Reviewerly/exHarmony huggingface dataset:
Geospatial Assessment of Land Use and Land Cover Patterns in the Black Volta Basin, Ghana

The Black Volta Basin in Ghana has experienced some losses in its productive lands due to mining.This study assessed the Land use/cover (LULC) changes within the Basin for a period of eighteen years, and mapped current and potential mining hotspots in the Basin.The study used multispectral Landsat images for the years 2000, 2015 and 2018.Supervised classification method and Spectral Angle Mapper were used to classify and map the LULC types.Feature-based extraction method was then used to delineate mining sites along the River in the Basin.Six (6) LULC types were classified.Findings from the study revealed that four of the classified LULC experienced some form of decline between the years 2000 and 2018, except Bareland and settlements which consistently increased within the period, with Barelands recording the highest increase of 21% between 2000 and 2015 and 18% between 2015 and 2018, signaling a sharp increase in the three year period.From the feature-based extraction, 312 segments of an average area of 8.4 km of each segment were found to be mining sites from the 2018 image analysis, which is about 80% of the total Bareland in 2018.Likewise, 146 segments of an average area of 3.9 km of each segment were found to be potential mining sites.This implies that mining within the basin is threatening other LULC and hence, reclamation and restoration activities need to be intensified.The outcome of this research could facilitate technological strategies towards restoration projects within the Basin.
Bayesian Estimation of the DINA Model With Pólya-Gamma Gibbs Sampling

With the increasing demanding for precision of test feedback, cognitive diagnosis models have attracted more and more attention to fine classify students whether has mastered some skills. The purpose of this paper is to propose a highly effective Pólya-Gamma Gibbs sampling algorithm based on auxiliary variables to estimate the deterministic inputs, noisy “and” gate model (DINA) model that have been widely used in cognitive diagnosis study. The new algorithm not only avoids the Metroplis-Hastings algorithm boring adjustment the turning parameters to achieve an appropriate acceptance probability, but also overcomes the dependence of the traditional Gibbs sampling algorithm on the conjugate prior distribution. There simulation studies are conducted and a detailed analysis of fraction subtraction data is carried out to further illustrate the proposed methodology.
A type-and-effect system for object initialization

Every newly created object goes through several initialization states: starting from a state where all fields are uninitialized until all of them are assigned. Any operation on the object during its initialization process, which usually happens in the constructor via this , has to observe the initialization states of the object for correctness, i.e. only initialized fields may be used. Checking safe usage of this statically, without manual annotation of initialization states in the source code, is a challenge, due to aliasing and virtual method calls on this . Mainstream languages either do not check initialization errors, such as Java, C++, Scala, or they defend against them by not supporting useful initialization patterns, such as Swift. In parallel, past research has shown that safe initialization can be achieved for varying degrees of expressiveness but by sacrificing syntactic simplicity. We approach the problem by upholding local reasoning about initialization which avoids whole-program analysis, and we achieve typestate polymorphism via subtyping. On this basis, we put forward a novel type-and-effect system that can effectively ensure initialization safety while allowing flexible initialization patterns. We implement an initialization checker in the Scala 3 compiler and evaluate on several real-world projects.
Semantics-based clustering approach for similar research area detection

The manual process of searching out individuals in an already existing research field is cumbersome and time-consuming. Prominent and rookie researchers alike are predisposed to seek existing research publications in a research field of interest before coming up with a thesis. From extant literature, automated similar research area detection systems have been developed to solve this problem. However, most of them use keyword-matching techniques, which do not sufficiently capture the implicit semantics of keywords thereby leaving out some research articles. In this study, we propose the use of Ontology-based pre-processing, Latent Semantic Indexing and K-Means Clustering to develop a prototype similar research area detection system, that can be used to determine similar research domain publications. Our proposed system solves the challenge of high dimensionality and data sparsity faced by the traditional document clustering technique. Our system is evaluated with randomly selected publications from faculties in Nigerian universities and results show that the integration of ontologies in preprocessing provides more accurate clustering results.
A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy

