Reviewerly/exHarmony huggingface dataset:
Convergence Properties of the (μ/μI, λ)-ES on the Rastrigin Function

The highly multimodal Rastrigin test function is analyzed by deriving a new aggregated progress rate measure. It is derived as a function of the residual distance to the optimizer by assuming normally distributed positional coordinates around the global optimizer. This assumption is justified for successful ES-runs operating with sufficiently slow step-size adaptation. The measure enables the investigation of further convergence properties. For moderately large mutation strengths a characteristic distance-dependent Rastrigin noise floor is derived. For small mutation strengths local attraction is analyzed and an escape condition is established. Both mutation strength regimes combined pose a major challenge optimizing the Rastrigin function, which can be counteracted by increasing the population size. Hence, a population scaling relation to achieve high global convergence rates is derived which shows good agreement with experimental data.
Advantages and disadvantages of using skype for educational purposes

Over the last decade, social networks have gained, gradually, considerable space and relevance not only concerning social interactions, but also concerning content transmission and knowledge generation, becoming a niche for propagation and research of various topics. This article aims to investigate the way the potential for interactivity present in Skype mechanisms of enunciation relate to the pedagogical nature of posts aimed at English teaching. For this, we base our reflections on social network studies by Recuero (2009, p.191), on teaching and technology reflections by Paiva (2001) and on virtual ethnography by Hine (2000). The purpose of this article is to provide knowledge about pros and cons connected with the usage of Skype during the process of study foreign languages among Russian people. Research in this sphere is not very developed as internet technologies are new method in education. Our task is to delight all sides of this research. A qualitative exploratory research method was applied to interviews and notes via Skype between students and their web teachers. The data were analysed using phenomenography. The findings are based on the pole in the Internet. The advantages were: being able to be at home; well-known being and convenience for the students; and attend ones possible regardless of geographical distance. Barriers were that the Internet connection works intermittently with audio or picture.
A Family of Decidable Bi-intuitionistic Modal Logics

We investigate intuitionistic logics extended both with the co-implication connective of Hilbert-Brouwer logic and with diamond and box modalities. We use a Kripke semantics based on frames with two 'forth' confluence conditions on the modal relation with respect to the intuitionistic relation. We give sound and strongly complete axiomatisations for entailment on this class of frames, and give similar axiomatisations for the subclasses of frames satisfying any combination of reflexivity, transitivity, and seriality. We then prove that all of these logics are decidable, by proving that they have the finite frame property.
Spatially localized sparse approximations of deep features for breast mass characterization

&lt;abstract&gt;&lt;p&gt;We propose a deep feature-based sparse approximation classification technique for classification of breast masses into benign and malignant categories in film screen mammographs. This is a significant application as breast cancer is a leading cause of death in the modern world and improvements in diagnosis may help to decrease rates of mortality for large populations. While deep learning techniques have produced remarkable results in the field of computer-aided diagnosis of breast cancer, there are several aspects of this field that remain under-studied. In this work, we investigate the applicability of deep-feature-generated dictionaries to sparse approximation-based classification. To this end we construct dictionaries from deep features and compute sparse approximations of Regions Of Interest (ROIs) of breast masses for classification. Furthermore, we propose block and patch decomposition methods to construct overcomplete dictionaries suitable for sparse coding. The effectiveness of our deep feature spatially localized ensemble sparse analysis (DF-SLESA) technique is evaluated on a merged dataset of mass ROIs from the CBIS-DDSM and MIAS datasets. Experimental results indicate that dictionaries of deep features yield more discriminative sparse approximations of mass characteristics than dictionaries of imaging patterns and dictionaries learned by unsupervised machine learning techniques such as K-SVD. Of note is that the proposed block and patch decomposition strategies may help to simplify the sparse coding problem and to find tractable solutions. The proposed technique achieves competitive performances with state-of-the-art techniques for benign/malignant breast mass classification, using 10-fold cross-validation in merged datasets of film screen mammograms.&lt;/p&gt;&lt;/abstract&gt;
Toward Generalized Artificial Intelligence by Assessment Aggregation With Applications to Standard and Extreme Classifications

