyses were done by using simple statistics calculations, codes, and categories. The results showed that pre-service English teachers applied various CALL applications, in which the majority of them used Hello English for speaking in the classroom because of the ease of use of the application. The most difficult skills learned by students were listening, and the easiest skill was reading. Another finding is related to hindrances during CALL applications which included internet connection, material understanding, CALL application procedures, assessment, discipline, cost, and learning style. However, those problems were eventually solved by pre-service English teachers. Furthermore, pre-service teachers wrote their teaching reflection to know their evaluation of their teaching. The reflections showed that they could solve the problems, made, and revised lesson plans, and brought good role models in the classroom.
Vibration Anomaly Detection using Deep Neural Network and Convolutional Neural Network

Identifying the “health state” of the equipment is the domain of condition monitoring. The paper proposes a study of two models: DNN (Deep Neural Network) and CNN (Convolutional Neural Network) over an existent dataset provided by Case Western Reserve University for analyzing vibrations in fault diagnosis. After the model is trained on the windowed dataset using an optimal learning rate, minimizing the cost function, and is tested by computing the loss, accuracy and precision across the results, the weights are saved, and the models can be tested on other real data. The trained model recognizes raw time series data collected by micro electro-mechanical accelerometer sensors and detects anomalies based on former times series entries.
Cost-Sensitive Variable Selection for Multi-Class Imbalanced Datasets Using Bayesian Networks

Multi-class classification in imbalanced datasets is a challenging problem. In these cases, common validation metrics (such as accuracy or recall) are often not suitable. In many of these problems, often real-world problems related to health, some classification errors may be tolerated, whereas others are to be avoided completely. Therefore, a cost-sensitive variable selection procedure for building a Bayesian network classifier is proposed. In it, a flexible validation metric (cost/loss function) encoding the impact of the different classification errors is employed. Thus, the model is learned to optimize the a priori specified cost function. The proposed approach was applied to forecasting an air quality index using current levels of air pollutants and climatic variables from a highly imbalanced dataset. For this problem, the method yielded better results than other standard validation metrics in the less frequent class states. The possibility of fine-tuning the objective validation function can improve the prediction quality in imbalanced data or when asymmetric misclassification costs have to be considered.
Chinese Character Image Completion Using a Generative Latent Variable Model

Chinese characters in ancient books have many corrupted characters, and there are cases in which objects are mixed in the process of extracting the characters into images. To use this incomplete image as accurate data, we use image completion technology, which removes unnecessary objects and restores corrupted images. In this paper, we propose a variational autoencoder with classification (VAE-C) model. This model is characterized by using classification areas and a class activation map (CAM). Through the classification area, the data distribution is disentangled, and then the node to be adjusted is tracked using CAM. Through the latent variable, with which the determined node value is reduced, an image from which unnecessary objects have been removed is created. The VAE-C model can be utilized not only to eliminate unnecessary objects but also to restore corrupted images. By comparing the performance of removing unnecessary objects with mask regions with convolutional neural networks (Mask R-CNN), one of the prevalent object detection technologies, and also comparing the image restoration performance with the partial convolution model (PConv) and the gated convolution model (GConv), which are image inpainting technologies, our model is proven to perform excellently in terms of removing objects and restoring corrupted areas.
Does the Order of Information Affect Investors' Investment Decisions? Experimental Investigation

Research aims: This study examines the order of information’s effect according to the Belief Adjustment Model. In particular, this study investigates the effect of the direction of the order and the pattern of presenting information in making investment decisions.Design/Methodology/Approach: The research applied an experimental method with web-based instruments using a 2x2 factorial design between subjects, involving 65 investors.Research findings: The investors’ investment valuation who received negative to positive information sequence direction was higher than the investors who received positive to negative information direction. Furthermore, there was an order effect in the form of recency on investors who received a partial presentation pattern. Meanwhile, investors who received a simultaneous pattern did not show an order effect in their assessment. These findings underline that the simultaneous pattern could reduce the order effect, so that investors need to generalize the information as a whole to avoid this bias.Theoretical contribution/Originality: This study extends the investigation of investment decisions using a long sequence of information perspectives and more varied types of information (e.g., financial information, corporate governance, and industry sectoral information that has an impact on company conditions) in making investment decisions on the belief adjustment model.Practitioner/Policy implication: Companies must maintain the direction of the order and the presentation patterns when issuing company information to maintain the quality of investors' decisions and avoid the risk of volatility in company shares.Research limitation/Implication: Participants who joined this research were active investors but had not yet had a comprehensive experience.
On the Maximum Order Complexity of Thue–Morse and Rudin–Shapiro Sequences along Polynomial Values

