Reviewerly/exHarmony huggingface dataset:
Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations.
DETERMINANTS OF BEHAVIORAL INTENTION TO USE BITCOIN IN MALAYSIA

Bitcoin is an emerging digital asset and is getting more attention in the media as of writing. Though, despite the regulatory matters, this paper is more concerned with the factors that influence the behavioral intention to use Bitcoin by the Malaysian. Although there are some recent studies on the intention and acceptance of Bitcoin, yet there is limited evidence from Malaysia. Therefore, this study intends on shedding some light on the current stance of the Malaysians on Bitcoin by examining the factors that affect the behavioral intention to use Bitcoin in Malaysia grounded on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The data was collected using an online self-administered questionnaire through the relevant local Malaysian forums and local cryptocurrency groups. The result shows that performance expectancy has the greatest influence on the users’ behavioral intention to use Bitcoin, followed by other factors such as social influence and facilitating conditions. Additional analysis of variance and moderation analyses show no significant difference in the mean of the behavioral intention between different demographic characteristics, and the interactions of the demographic characteristics and the UTAUT factors also demonstrate an insignificant effect on the behavioral intention to use Bitcoin. The result suggests that the behavioral intention to use Bitcoin by the Malaysian may increase if the use of Bitcoin can provide convenience to the public and businesses, but it is also influenced by the social and facilitating conditions. The result well reflects the main function of Bitcoin, that is as a currency and also as an investment tool. The finding echoes the effort and policies of the government of Malaysia to find an equilibrium between the use and the abuse of digital currencies as a currency, as well as the risks and returns of digital currencies as an investment tool.
Graph Attention Networks With Local Structure Awareness for Knowledge Graph Completion

Graph neural networks have been proven to be very effective for representation learning of knowledge graphs. Recent methods such as SACN and CompGCN, have achieved the most advanced results in knowledge graph completion. However, previous efforts mostly rely on localized first-order approximations of spectral graph convolutions or first-order neighborhoods, ignoring the abundant local structures like cycles and stars. Therefore, the diverse semantic information beneath these structures is not well-captured, leaving opportunities for better knowledge representation which will finally help KGC. In this work, we propose LSA-GAT, a graph attention network with a novel neighborhood aggregation strategy for knowledge graph completion. The model can take special local structures into account, and derive a sophisticated representation covering both the semantic and structural information. Moreover, the LSA-GAT model is combined with a CNN-based decoder to form an encoder-decoder framework with a carefully designed training process. The experimental results show significant improvement of the proposed LSA-GAT compared to current state-of-the-art methods on FB15k-237 and WN18RR datasets.
Does Herding Behaviour Among Traders Increase During Covid 19 Pandemic? Evidence from the Cryptocurrency Market

Cryptocurrencies are digital currencies and trading these currencies have gained huge momentum in recent years. The sophisticated features, complexities on regulatory framework, and high volatility of Cryptocurrencies would pose trading challenges to new investors and/or investors with limited knowledge. Investors generally are influenced by fund managers, financial analysts or other investors who are considered as well informed and highly knowledgeable peers. Investors mimic their behaviour to perform trading activities and such behaviour is termed as Herding. Covid 19 pandemic triggered severe uncertainties in the cryptocurrencies market and has led to wide fluctuations in prices causing severe volatility and market crashes. This paper aims to examine the herding behaviour in cryptocurrency market during the pre Covid 19 and Covid 19 pandemic period using the Cross-Sectional Standard Deviation (CSSD) approach. The findings of the paper reveal that herding was evident among all the ten crypto-currencies in normal market conditions of the entire sample period but not during market upswing or downswing. However, the herding behaviour was present in the cryptocurrencies Litecoin, Cardano and Dash during the Covid 19 pandemic period in all market conditions.
Contrastive Transformation for Self-supervised Correspondence Learning