The article proposes a plural learning framework combining the ingredients found in a tribunal for the derivation of a more generalized artificial intelligence (GAI) when starting from a specialized set of convolutional neural networks (CNNs). This framework involves at least two different training stages called, respectively, specialization and generalization. In the specialization stage, any CNN considered in a given set learns to predict independently of other elements of the set. In the second stage called generalization, an integration network learns to predict from assessment measures fed by downstream specialized CNNs. The assessment measures considered are categorical softmax probabilities and learning to judge from these assessments relies on independent CNNs. Generalization proof of concepts is provided in terms of multimodel, multimodal, and distributed schemes. The multimodel framework is such that different CNN models operating on the same modality cooperate for decision purpose. The multimodal framework implies specializations of CNN with respect to different input modalities. The distributed framework proposed is associated with assessment exchanges: it such that the aggregation aims at determining relevant joint assessments for mapping a given input to a single or a multiple output category. The performance of these aggregation frameworks is shown to be outstanding for both standard and extreme classification issues.
Automated Methodology for Classifying Non-functional Requirements: Challenges, Validation Aspects &amp; New Research Directions

The development of higher-quality software is thought to be greatly influenced by non-functional requirements or software quality traits. How to classify non-functional requirements automatically and efficiently has become the top priority. This systematic review aims to outline the cutting-edge automated approaches, methodologies, and systems for detecting and classifying non-functional requirements or quality attributes. Moreover, this study seeks to identify the methods used to validate the proposed non-functional requirement evaluation approaches, highlight limitations in the proposed non-functional requirement evaluation approaches, and offer new research recommendations. The suitable articles are extracted from leading citation databases specifically, IEEE Xplore, ScienceDirect, and Web of science. Directed by three research questions, a total of 66 studies were carefully reviewed. Supervised learning algorithms are the most popular, and cross-validation with ten folds is widely used to validate the results of the approaches. Precision and recall are the most used matrices to measure the performance of these approaches. This systematic review calls for close cooperation between non-requirement analysis, and machine learning researchers to address the need for the development of real-time automated tools.
Distant Co-occurrence Patterns of Connectives: a Corpus Study of Formulaicity in Japanese

Using corpus research methods, this study aims to establish whether there are two-item and, more generally, multi-item distant co-occurrence patterns of connectives in written Japanese, and further, to clarify the role these patterns play in discourse. The study is based on a hybrid corpus of written Japanese including Humanities and social science papers, Science and technology papers, and general written language data. The co-occurrence threshold was set at co-occurrence frequency &gt; 10, PMI value &gt; 2, and Dice coefficient &gt; 0.01. The distribution of the observed co-occurring pairs differed according to the genre. Visualization of the connectivity potential of co-occurring pairs as directed graphs showed that these co-occurring pairs constitute longer co-occurrence chains which can be interpreted as ready-made co-occurrence patterns. Two-item and multi-item co-occurrence patterns are considered a type of Bourdieu’s habitus and contribute to both discourse development and discourse prediction.
COVID-19 상황에서 중국 음악전공 학생들의 온라인 수업 인식 및 만족도에 관한 연구

Objectives The purpose of this study is to investigate the perceptions and satisfaction of Chinese music major students with online classes and the relationship between these variables in the COVID-19 situation. Methods The research participants consisted of 588 undergraduate and graduate students majoring in music from 62 universities located in 15 areas and 2 autonomous regions across China. In this study, a survey was conducted on the three areas of system perception, subjective norm perception, and perceived behavioral control in relation to the perception and the four areas of system, interaction and support, learning, and professional practice guidance in relation to satisfaction of online classes. The collected data were used for descriptive statistics, correlation analysis, and linear regression analysis using SPSS 25.0. Results First, the overall perceptions of Chinese music major students with online classes is relatively high. Second, the overall satisfaction of Chinese music major students with online classes is also relatively high. Third, there was a significant correlation between the perceptions and satisfaction of Chinese music major students on online classes. In addition, all 3 dimensions of perception, including system perception, subjective norm perception, and perceived behavioral control, had a statistically significant effect on satisfaction with online classes. Conclusions This study investigated the current status of university music major students' perceptions of online classes, which were rarely studied, in various dimensions such as system, learning, social relationship, and personal psychological aspects. It is expected that these various areas of perception will eventually become important basic data for developing highly satisfactory online classes.
Darknet Detection: A Darknet Traffic Detection Method Based on Improved Between-class Learning

With the development of the Internet, people have paid more attention to privacy protection, and privacy protection technology is widely used. However, it also breeds the darknet, which has become a tool that criminals can exploit, especially in the fields of economic crime and military intelligence. The darknet detection is becoming increasingly important, however, the darknet traffic is seriously unbalanced. The detection is difficult and the accuracy of the detection methods needs to be improved. To overcome these problems, in this paper, we first propose a novel learning method. The method is a Chebyshev distance based Between-class learning (CDBC), which can learn the spatial distribution of the darknet dataset, and generate "gap data". The gap data can be used to optimize the distribution boundaries of the dataset. Secondly, a novel darknet traffic detection scheme is proposed. We test the proposed method on the ISCXTor 2016 dataset and the CIC-Darknet 2020 dataset, and the results show that CDBC can help more than 10 existing methods improve accuracy, even up to 99.99%. Compared with other sampling methods, CDBC can also help the classifiers achieve higher recall.
Analysis of the evolution of advanced transformer-based language models: experiments on opinion mining

