Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Object Detection Using Complementary Learning and Instance Clustering

Supervised object detection schemes use fully annotated training data, which is fairly expensive to constitute. Whereas, weakly supervised object detection (WSOD) uses only image-level annotations for training which are much simpler to acquire. WSOD is a challenging task since it aims to learn object localization and detection with image-level labels. In line with this assertion, in this paper, we present an end-to-end framework for WSOD based on discriminative feature learning. We use the objectness technique to get initial proposals from the images. Afterwards, two complementary networks are trained in parallel to obtain discriminative image features, which are channel-wise concatenated with the features of the third network. We name this classification network designed for discriminative feature learning as fused complementary network. This network learns the proposals enclosing whole object instances by complementary features which ultimately learns to predict the high probabilities for whole objects than proposals containing only object parts. Clustering is then hierarchically performed on the region proposals. Our clustering method, named instance clustering, first performs inter-class clustering followed by iterative intra-class clustering using intersection-over-union metric to obtain spatially adjacent cluster members corresponding to each object instance. In each intra-class clustering iteration, the high scoring proposal is set as centroid from each intra-class cluster. Experiments are conducted on PASCAL VOC2007 and PASCAL VOC2012 datasets. Both qualitative and quantitative results have shown improved WSOD performance on these benchmarks.
Face Detection in MWIR Spectrum

The capability to perform face recognition in the visible and thermal spectra is of prime interest in many law enforcement and military organizations. Face detection is an important pre-processing step for face recognition. Though many algorithms are available for face detection in the visible spectrum, an assessment of how these algorithms can be retrained for the thermal spectrum is an important study. Current available visible-based face detection algorithms are very effective in daytime conditions, however, when there are extreme changes in illumination conditions like very low-light to no light (night-time), these become challenging. Due to limited amount of data available for researchers from sensors in the thermal band (due to the increased cost of having and operating state of the art thermal sensors), there are only a few proposed algorithms. In this work, we conducted a study to determine the impact of factors such as indoor/outdoor environment, distance from the camera, application of sunscreen, training set size, etc. on training deep-learning models for a face detection system in the thermal spectrum that simultaneously performs face detection and frontal/non-frontal classification. Existing deep learning models such as SSD (Single Shot Multi-box Detector), R-FCN (Region Based Fully Convolutional Network) and R-CNN (Region Based Convolutional Neural Network), are re-trained using thermal images for face detection and pose estimation tasks. Results from each model are compared, and the model with the best performance is further trained and tested on different datasets, including indoor, outdoor at different stand-off distances. The highest accuracy is achieved using a Faster R-CNN model with ResNet-101 and the accuracy is 99.4% after a 10-fold cross-validation. More experiments are performed to further study the efficiency and limitations of this model. The data set we use was collected under constrained indoor and unconstrained outdoor conditions.
Discovering Unmodeled Components in Astrodynamics with Symbolic Regression

The paper explores the use of symbolic regression to discover missing parts of the dynamics of space objects from tracking data. The starting assumption is that the differential equations governing the motion of an observable object are incomplete and do not allow a correct prediction of the future state of that object. Symbolic regression, making use of Genetic Programming (GP), coupled with a sensitivity analysis-based parameter estimation, is proposed to reconstruct the missing parts of the dynamic equations from sparse measurements of position and velocity. Furthermore, the paper explores the effect of uncertainty in tracking measurements on the ability of GP to recover the correct structure of the dynamic equations. The paper presents a simple, yet representative, example of incomplete orbital dynamics to test the use of symbolic regression.
A Question Detection Algorithm for Text Analysis

In this paper, an effective question detection algorithm for Vietnamese text analysis is proposed. The proposed algorithm takes an audio file as input, converts its speech to text, and returns question detection result. This is extremely useful for a text analyzer to determine if a given sentence generated from an audio file is a question or not, particularly in chatbot or voicebot systems where very often there are needs for automatic replies to questions queried by users. The algorithm uses two tiers of question words and a customized question phrases to achieve 88.64 % accuracy on a sub-dataset of 176 questions prepared based on FPT Open Speech Dataset.
The Educational Inclusion in the Deficit of Attention of Elementary Students

