Reviewerly/exHarmony huggingface dataset:
Advantages and Barriers of Applying Solar Nowcasting in Controlling Microgrids: Findings From A Survey in 2020

Variable distributed energy resources (DERs) such as solar and wind are rapidly becoming common in low-inertia microgrids (MGs) worldwide as the world explores cost-efficient and sustainable energy solutions. Additionally, the urgent need for greenhouse gas emission reductions and the availability of vast renewable energy resources serve as motivations to harvest these renewabies. Solar photovoltaic (PV) technology is one of the most utilised due to the significant drop in prices for solar PV systems. However, the variable nature of solar PV generation due to cloud movements introduces rapid ramp events thus affecting power system management. Nowcasting, which is defined as very short-term solar irradiance forecasting, together with controllable DERs, can be integrated into MGs to possibly address these ramp events and enable an increase in PV penetration levels in MGs. This study outlines the benefits and limitations of nowcasting and its applications in the control of MGs obtained from a survey. From the survey, it was evident that sky camera-based nowcasting technology is still new but has potential in MG applications. Applications of nowcasting in MGs included ramp rates control and scheduling of spinning reserves. Additionally, PV penetration levels may be increased if nowcasting tools are incorporated into the control of MGs. However, the main barrier impeding the utilisation of sky camera-based nowcasting technology is the lack of experience and lack of demonstrated reliability.
Contribution of virtual field trip and spatial intelligence toward the improvement in science learning achievement of elementary school student’s

The purpose of this study was to determine the contribution of the virtual field trip (VFT) and spatial intelligence to improving the science learning achievement of elementary school students. The study was conducted at the Integrated Islamic Elementary School (SDIT) Alhikmah Depok, with a sample of 24 fifth-grade students in the first semester of 2018/2019. This research is an experimental type with a descriptive method. The research design uses One Group Pretest Posttest Design. Data were collected through student questionnaire responses to VFT, spatial intelligence tests, and science learning achievement tests. Analyzes were performed with quantitative data and correlation techniques. The results showed that (1) student's responses to VFT obtained an average of 4.16 in the good category, (2) student's spatial intelligence in the sufficient high category obtained by 13 students from a total of 24 student's in the range of scores ≥ 83, (3) the average pretest results from science learning achievement of 64.92 and post-test results 89.75 increased by 27.67% and indicated by N-gain of 0.73 high categories; (4) VFT's contribution to science learning achievement is 86.8%; (5) the contribution of spatial intelligence to science achievement was 84.4%; and (6) the contribution of VFT and spatial intelligence together to science learning achievement is 94.6%. The results of this study prove that virtual field trips and spatial intelligence make a good contribution to the improvement of science learning achievements of elementary school students in the ecosystem material.
Collapse Type and Processes of the Geumosan Caldera in the Southern Gumi, Korea

The Gumi basin, situated in the mid-southeastern Yeongnam Massif, has the Cretaceous stratigraphy that is divided into Gumi Formation, andesitic rocks (Yeongamsan Tuff, Busangni Andesite), rhyolitic rocks (Obongni Tuff, Doseongul Rhyolite, Geumosan Tuff) and Intrusives (ring dikes, other dikes) in ascending order.The Geumosan Tuff is composed mostly of many ash-flow tuffs which are associated with Geumosan caldera along with the ring dikes.The caldera is outlined by ring faults and dikes and has about 3.5 × 5.6 km in diameters.The intracaldera volcanics show a downsag structure that is dipped inward in their flow and welding foliations.The caldera block represent an asymmetric subsidence, which drops 350 m in the northern margin and 600 m in the southern one.Based on these data, the Geumosan caldera is geometrically classified as an asymmetric piston subsidence caldera that suggests a single caldera cycle.The caldera reflects the piston subsidence of the caldera block bounded by the outward-dipping ring faults following a voluminous eruption of magma from the chamber.The downsag in the caldera block refers to the downsagging during the initial subsidence at the same time as the full development of the bound fault.In the ring fissures following the sagging, magma was injected due to the overpressure of magma chamber caused by subsidence.
Knowledge Transfer in UAE Higher Education Institutions During Covid-19 Pandemic: Learners' Cannot Learn Surgery by Watching

