Reviewerly/exHarmony huggingface dataset:
Experience and Prospects of Implementing a Model of Mathematics Distance Learning in North-Caucasus Federal University Training e-Environment

The article offers a view at relevant issues of contemporary trends in employing remote learning technologies in academic environment, and transformation of higher education based on the example of the North-Caucasus Federal University, including through the given epidemic situation due to COVID-19 in the Russian Federation. The aim of the study is to analyze the implementation of the educational content developed in 2018 at the North-Caucasus Federal University in order to teach Mathematics via distance learning technologies (DLT), as well as to evaluate the experience of using the said remote model within the new academic paradigm, which is due to the coronavirus pandemic. There is a view at a modified distance learning model involving mixed learning, which has been introduced into the educational process and tested. Its implementation has relied on LMS Moodle (LMS—Learning Management System; Moodle—Modular Object-Oriented Distance Learning Environment). There is also a review of the virtual academic environment, the model of structure, its major principles and specific of its functioning and management, as well as a statistical analysis of the outcomes obtained through introducing the model in education. The article focuses on the main differences between the concepts of “distance learning” and “modified distance learning model”, analyzes their strong and weak points, defines the major benefits of using the latter in the educational environment. The issue in question is view of from the stance of relevance and possible introduction of the said methodology for employing the potential contained in the distance learning technology. There is a special individual view offered at the issue mentioned above, as well as a possible way to resolve that.
Deep ordinal regression for automatic tumor cellularity assessment from pathological images

Breast cancer is one of the most common occurring cancers in women. Residual cancer burden measures tumor response to the therapy and is shown to be prognostic for long term survival. The percentage of invasive or in situ tumor cellularity is an important component of tumor burden assessment. In the current clinical practice, tumor cellularity is manually estimated by pathologists on hematoxylin and eosin (H&E) stained slides. In this paper, we present a deep ordinal regression framework to automatically assess cellularity from pathological images. We formulate the cellularity assessment as an ordinal regression problem and address by an end-to-end learning approach using deep convolutional neural networks. We evaluated the proposed methods on the SPIE BreastPathQ challenge dataset and achieved significant higher agreement with expert pathologist scoring in terms of intraclass correlation (ICC) of 0.94 (vs. 0.89 of the inter-rater agreements between pathologists).
Development of a wordpress CMS-based school website as a medium of information and promotion for SMAN 1 Poco Ranaka, NTT

In East Manggarai Regency, NTT there are still many schools that haven’t a school website. One of them is SMA Negeri 1 Poco Ranaka. The purpose of this community service is to develop a school website for SMA Negeri 1 Poco Ranaka. This school website development activity is carried out with Focused Group Discussions, presentation of materials, and website development. The results of this activity indicate that partners' understanding of the importance of the school website has increased so that the collaboration to develop school websites can be established. In addition, the developed school website prioritizes features that suit the needs of the school, both for communication purposes with stakeholders and to facilitate school promotions.
IMPLICATIONS OF ONLINE LEARNING DURING THE COVID-19 PANDEMIC: A SURVEY ON THE PERCEPTION OF TEACHERS IN SURROUNDING SCHOOLS DISTRICT OF KOTA KINABALU, SABAH

Education is one of the most important aspects of the process of insulation of knowledge to students, not to mention in the covid-19 pandemic situation. Without adequate knowledge, they would have drifted away with the waves of a borderless world. Strong knowledge will be able to spur a student to stay on track and to be able to build a good life landscape. Therefore, this study is aimed at looking at the implications of PdPr through online during the Covid-19 pandemic survey on the perception of teachers in schools around the district of Kota Kinabalu, Sabah. The study used quantitative methodology based on questionnaires distributed through Google Form. There were 255 respondents among the teachers in schools around Kota Kinabalu district who took the time to participate in the study. The findings show that there are nine positive implications of PdPr through online such as better implemented PdPr online than no classes, parents help students in PdPr, students can apply technology in Education well and so on. The study also found eight negative implications in online PdPr based on the feedback of teachers in schools around Kota Kinabalu district. The main negative implication is that students do not have sufficient facilities to participate in PdPr because they do not have gadgets, do not have enough data, uncertain telecommunication networks and family socio-economic problems. This finding clearly illustrates the actual situation of student achievement and involvement during the implementation of PdPr online. Hopefully, the findings of this study will help the Ministry of Education specially to restructure the online teaching and learning methods in the future to ensure that no students are left behind in daily lessons.
Comparison of Deep Learning Preprocessing Algorithms of Nuclei Segmentation on Fluorescence Immunohistology Images of Cancer Cells