Over time, society has undergone various changes and transformations, with the school being the&#x0D; space in which they have been most directly reflected. We go from positivist educational models to&#x0D; constructivist educational models, in order to respond to social needs. Currently, these changes are&#x0D; heading towards a model with new opportunities in learning, with the proposal of attending to&#x0D; social and school diversity, which is increasingly late. However, we stop to ask ourselves, is there&#x0D; educational inclusion?For this, a quantitative study was carried out in a Primary Education center in&#x0D; Madrid, the sample was made up of 100 students in the primary education stage, the objective that&#x0D; was intended to understand and know, are the educational responses The analysis of the results&#x0D; have allowed verifying the difficulties in learning in a great challenge in the classroom currently,&#x0D; introducing new methodologies in the classroom to promote educational inclusion. The results&#x0D; include the need for teacher training to include students in the first stage of their life
Estimation of Average Daily SolarRadiation for Rural Areas Using ANN

To analyze any solar systems it is important to have an information regarding average solar radiation falling on the surface. Solar radiation measurement cost is high as well as its maintenance is difficult to manage in the rural areas. Present work represents the results neural network modeling for an estimation of average solar radiation in the rural areas of Jalgaon city located in INDIA. Model was developed with back propagation algorithm Levenberg-Marquardt (LM). Available metrological data from the year 1995 to 2019 (March) from the data source of the NASA. Performance characterization was obtained from the developed ANN model with error analysis as RMSE, MAPE and the regression value R. It is observed from the statistical analysis that R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> coefficient is close to 0.9 and the MAPE is 1.78. Thus the developed model has good conformity with the calculated values and model is useful for predicting Direct Normal Irradiance which would serve the energy analysis of the solar systems especially for rural applications.
Research to Resource: Evidence-Based Strategies for Improving Wind Intonation

Intonation is one of the most challenging performance elements for developing wind instrumentalists. There is a need to provide in-service band directors with evidence-based instructional strategies that can be used when teaching their students to perform with accurate intonation. The purposes of this research-to-resource article are to provide band directors with a concise summary of selected research focused on wind instrument intonation, and to provide instructional recommendations based on those research findings that can be used in ensemble rehearsals. Recommendations on full-ensemble tuning strategies, vocalization, tuner usage, and timbral instructions are provided.
GDAGAN: An Anonymization Method for Graph Data Publishing Using Generative Adversarial Network

The publishing and sharing of big data such as social networks promotes the research and application of data mining, but the leakage of individual private information in the process of datasets sharing is also becoming more serious. The current popular anonymization method for graph data cannot make good trade-offs between data utility and privacy, and it is vulnerable in the face of the state-of-the-art de-anonymization method of the graph neural network. This paper proposes a graph data anonymization method using Generative Adversarial Network (GDAGAN), which learn the data characteristics comprehensively and generate anonymous graph data with similar feature distribution of the original. The anonymous graph data is constructed by adding differential privacy noise to the generate process. Experiments show that, the anonymous graph data generated by GDAGAN has strong anonymity performance and high data utility than exist method.
Eye-Tracking in Educational Multi-Touch Games

Collaborative learning with educational games on multi-touch tabletop devices opens chances, challenges and questions which make contemporary approaches in learning analytics meet their boundaries. Multi-modality might help here, and eye-tracking is a promising data source in the effort to a better understanding of the learners’ behaviour. This article describes our previous work regarding serious games on large multi-touch tabletop displays, developed to teach computer science theory topics in a collaborative and entertaining way, our previous research efforts and challenges and obstacles we met on our way. Eye-tracking will improve our understanding of the learners’ behaviour while they are not interacting with the game, enhance the construction of coherent learner models and might even provide a subtle way of control on a medium of public interaction. The benefits of our work can be used to enhance the game mechanics and support shy or students with disabilities. We present our plan of action which follows a design-based research approach and includes the motivation for our work and our short- and long-term goals.
Towards the Automation of Grading Textual Student Submissions to Open-ended Questions

Growing student numbers at universities worldwide pose new challenges for instructors. Providing feedback to textual exercises is a challenge in large courses while being important for student's learning success. Exercise submissions and their grading are a primary and individual communication channel between instructors and students. The pure amount of submissions makes it impossible for a single instructor to provide regular feedback to large student bodies. Employing tutors in the process introduces new challenges. Feedback should be consistent and fair for all students. Additionally, interactive teaching models strive for real-time feedback and multiple submissions.
HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue

