Reviewerly/exHarmony huggingface dataset:
Garnet Characteristics Associated with Jiama Porphyry-Skarn Cu Deposit 1# Skarn Orebody, Tibet, Using Thermal Infrared Spectroscopy

Field measurements of the thermal infrared (TIR) reflectance from drill hole samples proved to be an effective method to map variations in garnet species associated with hydrothermal alteration zonation of the Jiama porphyry-skarn Cu deposit 1# skarn orebody, Tibetan Plateau, China. The TIR mineral spectral information was combined with electron probe micro-analysis (EPMA) measurements to provide geological insights on effectively determining (a) garnet end components and providing a format for further research on the type and genesis of the deposit; (b) the significance of the characteristic spectrum of garnet to the variation of mineralization environment; (c) the relationship between the characteristic spectrum of garnet and Fe/Al content; (d) the garnet characteristic spectrum to the economic mineralization. The results suggest that garnet characteristics of the thermal infrared spectrum can be used as an indicator for skarn deposit prospecting.
Being Fast Means Being Chatty: The Local Information Cost of Graph Spanners

We introduce a new measure for quantifying the amount of information that the nodes in a network need to learn to solve a graph problem. We show that the local information cost (LIC) presents a natural lower bound on the communication complexity of distributed algorithms. For the synchronous CONGEST-KT1 model, where each node has initial knowledge of its neighbors' IDs, we prove that bits are required for solving a graph problem P with a τ-round algorithm that errs with probability at most γ. Our result is the first lower bound that yields a general trade-off between communication and time for graph problems in the CONGEST-KT1 model.We demonstrate how to apply the local information cost by deriving a lower bound on the communication complexity of computing a multiplicative spanner with stretch 2t – 1 that consists of at most edges, where ∊ = O(1/t2). Our main result is that any O(poly(n))-time algorithm must send at least bits in the CONGEST model under the KT1 assumption. Previously, only a trivial lower bound of bits was known for this problem; in fact, this is the first nontrivial lower bound on the communication complexity of a sparse subgraph problem in this setting.A consequence of our lower bound is that achieving both time- and communication-optimality is impossible when designing a distributed spanner algorithm. In light of the work of King, Kutten, and Thorup (2015), this shows that computing a minimum spanning tree can be done significantly faster than finding a spanner when considering algorithms with Õ(n) communication complexity. Our result also implies time complexity lower bounds for constructing a spanner in the node-congested clique of Augustine et al. (2019) and in the push-pull gossip model with limited bandwidth.
UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis

Memes are widely used on social media.They usually contain multi-modal information such as images and texts, serving as valuable data sources to analyse opinions and sentiment orientations of online communities.The provided memes data often face an imbalanced data problem, that is, some classes or labelled sentiment categories signi cantly outnumber other classes.This often results in di culty in applying machine learning techniques where balanced labelled input data are required.In this paper, a Gaussian Mixture Model sampling method is proposed to tackle the problem of class imbalance for the memes sentiment classi cation task.To utilise both text and image data, a multi-modal CNN-LSTM model is proposed to jointly learn latent features for positive, negative and neutral category predictions.The experiments show that the re-sampling model can slightly improve the accuracy on the trial data of sub-task A of Task 8.The multi-modal CNN-LSTM model can achieve macro F1 score 0.329 on the test set.
Blockchain-Enabled Decentralized Reliable Smart Industrial Internet of Things (BCIIoT)

The development of wireless communication in the information technological era, collecting data, and transfering it from unmanned systems or devices could be monitored by any application while it is online. Direct and aliveness of countless wireless devices in a cluster of the medium could legitimate unwanted users to interrupt easily in an information flow. It would lead to data loss and security breach. Many traditional algorithms are effectively contributed to the support of cryptography-based encryption to ensure the user's data security. IoT devices with limited transmission power constraints have to communicate with the base station, and the data collected from the zones would need optimal transmission power. There is a need for a machine learning-based algorithm or optimization algorithm to maximize data transfer in a secure and safe transmission.
Improving Cloud Data Encryption Using Customized Genetic Algorithm

