Reviewerly/exHarmony huggingface dataset:
Architecture Design for Plain CNNs

Using GAs in the architecture design of CNNs directly presents a number of challenges. On the one hand, the suitable architecture cannot be determined unless its performance is analyzed and compared to other architectures. However, measuring the effectiveness of a sole individual consumes a long time, and the situation becomes even more challenging when population-based methods are adopted. To hasten the process of evolution, significantly more computational resources would be needed. On the other hand, because the appropriate depth of CNNs for a certain issue is not known, it is difficult to confine the range of search space for design optimization. Consequently, a variable-length gene encoding approach may be the best alternative. However, a newly found challenge is identifying how to determine the crossover operation for distinct building blocks. In addition, the weight initialization values have a significant impact on the validated architecture performance, while tackling this issue necessitates a fine strategy of gene encoding as well as the optimization of hundreds of thousands of decision variables.
End-to-End Performance Predictors

In fact, common optimization problems in ENAS are computationally expensive and are usually handled using surrogate-assisted EAs(SAEAs) [1], employing inexpensive approximation regression and classification models, such as the Gaussian process model [2], radial basis network (RBN), etc., to replace the costly fitness evaluation [3]. SAEAs have proven to be useful and efficient in a variety of practical optimization applications [1].
Architecture Design for RBs and DBs Based CNNs

As mentioned in Part III, the research of CNN architectural design algorithms is now in the early phases, particularly for entirely automatic ones with great performance and using limited CPU resources. In this chapter, a novel GA-based method to automatically design CNN architectures named AE-CNN will be introduced with the following highlights. Firstly, the use of AE-CNN has no pre-conditions on any prior knowledge of basic CNN design, examined datasets, GA, pre-processing, re-composition, and post-processing. Secondly, the variable-length encoding technique is used to estimate the best CNN depth. The new operators for crossover and mutation are invented and included in for exploring and exploiting the search space in discovering the ideal CNN architectures in order to achieve variable-length encoding. Thirdly, for accelerating the architecture design, an efficient encoding approach according to RBs and DBs are designed, and limited computational resources are used, while AE-CNN achieves promising results. It is worth noting that, while the RBs and DBs are employed in AE-CNN, users are no need to be familiar with these blocks in order to use it.
Deep Neural Networks

The neural networks (NNs) with deep architectures are referred to as DNNs. In general, there is no universal standard of how deep a CNN must be to be considered deep. In practice, a DNN is defined as a NN with at least four layers.
Architecture Design for Stacked AEs and DBNs

As introduced in Part II, altering n in Eq. (1) could learn numerous different representations, but only those that perform exceptionally well on the machine learning tasks linked with them are given attention.
Hybrid GA and PSO for Architecture Design

In this chapter, a new approach based on EC is introduced for automatically searching for the optimal CNN architecture and determining whether or not to use shortcut connections between one layer and its forward layer. After that, a two-level encoding strategy is applied to a hybrid EC methodology that is composed of a GA and a PSO. This allows for the generation of both the network architecture and the shortcut connections within it. The technique is referred to as DynamicNet because to the fact that during the course of the evolutionary process, both the architecture and the shortcut connections are determined dynamically without any involvement from a human being. On three widely used datasets that have differing degrees of complexity, DynamicNet will be evaluated in comparison with one method that is based on EC and 12 methods that are considered to be state-of-the-art.
Distribution Training Framework for Architecture Design

As discussed in Part I, for the time-consuming issue of the ENAS methods, there are two primary categories of available acceleration methods. First, various acceleration approaches for DNN evaluation are proposed, including weights sharing [1], low fidelity [2], and prediction model [3]. However, the reduction of training in these methods introduces bias in the estimation of DNN performance. Second, many well-known distributed parallelism techniques have been developed to accelerate large-scale DNN training, the most common ones include model parallelism and data parallelism.
Differential Evolution for Architecture Design

The general goal of this chapter is to explore the capacity of DE, named DECNN, to evolve deep CNN architectures and parameters automatically. Designing new crossover and mutation operators of DE, as well as an encoding scheme, and a second crossover operator will help to achieve the goal. DECNN will be evaluated on six datasets of various complexity that are widely used and compared to 12 state-of-the-art methods.
Architecture Design for Variational Auto-Encoders