Abstract Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing steps that underlie the generation of the final mosaic, e.g. registration and blending of the contained image tiles. We introduce HISTOBREAST, an extensive collection of brightfield microscopy images that we collected in a principled manner under different acquisition conditions on Haematoxylin - Eosin (H&amp;E) stained breast tissue. HISTOBREAST is comprised of neighbour image tiles and ensemble of mosaics composed from different combinations of the available image tiles, exhibiting progressively degraded quality levels. HISTOBREAST can be used to benchmark image processing and computer vision techniques with respect to their robustness to image modifications specific to brightfield microscopy of H&amp;E stained tissues. Furthermore, HISTOBREAST can serve in the development of new image processing methods, with the purpose of ensuring robustness to typical image artefacts that raise interpretation problems for expert histopathologists and affect the results of computerized image analysis.
The development of “I Sangging Lobangkara” balinese folklore as an android based game

Abstract The main goal of this study was to create an android game that introduces and maintains Balinese Folklore I Sangging Lobangkara. Balinese Folklore I Sangging Lobangkara has a lot of moral value that began to be forgotten. This folklore was about a very famous painter, I Sangging Lobangkara. Hearing the news of I Sangging Lobangkara’s skill in drawing, The King invited him to The Palace to test his skill. Those challenges from The King were packed as the challenge of the game. The game of Balinese folklore I Sangging Lobangkara was developed using Android Studio with library libGdx. The testing of the application was done by white box testing, black box testing, content expert testing, media expert testing, and user usability testing. Balinese folklore “I Sangging Lobangkara” game in Android successfully applied in all of the android’s features which is tested and there was not any error found.
The Memory Challenge in Ultra-Low Power Deep Learning

One of the key goals for the next decade is to push machineMachine learning learningLearning into sensors at the edge, for always-on operation within a sub-mW power budget. However, to achieve this goal, we need to address memory organization challenges, as current machineMachine learning learningLearning (ML) models (e.g., deep neural networksNeural network) have storage requirements for both weightsWeights and activations that are often not compatible with on-chip memories and/or low costCost, low power external memory options. In this chapter, we outline key ideas, recent achievements, and directions for future research towards taming the memory bottleneck for ultra-low power ML circuits and systems.
Detection of Semantic Risk Situations in Lifelog Data for Improving Life of Frail People

The automatic recognition of risk situations for frail people is an urgent research topic for the interdisciplinary artificial intelligence and multimedia community. Risky situations can be recognized from lifelog data recorded with wearable devices. In this paper, we present a new approach for the detection of semantic risk situations for frail people in lifelog data. Concept matching between general lifelog and risk taxonomies was realized and tuned AlexNet was deployed for detection of two semantic risks situations such as risk of domestic accident and risk of fraud with promising results.
Development of a Neural Network Based PV Power Output Prediction Application Using Reduced Features and Tansig Activation Function

Many research works were done on the prediction of this PV power using Artificial Neural Network and other Artificial Intelligence method to somehow address some issues encountered in injecting the excess power of a Grid Connected Solar PV system into the grid. However, it was observed that simulation processes posed a cumbersome task. Thus, this paper attempts to develop PV power output prediction application software. It presents a methodology used in developing a NN - based PV-output prediction application with reduced features. It integrates techniques used in previous studies and utilizes some common Software applications such as Soft Computing tools, spreadsheets and IDE in the application development. As a result, the developed application software was able to perform PV output forecasting easily and dynamically.
A Combination of DWT CLAHE and Wiener Filter for Effective Scene to Text Conversion and Pronunciation

An effective scene to text conversion and its pronunciation is realized. An intelligent combination of Discrete Wavelet Transform (DWT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Wiener filter and adaptive weighted average is utilized for the image enhancement. Subsequently, the Maximally Stable Extremal Region (MSER) is used to detect the text regions. Afterward, the geometrical and contour based approaches filter out the non-text MSERs. The connected component concept is used to group the text candidates. In next step the Optical Character Recognition (OCR) recognizes the text. The Microsoft speech to text synthesizer pronounces the extracted text. The system applicability is tested by using the standard robust reading competition dataset. The designed method secures 93% precision in text segmentation and 89.9% precision in end-to-end recognition.
Model-Guided Multi-Path Knowledge Aggregation for Aerial Saliency Prediction

