Reviewerly/exHarmony huggingface dataset:
Understanding Which Images Degrade Deep Learning Classification Performance

In recent years, deep neural networks (DNNs) have shown high success and accuracy in various classification tasks such as speech recognition, image classification, segmentation, etc. However, DNNs provide low classification performance when the train and test distributions differ (i.e., out-of-distribution). In this study, we investigate each visual characteristic of the correctly classified and misclassified images (referred to as natural adversarial examples) on CIFAR10 to understand which images substantially degrade machine learning and deep learning models’ classification performances. To verify this, we divide the classification results into two groups per class: correct prediction and incorrect prediction. Then, we compare the visual appearance (e.g., image point of view) of examples of correct predictions with those of incorrect predictions. Our experimental results demonstrated that correctly classified examples have several common visual characteristics, which differ from those of natural adversarial examples.
PROFESSIONAL COMPETENCE OF FUTURE EDUCATIONAL MANAGERS

This article presents the results of a study of the development of professional competence of future education managers. The relevance of the problem under study lies in the demand for education managers in educational institutions. The personal and behavioral characteristics of future managers were studied using the SAMOAL test-questionnaire for diagnosing self-actualization of personality. The formation of the main professional competencies was determined by expert assessments of employers - school directors with many years of experience.
Short-term PV Power Forecasting Based on Time-Phased and Error Correction

The uncertainty of PV output power brings a series of scheduling operation problems, and accurate prediction of PV output power is an effective means to reduce the influence of uncertainty. A short-term PV power prediction method based on time-phased and error correction is proposed. The representative meteorological factors selected by GRA are used to classify the days to be predicted, and the improved sparrow search algorithm (CMSSA) is used to optimize the Elman model, and Elman, CMSSA-Elman, and CMSSA-Elman error correction models are established to select different prediction models for power prediction for different weather types. The results show that the method proposed in this paper has obvious improvement in the accuracy of PV system output power prediction for different weather conditions and has some practical value.
Deferred Dropout: An Algorithm-Hardware Co-Design DNN Training Method Provisioning Consistent High Activation Sparsity

This paper proposes a deep neural network training method that provisions consistent high activation sparsity and the ability to adjust the sparsity. To improve training performance, prior work reduces the memory footprint for training by exploiting input activation sparsity which is observed due to the ReLU function. However, the previous approach relies solely on the inherent sparsity caused by the function, and thus the footprint reduction is not guaranteed. In particular, models for natural language processing tasks like BERT do not use the function, so the models have almost zero activation sparsity and the previous approach loses its efficiency. In this paper, a new training method, Deferred Dropout, and its hardware architecture are proposed. With the proposed method, input activations are dropped out after the conventional forward-pass computation. In contrast to the conventional dropout where activations are zeroed before forward-pass computation, the dropping timing is deferred until the completion of the computation. Then, the sparsified activations are compressed and stashed in memory. This approach is based on our observation that networks preserve training quality even if only a few high magnitude activations are used in the backward pass. The hardware architecture enables designers to exploit the tradeoff between training quality and activation sparsity. Evaluation results demonstrate that the proposed method achieves 1.21-3.60 × memory footprint reduction and 1.06-1.43 x speedup on the TPUv3 architecture, compared to the prior work.
The mission of the University in the conditions of a multicultural region

The main goal of the presented article is to explicate the capabilities of a modern university to effectively pursue social policy in a multicultural region. The article examines the educational trends of recent years related to public demand and opportunities for the implementation of the third mission of the university. In this regard, attitudes towards communication, the practice of intellectual leisure and the active participation of universities in the creation of information networks were identified as the main educational trends. The article provides a description of socio-cultural markers that make it possible to speak with confidence about the criteria for identifying multicultural regions typical for Russia. The authors of the article draw attention to the need for universities to more actively participate in the formation of regional identities in order to ensure the cultural security of the country's residents. In conclusion, the article concludes that universities have a high potential to act as a resource for cultural stabilization and socialization in a multicultural region, that is, together with the state and civil society institutions, to implement programs aimed at harmonizing interethnic relations.
State of Art in Collaborative Ontology Development