Immunohistology fluorescence image analysis is an important method for cancer diagnosis. With the widespread application of convolutional neural networks in computer vision, segmentation of images of cancer cells has become an important topic in medical image analysis. Although there are many publications describing the success in application of deep learning models for segmentation of different kind of histology images, the universal model and algorithm of its application is still not developed. Since the histological images of cancer cells are very different, it is usually difficult to get a training set of a large size consisting of images of desired similarity with the studied ones. The image preprocessing consisting in splitting input images in smaller parts and its normalization plays an important role in deep learning especially when the training set is of a limited size. In this study, we compared several approaches to create the training set of a sufficient size while having a very limited number of labeled whole slide immunohistology fluorescence images of cancer cells. In addition, we compared different normalization methods and evaluated their influence on histological image segmentation.
Database and Storage Options

Of course, a data-driven application cannot be built without storing data in some way. Therefore, different storage patterns and concepts are at the heart of this chapter. When utilizing serverless services in the cloud, you might think that your choices are limited, but with SQL Database, Cosmos DB, and storage accounts (containing queues, blobs, and tables), the options are plenty.
Research on Job-shop Scheduling Problem Based-on Improved Genetic Algorithm

Abstract Considering minimizing completion time and maximizing equipment utilization of job shop scheduling problem, an improved genetic algorithm was proposed. First, constructing a two-layer chromosome coding structure, with which the initial population quality and the diversity of the population were strengthened and improved. Next, in order to enhance the local search ability and improve the algorithm convergence speed, the population generated after selection, crossover, and mutation was divided into families, and the elite population were combined with explosive fireworks of the firework algorithm. Then, in order to prevent the algorithm from maturing prematurely, the disadvantaged groups were mutated through(by) the dynamic mutation mechanism to narrow the gap between the super individual and the average. Last, the parameters of the algorithm were optimized by orthogonal tests, and performance of the algorithm was verified by classical test examples.
ALWars: Combat-Based Evaluation of Active Learning Strategies

The demand for annotated datasets for supervised machine learning (ML) projects is growing rapidly. Annotating a dataset often requires domain experts and is a timely and costly process. A premier method to reduce this overhead drastically is Active Learning (AL). Despite a tremendous potential for annotation cost savings, AL is still not used universally in ML projects. The large number of available AL strategies has significantly risen during the past years leading to an increased demand for thorough evaluations of AL strategies. Existing evaluations show in many cases contradicting results, without clear superior strategies. To help researchers in taming the AL zoo we present ALWars: an interactive system with a rich set of features to compare AL strategies in a novel replay view mode of all AL episodes with many available visualization and metrics. Under the hood we support a rich variety of AL strategies by supporting the API of the powerful AL framework ALiPy [21], amounting to over 25 AL strategies out-of-the-box.
A Toolkit to Assess the Level of Universal Competencies (Soft Skills) of Pedagogical University Students

As science and technology develops rapidly and the knowledge gained over the years of study devalues equally rapidly, a university should not only provide students with specific, highly professional knowledge and skills, but also develop universal competencies (soft skills), which include the ability to self-study, self-development, communication and cooperation, creativity, critical thinking and others. These are the skills that are increasingly valuable and will be more valuable in the future for the personal and professional success of a young specialist demanded by an employer. In the absence of unified and validated methods for diagnosing universal competencies, and with the uncertainty and inconsistency relating to the basic set of soft skills required for a young specialist at the start of his/her professional career, difficulties arise in organizing the educational process in high education to integrate the development of hard and soft skills. The aim of the study was to develop a toolkit for assessing and monitoring the level of development of universal competencies in students of a pedagogical university. The proposed toolkit was tested at the Institute of Natural Science and Sports Technologies of the Moscow City University. It made it possible to identify strengths and weaknesses in the educational process, acting as a sensitive diagnostic tool and facilitating the choice of a strategy aimed at increasing its effectiveness for developing students’ universal competencies. The proposed approach is universal and can be used in different contexts.
SSCGAN: speech style conversion based on GAN