Most VAEs were developed with symmetrical architecture in mind, which means that the encoder and decoder must have the same number of layers. However, when completing the step of unsupervised pre-training step, the decoder portion is deprecated and will never be employed when fine-tuning image classification problems. As a result, maintaining a symmetrical architecture is not nearly necessary [1]. However, new complications develop when the asymmetrical architecture is used, despite the fact that it is a quite appealing decision. This shows that if the asymmetrical architectures of CVAEs are planned to improve, one needs to evaluate the architectures of each partition on its own before making any changes to the architectures as a whole. In addition, for the correct forwarding of the asymmetrical CVAEs, it is essential to make certain that the decoder appropriately fits the resolution of the raw input data in the appropriate manner. This also adds another layer of complication to the manual design process for VAE designs.
Architecture Design for Skip-Connection Based CNNs

In this chapter, an efficient and effective algorithms employing GA is introduced, dubbed CNN-GA, to find the best CNN architectures for specific image classification tasks automatically, such that the found CNN can be directly employed without any need for manual tuning. CNN-GA is an algorithm for automating the architecture design of CNN. Please keep in note that the terms “automatic” and “automatic + manually + tuning” are discussed from the perspective of end-users, rather than developers. In developing high-performance CNN architecture design algorithms, however, adequate domain expertise should be promoted. This effort is not difficult to comprehend by comparing it to the design of the Windows Operating System by Microsoft scientists: to ensure the users could be able to effectively operate on computers even if they do not have considerable understanding of operating systems, the scientists should put as much of their professional knowledge as they possibly can while building a user-friendly operating system.
Architecture Design for Convolutional Auto-Encoders

Although the CAE and its variations have proven benefits in a variety of applications, one key restriction is that their stacked architectures are incompatible with those of state-of-the-art CNNs. The amount of convolutional and pooling layers in the stacked CNN is the same because each CAE contains a pooling layer and a convolutional layer in the encoder. State-of-the-art CNNs, on the other hand, have different amounts of convolutional and pooling layers. The constraint on the number of convolutional and pooling layers contained in CAEs ought to be lifted because the architecture of CNN is one of the important ingredients contributing to the final performance. However, because of the non-differentiable and non-convex properties in practice, it is intractable to determine optimal numbers for convolutional layers and pooling layers, which is related to NAS.
Deep Neural Architecture Compression

Various techniques have been proposed that take into account both the scale of parameters and performance [1–3]. Analysis of sensitivity or testing in a variety of configurations are commonly used to obtain a suitable compromise between model size and performance. Meanwhile, because convolutional operations take the majority of computing time, it has been investigated that pruning convolutional filters is a potentially promising method for striking a balance between the performance and the scale of parameters. As a result, several criteria for pruning filters in CNNs have been suggested [4–6] and implemented [7].
An analogue of Furstenberg–Sárközy’s theorem and an alternative solution to Waring’s problem over finite fields

In this paper, we use Cayley digraphs to obtain some new self-contained proofs for Waring's problem over finite fields, proving that any element of a finite field Fq can be written as a sum of m many kth powers as long as q>k2mm−1; and we also compute the smallest positive integers m such that every element of Fq can be written as a sum of m many kth powers for all q too small to be covered by the above mentioned results when 2⩽k⩽37. In the process of developing the proofs mentioned above, we arrive at another result (providing a finite field analogue of Furstenberg–Sárközy's Theorem) showing that any subset E of a finite field Fq for which |E|>qkq−1 must contain at least two distinct elements whose difference is a kth power.
Variasi Penggunaan Strategi Kesantunan sebagai Penanda Subordinasi Mahasiswa dalam Interaksi Lisan Tertulis

Studies on politeness strategies have been carried out in various fields. However, the study in the context of oral and written interactions is still limited. The purpose of this study is to present the use of students' language politeness strategies in oral and written interactions and to reveal the causes of differences in their choice of use. The primary data in this study were 50 instant message texts between students and lecturers. Data were analyzed using the categorization of politeness strategies from Brown &amp; Levinson (1987), which was supported by data from interviews with 7 participants. This study found that 4 out of 5 categories of politeness strategies were used by students, which were dominated by negative politeness strategies. It indicates an awareness of the asymmetric power relationship between the two, which makes them feel the need to be careful so that what is conveyed does not threaten their faces. In addition, it was found that the reasons behind the variation in the use of politeness strategies were related to educational ritualism, looseness of domination of power, the intensity of interaction, and the purpose of sending messages.
Smart Education with Deep Learning and Social Media for Disaster Management in the Pursuit of Environmental Sustainability While Avoiding Fake News