The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a fast Gibbs sampling (FGS) algorithm to learn the RBM by adding accelerated weights and adjustment coefficient. An important link based on Gibbs sampling theory was established between the update of the network weights and mixing rate of Gibbs sampling chain. The proposed FGS method was used to accelerate the mixing rate of Gibbs sampling chain by adding accelerated weights and adjustment coefficients. To further validate the FGS method, numerous experiments were performed to facilitate comparisons with the classical RBM algorithm. The experiments involved learning the RBM based on standard data. The results showed that the proposed FGS method outperformed the CD, PCD, PT5, PT10, and DGS algorithms, particularly with respect to the handwriting database. The findings of our study suggest the potential applications of FGS to real-world problems and demonstrate that the proposed method can build an improved RBM for classification.
The relationship between confusion and metacognitive strategies in Betty's Brain

Confusion has been shown to be prevalent during complex learning and has mixed effects on learning. Whether confusion facilitates or hampers learning may depend on whether it is resolved or not. Confusion resolution, behind which is the resolution of cognitive disequilibrium, requires learners to possess some skills, but it is unclear what these skills are. One possibility may be metacognitive strategies (MS), strategies for regulating cognition. This study examined the relationship between confusion and actions related to MS in Betty's Brain, a computer-based learning environment. The results revealed that MS behavior differed during and outside confusion. However, confusion resolution was not related to MS behavior, and MS did not moderate the effect of confusion on learning.
User Experience with a New Public Interface for an Integrated Library System

The purpose of this study was to understand the viewpoints and attitudes of researchers at Louisiana State University toward the new public search interface from SirsiDynix, Enterprise. Fifteen university constituents participated in user studies to provide feedback while completing common research tasks. Particularly of interest to the librarian observers were identifying and characterizing where problems were expressed by the participants as they utilized the new interface. This study was approached within the framework of the cognitive load theory and user experience (UX). Problems that were discovered are discussed along with remedies, in addition to areas for further study.
Question Mark Prediction By Bert

Punctuation resotration is important for Automatic Speech Recognition and the down-stream applications, e.g., speech translation. Despite the continuous progress on punctuation restoration, discriminating question marks and periods remains very hard. This difficulty can be largely attributed to the fact that interrogatives and narrative sentences are mostly characterized and distinguished by long-distance syntactic and semantic dependencies, which are cannot well modeled by existing models (e.g., RNN or n-gram). In this paper we propose to solve this problem by the self-attention mechanism of the Bert model. Our experiments demonstrated that compared the best baseline, the new approach improved the F1 score of question mark prediction from 30% to 90%.
Reading and Critical Thinking Techniques on Understanding Reading Skills for Early Grade Students in Elementary School

This study aims to determine the effect of reading and critical thinking techniques on students' reading comprehension skills.This study uses quantitative research methods with experimental methods with treatment with a 2 x 2 level design with 40 samples.The data used analysis variance and continued with Tukey and Least Square Differences (LSD) tests.The results showed that: (1) reading comprehension skills of students who learn to use reading techniques Directed Reading Thinking Activity is higher than students who learn to use reading techniques Preview, Question, Read, Reflect, Recite, and Review, (2) there is no interaction effect between reading and critical thinking techniques to students' reading comprehension skills, (3) for groups of students who think highly critically and use the technique of reading Directed Reading Thinking Activity higher than students who use reading techniques Preview, Question, Read, Reflect, Recite, and Review, (4) for groups of students who think critically low and use the techniques of reading Preview, Question, Read, Reflect, Recite, and Review are not higher than students who use the technique of reading Directed Reading Thinking Activity.Subsequent experiments are expected to find the right reading technique for students who think critically low on comprehension reading skills.
DeepRNNetSeg: Deep Residual Neural Network for Nuclei Segmentation on Breast Cancer Histopathological Images

Nuclear segmentation in the histopathological images is a very important and prerequisite step in computer aided breast cancer grading and diagnosis systems. Nuclei segmentation is very challenging in the complex histopathological images due to uneven color distribution, cell overlapping, variability in size, shape and texture. In this paper, we have developed a deep residual neural network (DeepRNNetSeg) model for automatic nuclei segmentation on the breast cancer histopathological images. DeepRNNetSeg learns high-level of discriminative features of the nuclei from the pixel intensities and produces probability maps. Annotated image mask is applied to the image in order to obtain the image patches, which are then fed to DeepRNNetSeg, which classify each image patches as nuclei or non-nuclei. We evaluate our proposed model on publicly available 143 H&E stain images of estrogen receptor positive (ER+) breast cancer. DeepRNNetSeg model has achieved an improved mean F1-score of 0.8513 and a mean accuracy of 86.87%.
A Novel Design and Implementation of Autonomous Robotic Car Based on ROS in Indoor Scenario