Ontologies are an important part of the Knowledge Representation (KR) and Knowledge Management (KM) processes. They are a meta description of the knowledge domain in which a certain activity expands, and are widely used in the discovery, analysis, and even in the creation of new knowledge. The development of an appropriate and high-quality ontology is a complex process in which both specialists in the respective domain area and specialists aware of the technology for building knowledge bases must work in cooperation. Along with the human factor, it is important to have and use quality tools that allow editing the ontology database in a way convenient for all participants in the process. In this review, we examine methods and tools currently available and usable in the collective development.
Generalized Morse Wavelet Frame Estimation Applied to Side-Channel Analysis

This paper proposes a maximum likelihood approach for the inference of a frame of Generalized Morse Wavelets. This way we abstract the limitation for some experiments to choose between many wavelet families, while answering the need to embed prior information on the time-frequency properties of signals. This method is tested on the retrieval of sensitive information in side-channel analysis.
Delving into the Scale Variance Problem in Object Detection

Object detection has made substantial progress in the last decade, due to the capability of convolution in extracting local context of objects. However, the scales of objects are diverse and current convolution can only process single-scale input. The capability of traditional convolution with a fixed receptive field in dealing with such a scale variance problem, is thus limited. Multiscale feature representation has been proven to be an effective way to mitigate the scale variance problem. Recent researches mainly adopt partial connection with certain scales, or aggregate features from all scales and focus on the global information across the scales. However, the information across spatial and depth dimensions is ignored. Inspired by this, we propose the multi-scale convolution (MSConv) to handle this problem. Taking into consideration scale, spatial and depth information at the same time, MSConv is able to process multi-scale input more comprehensively. MSConv is effective and computationally efficient, with only a small increase of computational cost. For most of the single-stage object detectors, replacing the traditional convolutions with MSConvs in the detection head can bring more than 2.5% improvement in AP (on COCO 2017 dataset), with only 3% increase of FLOPs. MSConv is also flexible and effective for two-stage object detectors. When extended to the mainstream two-stage object detectors, MSConv can bring up to 3.0% improvement in AP. Our best model under single-scale testing achieves 48.9% AP on COCO 2017 test-dev split, which surpasses many state-of-the-art methods.
A Template for Alternative Proof of Work for Cryptocurrencies

Many popular cryptocurrencies, such as Bitcoin, form consensus through a method known as Proof of Work. Problematically, current implementations of Proof of Work require immense amounts of energy consumption, where a majority of this energy is spent solely on securing consensus. Our focus is not to directly decrease energy consumption, but to allow for more useful and pragmatic computation to come from Proof of Work, such that energy is saved by not running these computational tasks separately. In this paper, we create a template for Proof of Work protocols, such that if followed, can guarantee similar security assurances as to the Proof of Work present in Bitcoin. Secondarily, we also develop “useful” prototypes based on this template.
A Study on the Improvement of Fine-grained Ship Classification through Data Augmentation Using Generative Adversarial Networks

Identification of the type of ship is an important issue in maritime surveillance. However, unlike the land environment where data sets are easy to build, it is difficult to build large amounts of marine environment data where it is difficult to collect images. In this situation, the use of large-scale data obtained in the surveillance and defense fields is essential for research, but the use of data for private research is impossible due to security issues. In this paper, a ship dataset free from security problems was constructed through data augmentation using GANs. We conduct an experiment on improving fine-grained ship classification performance through the use of a small amount of real ship images and augmented data, and try to show that the augmented data is useful for ship classification network training.
<i>The Big Misconception About Electricity (or how do circuits transport energy?)</i>

First Page
Bitcoin

In the previous chapter, you saw how blockchain technology, without the need for a central authority, and thanks to the techniques and algorithms discussed, can establish trust between participants and certify the immutability of the distributed ledger. The blockchain lends itself to being an ideal model for digital currencies, where there is no need for intermediaries and third parties such as banks, insurance companies, and other central organizations. In fact, blockchain was born and spread thanks to Bitcoin.
Blockchain-Based Smart Contracts Use for Photovoltaic Energy Trade Transactions

In recent decades, interest in renewable energy via solar energy has increased, especially in California and Nevada. In a region that can produce enough solar power for self-use, prosumers must use or sell surplus energy. Solar energy self-consumption is essential in modern energy transactions in the grid. This paper proposes an approach towards grid services that includes photovoltaic hardware to store the excess energy in battery storage. Such energy can then be sold to power companies while the transaction is handled using blockchain. Software, security architecture and data flow requirements based on the Ethereum Blockchain are included. This solution protects the private information of seller and buyer and secures transactions.
Expert System For Comparative Diagnosis Probability Of Lung Disease Using Nearest Neighbour Algorithm