Networking continues to increase day by day to invest increasingly our daily, creating huge volume of various and precise data: it is not easy to collect content, especially in crisis times. We focus on Smart Education proposed as primary tool of a hybrid of Deep Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM)-based model to retrieving content efficiently: CNN is used to extract meaningful features from multiple sources, enabling to have qualitative and sure information, notably with an efficient fake news security, and LSTM is used to maintain long-term dependencies in the extracted features with recurrent connections. This model has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach makes it possible to integrate artificial intelligence technologies, deep learning, social media and detecting or avoiding fake news into the crisis management model. It is based on an extension of our previous approach, namely disaster management based on short-term memory and education: this experience constitutes a background for this model. It combines representation training with awareness and education, while retrieving pattern information by combining various search results from multiple sources. We have extended it to improve our disaster management model and evaluate it in the case of Covid-19 while obtaining promising results, through past programs and experience that have shown overwhelmingly positive effects of education for vulnerability reduction and disaster risk management, in the pusuit of Environmental Sustainability
Analysis of the Micro-market Environment for STEM Education Based on the Porter Five-Force Model

In the Internet age, STEM education is quietly changing teaching and learning. However, the idea of education reform of STEM integration has sparked controversy after it was introduced to our country. Based on the PEST and Porter Five Forces models, this paper analyzes the macro-micro environment of STEM education, aiming to analyze China The current situation of STEM education, as well as its advantages and disadvantages, so as to actively build a STEM education system suitable for China's national conditions.
Increasing depth, distribution distillation, and model soup: erasing backdoor triggers for deep neural networks

Deep neural networks are vulnerable to backdoor attacks, in which the adversary injects a trigger embedded set into the training process. Inputs marked with the trigger provide incorrect predictions, whereas clean inputs remain unaffected. To erase the latent triggers in models, increasing depth, distribution distillation, and model soup (ID3MS), a defensive solution that operates without prior knowledge of triggers and relies on a small clean set is introduced. The depth of the backdoor model is increased by adding fully connected layer(s) at the penultimate layer. Without a classification layer, the original backdoor and increased depth models are considered as teacher and student, respectively. The student model applies distribution distillation to refit the distribution of the clean set and erase the backdoor triggers. The distilled student model is then recovered with the classification layer and model soup is used to ensemble a collection of models generated by various fine-tuning hyperparameters. The experimental results validate the superior performance of the ID3MS compared with existing defensive techniques against several attacks across datasets.
ORGANIZATIONAL AND PEDAGOGICAL CONDITIONS FOR THE FORMATION OF THE READINESS OF FUTURE PRIMARY SCHOOL TEACHERS TO DESIGN AN EDUCATIONAL ENVIRONMENT

The New Ukrainian school needs a new teacher – motivated, responsible and creative, who can become an «agent of change» in the educational space. Having academic freedom, he will prepare his own author's educational programs, choose his own textbooks, methods, strategies, methods and means of learning, diversify the options for organizing the learning space in the classroom using new IT technologies, new multimedia learning tools, updating the laboratory base for studying natural science subjects. mathematical cycle. That is why the process and content of teacher training capable of providing the necessary conditions, means and technologies for teaching students in the context of reforming general secondary education must undergo significant changes. At the article, a choice of organizational and pedagogical minds was framed for the formation of the readiness of the future teachers of the cob school to the design of the primary medium. It has been shown that the organizational and pedagogical mind will play the role of a catalyst for the formation of the readiness of future teachers in the early school to design the initial medium and determine the results, objectively ensuring the possibility of their attainment. On the basis of the analysis of legal documents, modern theory and practice, it was formulated organizational and pedagogical minds and the formation of the readiness of future teachers in the post-primary school to the design of the initial medium, as it appears to be safe positive motivation for education; updating the practical-oriented warehouse disciplines, the cycle of professional and practical training; directing the forms of organization of the lighting process and methods of navigating to the understanding of projective tasks, knowledge of the methods of their achievement and the means of reaching the goal; educated to pedagogical reflection. Visobncheni Organized-Pedagogychi, I am mutual, organically, one and one of the one to forget the fiber of the component of the component of the Maybutovsky schools, and the motivational-anniversary, the digestive, the digestive, the digestive, The prospect of further achievements for this one is directly evident from the expansive model of the formation of the readiness of the future teachers of the cob school to the design of the initial middle school. Key words: readiness, organizational and pedagogical conditions, primary school, teacher.
Implementation of Web Service Security Using Oauth2 in the Financial Facility Search System

