Reviewerly/exHarmony huggingface dataset:
Spatial Resolution-Independent CNN-Based Person Detection in Agricultural Image Data

Advanced object detectors based on Convolutional Neural Networks (CNNs) offer high detection rates for many application scenarios but only within their respective training, validation and test data. Recent studies show that such methods provide a limited generalization ability for unknown data, even for small image modifications including a limited scale invariance. Reliable person detection with aerial robots (Unmanned Aerial Vehicles, UAVs) is an essential task to fulfill high security requirements or to support robot control, communication, and human-robot interaction. Particularly in an agricultural context, persons need to be detected from a long distance and a high altitude to allow the UAV an adequate and timely response. While UAVs are able to produce high resolution images that enable the detection of persons from a longer distance, typical CNN input layer sizes are comparably small. The inevitable scaling of images to match the input-layer size can lead to a further reduction in person sizes. We investigate the reliability of different YOLOv3 architectures for person detection in regard to those input-scaling effects. The popular VisDrone data set with its varying image resolutions and relatively small depiction of humans is used as well as high resolution UAV images from an agricultural data set. To overcome the scaling problem, an algorithm is presented for segmenting high resolution images in overlapping tiles that match the input-layer size. The number and overlap of the tiles are dynamically determined based on the image resolution. It is shown that the detection rate of very small persons in high resolution images can be improved using this tiling approach.
Bio-inspired technique for improving machine learning speed and big data processing

Big data analytics (BDA) is progressively becoming a popular practice implemented by many organizations, because of its potential to discover treasured insights for improved decision-making. Machine Learning (ML) algorithms are one of the effective tools used for BDA, however, their computational complexity increases with an increase in data size. Therefore, this paper introduces a boundary detection and instance selection technique for improving the speed of MLbased big data classification models. The proposed technique (called ACOISA_ML) is inspired by edge selection in ant colony optimization. ACOISA_ML is evaluated on five ML algorithms and ten large- or medium-scale datasets, and the results show that it has the potential to reduce the training speed of ML algorithms by over 94% without significantly affecting their prediction accuracy. Moreover, the results show that it reduced the storage size of big datasets by over 55% (in most cases), thus improving the speed of big data processing.
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when model is encouraged to remember \textit{evidence} for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for Right Reasons (RRR), that additionally stores visual model explanations for each example in buffer and ensures model has the right reasons for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at this https URL.
Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities

The advances and successes in deep learning in recent years have led to considerable efforts and investments into its widespread ubiquitous adoption for a wide variety of applications, ranging from personal assistants and intelligent navigation to search and product recommendation in e-commerce. With this tremendous rise in deep learning adoption comes questions about the trustworthiness of the deep neural networks that power these applications. Motivated to answer such questions, there has been a very recent interest in trust quantification. In this work, we introduce the concept of trust matrix, a novel trust quantification strategy that leverages the recently introduced question-answer trust metric by Wong et al. to provide deeper, more detailed insights into where trust breaks down for a given deep neural network given a set of questions. More specifically, a trust matrix defines the expected question-answer trust for a given actor-oracle answer scenario, allowing one to quickly spot areas of low trust that needs to be addressed to improve the trustworthiness of a deep neural network. The proposed trust matrix is simple to calculate, humanly interpretable, and to the best of the authors' knowledge is the first to study trust at the actor-oracle answer level. We further extend the concept of trust densities with the notion of conditional trust densities. We experimentally leverage trust matrices to study several well-known deep neural network architectures for image recognition, and further study the trust density and conditional trust densities for an interesting actor-oracle answer scenario. The results illustrate that trust matrices, along with conditional trust densities, can be useful tools in addition to the existing suite of trust quantification metrics for guiding practitioners and regulators in creating and certifying deep learning solutions for trusted operation.
"Individualized learning in a course with a tight schedule"