Lung disease is a serious threat in Indonesia and has even claimed many lives. Lung disease itself is quite diverse such as tuberculosis, pneumonia, bronchitis, lung cancer and lung disease which recently threatens the health of all citizens of the world namely Coronavirus Disease 2019 (Covid-19). even so, especially in the City of Tasikmalaya the availability of a lung specialist doctor is very minimal and can even be said to be missing. For the treatment of lung disease in hospitals in the city of Tasikmalaya, it is usually done by a specialist in internal medicine. During the industrial revolution 4.0 as it is now the actual availability of experts can be overcome with artificial intelligence technology, namely through expert systems. Therefore, the authors try to build an expert system that can diagnose lung disease with nearest neighbour retrieval algorithm in order to represent the knowledge of pulmonary specialists and can benefit the community. This system can diagnose 25 symptoms or conditions related to lung disease, so that the diagnosis of these symptoms will produce an output in the form of the probability of the disease in accordance with the correlation of symptoms that are related. This system is quite effective as an alternative solution for the community in the diagnosis of lung disease, because this system has been tested by experts and produces an accuracy rate of 96.66&#x0025
A Secret Sharing Scheme to Reduce the Total Data Size

Secret sharing schemes have been proposed to prevent the loss or theft of secret data such as secret keys and files including highly private information. In the schemes, for given integer numbers of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> and k (n ≥ k), n data called shares are constructed from the secret data so that the sharing conditions are satisfied, where the conditions are (i) the secret data can be reconstructed from arbitrary <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> or more shares and (ii) it can not be reconstructed from arbitrary less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> shares. However, the total data size (the number of bytes) to store all the shares is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> times as large as the size of the secret data because the size of each share is equal to that of the secret data. In this paper, we propose a secret sharing scheme to reduce the total data size. In our proposed scheme, we partition the secret data into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> blocks, and we construct each share from n-k+1 blocks so that the sharing conditions are satisfied. Our proposed scheme can reduce the total data size by a maximum of 2/n of the previous schemes when n = k.
Mineralogical and Geochemical Features of Native Gold From Placers of the Evota Gold-Bearing Region (Russia, Aldan Shield)

Abstract Evota gold-bearing region is located in south of Republic Sakha (Yakutia) within the Nimnyr terrane of Aldan shield. A large numbers of gold-bearing placers are known on studied territory, but the primary sources for them have not been established. In this work, based on the study of the mineralogical and geochemical features of gold from alluvial deposits of the Evota gold-bearing region, possible genetic types of primary sources are considered. Obtained data showed that native gold in the studied objects has a very high, high and medium fineness. The roundness of gold is different. Almost ore crystals with sharp edges and well-rounded individuals with polished faces were found. The fineness of the studied gold grains was determined by microprobe analyzer Cameca Camebax-micro and varies in the range from 812 to 1000 %⁰. A thin (up to 20 μm) high-grade rim was found in two grains (cr. Zolotoy). The central part of one of them has a fineness of 865 %o, and in the edge part it reaches 1000 % ⁰ , which indicates that this gold was in the hypergenesis zone. Admixture elements determined as traces and are presented Cu, Pd, Fe, Ni. Minerals-microinclusions - quartz, potassium feldspar, pyroxene, staurolite, maldonite, bismuthite revealed in gold grains. In some watercourses, for example, in the creek Sukhoi, only very high fineness gold (993-1000 % ⁰ ) was found. The presence of high-grade gold (cr. Sukhoi), intergrowths of gold with bismuthite (cr. Zolotoy), as well as inclusions of maldonite (cr. Yagodny) gives opportunity for assuming that primary sources could be basic ores such presented in the P. Pinigin deposit. At the sites (cr. Elovyi, r. Evota), both medium-grade and very high-grade gold were found, the fineness range varied from 827 to 998 % ⁰ . The presence of gold with medium fineness and good roundness in studied watercourses probably indicates an additional supply of gold from primary sources formed as a result of the alkaline magmatism development of the Mesozoic age. Thus, for gold with high and very high fineness by admixture-elements, chemical composition and microinclusions, a genetic relationship with primary sources similar to the P. Pinigin deposit was found; for gold grains with medium fineness, formation in gold-ore mineralization characterized for the Mesozoic stage ore development such in deposits of Central-Aldan ore region is assumed.
Constructive Interaction of Teacher and Student in the Process of Learning to Play the Clarinet As a Condition for Formation of Ensemble Music Culture