Data Encryption is widely utilized for ensuring data privacy, integrity, and confidentiality. Nowadays, a large volume of data is uploaded to the cloud, which increases its vulnerability and adds to security breaches. These security breaches include circumstances where sensitive information is being exposed to third parties or any access to sensitive information by unauthorized personnel. The objective of this research is to propose a method for improving encryption by customizing the genetic algorithm (GA) with added steps of encryption. These added steps of encryption include the data being processed with local information (chromosome's value calculated with computer-generated random bits without human intervention). The improvement in the randomness of the key generated is based on altering the population size, number of generations, and mutation rate. The first step of encrypting is to convert sample data into binary form. Once the encryption process is complete, this binary result is converted back to get the encrypted data or cipher-text. Foremost, the GA operators (population size, number of generations, and mutation rate) are changed to determine the optimal values of each operator to bring forth a random key in the minimum possible time, then local intelligence is headed in the algorithm to further improve the outcomes. Local Intelligence consists of local information and a random bit generated in each iteration. Local Information is the current value of a parent in each iteration at the gene level. Both local information and random bit are then applied in a mathematical pattern to generate a randomized key. The local intelligence-based algorithm can operate better in terms of time with the same degree of randomness that is generated with the conventional GA technique. The result showed that the proposed method is at least 80% more efficient in terms of time while generating the secret key with the same randomness level as generated by a conventional GA. Therefore, when large data are intended to be encrypted, then using local intelligence can demonstrate to be better utilized time.
Exploring the zero-shot limit of FewRel

This paper proposes a general purpose relation extractor (RE) that uses Wikidata descriptions to represent the relation's surface form.The results are tested on the FewRel 1.0 dataset, which provides an excellent framework for training and evaluating the proposed zero-shot learning system in English.This relation extractor architecture exploits the implicit knowledge of a language model through a question-answering approach 1 .
Binarized Encoder-Decoder Network and Binarized Deconvolution Engine for Semantic Segmentation

Recently, semantic segmentation based on deep neural network (DNN) has attracted attention as it exhibits high accuracy, and many studies have been conducted on this.However, DNN-based segmentation studies focused mainly on improving accuracy, thus greatly increasing the computational demand and memory footprint of the segmentation network.For this reason, the segmentation network requires a lot of hardware resources and power consumption, and it is difficult to be applied to an environment where they are limited, such as an embedded system.In this paper, we propose a binarized encoder-decoder network (BEDN ) and a binarized deconvolution engine (BiDE) accelerating the network to realize lowpower, real-time semantic segmentation.BiDE implements a binarized segmentation network with custom hardware, greatly reducing the hardware resource usage and greatly increasing the throughput of network implementation.The deconvolution used for upsampling in a segmentation network includes zero padding.In order to enable deconvolution in a binarized segmentation network that cannot express zero, we introduce zero-aware binarized deconvolution which skips padded zero activations and zero-aware batch normalization embedded binary activation considering zero-skipped convolution.The BEDN, which is a binarized segmentation network proposed to be accelerated on BiDE, has acceptable accuracy while greatly reducing the computational and memory demands of the segmentation network through full-binarization and simple structure.BEDN has a network size of 0.21 MB, and its maximum memory usage is 1.38 MB.BiDE was implemented on Xilinx ZU7EV field-programmable gate array (FPGA) to operate at 187.5 MHz.BiDE accelerated the proposed BEDN within CamVid11 images of 480×360 size at 25.89 frames per second (FPS) achieving a performance of 1.682 Tera operations per second (TOPS) and 824 Giga operations per second per watt (GOPS/W).
The Effectiveness of Infographic via Interactive Smart Board on enhancing Creative Thinking: A Cognitive Load Perspective

The purpose of the present study was to examine The Effectiveness of the Infographic Pattern through the Interactive Smart Board on the Creative Thinking of Educational Technology Students: A Cognitive Load Perspective.This study utilized the quasi experimental type methodology.The independent variables were the Infographic Pattern methods (text and image, sound and image).The dependent variables were the creative thinking, which will be determined by the final mark on the post-test.The study samples consisted of 33 undergraduate educational technology students, and were randomly selected, via simple random sample.ANOVA procedure was used to determine the significant differences of the pretest scores among the two methods.An analysis of covariance, MANCOVA, was carried out to examine the main effects of the independent variables on the dependent variables.The findings of this study showed that students who have learned through the Infographic Pattern sound and image achieve design efficiently better in their post-test scores than those in the Infographic Pattern text and image.The results also showed that the effect of using infographic through the interactive smart board on the development of creative thinking skills among students of educational technology was medium degree.
Pointing to Select: A Fast Pointer-LSTM for Long Text Classification