The article presents a solution supporting individualised learning in courses with a tight schedule. Such courses pose additional organisational challenges and require appropriate tools. The presented solution is based on an Intelligent Tutoring System immersed in repository of e-learning content, which enables selection of content immediately before its provision to the student instead of at the beginning of a course. Thanks to this, the system, having identified the student’s needs, is able to make available the most suitable repository content at a given stage of education. The flexibility of the system is guaranteed by modularisation of content and its logical division using the UCTS taxonomy. The content has been described by means of concepts arranged according to the specificity of the domain to which the resources belong in order to ensure that the ITS is able to select relevant content. The proposed solution was used to set up an Applications of Fuzzy Logic course, which was part of an Artificial Intelligence class. The course was conducted within a very limited time frame resulting from the COVID-19 epidemic.
Experimental demonstration of time resolving quantum receiver for bandwidth and power efficient communications

We developed Coherent Frequency Shift Keying and Hybrid Frequency-Phase Shift Keying protocols optimized for quantum measurement. Paired with our new quantum receiver, these protocols experimentally demonstrate the record energy efficiency and improve communication channel capacity.
STUDENTS’ COGNITIVE AUTONOMY FORMATION IN THE PROCESS OF ENGLISH FOR SPECIFIC PURPOSES LEARNING: A MODEL OF IT IMPLEMENTATION INTO TRADITIONAL TEACHING

Due to the rapid development of new information technologies, educational techniques require rethinking of approaches to teaching methods and tools to fulfill the needs of learners regarding their training for the future job. The educators should consider not only the interests of students but the demands of employers who are looking for quick-thinking specialists ready to acquire new knowledge and skills in short terms. The article presents a model for teaching ESP (English for Specific Purposes) which facilitates formation of students’ cognitive autonomy. On the basis of scientific and theoretical analysis of this issue, the author considers the methodological approaches to its elucidating. To clarify the structure of students’ cognitive autonomy, the author provides the components and their subjective links on the subcomponents level on the hierarchical basis. The author determines the criteria and levels of students’ cognitive autonomy as well as subjective and objective factors influencing the efficiency of students’ cognitive autonomy in ESP study which were obtained through methods of mass survey (interviews, conversations, questioning), targeted observation of the formation of students’ cognitive autonomy; methods of testing and ranking. As a result, the author designed and implemented into practice a model for teaching ESP, which represents a flexible educational environment with a suitable choice of teaching techniques, modes, strategies, tools, and resources on the basis of the integral unity of information technologies and traditional teaching technologies. The stages of the experimental teaching included the following: preparing the experiment, implementing, processing the results, and interpreting them. For the analysis and correlation of the income and the outcome sample data, the author applied statistical methods. Thus, the synthesis stage of the experiment allowed confirming the effectiveness of the implemented model for teaching ESP, which facilitates students’ cognitive autonomy formation.
Detection method of sports scene conversion for MPEG compressed video based on fuzzy logic

At present, the majority of sports games video adopts MPEG image technology, and MPEG video compression is the current more mainstream approach. After compression, the quality of the video will decline, and other practical problems. However, the existing detection methods of sports video scene conversion, when dealing with MPEG compressed video, are not ideal, often appear the phenomenon of missing detection and wrong detection. In order to solve this problem, this paper proposes a detection method of sports scene conversion on MPEG compressed video based on fuzzy logic. Introducing fuzzy logic into the detection method of video scene conversion is the highlight of this method. Firstly, this paper preprocessed the video image according to the Convention. In this paper, the recognition of image features and specific extraction methods are introduced in detail, and the extraction algorithm of image color features is further optimized. For the design of the detection method, the main innovation is to fully combine the fuzzy logic and macroblock information. In the existing detection methods, different detection schemes are given for the abrupt change of video scene and the gradual change of scene. Finally, in order to verify the actual effect of the detection method in this paper, an experimental analysis based on the keyframe complexity detection method is established. After a number of experiments including the experimental results of scene transition, analysis, and processing time, through the analysis of data, a step-by-step proof of this method has good accuracy and recall.
Stakeholders of Large-scale Affordable Housing Projects in Coastal Cities Based on Social Network Analysis