Opinion mining, also known as sentiment analysis, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information in textual material. This can include determining the overall sentiment of a piece of text (e.g., positive or negative), as well as identifying specific emotions or opinions expressed in the text, that involves the use of advanced machine and deep learning techniques. Recently, transformer-based language models make this task of human emotion analysis intuitive, thanks to the attention mechanism and parallel computation. These advantages make such models very powerful on linguistic tasks, unlike recurrent neural networks that spend a lot of time on sequential processing, making them prone to fail when it comes to processing long text. The scope of our paper aims to study the behaviour of the cutting-edge Transformer-based language models on opinion mining and provide a high-level comparison between them to highlight their key particularities. Additionally, our comparative study shows leads and paves the way for production engineers regarding the approach to focus on and is useful for researchers as it provides guidelines for future research subjects.
Dusting Off Your Resume

Chatbots seem to be scaring people into potential job losses. Are we really there yet?
Development of an AR-Based Application for Training of Warehouse Operators

Nowadays, logistics is increasingly becoming a crucial process in the modern factories, and numerous companies are paying growing attention to the continuous improvement of quality and efficiency of logistic processes. Although Industry 4.0 concept introduced new digital technologies and led to high degree of automation of industrial and logistic systems, many tasks still rely on human work, especially in the warehouse. As a consequence, the consideration of the operators’ needs is definitely a key topic to guarantee high quality work performance, according to Industry 5.0 paradigm. In this context, the quality of the training procedures is fundamental to speed up the knowledge transfer and empower operators through the use of digital technologies. This paper presents the development of a novel training methodology based on Augmented Reality (AR), promoting a learning-by-doing approach to enhance both operators’ satisfaction and process efficiency and flexibility. A dedicated AR application has been developed for a spare parts warehouse of a large enterprise operating in the agriculture and construction vehicles sector, according to a User-Centered Design (UCD) approach. Results from field testing showed how the AR application is positively rated by the operators, guaranteeing high user satisfaction, and limiting the training effort.
Multi-station volcano tectonic earthquake monitoring based on transfer learning

Introduction: Developing reliable seismic catalogs for volcanoes is essential for investigating underlying volcanic structures. However, owing to the complexity and heterogeneity of volcanic environments, seismic signals are strongly affected by seismic attenuation, which modifies the seismic waveforms and their spectral content observed at different seismic stations. As a consequence, the ability to properly discriminate incoming information is compromised. To address this issue, multi-station operational frameworks that allow unequivocal real-time management of large volumes of volcano seismic data are needed. Methods: In this study, we developed a multi-station volcano tectonic earthquake monitoring approach based on transfer learning techniques. We applied two machine learning systems—a recurrent neural network based on long short-term memory cells (RNN–LSTM) and a temporal convolutional network (TCN)—both trained with a master dataset and catalogue belonging to Deception Island volcano (Antarctica), as blind-recognizers to a new volcanic environment (Mount Bezymianny, Kamchatka; 6 months of data collected from June to December 2017, including periods of quiescence and eruption). Results and discussion: When the systems were re-trained under a multi correlation-based approach (i.e., only seismic traces detected at the same time at different seismic stations were selected), the performances of the systems improved substantially. We found that the RNN-based system offered the most reliable recognition by excluding low confidence detections for seismic traces (i.e., those that were only partially similar to those of the baseline). In contrast, the TCN-based network was capable of detecting a greater number of events; however, many of those events were only partially similar to the master events of the baseline. Together, these two approaches offer complementary tools for volcano monitoring. Moreover, we found that our approach had a number of advantages over the classical short time average over long time-average (STA/LTA) algorithm. In particular, the systems automatically detect VTs in a seismic trace without searching for optimal parameter settings, which makes it a portable, scalable, and economical tool with relatively low computational cost. Moreover, besides obtaining a preliminary seismic catalog, it offers information on the confidence of the detected events. Finally, our approach provides a useful tentative label for subsequent analysis carried out by a human operator. Ultimately, this study contributes a new framework for rapid and easy volcano monitoring based on temporal changes in monitored seismic signals.
Your DRM Can Watch You Too: Exploring the Privacy Implications of Browsers (mis)Implementations of Widevine EME