In this paper, we focus on the self-supervised learning of visual correspondence using unlabeled videos in the wild. Our method simultaneously considers intra- and inter-video representation associations for reliable correspondence estimation. The intra-video learning transforms the image contents across frames within a single video via the frame pair-wise affinity. To obtain the discriminative representation for instance-level separation, we go beyond the intra-video analysis and construct the inter-video affinity to facilitate the contrastive transformation across different videos. By forcing the transformation consistency between intra- and inter-video levels, the fine-grained correspondence associations are well preserved and the instance-level feature discrimination is effectively reinforced. Our simple framework outperforms the recent self-supervised correspondence methods on a range of visual tasks including video object tracking (VOT), video object segmentation (VOS), pose keypoint tracking, etc. It is worth mentioning that our method also surpasses the fully-supervised affinity representation (e.g., ResNet) and performs competitively against the recent fully-supervised algorithms designed for the specific tasks (e.g., VOT and VOS).
Home Security System with Face Recognition based on Convolutional Neural Network

Security of house doors is very important and becomes the basis for the simplest and easiest security and sufficient to provide a sense of security to homeowners and along with technological developments, especially in the IoT field, which makes technological developments in locking house doors have developed a lot like locking house doors with faces and others.The development of facial recognition systems has also developed and has been implemented for home door locking systems and is an option that is quite simple and easy to use and is quite accurate in recognizing the face of homeowners.The development of the CNN method in facial recognition has become one of the face recognition systems that are easy to implement and have good accuracy in recognizing faces and has been used in object recognition systems and others.In this study, using the CNN Alexnet facial recognition system which is implemented in a door locking system, data collection is done by collecting 1048 facial data on the face of the homeowner using a system which is then used to train machine learning where the results are quite accurate where the accuracy is the result is 97.5% which is quite good compared to some other studies.The conclusion is the CNN Alexnet method can perform facial recognition which is quite accurate which can be implemented on the IoT device, namely, the Raspberry Pi.
Programmable Quantum Annealers as Noisy Gibbs Samplers

Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including powerful machine learning frameworks such as deep learning. The quest for discovering larger families of distributions for which sampling can be efficiently realized has inspired an exploration beyond established computing methods and turning to novel physical devices that leverage the principles of quantum computation. Quantum annealing embodies a promising computational paradigm that is intimately related to the complexity of energy landscapes in Gibbs distributions, which relate the probabilities of system states to the energies of these states. Here, we study the sampling properties of physical realizations of quantum annealers which are implemented through programmable lattices of superconducting flux qubits. Comprehensive statistical analysis of the data produced by these quantum machines shows that quantum annealers behave as samplers that generate independent configurations from low-temperature noisy Gibbs distributions. We show that the structure of the output distribution probes the intrinsic physical properties of the quantum device such as effective temperature of individual qubits and magnitude of local qubit noise, which result in a non-linear response function and spurious interactions that are absent in the hardware implementation. We anticipate that our methodology will find widespread use in characterization of future generations of quantum annealers and other emerging analog computing devices.
Establishment of Trust in Internet of Things by Integrating Trusted Platform Module: To Counter Cybersecurity Challenges

With the increasing day-to-day acceptance of IOT computing, the issues related to it are also getting more attention. The current IOT computing infrastructure brings some security challenges concerned with the users/customers and CSP. The users can store their confidential data at IOT storage and can access them anytime when they need. Lack of trust exists among IOT users and between IOT users and CSP. The prevention of this risk is a big research issue and it needs to be solved. There is a need for trusted IOT computing in recent times to provide trusted services. Here, we propose the integration of TPM in IOT computing to performs cryptographic operations and provide hardware-based security. In this domain, different schemes and methods have been proposed to build trust in IOT computing, but the suitable solution has not been presented by these schemes because these schemes lack in terms of some security services. A comparative study based on trusted computing schemes has also been presented in this paper along with different implementations of critical analysis. Our study is based on an overview of the main issues and summarizing the literature along with their strengths and limitations. In the end, we integrated the trusted platform module in the IOT architecture to establish the trust in IOT computing and to enhance the cybersecurity challenges and evaluated it with the help of mathematical/algorithms/graph theory/matrices and logical diagrams.
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning

