Reviewerly/exHarmony huggingface dataset:
A Nonlinear Gradient Domain-Guided Filter Optimized by Fractional-Order Gradient Descent with Momentum RBF Neural Network for Ship Image Dehazing

To avoid the blurred edges, noise, and halos caused by guided image filtering algorithm, this paper proposed a nonlinear gradient domain-guided image filtering algorithm for image dehazing. To dynamically adjust the edge preservation and smoothness of dehazed images, this paper proposed a fractional-order gradient descent with momentum RBF neural network to optimize the nonlinear gradient domain-guided filtering (NGDGIF-FOGDMRBF). Its convergence is proved. In order to speed up the convergence process, an adaptive learning rate is used to adjust the training process reasonably. The results verify the theoretical results of the proposed algorithm such as its monotonicity and convergence. The descending curve of error values by FOGDM is smoother than gradient descent and gradient descent with momentum method. The influence of regularization parameter is analyzed and compared. Compared with dark channel prior, histogram equalization, homomorphic filtering, and multiple exposure fusion, the halo and noise generated are significantly reduced with higher peak signal-to-noise ratio and structural similarity index.
Analysis of Earthen Road Construction and Land Degradation in the Tankhuwakhola Watershed of Dhankuta District

Earthen road construction has resulted land degradation in the Tankhuwakhola watershed of Dhankuta district, eastern hills of Nepal. The community living near the highway has dramatically changed in their way of living with the adoption of commercial crops. As a result, the people who lived far from the access of roads have interested in the expansion of agricultural link roads to their community. The local government had prepared a District Transport Master Plan (DTMP) and identified 25 rural earthen road schemes in the Tankhuwakhola watershed. The local people have also given high priority to road construction for increasing access to markets, education, health services and other facilities. The analysis of both spatial and non-spatial data reveals that the watershed area losses 1.8 million cubic meter soils due to the cause of earthen road construction. As a result, the area is suffering from the problem of the landslide, soil erosion that ultimately result of land degradation. This paper concludes that the acceleration of land degradation has exerted to the sustainability of population-resource relation. The watershed area has been producing more profitable commercial crops for exporting outside from the hills since the construction of the Koshi highway. The watershed needs an integrated watershed management program to address human-induced vulnerability and sustainability of watershed resources.
Rotation Invariant Networks for Image Classification for HPC and Embedded Systems

Convolutional Neural Network (CNNs) models’ size reduction has recently gained interest due to several advantages: energy cost reduction, embedded devices, and multi-core interfaces. One possible way to achieve model reduction is the usage of Rotation-invariant Convolutional Neural Networks because of the possibility of avoiding data augmentation techniques. In this work, we present the next step to obtain a general solution to endowing CNN architectures with the capability of classifying rotated objects and predicting the rotation angle without data-augmentation techniques. The principle consists of the concatenation of a representation mapping transforming rotation to translation and a shared weights predictor. This solution has the advantage of admitting different combinations of various basic, existing blocks. We present results obtained using a Gabor-filter bank and a ResNet feature backbone compared to previous other solutions. We also present the possibility to select between parallelizing the network in several threads for energy-aware High Performance Computing (HPC) applications or reducing the memory footprint for embedded systems. We obtain a competitive error rate on classifying rotated MNIST and outperform existing state-of-the-art results on CIFAR-10 when trained on up-right examples and validated on random orientations.
3D Yazıcı Kalem Teknolojisinin Matematik Dersinde Uygulanmasından Yansımalar

There are a lot of studies that have revealed students' problems in spatial reasoning and discovering properties of solid concepts.As it is known, the Turkey republic ministry of education has adopted a student-centered approach to provide more conceptual, effective and
Understanding Politics via Contextualized Discourse Processing

Politicians often have underlying agendas when reacting to events.Arguments in contexts of various events reflect a fairly consistent set of agendas for a given entity.In spite of recent advances in Pretrained Language Models, those text representations are not designed to capture such nuanced patterns.In this paper, we propose a Compositional Reader model consisting of encoder and composer modules, that captures and leverages such information to generate more effective representations for entities, issues, and events.These representations are contextualized by tweets, press releases, issues, news articles, and participating entities.Our model processes several documents at once and generates composed representations for multiple entities over several issues or events.Via qualitative and quantitative empirical analysis, we show that these representations are meaningful and effective.
Motion-blurred Image Restoration Based on Joint Invertibility of PSFs