Purpose - This study aims to explore various methods of knowledge Transfer (KT) in the UAE Higher Education Institutions (HEIs) (both pre and post COVD-19 pandemic) and to measure the satisfaction level of instructors and students towards these methods. This study also aims to identify the type of learning most and least supported by online KT process and to identify the challenges faced by instructors and students while using the online KT process during COVID-19 pandemic. The study revealed that more than 92 percent of both students and instructors expressed their satisfaction with the knowledge transfer methods introduced by higher education institutions in the UAE. The outcome of the research is worthwhile for both Emirati and foreign higher education institutions, which are operating in the UAE and even in the middle east, to promote their academic programs in their respective markets during and after the Covid-19 pandemic. This paper provides a through insight for the policy makers at higher institutions in UAE for considering online mode as a substitute/simultaneous approach for knowledge transfer.
A Learning-Based Optimization Algorithm: Image Registration Optimizer Network

Remote sensing image registration is valuable for image-based navigation system despite posing many challenges. As the search space of image registration is usually nonconvex, the optimization algorithm, which aims to find the optimal parameters in search space, is a challenging step. Conventional optimization algorithms can hardly reconcile the contradiction of rapid convergence and global optimization. In this letter, a novel learning-based optimization algorithm named image registration optimizer network (IRON) is proposed, which can predict the global optimum straightforwardly. The IRON is trained by a 3-D tensor ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9\times 9\,\,\times9$ </tex-math></inline-formula> ) which consists of similar metric values. Each value of the 3-D tensor corresponds to the initial parameters’ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$9 \times 9 \times 9$ </tex-math></inline-formula> neighbors in the search space. The 3-D tensor’s label is a vector which points to the global optimal parameters from the initial parameters. Because of the special design, our IRON could predict the global optimum directly. The experimental results demonstrate that the proposed algorithm performs better than other classical optimization algorithms as it shows higher accuracy, lower root-of-mean-square error (RMSE), and more convergence efficiency. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.github.com/jaxwangkd04/IRON</uri> .
A Training-Based Identification Approach to VIN Adversarial Examples in Path Planning

With the rapid development of Artificial Intelligence (AI), the problem of AI security has gradually emerged. Most existing machine learning algorithms may be attacked by adversarial examples. An adversarial example is a slightly modified input sample that can lead to a false result of machine learning algorithms. This poses a potential security threat for many AI applications. Especially in the domain of robot path planning, the adversarial maps may result in multiple harmful effects on the predicted path. However, there is no suitable approach to automatically identify them. To our knowledge, all previous works used manual observation method to identify the attack results of adversarial maps, which is time-consuming. Aiming at the existing problems, this paper explores a method to automatically identify the adversarial examples in Value Iteration Networks (VIN), which has a strong generalization ability. We analyze the possible scenarios caused by the adversarial maps. We propose a training-based identification approach to VIN adversarial examples by combining the path feature comparison and path image classification. Experiments show that our method can achieve a high-accuracy and effective identification on VIN adversarial examples.
Graph Mining with Graph Neural Networks

Graphs are ubiquitous data structures in various fields, such as social media, transportation, linguistics and chemistry. To solve downstream graph-related tasks, it is of great significance to learn effective representations for graphs. My research strives to help meet this demand; due to the huge success of deep learning methods, especially graph neural networks, in graph-related problems, my emphasis has primarily been on improving their power for graph representation learning. More specifically, my research spans across the following three main areas: (1) robustness of graph neural networks, where we seek to study the performance of them under random noise and carefully-crafted attacks; (2) self-supervised learning in graph neural networks, where we aim to alleviate their need for costly annotated data by constructing self-supervision to help them fully exploit unlabeled data; and (3) applications of graph neural networks, where my work is to apply graph neural networks in various applications such as traffic flow prediction. This research statement, 'Graph Mining with Graph Neural Networks', is focused on my research endeavors specifically related to the aforementioned three topics.
Development application of a quality assurance management information system for Paulus Indonesia Christian University