Xu, N.; Li, Q.M., and Xu, Q.Y., 2020. Stakeholders of large-scale affordable housing projects in coastal cities based on social network analysis. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 617–621. Coconut Creek (Florida), ISSN 0749-0208.Based on stakeholder theory, stakeholders in large affordable housing projects in coastal cities were studied. Through questionnaires and social network analysis, the stakeholder scope of the affordable housing project in coastal cities was identified and the stakeholder social relationship network was established. Through the social relationship matrix and the corresponding network centrality analysis, cohesion subgroup analysis, etc., the core stakeholders of the affordable housing project in coastal cities were defined.
NERO: A Text-based Tool for Content Annotation and Detection of Smells in Feature Requests

Utilizing massive user feedback, e.g. feature requests from Bugzilla, JIRA, or GitHub, to motivate software evolution has become a new trend in RE community. However, manually understanding and analyzing feature requests from issue tracking systems is a time-consuming and labor-intensive task. In this paper, we present NERO (coNtent annotation and smElly Feature Requests detection), an automated tool to support analysts to understand the semantic meaning of feature requests and detect the smells in feature requests. It can also provide an overall score based on the smell detection results to help analysts quickly judge the quality of feature requests.
Incorporating Temporal Cues and AC-GCN to Improve Temporal Relation Classification

Temporal relation classification, an important branch of relation extraction, aims to identify the time sequence among events. Currently, Shortest Dependency Path (SDP) is widely used in various kinds of neural network models to capture the crucial information from sentences. However, while eliminating irrelevant words in event sentences, SDP will miss some useful information, e.g., time expressions. To address the above issue, we propose a neural network method incorporating the temporal cues to AC-GCN (Augmented Contextualized Graph Convolutional Network) to classify temporal relations. Firstly, we introduce the semantic role labeling and heuristic rules to extract the time expressions corresponding to event triggers and other words in SDPs, respectively. Then, the SDP with time expression (i.e., T-SDP) is encoded by a Bi-LSTM with the parameter sharing mechanism and fed into GCN to classify temporal relations. The experimental results on TimeBank-Dense show that our proposed model outperforms all baselines significantly.
Syntax-Guided Sequence to Sequence Modeling for Discourse Segmentation

Previous studies on RST-style discourse segmentation have achieved impressive results. However, recent neural works either require a complex joint training process or heavily rely on powerful pre-trained word vectors. Under this condition, a simpler but more robust segmentation method is needed. In this work, we take a deeper look into intra-sentence dependencies to investigate if the syntax information is totally useless, or to what extent it can help improve the discourse segmentation performance. To achieve this, we propose a sequence-to-sequence model along with a GCN based encoder to well utilize intra-sentence dependencies and a multi-head biaffine attention based decoder to predict EDU boundaries. Experimental results on two benchmark corpora show that the syntax information we use is significantly useful and the resulting model is competitive when compared with the state-of-the-art.
Thinking development and aesthetic education of students in the process of teaching mathematics by example solutions for one problem

Abstract The article discusses one of the leading goals of teaching mathematics - the development of students’ thinking by means of mathematics, it is noted that this requires the creation of special situations, the resolution of which leads to the development of thinking. The possibility of developing logical and figurative thinking of students of secondary schools in the process of aesthetic education using the subject of mathematics is investigated. The beauty of problem solving allows you to increase interest in the study of mathematics. The development of intellectual and creative capabilities, their imagination, as well as broadening the horizons in the application of methodological programs in teaching and evaluating students’ knowledge and skills. The present work is presented on the example of solving one problem by several mathematical methods. Using a mathematical model to solve various kinds of problems with the greatest number of solutions contributes to aesthetic education, allowing you to develop a culture and logical thinking, to form in students the optimal analysis of the task in solving it. The work is presented in an experiment on organizing aesthetic education by knowing the beauty of problem solving, which will fully reveal the criteria for applying the individual approach to learning through the development of thinking in solving various kinds of mathematical problems. The organization of the intensification of the process of mastering knowledge and applying aesthetic beauty is shown by the example of solving problems in mathematics of grades 7-9. We carried out a number of experimental works on the development of thinking and aesthetic education, relying on the activity-based approaches of educational and developmental teaching of mathematics.
A Tri-Attention Neural Network Model-BasedRecommendation