&lt;p&gt;Implementation Of Web Service Security Using Oauth2 In Financial Facility Search System. The financial facility search system is an application that makes it easier for people to find the nearest financial facility. A similar application is Google maps which have been widely used to search for financial facilities, but for completeness of information and features, it is incomplete. So financial institutions need applications that can manage data on their financial facilities without other vendor intermediaries. This financial facility search system will be built on a mobile platform and website that is integrated with a web service. Financial facility data can be accessed publicly by the public so that the integrity of financial facility data needs to be maintained and secured from several threats that may occur. One such authentication process is the OAuth2 method. With the existence of OAuth2, this financial facility search system can overcome threats that may occur so that data integrity can be maintained.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
ALGORITHM FOR OPTIMAL SYMBOL-BY-SYMBOL RECEPTION OF FREQUENCY-EFFICIENT SIGNALS WITH CORRECTION CODES IN NON-BINARY GALOIS FIELDS

The description of the developed algorithm for optimal symbol-by-symbol reception of signal structures based on frequency-efficient signals with two-dimensional «constellations» intensively used in applications and block correction codes in non-binary Galois fields is given. It is shown that symbol-by-symbol reception minimizes the error probability for code symbol or for information bit in contrast to the well-known maximum likelihood rule which minimizes the error-probability for code word. It is proved that the basis of this decoding algorithm is the spectral transform in the Walsh-Hadamard basis and the resulting complexity of the algorithm is determined by the dimension of the dual code which makes it promising for block correction codes with a high code rate (with low redundancy). The study of the error-performance characteristics (probability of erroneous reception for information bit) of the considered symbol-by-symbol reception algorithm was carried out by simulating it for signal structures based on signals with multilevel phase shift keying (PSK-16 signals) and on signals with quadrature amplitude manipulation (QAM-16 signals) and on simple correcting code with a general parity check in non-binary Galois field of size 16. It is shown that the use of the symbol-by-symbol reception algorithm provides an energy gain for the bit error probability from 0.00001 to 1.25…1.5 in relation to the considered signals without coding. As the bit error probability decreases the energy gains increase.
Penerapan Media Pembelajaran Berbasis Animasi Flash Pada Mata Pelajaran Rangakain Listrik di SMK Negeri 1 Padang

This research is oriented to the learning outcomes of students who have not yet reached the KKM in the subject of electrical circuits for class X TITL at SMKN 1 Padang. Many factors affect the low student learning outcomes such as the learning process using conventional methods and the media used by the teacher in learning is a module and only uses the blackboard. Based on these factors, research is conducted in the form of experiments to improve student learning outcomes by using media-based flash animation learning media. The method used in this research is quasi-experimental with Pretest-Posttest One Group Design. The subjects of this study were students of class X SMKN 1 Padang semester January-June 2016, which amounted to 31 students. Collecting data in this study using a test (Pretest-Posttest), namely objective questions. The test questions used were tested first to determine the validity, reliability, level of difficulty, and discriminating power of the questions. Based on the results of the study obtained 25 items. The results showed that there was an increase in student learning outcomes of class X TITL SMKN 1 Padang in the subject of Electrical Engineering. The increase occurred at a high level (0.71) where 18 students were at a high level and 13 other students were at an increasing level. Based on the results of the study, it can be concluded that the application of flash animation media on electrical circuit subjects for class X TITL SMKN 5 Padang can improve student learning outcomes at a high level (0.71).
Analyzing model changes with Loupe

Models are software artifacts that are subject to continuous change. The extraction and analysis of these changes may significantly affect model maintenance. Existing work has already investigated processes and techniques to support, analyze and mitigate the impact of changes to models. However, most of these works focus on the analysis of changes between two sets of models and do not take a holistic approach to the entire version history of models. Furthermore, there is limited support for constructing user-defined queries that can be used to extract change information from models. This makes it challenging to analyse the changes over the version history of the models. To support change analysis across the entire version history, we developed a change analyzer named Loupe, which can be used to query and extract change information from all versions of a model. Loupe stores the changes across successive model versions and also provides a set of language constructs to query the changes. Loupe has been compared against a generic model indexing tool (Eclipse Hawk), evaluating its capability to extract changes across two successive model versions and its capability to detect instances of a refactoring operation that has been executed across a model's history. Loupe was able to extract this information using queries that were more concise and ran faster than what could be written for Hawk.