To implement restoration in a single motion blurred image, the PSF (Point Spread Function) is difficult to estimate and the image deconvolution is ill-posed as a result that a good recovery effect cannot be obtained.Considering that several different PSFs can get joint invertibility to make restoration wellposed, we proposed a motion-blurred image restoration method based on joint invertibility of PSFs by means of computational photography.Firstly, we designed a set of observation device which composed by multiple cameras with the same parameters to shoot the moving target in the same field of view continuously to obtain the target images with the same background.The target images have the same brightness, but different exposure time and different motion blur length.It is easy to estimate the blur PSFs of the target images make use of the sequence frames obtained by one camera.According to the motion blur superposition feature of the target and its background, the complete blurred target images can be extracted from the observed images respectively.Finally, for the same target images with different PSFs, the iterative restoration is solved by joint solution of multiple images in spatial domain.The experiments showed that the moving target observation device designed by this method had lower requirements on hardware conditions, and the observed images are more convenient to use joint-PSF solution for image restoration, and the restoration results maintained details well and had lower signal noise ratio (SNR).
A Hybrid Classifier for Handwriting Recognition on Multi-domain Financial Bills Based on DCNN and SVM

With the rapid growth of the global economy, the automatic recognition of financial bills becomes the primary way to reduce the burden of the traditional manual approach for bill recognition and classification. However, most automatic recognition methods cannot effectively recognize the handwritten characters on financial bills, especially when the bills come from different financial companies. To solve the problem, this paper fully explores the bill system in banks and the operations of bill number recognition, and then develops a hybrid classifier based on deep convolutional neural network (DCNN) and support vector machine (SVM), with the aim to recognize the handwritten numbers on financial bills in different domains. The DCNN with different channels was adopted to effectively mine the local handwritten numbers on financial bills from varied sources. Then, the extracted information was fed to the SVM to realize accurate classification of numbers. Our method makes full use of the distribution difference between information in different fields, and adapts to different fields based on the parameter sharing mechanism. Experimental results show that our method can recognize the handwritten numbers on financial bills more accurately (&gt;3%) than benchmark methods.
Towards the Development of an Adaptive System for Detecting Anomaly in Human Activities

As the global population of older adults increases, measures are put in place to monitor their well-being, promote independent living and improve their quality of life. Among those measures are in-house monitoring system that allows for the collection of data in a non-invasive form to identify the activities of daily living of the older adults and detect abnormalities in their daily routines. Abnormalities can be an early sign of health decline or other related challenges, thereby informing the family and carers of the need for intervention. However, existing anomaly detection systems are unable to adapt to the dynamic nature of human activities which are subject to changes due to different factors, resulting in an increased false prediction rate. To address this deficiency, the anomaly detection system must be adaptive to the changes in human routines as well as factors leading to the changes. This paper presents a consolidation of the achievements recorded in the development of an adaptive anomaly detection system. This system consists of a data collection and interpretation component, anomaly detection component and a feedback component. An ensemble of novelty detection model based on a consensus approach is utilised for the anomaly detection while the feedback component is based on a gesture recognition model implemented on an assistive robot platform. The results of our proposed approach for anomaly detection and gesture recognition performs better when compared to other existing approaches. The obtained results for the different system components show the potential of the system for in-house monitoring of older adults.
Enhancing Image Retrieval and Re-ranking Efficiency using Hybrid approach

Many Image search engines are used to share images on social media websites on different occasion. It became very essential for user to retrieve more relevant image from web as per user intention, To search and retrieve images from web which are more relevant to user requirement we have proposed a new system using user intention and hybrid features of images to retrieve and re-rank images to user so that user can use these images for his purpose. A novel data fusion technique to support text-based and content based image recovery combining different heterogeneous features is proposed. The user need to provide just a single tick on an inquiry image and pictures recovered by content based pursuit are re-positioned dependent on their visual and texture similitudes to the enquiry image. Word-based and pictorial expansions are incorporated to detect user intention without additional human feedback. Expanded keywords helps in extending positive model images and furthermore develop the image pool to include additional related images. A lot of visual features which are both efficient and effective for image search are chosen. The n-dimensional feature vector for both colour and texture is reduced to single dimension each, used for comparing the similarity with query image using suitable distance metrics. Further only the images retrieved as a outcome of text created pursuit and image re-position process are compared during run time for finding the similar images; not the entire database. This considerably reduces the computational complexity and improves the search efficiency. With improved feature extraction capturing textual and visual similarities, the proposed one click image search framework gives a productive robotized recovery of comparable images giving promising results with improvement in retrieval efficiency.
Prediction of daily global solar radiation using different empirical models on the basis of meteorological parameters at Trans Himalaya Region, Nepal