Speech conversion has significant applications in medical, robotics, and other industries. With the rise of deep learning, CycleGAN is widely used in speech conversion technology. However, the existing CycleGAN-based methods do not consider the speech signal’s temporal and spatial features. In addition, the training of CycleGAN is difficult to converge due to the gradient disappearance problem of the generator. We propose SSCGAN, whose generator is a U-shaped encoder-decoder network that extracts the temporal and spatial features by using 1DCNN and 2DCNN in parallel. A feature fusion module based on multi-scale mixed convolution is embedded between encoder and decoder to achieve high-level fusion of spatial features and temporal features. To make the network training more stable and easier to converge, SSCGAN uses Wasserstein distance instead of the original Jensen–Shannon divergence to calculate the distance of the probability distribution, which can alleviate the gradient extinction problem for generators. In addition, SSCGAN utilizes the PatchGAN structure in the discriminator, which considers the samples’ local details by dividing them into different patches. It can improve the discriminative ability of SSCGAN. The experiment results in the nonparallel corpus database VCC 2018 show that SSCGAN is superior to existing methods such as CycleGAN-VC, StarGan-VC. In inter-gender speech conversion, the MSD of SSCGAN is decreased by 0.162 on average compared to other methods, and in intra-gender speech conversion, the MSD is decreased by 0.118 on average. In subjective evaluation, participants also think SSCGAN is the best.
Neural PCA for Flow-Based Representation Learning

Of particular interest is to discover useful representations solely from observations in an unsupervised generative manner. However, the question of whether existing normalizing flows provide effective representations for downstream tasks remains mostly unanswered despite their strong ability for sample generation and density estimation. This paper investigates this problem for such a family of generative models that admits exact invertibility. We propose Neural Principal Component Analysis (Neural-PCA) that operates in full dimensionality while capturing principal components in descending order. Without exploiting any label information, the principal components recovered store the most informative elements in their leading dimensions and leave the negligible in the trailing ones, allowing for clear performance improvements of 5%-10% in downstream tasks. Such improvements are empirically found consistent irrespective of the number of latent trailing dimensions dropped. Our work suggests that necessary inductive bias be introduced into generative modeling when representation quality is of interest.
Deep Learning Architectures Comparison for the 3D Generation Data

There is a need to identify the best artificial images for each use case faced with several Deep Learning architectures for generating them. Twelve models with different hyperparameters were created to compare several networks with the generative architectures Autoencoder, Variational Autoencoder, and Generative Adversarial Networks in the 3D MNIST dataset. After training, the models were compared with loss functions to assess the difference between the original and artificial data, so that greater complexity did not translate into better performance, indicating the Autoencoder models as the best cost-benefit.
Development of a computer network-based teaching and testing system

The quality of teaching is an important indicator to measure whether the computer network education system is perfect, whether it can achieve the expected results, and to what extent it has developed. Under the traditional mode of China's colleges and universities, the main method of teaching inspection is manual. Due to the information construction of university teaching, the establishment of a computer network-based teaching testing system is the right thing to do. The teaching testing system studied in this paper is an open platform which inputs all teachers into it and then analyzes the course content according to different users to derive the learning effect, knowledge mastery and ability level of each student and finally evaluate them. The system described in this thesis is developed by computer network technology, which has the following functional modules: login management; data acquisition; a series of operations such as generating education class grade information reports after the initial processing of the system is completed through the WEB front-end page.
Design of Multi Model Interface to Establish Communication Among Differently Abled People Using IOT

In daily life, for the people who are deaf, dumb and blind, the communication among them is difficult. Communication is possible only in sign language which is difficult to understand and interpret. So, to prevent this, an electronic frame work is developed using WIFI Module/IOT. The dumb uses the phone to send / receive text messages which are sent to the WIFI Module and the result is displayed using controller which is connected to the LCD display and played over the speaker, i.e. the messages are transferred from the phones to a controller via cloud and played on speaker for the blind who cannot use a phone. Also, when the blind person wants to speak, a mike is provided which in turn converts the audio into text and sends message over the phones.
Corpus Construction

Chapter 5 describes the process of constructing the MI 1984–2014 Corpus, from compiling the sampling frame (e.g. search terms, dates covered) to the compilation procedure for the illness and year subcorpora. In particular, a detailed discussion of the interpretative status of search terms is provided. Practical issues related to compiling corpora such as cleaning the data are discussed. Furthermore, the problems that the interdisciplinary nature of corpus construction poses for the researcher are outlined.
BiasHacker: Voice Command Disruption by Exploiting Speaker Biases in Automatic Speech Recognition