Thanks to HTML5, users can now view videos on Web browsers without installing plug-ins or relying on specific devices. In 2017, W3C published Encrypted Media Extensions (EME) as the first official Web standard for Digital Rights Management (DRM), with the overarching goal of allowing seamless integration of DRM systems on browsers. EME has prompted numerous voices of dissent with respect to the inadequate protection of users. Of particular interest, privacy concerns were articulated, especially that DRM systems inherently require uniquely identifying information on users' devices to control content distribution better. Despite this anecdotal evidence, we lack a comprehensive overview of how browsers have supported EME in practice and what privacy implications are caused by their implementations. In this paper, we fill this gap by investigating privacy leakage caused by EME relying on proprietary and closed-source DRM systems. We focus on Google Widevine because of its versatility and wide adoption. We conduct empirical experiments to show that browsers diverge when complying EME privacy guidelines, which might undermine users' privacy. For instance, we find that many browsers gladly give away the identifying Widevine Client ID with no or little explicit consent from users. Moreover, we characterize the privacy risks of users tracking when browsers miss applying EME guidelines regarding privacy. Because of being closed-source, our work involves reverse engineering to dissect the contents of EME messages as instantiated by Widevine. Finally, we implement EME Track, a tool that automatically exploits bad Widevine-based implementations to break privacy.
Lights, Camera, Interaction! The Real-Time Technology of ILM StageCraft

LED volume virtual production brings its own unique set of challenges that differ from typical applications of real-time rendering. ILM StageCraft is a complete suite of virtual production tools that incorporates powerful off-the-shelf systems, such as Unreal, with proprietary solutions that allow us to overcome these challenges—always with the goal of using the very best tool for the job.
How much cloud is too much cloud?

Avatar: The Way of Water, presented Wētā FX with the challenge of delivering visuals of an unprecedented level of complexity and quality. To support the filmmaker's creative vision, Wētā FX sought to ensure it had burst compute capacity available through scalable cloud resource.
Coil to Coil: Self-supervised denoising using phased-array coil images

A self-supervised learning framework, Coil to Coil (C2C), is proposed. This method generates two noise-corrupted images from single phased-array coil data to train a denoising network and, therefore, requires no clean image nor acquisition of a pair of noisy images. The two images are processed to have the same signals and independent noises, satisfying conditions for the noise to noise algorithm, which requires paired noise-corrupted images. C2C shows the best performance among popular self-supervised denoising methods in both real and synthetic noised images, revealing little or no structure in the noise map.
TEACHER MOTIVATION AND SCHOOL ADMINISTRATION- A STUDY WITH REFERENCE TO TEACHERS FROM SELECTED PRIMARY SCHOOLS OF MANGALORE IN THE STATE OF KARNATAKA

Education is the most important invention of mankind. It is more important than his invention of tools, machines, spacecraft, medicine, weapons and even of language. Because language is the product of his education. Man without education would still be living like an animal. In imparting education, the role of teachers assumes special significance. The teachers role has always been emphasized as a morale booster for harmonising the worldly agonies and celestial bliss. The quality of a nation depends upon the quality of its school and the quality of schools depend considerably on the quality of the teachers as well as on the interaction of collective internal and external forces that intervene in the fulfilment of the purpose of the schools. This paper tries to analyse the interrelation between School administration and motivation of teachers in primary schools of Mangalore in Karnataka State. A semi-structured questionnaire was prepared for the teachers. Depending on the type of information to be collected, open-ended, multiple-choice type questions, and Likert scale (1932) were included in the questionnaire. The data collected from the teacher are evaluated and analysed with appropriate statistical tools to arrive at the conclusion. It was found that favourable support and guidance from head of the school and education officer along with periodic inspection plays a vital role in motivating teachers in primary schools. KEY WORDS: School support, School administration, Teacher motivation, Education, Primary School.
Findings of the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages

In this work, we present the results of the Amer-icasNLP 2023 Shared Task on Machine Translation into Indigenous Languages.This edition of the shared task features eleven language pairs, one of which -Chatino-Spanish -uses a newly collected evaluation dataset, consisting of professionally translated text from the legal domain.Seven teams participated in the shared task, with a total of 181 submissions.Additionally, we conduct a human evaluation of the best system outputs and compare them to the best submissions from the 2021 shared task.We find that this analysis agrees with the quantitative measure we use to rank submissions, ChrF, which itself shows an improvement of 9.64 points on average across all languages, compared to the prior winning system.