Global Solar Radiation (GSR) is the cleanest and freely available energy resource on the earth. GSR was measured for six years (2010 -2015) at the horizontal surface using calibrated first-class CMP6 pyranometer at Kathmandu (Lat. 27.70o N, Long. 85.5oE and Alt. 1350m). This paper explains the daily, monthly, and seasonal variations of GSR and also compares with sunshine hour, ambient temperature, relative humidity, and precipitation to GSR. The annual average global solar radiation is about 4.16 kWh/m2/day which is a significant amount to promote solar active and passive energy technologies at the Trans-Himalaya region. In this study, the meteorological parameters are utilized in the regression technique for four different empirical models and finally, the empirical constants are found. Thus obtained coefficients are utilized to predict the GSR using meteorological parameters for the years to come. In addition, the predicted GSR is found to be closer to the measured value of GSR. The values are justified by using statistical tools such as coefficient of determination (R2), root mean square error (RMSE), mean percentage error (MPE), and mean bias error (MBE). Finally, the values of R2, RMSE, MPE, and MBE are found to be 0.792, 1.405, -1.014, and 0.011, respectively for the model (D), which are based on sunshine hour, temperature and relative humidity. In this model, the empirical constants, a = 0.155, b = 0.134, c = 0.014 and d = 0.0007 are determined which can be utilized at the similar geographical locations of Nepal.&#x0D; BIBECHANA 18 (2021) 159-169
An Area- and Power-Efficient Stochastic Number Generator for Bayesian Sensor Fusion Circuits

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Editor’s note:</i> This article introduces a new stochastic number generator module with low autocorrelation properties. This module is systematically compared with earlier proposals regarding its implementation cost, its theoretical properties, and its performance in a specific application: Bayesian sensor fusion. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">—Ilia Polian, University of Stuttgart</i>
Part Segmentation of Unseen Objects using Keypoint Guidance

While object part segmentation is useful for many applications, typical approaches require a large amount of labeled data to train a model for good performance. To reduce the labeling effort, weak supervision cues such as object keypoints have been used to generate pseudo-part annotations which can subsequently be used to train larger models. However, previous weakly-supervised part segmentation methods require the same object classes during both training and testing. We propose a new model to use key-point guidance for segmenting parts of novel object classes given that they have similar structures as seen objects - different types of four-legged animals, for example. We show that a non-parametric template matching approach is more effective than pixel classification for part segmentation, especially for small or less frequent parts. To evaluate the generalizability of our approach, we introduce two new datasets that contain 200 quadrupeds in total with both key-point and part segmentation annotations. We show that our approach can outperform existing models by a large margin on the novel object part segmentation task using limited part segmentation labels during training.
Development of Interactive Learning Multimedia Reading Early Children's Beginning

Development of Interactive Learning Multimedia Learning Beginning for early childhood (5-6 years) in Christian Kindergarten 3 District Medan Kota, Medan City. Postgraduate Basic Education Study Program, State University of Medan. The purpose of this research and development was to determine the feasibility and effectiveness of interactive learning multimedia for early childhood reading in Group B TK Nasrani 3, Medan Kota District, and Medan City. The development model in this study refers to the 4D (Four-D) researches and development model which consists of 4 main stages, namely define, design, develop, and disseminate. The subjects in this study were 3 expert validators and 20 students of Group B TK Nasrani 3 Medan. The product of this research is a multimedia interactive learning software product on the material of celestial objects for group B students TK Nasrani 3 Medan. The results showed that (1) the feasibility level of interactive multimedia learning pre-reading based on: a) the validation results of the material experts on the feasibility of content by instructional media experts by 3.6, learning material experts by 3.7, while design experts 3.5 in the category with " very worthy ”. b) The practical results of the teacher state that children get very good responses and responses. This can happen because the steps loaded in multimedia learning have instructions and are easily understood by children. 2) the results of the effectiveness of interactive learning multimedia that were developed showed the average result of reading the beginning of the child on the results of initial observations was 45%, which means the child began to develop and the result of the final observation was 83%, which means that the child developed very well.
Automatic scoring of Chinese fill-in-the-blank questions based on improved P-means