Modern speech recognition systems that are widely deployed today still suffer from known gender and racial biases. In this work, we demonstrate the potential to exploit the existing biases in these systems to achieve a new attack goal. We consider the potential for command disruption by an attacker that can be conducted in a manner that allows for access and control of a victim's voice assistant device. We present a novel attack, BiasHacker, which crafts specialized chatter noise to exploit racial and gender biases in speech recognition systems for the purposes of command disruption. Our experimental results confirm both racial and gender bias that is still present in the speech recognition systems of two modern smart speaker devices. We also evaluated the effectiveness of three types of chatter noise (American English (AE)-Male, Nigerian-Female, Korean-Female) for disruption and demonstrate that the AE-Male chatter is consistently more successful. Comparing the average success rate of each chatter type, in scenarios where disruption was achieved, we find that when targeting the Google Home mini smart speaker, the AE-Male chatter noise increases average disruption success compared to the Nigerian-Female and Korean-Female chatter noises by 112% and 121%, respectively. Also, when targeting the Amazon Echo Dot 2 the AE-Male chatter noise increases average disruption success compared to the Nigerian-Female and Korean-Female chatter noises by 42% and 69%, respectively.
Video-Based Detection, Counting and Classification of Vehicles Using OpenCV

In this era people using vehicles is getting increased day by day. To plan, monitor and also controlling of these vehicles is becoming a big challenge. A system is to be implemented without altering the infrastructure, so a video- based vehicle capturing and analysis of that video without affecting the traffic is required, by which traffic accidents and congestion can be determined. In this paper, we have come up with a solution for the above problem using the video surveillance considering the video data from the traffic cameras. We have used adaptive thresholding method, Gaussian based background subtraction with tracking methods such as blob tracking and virtual detector. The implementation was done using OpenCV Python as a tool. Our proposed system can identify, track the congestion and help in counting the objects precisely.
Anomaly detection to improve security of big data analytics

Big data analytics largely rely on data. Because of their central role, it is fundamental to ensure the security and correctness of data used in these applications. Anomaly detection could help to increase the security of big data analytics applications. However, these applications are very diverse both for the properties of the data analyzed and for the computations to be carried out on them. As a result, the selection of the most appropriate anomaly detection method is a challenging and time consuming task for designers. Hierarchical Temporal Memory (HTM) is as an anomaly detection technique sufficiently generic to achieve satisfactory performance on a wide range of applications, thus suitable to ease the burden of selecting the anomaly detection method. To confirm this, in this paper we explore the performance of HTM on a dataset used for air quality prediction. Our preliminary results show that HTM achieves excellent performance when compared to other popular anomaly detection methods.
Constrained Artificial Intelligence - Welcome and Committees

The PeRConAI workshop aims at promoting the circulation of new ideas and research directions on pervasive and resource-constrained artificial intelligence.PeRConAI will serve as a forum for practitioners and researchers working on the intersection between pervasive computing and machine learning, including deep learning and artificial intelligence.The workshop welcomes theoretical and applied research sharing the common objective of investigating solutions advancing towards a truly pervasive and liquid AI.In the long term, we envision a future where every device at the edge of the Internet, regardless of its computing capabilities, will have an active role in the AI process by processing its data and collaborating with other devices to extract knowledge from them.The workshops' international programme committee refereed the submitted full papers by their quality and relevance to the workshop theme.In total, the workshop program includes 7 papers that were carefully selected out of 14 submissions, including one redirected contribution from the main conference.The workshop includes two sessions covering the two main aspects of AI in pervasive computing: the papers in the first session address problems related to on-device learning, targeting, in some cases, smart-vision related problems.The following session covers aspects of distributed and federated learning in pervasive contexts.Between the two sessions, a keynote speech will take place.We would like to thank the program committee members for providing detailed and thoughtful reviews and thus making it possible to assemble an interesting program of high-quality papers.We would also like to thank the PerCom organizers and, in particular, Antinisca Di Marco and Frank Dürr, the PerCom workshop co-chairs, for supporting the workshop and assisting with its organization.Finally, we thank all authors and workshop attendees for their contributions and participation.
Sign Language Recognition

There seems to be an issue between the communication of differently abled and others. That is why we have decided to make an application to mitigate this issue and reduce the dependencies on third parties like translators. We have studied different approaches taken and discussed them. Recognition of hand signs is a popular topic since the 90s and various findings have been uncovered in the following years. We have written about popular methods for this. For this application to work we have to weigh in feature extraction, object detection, and predictions using a machine learning model.