Recurrent neural networks (RNNs) suffer from well-known limitations and complications which include slow inference and vanishing gradients when processing long sequences in text classification.Recent studies have attempted to accelerate RNNs via various ad hoc mechanisms to skip irrelevant words in the input.However, word skipping approaches proposed to date effectively stop at each or a given time step to decide whether or not a given input word should be skipped, breaking the coherence of input processing in RNNs.Furthermore, current methods cannot change skip rates during inference and are consequently unable to support different skip rates in demanding real-world conditions.To overcome these limitations, we propose Pointer-LSTM, a novel LSTM framework which relies on a pointer network to select important words for target prediction.The model maintains a coherent input process for the LSTM modules and makes it possible to change the skip rate during inference.Our evaluation on four public data sets demonstrates that Pointer-LSTM (a) is 1.1x∼3.5xfaster than the standard LSTM architecture; (b) is more accurate than Leap-LSTM (the state-of-the-art LSTM skipping model) at high skip rates; and (c) reaches robust accuracy levels even when the skip rate is changed during inference.
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models

We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model.We download extra training data from Twitter in English, Danish, and Turkish, and use it to re-train the model.We then fine-tuned the model on the provided training data and, in some configurations, implement transfer learning approach exploiting the typological relatedness between English and Danish.Our systems obtained good results across the three languages (.
Multiple Kernel Learning With Minority Oversampling for Classifying Imbalanced Data

Class imbalance problems, developed due to the sampling bias or measurement error, occur frequently in real-world pattern classification tasks. The traditional classifiers focus on the overall classification accuracy and ignore the minority class, which may degrade the classification performance. However, existing oversampling algorithms generally make specific assumptions to balance the class size and do not sufficiently consider irregularities present in imbalanced data. As a result, these methods can perform well only on certain benchmarks. In this paper, by incorporating minority oversampling and cost-sensitive learning, we propose multiple kernel learning with minority oversampling (MKLMO), for efficiently handling the class imbalance problem with small disjuncts, overlapping, and nonlinear shape. Unlike existing methods where oversampling of the minority class is performed first and then a standard classifier is deployed on the rebalanced data, the proposed MKLMO generates synthetic instances and trains classifier synchronously in the same feature space. Specially, we define a distance metric in the optimal feature space by multiple kernel learning and use kernel trick to expand the original Gram matrix. Moreover, we assign different weights to instances, based on the imbalance ratio, for reducing the bias of the classifier towards the majority class. In order to evaluate the proposed MKLMO method, several experiments are performed with nine artificial and twenty-one real-world datasets. The experimental results show that our algorithm outperforms other baseline algorithms significantly in terms of the assessment metric geometric mean (G-mean), especially in the presence of data irregularities.
Multimodal speech recognition for unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) are becoming widespread with applications ranging from film-making and journalism to rescue operations and surveillance. Research communities (speech processing, computer vision, control) are starting to explore the limits of UAVs, but their efforts remain somewhat isolated. In this paper we unify multiple modalities (speech, vision, language) into a speech interface for UAV control. Our goal is to perform unconstrained speech recognition while leveraging the visual context. To this end, we introduce a multimodal evaluation dataset, consisting of spoken commands and associated images, which represent the visual context of what the UAV “sees” when the pilot utters the command. We provide baseline results and address two main research directions. First, we investigate the robustness of the system by (i) training it with a partial list of commands, and (ii) corrupting the recordings with outdoor noise. We perform a controlled set of experiments by varying the size of the training data and the signal-to-noise ratio. Second, we look at how to incorporate visual information into our model. We show that we can incorporate visual cues in the pipeline through the language model, which we implemented using a recurrent neural network. Moreover, by using gradient activation maps the system can provide visual feedback to the pilot regarding the UAV’s understanding of the command. Our conclusions are that multimodal speech recognition can be successfully used in this scenario and that visual information helps especially when the noise level is high. The dataset and our code are available at http://kite.speed.pub.ro.
Automated Features Extraction from Software Requirements Specification (SRS) Documents as The Basis of Software Product Line (SPL) Engineering

Extractive Software Product Line Engineering (SPLE) puts features on the foremost aspect in domain analysis that needs to be extracted from the existing system's artifact. Feature in SPLE, which is closely related to system functionality, has been previously studied to be extracted from source code, models, and various text documents that exist along the software development process. Source code, with its concise and normative standard, has become the most focus target for feature extraction source on many kinds of research. However, in the software engineering principle, the Software Requirements Specification (SRS) document is the basis or main reference for system functionality conformance. Meanwhile, previous researches of feature extraction from text document are conducted on a list of functional requirement sentences that have been previously prepared, not literally SRS as a whole document. So, this research proposes direct processing on the SRS document that uses requirement boilerplates for requirement sentence statement. The proposed method uses Natural Language Processing (NLP) approach on the SRS document. Sequence Part-of-Speech (POS) tagging technique is used for automatic requirement sentence identification and extraction. The features are acquired afterward from extracted requirement sentences automatically using the word dependency parsing technique. Besides, mostly the previous researches about feature extraction were using non-public available SRS document that remains classified or not accessible, so this work uses selected SRS from publicly available SRS dataset to add reproducible research value. This research proves that requirement sentence extraction directly from the SRS document is viable with precision value from 64% to 100% and recall value from 64% to 89%. While features extraction from extracted requirement sentences has success rate from 65% to 88%.
ANALISIS PENERAPAN PEMBELAJARAN DARING PADA SISWA KELAS V SDN 009 SAMARINDA ULU

