Reviewerly/exHarmony huggingface dataset:
Protecting Image-classifiers from Adversarial Attacks using an Adaptive Clustering Buffer-based Detection

Endeavoring to defend image classifier models from query-based adversarial attacks, in this paper, we present a novel buffer-based defender that is adaptively optimized using an unsupervised neural network clustering technique. The proposed defender can be considered as a complementary and independent firewall protector that is integrable with almost all available defense methods. We empirically evaluate the detection accuracy besides the performance of the buffer on three image classifier models (MLP, VGG19, and ResNet50) against various white/black-box attack methods. Results revealed that our buffer-based detection method could relatively improve the protection rate without sacrificing the overall performance.
Assessing the effectiveness of smartphones in education: A Meta-analysis of recent studies

The usage of mobile devices is increasing in frequency and scope. The percentage of students who use smartphones is quite high, in particular among those attending institutions of higher education. Like they would with any other technology, educators are doing research on the efficacy of using smartphones in the classroom. Studies have been conducted on the efficacy of using smartphones in face-to-face education as well as in the process of distant education, which has grown more common as a direct result of COVID-19. The purpose of this study is to do a meta-analysis of the data from previous experimental studies that looked at how well smartphones have been used over the past five years. The total effect size that has been calculated is 3.73. Since p = 0.05, this effect's size is statistically important. This finding has a big effect, as can be seen. For each study, an effect size calculation was done based on Hedges' g. The size of the effect is between -8 and 25.70.
PTcomp: Post-Training Compression Technique for Generative Adversarial Networks

In a time of virtual spaces, the usage of generative adversarial networks is inevitable.Generative adversarial networks (GANs) are generative deep-learning models that can generate realistic data.GANs have been used in many applications like text-to-image, image-to-image, image synthesis, speech synthesis, etc.Its power lies in the diversity and novelty of the generated data.Despite their advantages, GANs are resource-hungry.GANs' output resolution and high correlation make it more challenging to compress and fit on edge-devices storage and power budget.Hence, traditional compression techniques are not the best fit to use with GANs.Additionally, GANs training instability adds another dimension of difficulty.Therefore, compression techniques that require retraining are challenging for GANs.In this paper, we developed a weight clustering technique to compress GANs without the need for retraining, hence the name post-training compression technique (PTcomp).We also proposed a clustered-based pruning which adds more savings.Experiments on Cyclegan, Deep convolution gan (DCGAN), and Stargan using several datasets show the superiority of our technique against traditional post-training quantization.Our technique provides a 4x to 8x compression ratio with comparable quality to original models and 14% fewer mac operations due to pruning.
Neural Quality Estimation Based on Multiple Hypotheses Interaction and Self-Attention for Grammatical Error Correction

The English grammatical error correction system is suitable for the English learning environment, with the goal of accurately correcting errors in learners’ writing. However, false corrections are often generated in practical applications, and many errors cannot be corrected, thus misleading learners. The quality estimation model is beneficial to ensure that learners obtain accurate grammatical error correction results and avoid misleading sentences caused by error corrections. Grammatical error correction models can generate multiple hypotheses of higher quality, but existing quality estimation models do not consider interactions between different hypotheses. Based on this, we propose a model based on multiple hypotheses interaction and self-attention, BGANet, for English grammatical error correction quality estimation. BGANet builds interactions between multiple hypotheses, extracts and aggregates grammatical error correction evidence in hypotheses through two kinds of self-attention mechanisms, and evaluates the quality of the generated hypotheses. Experiments on four grammatical error correction datasets show that BGANet has better quality estimation performance.
Deleuze and AlphaGo

It is time to update Deleuze and Guattari's contrast between Chess and Go in the ‘Nomadology’ Plateau with a discussion of AlphaGo, the artificial intelligence that revolutionised Go in 2016. I focus less on the political issues in Go nomadology, central as they are, and more on smooth space and time. I explain and speculate on some details in Go strategy, as well as some processes of machine learning. The relations between human Go, computer Go, and smooth-time nomadology remain unsettled, in my view, but many resources for nomadological analysis open up for readers who learn to play, and program, Go.
Transaction-Oriented Approach to the Design of Information Systems Based on Formal Grammar

