Reviewerly/exHarmony huggingface dataset:
FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation

Recent methods for long-tailed instance segmentation still struggle on rare object classes with few training data. We propose a simple yet effective method, Feature Augmentation and Sampling Adaptation (FASA), that addresses the data scarcity issue by augmenting the feature space especially for rare classes. Both the Feature Augmentation (FA) and feature sampling components are adaptive to the actual training status -- FA is informed by the feature mean and variance of observed real samples from past iterations, and we sample the generated virtual features in a loss-adapted manner to avoid over-fitting. FASA does not require any elaborate loss design, and removes the need for inter-class transfer learning that often involves large cost and manually-defined head/tail class groups. We show FASA is a fast, generic method that can be easily plugged into standard or long-tailed segmentation frameworks, with consistent performance gains and little added cost. FASA is also applicable to other tasks like long-tailed classification with state-of-the-art performance.
To the question of formation of cultural-educational space of a university (based on experience of the Azov-Black Sea Engineering Institute)

The object of this research is the cultural-educational space, while the subject is the structure and components of cultural-educational space of a modern University based on the example of Azov-Black Sea Engineering Institute. The author understands cultural-educational space as an organized in a specific way sociocultural and pedagogical environment aimed at stimulation of the development and personal growth of each individual therein. Emphasis is made on the importance of formation and development of cultural-educational environment by each university as a condition for successful teaching of students and their full integration into the cultural community. The scientific novelty of this work consists in raising a question on the principles of finding optimal components of cultural-educational space of modern university; as the basic principle, the author advances the principle of close connection between the components of cultural-educational space. It is established that the elements of cultural-educational space is the physical space of the university, curriculum, educational techniques, methods, forms and means of teaching and learning, cultural situation, students self-governance, sphere of professional self-determination, personal fulfillment, sports and recreation activities, volunteer work, leisure, Internet component of cultural-educational space, etc. The obtained results can be used by different universities in formation of their own cultural-educational space that influences the performance of the university. It is concluded that the systemically organized components of cultural-educational space interact with each other and produce comprehensive impact upon personality of a student.
Expert system with Fuzzy logic for protecting Scientific Information Resources

Analysis of the state of development of research on the protection of valuable scientific and educational databases, library resources, information centers, publishers show the importance of information security, especially in corporate information networks and systems for data exchange. Corporate library networks include dozens and even hundreds of libraries for active information exchange, and they (libraries) are equipped with information security tools to varying degrees. The purpose of the research is to create effective methods and tools to protect the databases of the scientific and educational resources from unauthorized access in libraries and library networks using fuzzy logic methods.
Development and Validation of Learning Resource Materials in Upgrading Comprehension Skills of Senior High School Students

This study sought to develop and evaluate learning resource material in upgrading reading comprehension skills of Grade 11 to meet the pressing need of Doňa Candelaria Meneses Duque National High School. The researcher utilized the descriptive survey method which uses questionnaires as the primary tools in gathering desired data information. There were two sets of questionnaire which were used in the study. The first questionnaire is the Reading Diagnostic Test while the second research instrument is the evaluation tool for the developed learning resource materials. The subjects of this study are in two groups: one hundred twenty three (123) randomly selected Grade 11 senior high school students at Doña Candelaria Meneses Duque National High School (DCMDNHS) at Bulakan, Bulacan during the school year 2017-2018; and five ESL teachers, who evaluated the resource materials. The data collected were encoded and entered into the matrix using the computer software of Microsoft Excel with the following statistical procedures: frequency distribution, percentage score, mean, total mean and standard deviation. Based on the results of the diagnostic test, the researcher developed the learning resource materials, and these were validated by five ESL teachers using a 5- point Likert Scale in terms of the following criteria: objectives, contents, vocabulary, usefulness and presentation. Generally, after the evaluation, the overall mean gained of the instructional materials is 4.72, interpreted as “Excellent”. This computed overall mean attests and suggests that the developed learning resource materials in reading are acceptable. The study recommends that the language teachers need to provide the students the means towards maximum growth in accordance with their reading ability. The teachers must help students acquire basic comprehension skills and strategies in sequential reading program designed to reinforce the skills and appreciation of previous skills acquired and to develop new skills that are needed. Likewise, it is also suggested that the administrators should conduct or sponsor programs and inservice trainings to ensure the necessary updated innovations on the preparation and development of instructional materials, particularly in reading comprehension skills. Further, future researchers should conduct a research-driven strategy that can improve the reading comprehension skills of the students.
Exploring the Improvement Direction by Analyzing the Degree of Change in the Composition of the Content of Korean National-Level Music Curriculum