Recently, leveraging pre-trained Transformer based language models in down stream, task specific models has advanced state of the art results in natural language understanding tasks.However, only a little research has explored the suitability of this approach in low resource settings with less than 1,000 training data points.In this work, we explore fine-tuning methods of BERT -a pre-trained Transformer based language model -by utilizing pool-based active learning to speed up training while keeping the cost of labeling new data constant.Our experimental results on the GLUE data set show an advantage in model performance by maximizing the approximate knowledge gain of the model when querying from the pool of unlabeled data.Finally, we demonstrate and analyze the benefits of freezing layers of the language model during fine-tuning to reduce the number of trainable parameters, making it more suitable for low-resource settings.
Cloud height measurement by a network of all-sky imagers

Abstract. Cloud base height (CBH) is an important parameter for many applications such as aviation, climatology or solar irradiance nowcasting (forecasting for the next seconds to hours ahead). The latter application is of increasing importance for the operation of distribution grids and photovoltaic power plants, energy storage systems and flexible consumers. To nowcast solar irradiance, systems based on all-sky imagers (ASIs), cameras monitoring the entire sky dome above their point of installation, have been demonstrated. Accurate knowledge of the CBH is required to nowcast the spatial distribution of solar irradiance around the ASI's location at a resolution down to 5 m. To measure the CBH, two ASIs located at a distance of usually less than 6 km can be combined into an ASI pair. However, the accuracy of such systems is limited. We present and validate a method to measure the CBH using a network of ASIs to enhance accuracy. To the best of our knowledge, this is the first method to measure the CBH with a network of ASIs which is demonstrated experimentally. In this study, the deviations of 42 ASI pairs are studied in comparison to a ceilometer and are characterized by camera distance. The ASI pairs are formed from seven ASIs and feature camera distances of 0.8…5.7 km. Each of the 21 tuples of two ASIs formed from seven ASIs yields two independent ASI pairs as the ASI used as the main and auxiliary camera, respectively, is swapped. Deviations found are compiled into conditional probabilities that tell how probable it is to receive a certain reading of the CBH from an ASI pair given that the true CBH takes on some specific value. Based on such statistical knowledge, in the inference, the likeliest actual CBH is estimated from the readings of all 42 ASI pairs. Based on the validation results, ASI pairs with a small camera distance (especially if &lt;1.2 km) are accurate for low clouds (CBH&lt;4 km). In contrast, ASI pairs with a camera distance of more than 3 km provide smaller deviations for greater CBH. No ASI pair provides the most accurate measurements under all conditions. The presented network of ASIs at different distances proves that, under all cloud conditions, the measurements of the CBH are more accurate than using a single ASI pair.
Offensive Language Detection on Video Live Streaming Chat

This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time.Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions.We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful).To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning).We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity.Our results show that the transfer of features from social media to live streaming is effective.However, the two measurements show less correlation in the transferability prediction.
Multitask Learning-Based Neural Bridging Reference Resolution

We propose a multi task learning-based neural model for resolving bridging references tackling two key challenges.The first challenge is the lack of large corpora annotated with bridging references.To address this, we use multi-task learning to help bridging reference resolution with coreference resolution.We show that substantial improvements of up to 8 p.p. can be achieved on full bridging resolution with this architecture.The second challenge is the different definitions of bridging used in different corpora, meaning that hand-coded systems or systems using special features designed for one corpus do not work well with other corpora.Our neural model only uses a small number of corpus independent features, thus can be applied to different corpora.Evaluations with very different bridging corpora (ARRAU, ISNOTES, BASHI and SCICORP) suggest that our architecture works equally well on all corpora, and achieves the SoTA results on full bridging resolution for all corpora, outperforming the best reported results by up to 36.3 p.p..
Perkembangan Bahasa Pada Anak Usia Dini

Language development in early childhood is the most important aspect for early childhood. Through language, children can develop the ability to communicate with other people and children can express their thoughts using language so that other people can understand what the child is thinking. On the other hand, it is not surprising that language is considered as an indicator of a child's success. In the aspect of language development, the expected competence and results are that children are able to use language as a passive understanding of language and can communicate effectively which is useful for thinking and learning well. The role of parents is very important for children's language development by providing appropriate stimulations so that language development can develop optimally
Word Cloud and Sentiment Analysis of Amazon Earphones Reviews with R Programming Language