Heterogeneous information network (HIN), which contains various types of nodes and links, has been applied in recommender systems. Although HIN-based recommendation approaches perform better than the traditional recommendation approaches, they still have the following problems: for example, meta-paths are manually selected, not automatically; meta-path representations are rarely explicitly learned; and the global and local information of each node in HIN has not been simultaneously explored. To solve the above deficiencies, we propose a tri-attention neural network (TANN) model for recommendation task. The proposed TANN model applies the stud genetic algorithm to automatically select meta-paths at first. Then, it learns global and local representations of each node, as well as the representations of meta-paths existing in HIN. After that, a tri-attention mechanism is proposed to enhance the mutual influence among users, items, and their related meta-paths. Finally, the encoded interaction information among the user, the item, and their related meta-paths, which contain more semantic information can be used for recommendation task. Extensive experiments on the Douban Movie, MovieLens, and Yelp datasets have demonstrated the outstanding performance of the proposed approach.
Migration Learning Based on Computer Vision and Its Application in Ocean Image Processing

Chen, S.D. and Liu, Y., 2020. Migration learning based on computer vision and its application in ocean image processing. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 281–285. Coconut Creek (Florida), ISSN 0749-0208.Transfer learning is commonly used in computer vision, we want to apply it on visual question answering. We build up a QA pre-trained model to extract QA features from different source datasets and finally find the optimal pre-training for next experiments. We select LSTM model as our QA pre-train model since LSTM could fit different length of questions and has the same structure as our baseline model. We also do experiments on VQA baseline with LSTM+CNN model and pre-train + CNN model on different scales of datasets to find out the effectiveness of transfer learning. We figure out that baseline + pre-train models with different features have different influence. At last, we build up a VQA model with attention mechanism to combine QA pre-train features and image pre-train features, and studied its application in ocean image processing.
Automatic Recognition of Legal Amounts on Indian Bank Cheques

Holistic-based approaches attempt to represent an entire handwritten word as an indivisible entity by representing it with feature representations. Despite the presence of various feature representations, it still remains a challenge to get the effective representation for Devanagari Legal amounts. In this paper, an attempt is made to represent legal amounts with histogram of oriented gradients (HOG) and local binary patterns (LBP) for their characterization. Thereafter, two fusion-based models are proposed. In the first model, HOG and LBP are fused at feature level and, in second, at decision level. Later, recognition is performed with the nearest neighbor and support vector machine classifiers. For corroboration of the efficacy of the proposed models several experiments have been conducted on ICDAR ' 11 Devanagari Legal amount dataset. Experimental results demonstrate that fusion based approaches are effective by achieving significant improvement in recognition accuracy as compared to individual feature representations and other contemporary approaches employed on the data set.
MODELS OF APPROACHES TO DIGITAL COMPETENCES

The review study analyzes 15 papers from the SCOPUS database, which deal with the issue of digital competences in the field of education.Based on this analysis, it offers a basic view of both the research field itself and the topics described in these articles.They show that, although the issue is current and socially relevant, it is still not given due attention in the field of educationneither researching nor theoretically.At the same time, the review study points to some broader issues of competence research as such.
An Improved Method for Face Recognition with Incremental Approach in Illumination Invariant Conditions

In this paper we propose an enhanced method with an acceptable level of accuracy for face recognition with an incremental approach in invariant conditions like illumination, pose, expressions and occlusions. The proposed method hold the class-separation criterion for maximizing the input samples as well as the asymmetrical characteristics for training data distributions. This enhanced approach helps the learning model to get adjusted the weak features inline with enhanced or boosted feature classifier for online samples. This enhanced model also helps in calculating feature loses during the training process of offline samples. For representing the illumination invariant face features local binary pattern (LBP) are extracted from the input samples and IFLDA is used for representation and classification. This modified algorithm with incremental approach gives the acceptable results by detecting and recognizing the faces in extreme illuminations varying conditions.