Pervasive deployment of autonomous vehicle all over the world is an undisputed trend in the future. Autonomous vehicle will inevitably play an essential role in decreasing traffic jams, reducing threats from driving while intoxicated (DWI), and assisting the handicapped to get around. At the same time, the new energy vehicles (NEV) especially the electromobile is gradually adopted by several governments like Germany, USA and China as compulsive transportation tools from the standpoint of environmental friendliness. Taking these two crucial trends into consideration, this article proposes a scheme of autonomous robotic car based on robot operation system (ROS) in electromobile-like car which can be easily transplanted to commercial electromobile. In this article, the design and implementation of robotic car are demonstrated in detail which involves overall architecture of functional modules, hardware design, obstacle avoidance, localization and mapping, land detection and tracking, velocity control and indoor navigation. All software modules and hardware are integrated in NVIDIA Jetson TX1 and TRAXXAS car.
Representation Learning for Imbalanced Cross-Domain Classification

Deep architectures are trained on massive amounts of labeled data to guarantee the performance of classification. In the absence of labeled data, domain adaptation often provides an attractive option given that labeled data of a similar nature but from a different domain is available. Previous work has chiefly focused on learning domain invariant representations but overlooked the issues of label imbalance in a single domain or across domains, which are common in many machine learning applications such as fake news detection. In this paper, we study a new cross-domain classification problem where data in each domain can be imbalanced (data imbalance), i.e., the classes are not evenly distributed, and the ratio of the number of positive over negative samples varies across domains (domain imbalance). This cross-domain problem is challenging as it entails covariate bias in the input feature space and representation bias in the latent space where domain invariant representations are learned. To address the challenge, in this paper, we propose an effective approach that leverages a doubly balancing strategy to simultaneously control these two types of bias and learn domain invariant representations. To this end, the proposed method aims to learn representations that are (i) robust to data and domain imbalance, (ii) discriminative between classes, and (iii) invariant across domains. Extensive evaluations of two important real-world applications corroborate the effectiveness of the proposed framework.MSC codesUnsupervised Domain AdaptationData ImbalanceDomain ImbalanceRepresentation Learning
Efficient and Effective Graph Convolution Networks

Graph convolution is a generalization of the convolution operation from structured grid data to unstructured graph data. Because any type of data can be represented on a feature graph, graph convolution has been a powerful tool for modeling various types of data. However, such flexibility comes with a price: expensive time and space complexities. Even with state-of-the-art scalable graph convolution algorithms, it remains challenging to scale graph convolution for practical applications. Hence, we propose using Diverse Power Iteration Embeddings (DPIE) to construct scalable graph convolution neural networks. DPIE is an approximated spectral embedding with orders of magnitude faster speed that does not incur additional space complexity, resulting in efficient and effective graph convolution approximation. DPIE-based graph convolution avoids expensive convolution operation in the form of matrix-vector multiplication using the embedding of a lower dimension. At the same time, DPIE generates graphs implicitly, which dramatically reduces space cost when building graphs from unstructured data. The method is tested on various types of data. We also extend the graph convolution to extreme-scale data never-before studied in the graph convolution field. Experiment results show the scalability and effectiveness of DPIE-based graph convolution.MSC codesGraph EmbeddingNeural Networks
Towards using Virtual Reality for Replicating HRI Studies

As a first step into utilizing Virtual Reality (VR) for Human-Robot interaction(HRI) studies we attempted to replicate a study from Kahn et al., which was interested in peoples' secret keeping behaviour with regards to robotic tour guides compared to human tour guides. Some changes had to be made to the original study but the essence of the experiment was maintained. Results suggest that there are many differences in how the participants in this study viewed the various robot behaviours when compared to how participants viewed the guides in the original study. As such no conclusive statements can be made about the overall suitability of VR as a platform for conducting HRI studies.
Mediated Interpersonal Communication: A New Way of Social Interaction in the Digital Age