The internet has opened a very wide range of ways for exchanging information or data.The development of internet influenced our daily lives to share our opinion on internet.We can share our opinions in social media like twitter, Facebook, LinkedIn or micro blogging site.We can give reviews about any product or we can share what things we are expecting.The sharing of information in internet makes internet a rich resource.Large organization or any type of business who want to do business in a customer centric way needs to know what peoples are thinking.To know that, we can use online resources but to analyze all the data in a short time is not easy if we try to figure out everyone is thought one by one.Sentiment analysis and word cloud in text mining is introduced to eradicate this problem.It helps to know what peoples are thinking and helps to develop the client experience and helps to take decision in a customer centric way.The project on word cloud and sentiment analysis of amazon earphones reviews is basically done to know the process which we can used in our practical life to know the people's attitude, opinions, reviews, sentiment towards something from unstructured big data from online resources.
PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction.In this paper, we design the positional dependency-based word embedding (POD) which considers both dependency context and positional context for aspect term extraction.Specifically, the positional context is modeled via relative position encoding.Besides, we enhance the dependency context by integrating more lexical information (e.g., POS tags) along dependency paths.Experiments on SemEval 2014/2015/2016 datasets show that our approach outperforms other embedding methods in aspect term extraction.
New Benchmark Corpus and Models for Fine-grained Event Classification: To BERT or not to BERT?

We introduce a new set of benchmark datasets derived from ACLED data for fine-grained event classification and compare the performance of various state-of-the-art machine learning models on these datasets, including SVM based on TF-IDF character n-grams and neural context-free embeddings (GLOVE and FASTTEXT) as well as deep learning-based BERT with its contextual embeddings.The best results in terms of micro (94.3-94.9%)and macro F 1 (86.0-88.9%)were obtained using BERT transformer, with simpler TF-IDF character n-gram based SVM being an interesting alternative.Further, we discuss the pros and cons of the considered benchmark models in terms of their robustness and the dependence of the classification performance on the size of training data.
A New Extension of Thinning-Based Integer-Valued Autoregressive Models for Count Data

The thinning operators play an important role in the analysis of integer-valued autoregressive models, and the most widely used is the binomial thinning. Inspired by the theory about extended Pascal triangles, a new thinning operator named extended binomial is introduced, which is a general case of the binomial thinning. Compared to the binomial thinning operator, the extended binomial thinning operator has two parameters and is more flexible in modeling. Based on the proposed operator, a new integer-valued autoregressive model is introduced, which can accurately and flexibly capture the dispersed features of counting time series. Two-step conditional least squares (CLS) estimation is investigated for the innovation-free case and the conditional maximum likelihood estimation is also discussed. We have also obtained the asymptotic property of the two-step CLS estimator. Finally, three overdispersed or underdispersed real data sets are considered to illustrate a superior performance of the proposed model.
A Novel Comprehensive Watermarking Scheme for Color Images

Watermarking technology is commonly used to solve various problems in digital rights management and multimedia security. If a watermarking scheme with multiple purposes applies single method, it will easily cause the destruction of the hidden messages in particular attacks. For the copyright protection and tamper detection of color images, this research proposed a robust-fragile watermarking scheme. The two different embedding schemes embed the watermark into the R layer and G layer after NSST (nonsubsampled shearlet transform) and DWT (discrete wavelet transform) transformation. The hash sequence generated by the R layer and the G layer is served as fragile watermarks and is embedded into the B layer by the LSB (least significant bit) method. Finally, an improved rotation correction is applied to better extract the watermark under the rotation attack. Experimental results show that the proposed method is more accurate than the existing ones in terms of rotation angle correction and can effectively resist general attacks such as noise, filtering, and JEPG compression. Moreover, the proposed fragile watermark can locate the tamper position when malicious tamper occurs. Except cropping attack, the true-positive rate (TPR) reaches 1 for all attacks.