The article discusses some important aspects of designing cloud information systems based on formal grammars. The proposed approach is based on the formalization of the information exchange unit and the development of the fundamental principles of client-server interaction. The proposed universal unit of information exchange is compatible with both relational and non-relational databases. The data transfer protocol between all components of the information system is described. A transaction is chosen as an informational message, as some atomic operation consisting of a context and a descriptor. Two protocols for transferring a transaction between a client and a server are outlined, guaranteeing the integrity and security of the transmitted data. The approaches differ in the implementation of the access control system and the way the transaction is described. The advantages and disadvantages of each of the approaches are considered. As a description of the transaction, a model language is proposed, which is based on a context-free formal grammar. The language supports a full set of operations for interacting with the database, as well as control structures, and provides data type control. A translator of a model transaction description language has been implemented that generates a target code for programming languages popular with hosting providers.
Calculation of aircraft lever-float valves using neural networks

The possibility of using neural networks in aviation is shown, in particular in products intended for use in aviation technology. The possibility of using neural networks throughout the entire life cycle of aviation equipment products is analyzed. The advantages that can be obtained using neural networks are described. The main stages of creating a neural network model are analyzed and a description of each stage is presented. The difficulties associated with the practical application of models based on artificial intelligence are shown. The calculation of the operation of a lever-float valve is presented and a neural network model is made for its calculation using real operation and test data.
Pedagogical Conditions for Soft Skills Formation among Younger Schoolchildren Based on the Cooperative Learning Method

The aim of the study is to identify and experimentally test the pedagogical conditions for soft skills formation among younger schoolchildren based on the cooperative learning method. The paper presents the substantiation for these conditions and discusses the results of assessing the effectiveness of their implementation. Scientific novelty of the study lies in selecting and structuring the content of the extracurricular activities programme for soft skills formation among younger schoolchildren in the conditions of cooperative learning taking into account constructive foreign experience. The cooperative learning method was chosen as the central method of forming soft skills in younger schoolchildren, its use contributed to improving the effectiveness of the indicators of the process under consideration. As a result of analysing the data obtained, it has been proved that the proposed extracurricular activities programme for soft skills formation among younger schoolchildren based on the cooperative learning method helped to develop their creative and critical thinking, skills of effective interaction with others, as well as the abilities to solve situational tasks that determine the life success of primary school graduates.
Structural and Content Model for Ecological Culture Formation among the Subjects of the Educational Process at School in the Context of the Idea of Modern Ecology

The aim of the study is to substantiate the possibility of ecological culture formation among the subjects of the educational process at school on the basis of a specialised pedagogical model in the context of the idea of modern ecology. The paper explains the difference between modern ecological education and ecological education as a subject area. The necessity of creating a model is considered and its description is given. The scientific novelty of the study lies in developing a structural and content model for ecological culture formation among the subjects of the educational process at school, which consists of the normative-target, content, procedural-technological and efficient-corrective blocks. As a result, the pedagogical conditions that contribute to ecological culture formation among the subjects of the educational process at school in the context of the idea of modern ecology have been formulated.
Role-Playing as a Way to Expand Vocabulary in English Classes in Conditions of Social Deprivation

The aim of the study is to substantiate that role-playing is an effective way to expand vocabulary in English classes for students experiencing social deprivation at different levels of education. The paper considers pedagogical methods of forming English language vocabulary among students (creative retelling and role-playing), identifies the specifics of the work on expanding English language vocabulary among students experiencing social deprivation. The scientific novelty of the paper lies in determining pedagogical conditions for the effective organisation of English language teaching among students experiencing social deprivation in order to expand their foreign language vocabulary. As a result, it has been shown that retelling can act as a preliminary stage of preparing students with social deprivation to actively participate in role-playing in order to expand their vocabulary in English classes, the specifics of organising role-playing with students in conditions of social deprivation have been presented.
Talk of the Town mobile app platform: New method for engaging family in STEM learning and research in homes and communities