This study was conducted to derive issues surrounding the 2015 revised music curriculum in Korea and propose a future-oriented music curriculum revision direction that can solve them.Using the content analysis and meta-analysis method, studies related to music curriculum in Korea and abroad were analyzed.According to the results of content analysis, issues and improvement directions in terms of the curriculum content and document system were suggested.First, in terms of the content of the curriculum, the main matters were derived with a focus on core competencies and subject competencies, goal setting, content system composition, teaching and learning and evaluation.Second, in the form of curriculum documents, major issues were reviewed with a focus on the linkage of documents, the linkage of competencies and contents, and the concreteness of the system.As a result of the analysis, the main issues were as follows: ambiguity in the role of competency, lack of musical characteristics, lack of thinking base, lack of practicality, and lack of school presence.In conclusion, improvement directions were suggested to further enhance the competencies of music subject, restructuring the area of music content, supplementing digital teaching and learning, and increasing the readability of documents.
Explainable and Efficient Sequential Correlation Network for 3D Single Person Concurrent Activity Detection

We present the sequential correlation network (SCN) to improve concurrent activity detection. SCN combines a recurrent neural network and a correlation model hierarchically to model the complex correlations and temporal dynamics of concurrent activities. SCN has several advantages that enable effective learning even from a small dataset for real-world deployment. Unlike the majority of approaches assuming that each subject performs one activity at a time, SCN is end-to- end trainable, i.e., it can automatically learn the inclusive or exclusive relations of concurrent activities. SCN is lightweight in design using only a small set of learnable parameters to model the spatio-temporal correlations of activities. This also enhances the explainability of the learned parameters. Furthermore, the learning of SCN can benefit from the initialization using semantically meaningful priors. We evaluate the proposed method against the state-of-the-art method on two benchmark datasets with human skeletal data, SCN achieves comparable performance to the SOTA but with much faster inference speed and less memory usage.
Restart of accelerated first order methods with linear convergence for non-strongly convex optimization

Accelerated first order methods, also called fast gradient methods, are popular optimization methods in the field of convex optimization. However, they are prone to suffer from oscillatory behaviour that slows their convergence when medium to high accuracy is desired. In order to address this, restart schemes have been proposed in the literature, which seek to improve the practical convergence by suppressing the oscillatory behaviour. This paper presents a restart scheme applicable to a broad class of accelerated first order methods. Under a quadratic functional growth condition, linear convergence rate is proved for a large class of non-strongly convex functions. Moreover, the worst-case convergence rate is comparable to the one obtained using a (generally non-implementable) optimal fixed-rate restart strategy. We show numerical results comparing the proposed algorithm with other restart schemes.
Sequential Adversarial Anomaly Detection for One-Class Event Data

We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator. The generator captures the dependence in sequential events using the marked point process model. The detector sequentially evaluates the likelihood of a test sequence and compares it with a time-varying threshold, also learned from data through the minimax problem. We demonstrate our proposed method's good performance using numerical experiments on simulations and proprietary large-scale credit card fraud datasets. The proposed method can generally apply to detecting anomalous sequences.
Correction to: Analyzing ipsative data in psychological research

The article Some Mathematical Concepts of the Analytic Hierarchy Process, written by Thomas L. Saaty, was originally published Online First without Open Access.
A genetic-based pairwise trip planner recommender system

Abstract The massive growth of internet users nowadays can be a big opportunity for the businesses to promote their services. This opportunity is not only for e-commerce, but also for other e-services, such as e-tourism. In this paper, we propose an approach of personalized recommender system with pairwise preference elicitation for the e-tourism domain area. We used a combination of Genetic Agorithm with pairwise user preference elicitation approach. The advantages of pairwise preference elicitation method, as opposed to the pointwise method, have been shown in many studies, including to reduce incosistency and confusion of a rating number. We also performed a user evaluation study by inviting 24 participants to examine the proposed system and publish the POIs dataset which contains 201 attractions used in this study.
Adaptive Gradient Methods for Constrained Convex Optimization and Variational Inequalities