Chinese fill-in-the-blank questions contain both objective and subjective characteristics, and thus it has always been difficult to score them automatically. In this paper, fill-in-the-blank items are divided into those with word-level or sentence-level granularity; then, the items are automatically scored by different strategies. The automatic scoring framework combines semantic dictionary matching and semantic similarity calculations. First, fill-in-the-blank items with word-level granularity are divided into two types of test sites: the subject term test site, and the common word test site. We propose an algorithm for identifying an item’s test site. Then, a subject term dictionary with self-feedback learning ability is constructed to support the scoring of subject term test sites. The Tongyici Cilin semantic dictionary is used for scoring common word test sites. For fill-in-the-blank items with sentence-level granularity, an improved P-means model is used to generate a sentence vector of the standard answer and the examinee’s answer, and then the semantic similarity between the two answers is obtained by calculating the cosine distance of the sentence vector. Experimental results on actual test data show that the proposed algorithm has a maximum accuracy of 94.3% and achieves good results.
MAPSOFT: A Multi-Agent based Particle Swarm Optimization Framework for Travelling Salesman Problem

Abstract This paper proposes a Multi-Agent based Particle Swarm Optimization (PSO) Framework for the Traveling salesman problem (MAPSOFT). The framework is a deployment of the recently proposed intelligent multi-agent based PSO model by the authors. MAPSOFT is made up of groups of agents that interact with one another in a coordinated search effort within their environment and the solution space. A discrete version of the original multi-agent model is presented and applied to the Travelling Salesman Problem. Based on the simulation results obtained, it was observed that agents retrospectively decide on their next moves based on consistent better fitness values obtained from present and prospective neighborhoods, and by reflecting back to previous behaviors and sticking to historically better results. These overall attributes help enhance the conventional PSO by providing more intelligence and autonomy within the swarm and thus contributed to the emergence of good results for the studied problem.
LectureCast as 360 Degree Video: What Impact do Immersion and Presence Experience have on Learning Performance?

In times of PodCast and MOOC's, a rebirth of the "classical" academic lecture in the form of modern technology-mediated communication as video-conserve can be observed. Current technological developments in the field of audiovisual media reduce the distance between a recorded action and its reception. By using high immersive technologies presence experiences can introduc new qualities of communication. In a study undertaken with freshmen we first explored the everyday use of video technologies by students and their experiences and expectations with regard to their use in formal learning contexts. I addition we investigated the question whether and how learning abilities of the recipients are impacted by immersive technologies (360-degree video on Head Mounted Display/VR headset) compared to classical LectureCast technologies (16:9 Video via Desktop). All things considered, it can be stated that an immersive media format alone does not generate added value: there is no increase of attention that further enhances learning, which can be derived from the feeling (of presence) of sitting in a lecture room, even beyond the possibly initial distraction of the appeal of novelty. Furthermore, the teacher-centered mode is unsuitable for a spherical projection due to its spatial setting, where only one direction is being employed. In order to exploit possible media-specific values of 360-degree video in the context of LectureCasts, it is consequently necessary to work either with a modified concept of a teacher-centered lecture, using the entire space, or to set a focus on the content by combining the recorded lecture with additional visual elements. Keywords: LectureCast, Immersion, Sense of Presence, 360-degree video, VR headset, Video-Learning
Novel Machine Learning Approach for Sentiment Analysis of Real Time Twitter Data with Apache Flume

A lot of data is generated from multiple sources. This data contains many hidden patterns and information. Many researchers are trying to get meaningful insights out of these patterns. Data from these sources mostly contains opinions. Opinions can be mined to lead various extractions from organizational point of view. One approach is to use Sentiment Analysis. In this paper, the authors are storing the Twitter Streaming Data into HDFS of Hadoop by using Flume and then extracting with Apache Hive. Later, Machine Learning classification algorithms are applied to decode the sentiment in this data using Apache Mahout. A novel approach based on hybrid Naïve Bayes and Decision Tree Algorithms are used to enhance the performance of sentiment analysis of streaming twitter data. The implemented research approach achieved an accuracy of 86.44% in comparison to 81.11% for Naïve Bayes Classifier.