Abstract University of Christian Indonesia Paulus (UKI Paulus) currently already has the standard documents, policies and quality documents. Yet the implementation is not supported by an information system, so the socialization process for standards, policies and quality documents is ineffective and causes limited access for those who need it. Furthermore, the implementation of monitoring and evaluation quality audits is also inefficient and brings obstacles to the implementation of the quality assurance system. The Unified Modeling Language (UML) is used for the information and communication technology (ICT) based quality assurance management information system. The development of the UKI Paulus Quality Assurance Management Information System (SIM-MUTU UKI Paulus) uses a System Development Life Cycle (SDLC) paradigm approach which consists of 5 stages: (1) identification of system requirements; (2) analysis and modeling system; (3) the system creation; (4) the system testing and restoration; and (5) implementation. The SIM-MUTU UKI Paulus which was developed is a web-based information system that provides convenience in managing the quality assurance at the university level. Various features developed in this system include: accreditation features, internal academic quality audit features and library features. System testing is carried out using white box testing and black box testing methods. 5 aspects usability testing questioners were given to 30 respondents, they are: (a) learnability, (b) efficiency, (c) memorability, (d) errors, (e) satisfaction and the result were 86.5%, 78, 2%, 75.0%, 72.5%, and 75.2% for each aspect. Based on the ISO 9241-11 indicator, the average level of usability is 77.5%. The results of this test indicated that the system run optimally, has a high level of usability with a feasibility rate of 86%. Higher education quality assurance management information system based on information and communication technology able to facilitate auditing and provide information with complete documentation related to the quality management business process in Higher Education. The utilization of technology in the form of implementing an information system support the quality assurance management process to be more automated without eliminating the business processes that have been running so far. This Information system also increase the value of an organization’s competitive advantage and facilitate the users in the decision-making processes.
Event Detection and Knowledge Mining from Unlabelled Bengali News Articles

Text mining is one of the most important tasks in Natural Language Processing (NLP). It is often very difficult to detect events of interest from vast unstructured or data that is not properly labeled. In this paper, we present a machine learning-based approach to discriminate particular event-related news from its text content. We focus here particularly on violence and related events. We have created a dataset from popular Bengali newspapers and used a keyword-based search method to select violence-related news articles. Based on the dataset, we first trained a supervised learning model. We have experimented with six classification algorithms where Logistic Regression outperformed each of the contemporary algorithms in both full and reduced feature set acquiring 75.7% and 83.4% accuracy respectively. Using the models, we then further analyzed violence- related news data to find out important insights from unlabeled data.
Energy Efficient Aspects of Federated Learning – Mechanisms and Opportunities

The role of machine learning in IoT-enabled applications, most predominantly in societal applications, is instrumental. That the active researchers take a series of steps utilizing innovative technologies to override emerging challenges such as data privacy, latency, scalability, energy consumption, and so forth. Federated Learning (FL), a subset of machine learning paradigm, has shifted the mindset of researchers, including system architects, in recent years while solving the existing challenges of the inclusion of machine learning in applications; it has taken a more distinct shape in terms of handling learning mechanisms in a decentralized fashion. In fact, energy-efficient mechanisms need to be incorporated into the FL frameworks/architectures. There is still no work that expresses the energy reduction opportunities of FL algorithms or architectures. This paper has primarily focused on revealing the energy-efficient aspects of FL. Besides, it is reinforced with the discussions on the recent developments of FL and prominent future research contributions in various application domains with an emphasis on energy efficiency. The article would benefit researchers, more specifically the system engineers or tool developers, who deal with the inclusion of FL for applications.
Acoustic Word Embedding System for Code-Switching Query-by-example Spoken Term Detection

In this paper, we propose a deep convolutional neural network-based acoustic word embedding system for code-switching query by example spoken term detection. Different from previous configurations, we combine audio data in two languages for training instead of only using one single language. We trans-form the acoustic features of keyword templates and searching content segments obtained in a sliding manner to fixed-dimensional vectors and calculate the distances between them. An auxiliary variability-invariant loss is also applied to training data within the same word but different speakers. This strategy is used to prevent the extractor from encoding undesired speaker- or accent-related information into the acoustic word embeddings. Experimental results show that our proposed sys-tem produces promising searching results in the code-switching test scenario. With the employment of variability-invariant loss, the searching performance is further enhanced.
Face Template Security: LBP-Based LSB Watermarking Technique for Multi-class SVM Classification Using HoG

A digital watermarking is a technique which is protecting the user authenticity by embedding the digital code in the image. In this paper, an attempt is made to invisible watermarking technique based on Local Binary Pattern (LBP), Least Significant Bit (LSB), Gabor, Histogram of Gradient (HoG), and Multi-class Support Vector Machine (SVM). The embedded image is classified through multi-class SVM with magnitude and direction by HoG features. It is a kind of invisible watermarking technique as we need the original image for identification. The Peak Signal Noise Ratio (PSNR) 31.12 dB is used to evaluate the strength of noise between the watermarked image and original image. Subsequently, the image identification is done by using multi SVM classification and measured with 98.75% accuracy of watermarked image.
LiSP: A Lightweight Signcryption using PHOTON hash for Internet-of-Things Infrastructure