Abstract Both the Thue–Morse and Rudin–Shapiro sequences are not suitable sequences for cryptography since their expansion complexity is small and their correlation measure of order 2 is large. These facts imply that these sequences are highly predictable despite the fact that they have a large maximum order complexity. Sun and Winterhof (2019) showed that the Thue–Morse sequence along squares keeps a large maximum order complexity. Since, by Christol’s theorem, the expansion complexity of this rarefied sequence is no longer bounded, this provides a potentially better candidate for cryptographic applications. Similar results are known for the Rudin–Shapiro sequence and more general pattern sequences. In this paper we generalize these results to any polynomial subsequence (instead of squares) and thereby answer an open problem of Sun and Winterhof. We conclude this paper by some open problems.
A Research-Oriented Teaching Model for Public Physical Education in Colleges Based on Extenics Theory

This paper mainly designs a research-oriented teaching (ROT) model for public physical education (PE) in colleges based on extenics theory, which overcomes the limits and defects of the current ROT model for public PE in colleges. From the theoretical perspective, the extenics theory and method were discussed in details for implementing the ROT model for public PE in colleges. From the angle of engineering application, the authors designed an improved evaluation index system (EIS) for the implementation effect of the said ROT model, and developed the corresponding extenics superiority model, which quantifies the implementation effect. The research results further improve the ROT model for public PE in colleges.
Facebook Profile Credibility Detection using Machine and Deep Learning Techniques based on User’s Sentiment Response on Status Message

Recently, the impact of online Social Network sites (SNS) has dramatically changed, and fake accounts became a vital issue that has rapidly evolved.This issue gives rise to how to assess and measure the credibility of User-Generated Content (UGC).This content is used in finding trusted sources of information on SNS like Facebook, Twitter, etc. Consequently, classifying users' profiles and analyzing each user's behavior response based on the content generated became a challenge that must be solved.One of the most significant approaches is Sentiment Analysis (SA) which plays a major role in assessing and detecting the credibility degree of each user account behavior.In this paper, the aim of the study is to measure and predict the user's profile credibility by declaring the correlation degree among the UGC features that affect users' responses to status messages.The proposed models were implemented using six Supervised Machine Learning classifiers, an Unsupervised Machine Learning cluster model, and a Deep Learning Neural Network (NN) model.The research paper presents two experiments to evaluate Facebook profile credibility.At first, we applied a binary classification model to classify profiles into fake or genuine users.Then, we conducted a classification model on genuine users based on the credibility theory by using the Analytical Hierarchical Process (AHP) approach and computed the credibility score for each.Secondly, we selected and analyzed a public Facebook page (CNN public page) and obtained data from it for users' sentiment reactions and responses on statuses Messages relating to different topics on the period (2016/2017).Then, we performed LDA on the status corpus (Topic Modeling algorithm, Latent Dirichlet Allocation) to generate topic vectors.In addition, we performed Principal Component Analysis (PCA) method to visualize and classify each status topic distribution.Afterthought, we produced a status corpus cluster to classify users' behaviors through statuses posted and users' comments.As a conclusion of this study, the first experimental results achieved 95% and 99% accuracy to classify fake/genuine users and incredible/credible accounts, respectively.The second experiment outcome identified the clusters for the status corpus in 10 topic-features distribution and classified users' contents into credible or not according to the final calculated credibility score.
One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms

Path planning plays a crucial role in many applications in robotics for example for planning an arm movement or for navigation. Most of the existing approaches to solve this problem are iterative, where a path is generated by prediction of the next state from the current state. Moreover, in case of multi-agent systems, paths are usually planned for each agent separately (decentralized approach). In case of centralized approaches, paths are computed for each agent simultaneously by solving a complex optimization problem, which does not scale well when the number of agents increases. In contrast to this, we propose a novel method, using a homogeneous, convolutional neural network, which allows generation of complete paths, even for more than one agent, in one-shot, i.e., with a single prediction step. First we consider single path planning in 2D and 3D mazes. Here, we show that our method is able to successfully generate optimal or close to optimal (in most of the cases &amp;lt;10% longer) paths in more than 99.5% of the cases. Next we analyze multi-paths either from a single source to multiple end-points or vice versa. Although the model has never been trained on multiple paths, it is also able to generate optimal or near-optimal (&amp;lt;22% longer) paths in 96.4 and 83.9% of the cases when generating two and three paths, respectively. Performance is then also compared to several state of the art algorithms.
Analysis and implementation of a solar tracking rack system