Children do not just learn in the classroom. They engage in "informal learning" every day just by spending time with their family and peers. However, while researchers know this occurs, less is known about the science of this learning-how this learning works. This is so because investigators lack access to those moments of informal learning. In this mini-review we present a technical solution: a mobile-based research platform called "Talk of the Town" that will provide a window into children's informal learning. The tool will be open to all researchers and educators and is flexibly adaptable to these needs. It allows access to data that have never been studied before, providing a means for developing and testing vast educational interventions, and providing access to much more diverse samples than are typically studied in laboratories, homes, and science museums. The review details the promise and challenges associated with these new methods of data collection and family engagement in STEM learning sciences.
Collision-probability-based hopping trajectory optimization on hazardous terrain of small bodies

This paper studies the safe motion of a hopping rover on hazardous terrains of small bodies in the presence of initial state errors and dynamic parameter uncertainties. The collision probability is adopted to optimize the hopping trajectory. Firstly, the hazardous terrain is characterized by the least square median method, and the feasible path point zone is defined for hopping motion. Meanwhile, the hopping trajectory optimization is converted into sequence path point planning and optimal trajectory design problems based on the classical path planning algorithm. Then, considering the effects of errors and uncertainties, a performance index is derived to design a continuous hopping trajectory of sequence path points. Taking the collision probability as an index to indicate safety, local path points are optimized through path point decomposition and replacement strategy. Therefore, a collision-probability-based continuous optimal hopping trajectory can be generated to realize safe and precise motion. Finally, with the simulated hazardous terrain based on the small body 433 Eros, the effectiveness of the proposed method is verified by numerical analysis.
Optimization integrated generative adversarial network for occluded text recognition with language modeling

Summary Text recognition has attracted increased attention recently as a result of the complexity of natural settings and the variety of text instances. Various text or character recognition methods are introduced to distinguish the text from the natural scene, but existing methods struggle with the distorted and highly curved text instances. Consequently, an effective method for occluded text or character detection from object‐background images is developed using the suggested elephant herding exponential sailfish optimizer‐based generative adversarial network (EHESFO‐based GAN). In order to build the proposed EHESFO, elephant herding optimization and Exponential SailFish Optimizer (ESFO) are merged. ESFO was created by fusing exponentially weighted moving average and SailFish Optimizer. With GAN, features extracted from the background and foreground of an image are efficiently used for image annotation and text recognition. The best features from the background and foreground images are extracted to create the optimal solution, which increases the efficacy and efficiency of text recognition. While taking the occlusion as 0.4, the proposed EHESFO‐based GAN achieved higher accuracy of 98.1090% and lower error of 1.4%.
Prediction Model of Elderly Care Willingness Based on Machine Learning

At present, the problem of an aging population in China is severe. The integration of existing healthcare services with elderly care services is inefficient and cannot meet the needs of the elderly. As such, China urgently needs the concerted efforts of various social forces to cope with the increasingly serious problem of aging. In accordance with Andersen’s behavioral model, a survey was conducted in Tangshan City among seniors 60 years of age and older. Using logistic regression models, decision tree models, and random forest models, we examined the factors impacting senior people’s desire to choose the integrated medical care and nursing care model. The results of the three models displayed that the elderly’s propensity to choose the combined medical care and nursing care model is significantly influenced by the amount of insurance, life care needs, and healthcare needs. Moreover, the study found that the willingness of the elderly in Tangshan to improve the combined medical and nursing care service system is low. The government should appeal to the community to participate in multiple developments to improve the integrated medical and nursing service system.
Analysis of Fintech Use at Early Working Age in Indonesia

Financial technology (fintech) is a growing industry in Indonesia, supported by advances in technological infrastructure. Until the end of 2021, according to data obtained from the Financial Services Authority (OJK), loan distribution grew by 89.77% on an annual basis or equivalent to IDR 295.85 trillion. With the highest number of users aged 19 - 34 years or referred to as the early working age of 9 million. The existence of this phenomenon makes researchers ask why many early working age use this technology. The method used in this research is descriptive quantitative. This study found that the early working age used this technology to fulfill secondary needs and they expressed satisfaction with Fintech technology.
Sampling Algorithms for Unsupervised Prototype Selection