Internet-of-Things (IoTs) are developing each day and proving their potential value in various applications. The increasing number of devices for IoTs also posing a high security concern and authentication is one of them. Along with the traditional sign-then encryption techniques, signcryption, light weight authentication methods are also developed in recent years. The various cons among those existing algorithms have urged the need to extend and strengthen the algorithm. We have, therefore, proposed a light weight signcryption scheme that uses PHOTON-256 for hashing. We have theoretically analysed the scheme and have been found significant for IoT perception layer usage.
Research on Named Entity Recognition in Chinese EMR Based on Semi-Supervised Learning with Dual Selected Strategy

With the construction of the electronic medical record system, medical record data begins to accumulate, and how to extract essential information from these resources has become a concern. And named entity recognition(NER) is the first step. With the help of doctors, we built a small Chinese electronic medical record annotation corpus. But the NER supervision method requires a large amount of manually labeled corpus. So to reduce the cost of it and make better use of the unlabeled corpus, this paper proposes a semi-supervised Chinese electronic medical record NER model based on ALBERT-BiLSTM-CRF which named CEMRNER. The model uses a Bidirectional Long Short Term Memory network (BiLSTM) and a Conditional Random Field model (CRF) to train the data and introduces the pre-training language model ALBERT to solve the problem of Chinese representation. At the same time, we propose a dual selected strategy to select the high confidence samples and expand the training set. The dual strategy can ensure the accuracy i automatically labeled data, and reduce the error iteration in semi-supervised learning. The experiment and analysis show that compared with other models, this method is more accurate and comprehensive. The precision, recall rate, and F1Score are 85.45%, 87.81%, and 86.61%, respectively. The paper proves that using a semi-supervised method and pre-training ALBERT can improve the accuracy of recognition under the condition of less labeled data.
Violent Event Detection: An Approach Using Fusion GHOG-GIST Descriptor

In this paper, we propose violent event detection in video using fusion of global histograms of oriented gradients (GHOG) and GIST feature descriptors. The significant features are extracted from GHOG, GIST, and GHOG + GIST. Finally, significant features are used with the support vector machine (SVM) classifier for the detection of a violent event. The proposed feature descriptor method used Hockey Fight and Violent-Flows dataset for the experimentation, and empirical results show that the proposed GHOG + GIST fusion feature descriptor is effective and efficient than other state of the art techniques.
Chinese Implicit Sentiment Analysis based on Hybrid Neural Networks

Abstract Implicit sentiment analysis is an important part of sentiment computing, especially sentiment analysis based on deep learning has become a research hotspot in recent years. In this paper, convolutional neural networks are used to extract text features, combined with long-term and short-term memory network (LSTM) structure to extract context information, and add attention mechanism to the network to construct a new hybrid neural network model to achieve implicit emotion for text analysis. The hybrid neural network model extracts more meaningful sentence semantic and structural hidden features from the word-level and sentence-level hierarchies, respectively, and pays attention to the features with a large emotional contribution rate through the attention mechanism. The classification accuracy rate of the model on the public implicit sentiment dataset has reached 77%. The research of implicit sentiment analysis can improve the effect of text sentiment analysis more comprehensively, and further promote the application of text sentiment analysis in the fields of knowledge embedding, text representation learning, user modeling and natural language.
Leveraging Different Context for Response Generation through Topic-guided Multi-head Attention

Multi-turn dialogue system plays an important role in intelligent interaction. In particular, the subtask response generation in a multi- turn conversation system is a challenging task, which aims to generate more diverse and contextually relevant responses. Most of the methods focus on the sequential connection between sentence levels by using hierarchical framework and attention mechanism, but lack reflection from the overall semantic level such as topical information. Previous work would lead to a lack of full understanding of the dialogue history. In this paper, we propose a context-augmented model, named TGMA-RG, which leverages the conversational context to promote interactivity and persistence of multi-turn dialogues through topic-guided multi-head attention mechanism. Especially, we extract the topics from conversational context and design a hierarchical encoder-decoder models with a multi-head attention mechanism. Among them, we utilize topics vectors as queries of attention mechanism to obtain the corresponding weights between each utterance and each topic. Our experimental results on two publicly available datasets show that TGMA-RG improves the performance than other baselines in terms of BLEU-1, BLEU-2, Distinct-1, Distinct-2 and PPL.