Constructivity in the interaction of a teacher and a student contributes to an increase in the level of musical and performing skills, therefore, the problem of research is relevant in connection with the disclosure in the conditions of constructive interaction of the potential capabilities of the student when playing the instrument, the influence on its creative development, including in the field of ensemble music. Such interaction encourages the acquisition of the entire complex of competencies and its own professional position. The constructive interaction of the teacher and the student in the course of classes in the clarinet class as a condition for the formation of a culture of ensemble music was not previously considered, therefore this article is devoted to a theoretical study of the problem of forming the skill of constructive interaction of the teacher and student and illuminating the method of its formation in the clarinet class
Digitalization in Higher Education

Education plays an important role in the development of stable and civilized society, polishing human skills and developing the personality which helps an individual become knowledgeable and skillful. Digitalization in higher education can be defined as the use of a combination of different technologies and instruction to make the education system more effective than the traditional education system. This chapter discusses the pros and cons of the digitalization of the education system and the various issues and challenges faced by the students as a result. We will also discuss how digitalization is affecting the student’s learning process.
TextRank keyword extraction method weighted by multivariate quantitative indexes

In the process of keyword extraction, news text has its uniqueness. Keywords extraction of news text not only needs to pay attention to the difference of quantitative indexes of words, but also needs to consider the influence of phrases. In order to improve the keyword extraction effect of news texts, this paper constructs a keyword graph based on TextRank, improves the probability transition matrix by combining four quantitative indicators of node frequency, location, span and part of speech, realizing the weight difference of words. Considering the influence of word segmentation technology on phrases extraction, the reconstruction of phrases is completed according to the law of recombination and the concept of combinatorial entropy is defined to realize the filtering of reconstructed phrases. According to the statistical quantitative index of phrases, the linear weighted value is assigned to the reconstructed phrases, and finally, the TopN words or phrases are selected as keywords according to their weight value. Experimental results show that the proposed algorithm is not only superior to the traditional TextRank and TF-IDF algorithms, but also has great advantages compared with the improved PositionRank and MyWPMWRank algorithms, the F value of which can be increased by 9.75% at most, which effectively improves the keywords extraction effect of news text.
Visitor Forecasting Wisata Bahari Lamongan (WBL) Using Hybrid Particle Swarm Optimization (PSO) and Seasonal ARIMA

The revenue of city is determined by some factors, one of them is tourism sector. A problem of tourism sector is forecasting visitors Wisata Bahari Lamongan (WBL). Because data of the number of visitors WBL are fluctuating and seasonal, then it is required Seasonal ARIMA method. In the Seasonal ARIMA method, there are some parameters that should be optimized for producing forecasting with small mean square error (MSE). In this research, Seasonal ARIMA parameters will be optimized by Particle Swarm Optimization (PSO). PSO is optimization algorithm inspired by behavior of birds group in searching food. Based on simulation results, PSO algorithm can optimize Seasonal ARIMA parameter which is optimal and it can produce forecasting result with small MSE.
Model Explainability and Interpretability

In this book, we will begin with an introduction to model explainability and interpretability basics, ethical considerations in AI applications, and biases in the predictions generated by AI models. We will cover the reliability of AI models in generating predictions in different use cases. Then we will cover the methods and systems to interpret the linear models that are used in AI, such as non-linear models and time series models. Next, we will explore the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, and more. Then we will cover model explainability for unstructured data and natural language processing-related tasks.
Gamification in CollectiveEyes

In this paper, we present our ongoing investigation of human motivation design to offer a user’s visual perspectives to other people based on a novel abstraction named theme channel in CollectiveEyes, which is a digital platform to collectively share human seeing. CollectiveEyes captures people’s visual perspectives in the world anytime, anywhere, and a user simultaneously watches multiple captured visual perspectives in a virtual space, which necessitates motivating people to allow CollectiveEyes to capture their visual perspectives.