The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> Nearest Neighbor classifier ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -NN) is one of the most widely used classification algorithms. It is simple, non-parametric, and does not have a training phase, but it requires storing all observations (training set). The class for an unknown sample <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x_{q}$</tex> is labeled using a similarity/distance function <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> to compute the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> most similar elements (neighbors). The latter makes that <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -NN prediction process unfeasible for large datasets. Two main approaches are studied to optimize the prediction process: a pre-computed index to speed up the neighbors' computation and by generating a subset of representative elements called prototypes. This work presents an analysis of sampling algorithms to perform unsupervised prototype selection. Each proposed algorithm's performance is measured by the reproduction capacity of the original data set class distribution and the performance in several classification tasks.
2D automatic alignment of optical spatial filtering system by PID control

In this work, an interface that allows the automation of the alignment process in spatial filter systems is proposed. Optical spatial filtering is a method used in interferometry for the generation of regular laser illumination beams, where a pinhole aperture is focused on a convergence point of a lens. For the experimental setup, an emerging laser beam is focused to a point, where the correct position of the aperture can be found by adjusting the linear displacement with motorized devices. To obtain the position of the pinhole in an (X, Y) plane, a displacement arrangement implemented with micrometric servomotors and a CCD camera; controlled by a PID control algorithm, is used. The automatic adjustment of the position of the resulting beam intensity pattern registered in the camera is carried out by the servo motors until the optimal position coordinates are found. Process control related to positioning and subsequent estimation is executed with Python and synchronized in Lab View.
Watermarking Scheme With Bounded-Exhaustive Self-Recovery Approach

This paper revisits a fragile watermarking scheme with exact self-recovery capabilities and proposes a novel restoration process. The restoration mechanism is aimed at solving Systems of Binary Linear Equations (SBLE) associated with altered subsets of bits, using arithmetic module-2. The chances that those SBLE will be linearly dependent increase for more significant tampering rates. While such subsets are deemed to be unrecoverable by schemes in the current watermarking literature, the proposed restoration process calculates the Hermite standard form of the matrix representation of the SBLE to find both the pivot and the free variables. Since the number of solutions is finite when using arithmetic module-2, all the answers are calculated by exhaustively replacing the free variables with all the possible combinations of bits. Finally, the solutions are analyzed to possibly restore some tampered bits to enhance the recovery performance in every iteration. Experimental results demonstrate that the proposed scheme outperforms the best methods with exact self-recovery capabilities in the state-of-the-art in terms of embedding time, restoration time, and restoration performance.
Watermarks based on Pyramidal Images for Tampering Image Self-Restoration

This article presents the Pyramidal Image Water-marking (PIW) and Pyramidal Image Restoration (PIR) algorithms based on a pyramidal image representation. The PIW algorithm watermark a digital image with an array of compressed images corresponding to a pyramidal representation and the PIR algorithm is capable of performing automatic image restoration using the watermark images computed by the PIW algorithm. The use of pyramids avoids the quantization procedure for the reference information bits and allows us to identify pixels erroneously deemed as altered by the block-wise tampering detection scheme. PIW and PIR outperform some state-of-the-art algorithms used for automatic image restoration and can reconstruct images with a tampering rate of up to 0.95.
Raspberry-based Low-resolution Thermal image system using a Smoothing Filter-based Kalman

Since the emergence of global epidemics such as SARS-CoV-2, H1N1, SARS and MERS, a wide range of systems for measuring temperature have been developed based on computer vision to reduce and prevent the virus contagious. By implementing a Raspberry-based Low-resolution embedded system based and a FLIR Lepton® sensor human body temperature is measured and improved by four different algorithms implemented. Firstly, three traditional time-series processes solving such as, Simple Mean (SM), Simple Moving Average (SMA), and Multi Lineal Regression (MLR), and secondly, and online filter-based Kalman predictor were implemented to increase the signal to noise ratio of the acquired temperature magnitude. Results of average prediction for different benchmarks demonstrate the best performance of Kalman Filter upon traditional processes. In addition, this algorithm achieves to smooth output temperature with fewer samples (∼10% of total samples) in comparison MLR and SMA. Finally, Raspberry-based Low-resolution Thermal image system is a feasible tool as a high-speed temperature estimator, by implementation of algorithms codified in Python language.