We provide new adaptive first-order methods for constrained convex optimization. Our main algorithms AdaACSA and AdaAGD+ are accelerated methods, which are universal in the sense that they achieve nearly-optimal convergence rates for both smooth and non-smooth functions, even when they only have access to stochastic gradients. In addition, they do not require any prior knowledge on how the objective function is parametrized, since they automatically adjust their per-coordinate learning rate. These can be seen as truly accelerated Adagrad methods for constrained optimization. We complement them with a simpler algorithm AdaGrad+ which enjoys the same features, and achieves the standard non-accelerated convergence rate. We also present a set of new results involving adaptive methods for unconstrained optimization and monotone operators.
Unsupervised Meta-Learning through Latent-Space Interpolation in Generative Models

Unsupervised meta-learning approaches rely on synthetic meta-tasks that are created using techniques such as random selection, clustering and/or augmentation. Unfortunately, clustering and augmentation are domain-dependent, and thus they require either manual tweaking or expensive learning. In this work, we describe an approach that generates meta-tasks using generative models. A critical component is a novel approach of sampling from the latent space that generates objects grouped into synthetic classes forming the training and validation data of a meta-task. We find that the proposed approach, LAtent Space Interpolation Unsupervised Meta-learning (LASIUM), outperforms or is competitive with current unsupervised learning baselines on few-shot classification tasks on the most widely used benchmark datasets. In addition, the approach promises to be applicable without manual tweaking over a wider range of domains than previous approaches.
SISE-PC: Semi-supervised Image Subsampling for Explainable Pathology

Although automated pathology classification using deep learning (DL) has proved to be predictively efficient, DL methods are found to be data and compute cost intensive. In this work, we aim to reduce DL training costs by pre-training a Resnet feature extractor using SimCLR contrastive loss for latent encoding of OCT images. We propose a novel active learning framework that identifies a minimal sub-sampled dataset containing the most uncertain OCT image samples using label propagation on the SimCLR latent encodings. The pre-trained Resnet model is then fine-tuned with the labelled minimal sub-sampled data and the underlying pathological sites are visually explained. Our framework identifies upto 2% of OCT images to be most uncertain that need prioritized specialist attention and that can fine-tune a Resnet model to achieve upto 97% classification accuracy. The proposed method can be extended to other medical images to minimize prediction costs.
An optimal machine learning model for breast lesion classification based on random projection algorithm for feature optimization

The purpose of this study is to develop a machine learning model with the optimal features computed from mammograms to classify suspicious regions as benign and malignant. To this aim, we investigate the benefits of implementing a machine learning approach embedded with a random projection algorithm to generate an optimal feature vector and improve classification performance. A retrospective dataset involving 1,487 cases is used. Among them, 644 cases depict malignant lesions, while the rest 843 cases are benign. The locations of all suspicious regions have been annotated by radiologists before. A computer-aided detection scheme is applied to pre-process the images and compute an initial set of 181 features. Then, three support vector machine (SVM) models are built using the initial feature set and embedded with two feature regeneration methods, namely, principal component analysis and random projection algorithm, to reduce dimensionality of feature space and generate smaller optimal feature vectors. All SVM models are trained and tested using the leave-one-case-out cross-validation method to classify between malignant and benign cases. The data analysis results show that three SVM models yield the areas under ROC curves of AUC = 0.72&plusmn;0.02, 0.79&plusmn;0.01 and 0.84&plusmn;0.018, respectively. Thus, this study demonstrates that applying a random projection algorithm enables to generate optimal feature vectors and significantly improve machine learning model (i.e., SVM) performance (p&lt;0.02) to classify mammographic lesions. The similar approach can also been applied to help more effectively train and improve performance of machine learning models applying to other types of medical image applications.
Multi Color Channel vs. Multi Spectral Band Representations for Texture Classification

Texture and color are salient visual cues of human perception and are widely used in many image analysis applications. Multi color spaces (MCS) approaches enrich the color texture representation and improve the performances of color texture classification applications. In these approaches, textures are represented by color texture features computed from descriptors that are extracted from images coded in several color spaces. When spectral characteristics of the texture of materials need to be analyzed, hyperspectral imaging (HSI) devices are chosen to address industrial applications that conventional color imaging is unable to solve. This paper aims to evaluate the contribution of HSI in the performances of texture classification methods compared to color imaging. For this purpose, we propose to extend the MCS approach to HSI in order to extract relevant spectral texture features computed from images of different spectral bands. Since these approaches both require to process high-dimensional data, they need to reduce the dimensionality of the feature space by selecting the most discriminating features, leading to a multi color channel (MCC) representation and a multi spectral band (MSB) representation respectively. This paper presents a unified representation of textures contained in color or hyperspectral images and compares the MCC and MSB representations for classification issues. Experimental results carried out on two hyperspectral texture databases show that the MCC representation is able to outperform the MSB ones.
Defining Preferred and Natural Robot Motions in Immersive Telepresence from a First-Person Perspective