Solar energy is the most feasible source of renewable energy especially in sun rich regions like Africa. In particular, electrically epileptic country like Nigeria with current power output of only about 3.5GW of energy has a relatively long sunny period for about 90% of the year, making Nigeria very rich in solar power. Maximally harnessing solar energy into usable electricity however, is still a fundamental problem as the existing models can harness only about 70% of the available energy. To maximally harness solar energy, a solar panel must be perpendicular to the energy source for all 12hours of sunlight availability, and must be able to follow the sun’s movement all day long. Accordingly, this work considers the analysis, construction, and implementation of a single axis solar tracking dynamic system. Experimental result show that the proposed system outperforms the conventional static solar tracking system.&#x0D; Keywords: solar energy, single axis, harvesting-solar energy.
Exploiting Mixed Binaries

Unsafe programming systems are still very popular, despite the shortcomings due to several published memory-corruption vulnerabilities. Toward defending memory corruption, compilers have started to employ advanced software hardening such as Control-flow Integrity (CFI) and SafeStack. However, there is a broad interest for realizing compilers that impose memory safety with no heavy runtime support (e.g., garbage collection). Representative examples of this category are Rust and Go, which enforce memory safety primarily statically at compile time. Software hardening and Rust/Go are promising directions for defending memory corruption, albeit combining the two is questionable. In this article, we consider hardened mixed binaries, i.e., machine code that has been produced from different compilers and, in particular, from hardened C/C++ and Rust/Go (e.g., Mozilla Firefox, Dropbox, npm, and Docker). Our analysis is focused on Mozilla Firefox, which outsources significant code to Rust and is open source with known public vulnerabilities (with assigned CVE). Furthermore, we extend our analysis in mixed binaries that leverage Go, and we derive similar results. The attacks explored in this article do not exploit Rust or Go binaries that depend on some legacy (vulnerable) C/C++ code. In contrast, we explore how Rust/Go compiled code can stand as a vehicle for bypassing hardening in C/C++ code. In particular, we discuss CFI and SafeStack, which are available in the latest Clang. Our assessment concludes that CFI can be completely nullified through Rust or Go code by constructing much simpler attacks than state-of-the-art CFI bypasses.
Technology and research results of vocational education and training systems in the context of region socio-economic development

Introduction. The level of orientation of regional secondary vocational education systems to the needs of the territories in which they are located significantly affects the economic parameters of the development of the constituent entities of the Russian Federation, the dynamic characteristics of personnel demand and supply in the regional labor market. The technology and tools developed by the authors for studying the degree of compliance of vocational education and training systems with the tasks of economic and social development has been implemented in three subjects of the Russian Federation featuring different levels of socio-economic development: the Samara region, the Khanty-Mansi Autonomous District and the Republic of Ingushetia. Methodology and methods. A methodological approach was used that considers the current situation of uncertainty in the external environment of regional secondary vocational education systems. Hence, the arrangement of its educational resources, which provides the properties of a self-organizing system, becomes an effective tool for stimulating the regionally oriented development of the vocational education and training system. Achieving the compliance of the VET-system with external requests is determined by the parameters of its adaptive potential. Research methods: survey (questioning), expert evaluation method, content analysis, scaling. Results. To achieve the goal of the research, the compliance criteria were formed, as well as indicators that reflect the properties of the organization of educational resources of the system, corresponding to the principles of adaptive systems. It is established that the achieved compliance values in each region are formed by various sets of factors and conditions for the organization of educational resources. The following trends are identified for the regions: a) a decrease in employers' effective demand for vocational education programs; b) insufficient use of educational technologies and forms of training organization that optimize the resource support of the activities of professional educational organizations, including network formats for the implementation of educational programs using educational resources of enterprises in the real sector of the economy. On average, 20% of professional educational organizations in pilot regions practice a network format for implementing open source programs (as a rule, without using employers' resources). At the same time, only 6% of the student contingent are trained in such programs. Discussion and conclusion. The practical significance of the study is determined by the possibilities of applying the results of assessing the level of compliance of the VET-systems with the tasks of socio-economic development to work out and implement effective regional educational policy in the sphere of vocational education.
Quantifying Differences in Reward Functions

For many tasks, the reward function is too complex to be specified procedurally, and must instead be learned from user data. Prior work has evaluated learned reward functions by examining rollouts from a policy optimized for the learned reward. However, this method cannot distinguish between the learned reward function failing to reflect user preferences, and the reinforcement learning algorithm failing to optimize the learned reward. Moreover, the rollout method is highly sensitive to details of the environment the learned reward is evaluated in, which often differ in the deployment environment. To address these problems, we introduce the Equivalent-Policy Invariant Comparison (EPIC) distance to quantify the difference between two reward functions directly, without training a policy. We prove EPIC is invariant on an equivalence class of reward functions that always induce the same optimal policy. Furthermore, we find EPIC can be precisely approximated and is more robust than baselines to the choice of visitation distribution. Finally, we show that EPIC distance bounds the regret of optimal policies even under different transition dynamics, and confirm empirically that it predicts policy training success. Our source code is available at — double blind: supplementary material —.