The purpose of this study was to obtain information related to the online learning implementation for the 5th grade students of SDN 009 Samarinda Ulu during the Covid-19 pandemic. This research was a descriptive qualitative research. Respondents in this study were 9 people from SDN 009 Samarinda Ulu. The details of the 9 respondents in this study were 3 homeroom teachers of the 5th grade, 3 students and 3 parents. To maintain the confidentiality of the respondentsâ€™ identities, the researcher gave the names of the respondents R1, R2, R3, R4, R5, R6, R7, R8 and R9. The data techniques in this study were interviews and documentation. The interviews used in this study were interviews, interviews were conducted by telephone and WhatsApp. Documentation was used as supporting data for this research. The data analysis technique used in this study was the Miles and Huberman analysis technique with four stages, namely, data collection, data reduction, data presentation and conclusion; and data validity in the study used source triangulation. The results of this study indicated that the Covid-19 pandemic had a major impact on the learning process at SDN 009 Samarinda Ulu. Learning that was usually carried out through direct meeting turned into online learning; online learning was still ineffective due to confusion and unpreparedness of the teachers, students and parents in the implementation process and the lack of facilities and infrastructure.
Adaptive Kalman filtering for closed-loop systems based on the observation vector covariance

The Kalman filter is one of the most widely used methods for state estimation and control purposes. However, it requires correct knowledge of noise statistics, which are unknown or not known perfectly in real-life applications and then they need to be identified. Considering such background, this paper introduces a new adaptive Kalman filter algorithm in order to handle the unknown process noise covariance for linear discrete-time closed-loop systems. From the closed-loop joint analysis of the system and the a priori recursive form of the Kalman filter, we adaptively estimate the process noise covariance by relating it to the observation vector covariance. The latter is then obtained from an exponential moving average technique. Lastly, we also extend our adaptive methodology for a special class of nonlinear systems. The performance of the proposed adaptive method is demonstrated through numerical examples and it has been compared to other types of adaptive filtering algorithms.
An Approach for Information Security Risk Assessment in Cloud Environments

Cloud technology has revolutionized the way computational resources are accessed, offering benefits that have led to widespread adoption.The risk of losing important data is one of the reasons why some organizations do not adopt the migration to a public cloud or adopt the partial migration of information that is not critical.The process of risk assessment should be done since the initial stage of a project and become a continuous process.It is an essential process that help management structure to take strategic decision about security mechanisms needed to be implemented to avoid information leaks and about the costs and impact of unexpected events.This paper presents the process of information security risk assessment as well as the importance of knowledge of the associated risks.The paper also proposes a model for determining risk of data security according to their importance for the organization, which provides an overview of vulnerabilities and their real impact on assets.Furthermore, the proposed model helps organizations to choose the right methods to ensure the optimal level of security, in line with operational requirements and critical information.
Approach of a Japanese Co-Occurrence Words Collection Method for Construction of Linked Open Data for COVID-19

The Coronavirus Disease (COVID-19) is spreading around the world.There is a possibility that lifestyles will continue to change in the future, and it is essential to take COVID-19 into account in our daily lives as well as in business.However, COVID-19 is causing unprecedented changes in our lives, with no known cure and no established measures to prevent infection.Therefore, it is necessary to consider these response measures based on various information about COVID-19.In this paper, we propose a method for collecting Japanese co-occurrence words with the goal of construct a Linked Open Data (LOD) for COVID-19.
KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT

This research presents our team KEIS@JUST participation at SemEval-2020 Task 12 which represents shared task on multilingual offensive language.We participated in all the provided languages for all subtasks except sub-task-A for the English language.Two main approaches have been developed the first is performed to tackle both languages Arabic and English, a weighted ensemble consists of Bi-GRU and CNN followed by Gaussian noise and global pooling layer multiplied by weights to improve the overall performance.The second is performed for other languages, a transfer learning from BERT beside the recurrent neural networks such as Bi-LSTM and Bi-GRU followed by a global average pooling layer.Word embedding and contextual embedding have been used as features, moreover, data augmentation has been used only for the Arabic language .