Although previously intended to seek information, the Internet becomes a means to communicate and social interaction.Thus, it has since proven that social media mediates interpersonal communication, connecting people that never met in face-to-face situations.Social media continuously shaped and shifted the way netizens interact amongst themselves This paper seeks how the netizens interact with each other and how this interactivity takes place within social media realms.Therefore, Social Construction theory and Social Information theory will be used to answer these questions.Using this method of research netnography was done on Bening Society community Facebook account.The informants in this study were drawn from members of the active Bening Society community.Netnography is utilized as a method and the Bening society on Facebook which garnered thousands of members is used as a case study.Hence, the informants are those who are active in participating in this online society.The results showed that social media, in this case, Facebook is able to provide comfort for users, especially in managing social interaction with friends.The process of interpersonal communication that occurs through Facebook resembles those in face-to-face interpersonal communication.
Early detection of breast cancer using mammography images and software engineering process

The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
A Survey of Deep Learning and Its Application in Distribution Network

With the availability of data and improvement of computation ability, deep learning methods are promising in many areas. Due to the wide deployment of sensors and smart meters in distribution network, abundant data can be obtained, which provides the potential for the application of deep learning in distribution network. In this paper, concept and typical structures of deep learning networks are discussed. Meanwhile, several applications of deep learning in distribution network are summarized. Finally, challenges and prospects are proposed.
Smart Solar Home System with Solar Forecasting

Solar home systems (SHSs) have become a popular solution to solve the problem of energy access in areas with no electricity or poor electricity service. However, they face the challenge of daily and seasonal variation in solar irradiance and load usage leading to suboptimal energy use. Solar forecasting techniques are used to solve this problem and are often used in larger systems such as solar farms. In larger commercial systems, solar forecasting involves predicting solar irradiance either in a hour/day-ahead fashion. This technique works well for large systems where the solar power is often exported back to the utility grid. However, with smaller solar systems such as SHSs, the solar energy is often used to charge batteries (which are then used during the night) and the solar output from the panels depends not only on the weather but also the battery utilization. In addition to this challenge, most SHSs lack computing and communication systems making deployment of optimization techniques challenging. This study proposes a smart solar home system and a forecasting methodology which accounts for battery utilization leading to more optimal energy usage. Smart solar home systems have been deployed in real homes and field data is evaluated with the proposed forecasting technique.
Real-Time Industrial Bin-Picking with a Hybrid Deep Learning-Engineering Approach

The real-time pick and place of 3D industrial parts randomly filed in a part-bin plays an important role for manufacturing automation. Approaches based solely on the conventional engineering discipline have been shown limitations in terms of handling multiple parts of arbitrary 3D geometries in real-time. In this paper, we present a hybrid approach of deep learning and engineering as a means of breaking through the current limitations of industrial bin picking toward enabling the real-time pick and place of multiple 3D parts of arbitrary geometries. The proposed approach, first, makes use of deep-learning based object detectors configured in a cascaded form for both detecting parts in a bin and extracting features associated with the individual parts detected. The concatenation of the part label and the feature labels and their positions associated with the part allows the subsequent part net to have its part recognition rate close to 100%, Furthermore, the part features and their positions are to be fed directly into the estimation of the 3D pose of the corresponding part in a bin as well as its degree of occlusion. The experimental results demonstrate that the proposed approach is able to perform a real-time multiple part bin picking operation for multiple 3D parts of arbitrary geometries with a high precision.
Chaos-based Image Encryption using Integer Wavelet Transform

Since the last few decades, several chaotic encryption techniques are reported by different researchers. Although the cryptanalysis of some techniques shows the feebler resistance of those algorithms against any weaker attackers. However, different hyper-chaotic based and DNA-coding based encrypting methods are introduced recently. Though, these methods are efficient against several attacks, but, increase complexity as well. On account of these drawbacks, we have proposed a novel technique of chaotic encryption of an image using the integer wavelet transform (IWT) and global bit scrambling (GBS). Here, the image is transformed and decomposed by IWT. Thereafter, a chaotic map is used in the encryption algorithm. A key-dependent bit scrambling (GBS) is introduced rather than pixel scrambling to make the encryption stronger. It enhances key dependency along with the increased resistance against intruder attacks. To check the fragility and dependability of the algorithm, a sufficient number of tests are done, which have given reassuring results. Some tests are done to check the similarity between the original and decrypted image to ensure the excellent outcome of the decryption algorithm. The outcomes of the proposed algorithm are compared with some recent works' outputs to demonstrate its eligibility.