As an emerging vision platform, a drone can look from many abnormal viewpoints which brings many new challenges into the classic vision task of video saliency prediction. To investigate these challenges, this paper proposes a large-scale video dataset for aerial saliency prediction, which consists of ground-truth salient object regions of 1,000 aerial videos, annotated by 24 subjects. To the best of our knowledge, it is the first large-scale video dataset that focuses on visual saliency prediction on drones. Based on this dataset, we propose a Model-guided Multi-path Network (MM-Net) that serves as a baseline model for aerial video saliency prediction. Inspired by the annotation process in eye-tracking experiments, MM-Net adopts multiple information paths, each of which is initialized under the guidance of a classic saliency model. After that, the visual saliency knowledge encoded in the most representative paths is selected and aggregated to improve the capability of MM-Net in predicting spatial saliency in aerial scenarios. Finally, these spatial predictions are adaptively combined with the temporal saliency predictions via a spatiotemporal optimization algorithm. Experimental results show that MM-Net outperforms ten state-of-the-art models in predicting aerial video saliency.
An Axis Based Mean Filter for Removing High-Intensity Salt and Pepper Noise

In this work, we propose an Axis Based Mean Filtering (ABMF) method for removing high-intensity Salt and Pepper Noise from gray-scale images. The proposed method applies the concept of mean filter and uses only the terminal pixels within a window along a specific axis to predict the value of the central noisy pixel. The ABMF considers a fixed window size of 3×3. If the central pixel of the window is noisy, it tries to identify a straight line (referred to as axis) within the window passing through the central pixel such that the pixels on either side of the line are non-noisy. If such an axis is found, the noisy pixel is replaced by the mean of the pixels on either end of the axis. However, if such a line does not exist, the noisy pixel is replaced by the mean of all the non-noisy pixels within the window. Experimental results over a set of 34 images exhibit that the proposed ABMF outperforms the existing algorithms by 58% and 29% in terms of mean SSIM and mean PSNR respectively for noise-intensities ranging from 10%-90%.
WebApriori: A Web Application for Association Rules Mining

This paper presents a web application for Association Rules Mining (ARM). It utilizes Apriori that is the most widely used algorithm for this type of data mining tasks. The web application is called WebApriori and offers a modern responsive web interface and a web service to scientific communities working in the field of ARM. It is also appropriate for educational purposes. WebApriori implements an Apriori engine that can efficiently discover the hidden associations in data and it is capable to process different types of datasets. Part of the process involves the removal of redundant associations rules. The asynchronous communication between the front-end, back-end, web service and Apriori engine layers efficiently handles multiple concurrent user requests.
Foreign Language as Intellectual Resource and Value in Modern Conditions in Professional Activity of Manager

The article elaborates on the practical necessity of a high level foreign language skills for students taking courses in the program “080200 Management”. The thematic justification of the research stands up to the fact that foreign language is a basic tool in the professional and educational activities of such a specialist. The article focuses on the notion “communication performance of a specialist”. High communication performance is the goal and result of language studies at a higher education institution. The course of study “Foreign language” is an obligatory course playing a major role in professional training of students, aspiring to become managers. The result of higher education language training cannot be limited to acquiring language competence at the level A1-A2, which is standard for students, not specializing in linguistics. Future managers shall learn to perform efficiently within the process of intercultural business communication with an English-speaking partner and be able to represent economic interests of their country in an orderly manner. Fully formed communication competence facilitates successful participation in professional communication with English-speaking partners in situations of business communication, such as, but not limited to the following: 1) negotiations; 2) business meetings; 3) presentations of company services; 4) job interviews etc. The research methods are theoretical. The research is aimed to dwell on the structure of the notion of communicative performance, its peculiarities and components.
Avoiding Bias in Students’ Intrinsic Motivation Detection

Intrinsic motivation is the psychological construct that defines our reasons and interests to perform a set of actions. It has shown to be associated with positive outcomes across domains, especially in the academic context. Therefore, understanding and identifying peoples’ levels of intrinsic motivation can be crucial for professionals of many domains, e.g. teachers aiming to offer better support to students’ learning processes and enhance their academic outcomes. In a first attempt to tackle this issue, we propose an end-to-end approach for recognition of intrinsic motivation, using only facial expressions as input. Our results show that visual cues from students’ facial expressions are an important source of information to detect their levels of intrinsic motivation (AUC $$=0.570$$, $$F_1=0.556$$). We also show how to avoid potential bias that might be present in datasets. When dividing the training samples per gender, we achieved a substantial improvement for both genders (AUC $$=0.739$$ and $$F_1=0.852$$ for male students, AUC $$=0.721$$ and $$F_1=0.723$$ for female students).
Implementation of an Autonomous Self-Balancing Robot Using Cascaded PID Strategy

The hardware implementation of an autonomous self-balancing robot, using the open-source Ardupilot platform is presented here. The cascaded PID strategy is combined with a trajectory tracking controller to achieve autonomous driving. Motion and trajectory control performance is analyzed through simulation and hardware based experiments.