This paper presents some early work and future plans regarding how the autonomous motions of a telepresence robot affect a person embodied in the robot through a head-mounted display. We consider the preferences, comfort, and the perceived naturalness of aspects of piecewise linear paths compared to the same aspects on a smooth path. In a user study, thirty-six subjects (eighteen females) watched panoramic videos of three different paths through a simulated museum in virtual reality and responded to questionnaires regarding each path. We found that comfort had a strong effect on path preference, and that the subjective feeling of naturalness also had a strong effect on path preference, even though people consider different things as natural. We describe a categorization of the responses regarding the naturalness of the robot's motion and provide a recommendation on how this can be applied more broadly. Although immersive robotic telepresence is increasingly being used for remote education, clinical care, and to assist people with disabilities or mobility complications, the full potential of this technology is limited by issues related to user experience. Our work addresses these shortcomings and will enable the future personalization of telepresence experiences for the improvement of overall remote communication and the enhancement of the feeling of presence in a remote location.
INNOVATIVE PEDAGOGICAL TECHNOLOGIES AS A METHOD OF ACTIVATING THE COGNITIVE ACTIVITY OF STUDENTS IN PROFESSIONAL TRAINING

Different approaches to the interpretation of the essence of innovative pedagogical technologies have been highlighted in the article, as well as their influence on the activation of cognitive activity of students as future specialists. It has been analysed that methods of intensification of educational activities are aimed at forming positive motives for learning, stimulate cognitive activity and at the same time contribute to the enrichment of educational information. The implementing of innovative pedagogical technologies in the educational process will increase students' interest in learning, teach them to work independently, be communicative and mobile, adapt to the requirements of modern society. It has been considered that innovative pedagogical technologies are the purposeful, systematic and consistent implementation of innovative methods, techniques, tools that encompass a holistic educational process from determining its purpose to expected results. This is due to the fact that the training system of a future specialist must comply with modern trends in the development of society, its needs. Innovative learning is a learning and educational activity focused on dynamic changes in the world around us, based on the development of various forms of thinking, creative abilities, and high social and adaptive abilities of an individual. In the professional and practical training of future teachers, technologies are being introduced to form their professional competence, among which are: personality-oriented education and training, group educational technology, information technology, project training, problem-based learning theory, interactive technology, creative personality formation technology , game technology theory, specialized training, integrated technologies and others. Technologies contribute to the assimilation of knowledge and skills, the activation and development of mental actions, the system of effective practical sphere, the strengthening of emotionality, the development of a creative nature; independence in decision making; the desire to acquire skills in a relatively short period.
The Impact of COVID-19 on the Electronic Commerce Users Behavior

The global coronavirus (COVID-19) pandemic has created an incredibly difficult business climate Businesses are presented with many new challenges as international borders close, brick-and-mortar businesses shut their doors, and people are told to isolate at home Many companies face temporary or even permanent closure, with staff facing months of financial uncertainty and concerns Consumer behavior has been forced to immediately change on a massive scale People in isolation or under lockdown cannot perform their usual routines, because local shops have been forced to close their doors for safety reasons Concerns about the availability of goods have encouraged panic buying of items in bulk Financial uncertainty and the prospect of a severe and long-term recession make for a stark backdrop, which has led to an impact on consumer outlook, perceptions, and behaviors Around the world, businesses that continue to operate seek the best ways to cope with the prevailing trading conditions and the best ways to adapt their strategies However, no definite recommendation can be given, because the COVID-19 pandemic is unprecedented Hence, circumstances are changing fluidly on a daily basis Defining a strategy now is difficult due to limited evidence or precedent to base assumptions on Possible business and economic outcomes could be gauged by looking at data from countries that first suffered from the effects of COVID-19 Despite no guarantee that other countries will follow the same trajectory, an analysis of these countries can reveal useful patterns and insights This research attempts to explore the impacts of the extent of COVID-19 on the e-commerce firms in Saudi Arabia, where the five largest e-commerce firms in the Saudi Arabia were chosen in terms of incomes and market value, This research tests that e-commerce raised due to coronavirus