Reviewerly/exHarmony huggingface dataset:
Landsat 8: Utilizing sensitive response bands concept for image processing and mapping of basalts

The spectral signature curve behavior of basalt and its relationship with the surface reflectance (SR) values of Landsat 8 OLI data, (Operational Land Imager), is considered as the key factor for determination of the sensitive response bands for basalt interaction to be used in both Object-Oriented Principal Components (PCA) & Independent Component (ICA) analyses. Also, it is extremely beneficial in develop mathematical formulas which express the best band ratios for the precise illustration and mapping of basalts. The integration of those sensitive bands and the three mentioned methods are the main objectives of the present investigation. This should permit to create the basalt spatial distribution map for a given area. These methods were applied in west central Sinai, Egypt. The measured spectral signature reflectance curve of the basalt was resampled to meet the spectral characteristics of Landsat 8 bands, both curves were carefully examined to determine the most significant response bands for basalts; which were found to be bands 4, 5, 6 and 7. The examined spectral behavior led to three significant band ratios. Moreover, two false color composite (PC1, PC2, PC4) and (IC4, IC3, IC1) in RGB, were determined by execution and examination of the PCA and ICA images, respectively of the study area. ERDAS Imagine (2010) and ArcGIS 10.3 packages were used for digital/mathematical processing steps and to apply the resulted models on the study area.
Model-driven cloud resource management with OCCIware

Cloud computing has emerged as the main paradigm for hosting and delivering computing resources as services over Internet. It provides a delivery model for computing resources at infrastructure, platform, and software levels. However, there is a plethora of cloud providers offering different resource management interfaces. Due to this diversity, the consumption, provisioning, management, and supervision of cloud resources are subjected to four key issues, i.e., heterogeneity, interoperability, integration, and portability. To cope with these issues, Open Cloud Computing Interface (OCCI) is proposed as a community-based and open recommendation standard for managing any kind of cloud resources. Currently, only runtime implementations exist for OCCI, and each one targets a specific cloud service model such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS). Thus, OCCI lacks an approach to model and execute different OCCI artifacts. Our approach provides a generic modeling framework coupled with a generic runtime implementation. In this article, we propose the OCCIware approach, which represents the first approach to design, validate, generate, implement, deploy, execute, and supervise everything as a service with OCCI. This approach provides OCCIware Studio, the first model-driven tool chain for OCCI. It is built around OCCIware Metamodel, which defines the static semantics for the OCCI standard in Ecore and OCL. In addition, it proposes OCCIware Runtime, the first generic OCCI runtime implementation targeting all the cloud service models (IaaS, PaaS, and SaaS). OCCIware provides a unique and unified framework to manage OCCI artifacts and, at the same time, it represents a factory to build cloud domain-specific modeling frameworks where each framework targets a specific cloud domain. OCCIware has been applied in various cloud domains and use-cases, which validate its applicability.
Logic-statistical information models in control function of accounting

The article deals with theoretical substantiation and development of an approach to the control function implementation on the basis of automation control application process and the use of logicstatistical information models (LSÌM). The basis for development of the approach to the improvement of monitoring over the accounting objects is LSÌM which allows automatically identify deviations from the established norms. In contrast to the existing approaches the proposed here development provides instant formation of signal (signal document) about the critical state of an object. The use of LSÌM in the process of automation control allows quickly correct the imbalances in the state of objects and lead them in compliance with the accepted norms through appropriate management decisions. The result of this application development is holding objects within optimal limits. This will promote the rational use. Originality of the proposed approach lies in the fact that detailed analysis of the accounting object is conducted not for all factors, but only for those that go beyond the optimal zone. This greatly reduces the amount of information and data to be analyzed. In addition, the use of LSÌM allows controlling the volume (size) of the accounting object or each period of time, keeping them in a certain framework and providing the most optimal ratio by making appropriate management decisions. The use of the recommended here approach allows controlling the facility and gaps through the administrative impact on its status.
Digital Divide, Digital Inclusion and Inclusive Education

Digital divide refers to the gap of unequal accessibility and experience for access and use of modern information and communication technologies; digital inclusion has a focus on a practical, policy-driven approach in this context that addresses the needs of communities as a whole. Inclusive education is a process of strengthening the capacity of education systems to reach out all learners being welcomed in regular classes and to support them to learn, contribute and participate in all aspects of the life in schools and training institutions. Inclusive education is in many European countries at the beginning, even though it has numerous benefits and supports digital inclusion. This chapter presents some aspects of supporting inclusive education through digital technologies, the impact of digital divide and digital inclusion on inclusive education and how could digital platforms support digital inclusion. Some example of European projects with own participation of authors are presented where inclusive educational methodologies and technologies have been developed to minimize the negative impact of digital divide on people with disabilities and support inclusive education and training.
Impact of Information Literacy Skill on Students’ Academic Performance in Bangladesh

The study explores the level of academic performance and information literacy skill of undergraduate students in Bangladesh.In addition, the study examines the impact of information literacy skill on students' academic performance.To achieve the objectives, the study uses primary data collected from 325 students and employs several statistical and econometric methods.The study firstly uses information literacy skill index to measure the level of students' information literacy skill and secondly, uses a linear regression estimated by OLS method to examine the impact of information literacy skill on students' academic performance.The study finds that most of the students' Grade Point Average (GPA) is medium standard which ranges between 3.01 and 3.50 and the level of information literacy skill is lower which ranges between 10 and 20.Besides, the study also finds that study hour, family income, class attendance, past academic result and information literacy skill are the significant factors which influence students' academic performance.More specifically, the study finds that students' GPA may be increased by 0.012 if students' information literacy skill is increased by one unit.Therefore, the study suggests nursing students' information literacy skill to improve the students' academic performance.
Negative Lexically Constrained Decoding for Paraphrase Generation

Paraphrase generation can be regarded as monolingual translation.Unlike bilingual machine translation, paraphrase generation rewrites only a limited portion of an input sentence.Hence, previous methods based on machine translation often perform conservatively to fail to make necessary rewrites.To solve this problem, we propose a neural model for paraphrase generation that first identifies words in the source sentence that should be paraphrased.Then, these words are paraphrased by the negative lexically constrained decoding that avoids outputting these words as they are.Experiments on text simplification and formality transfer show that our model improves the quality of paraphrasing by making necessary rewrites to an input sentence.
Path Planning Algorithms for Unmanned Aerial Vehicles

In this paper, the shortest path for Unmanned Aerıal Vehicles (UAVs) is calculated with two -dimensional (2D) path planning algorithms in the environment including obstacles and thus the robots could perform their tasks as soon as possible in the environment.The aim of this paper is to avoid obstacles and to find the shortest way to the target point.Th e simulation environment was created to evaluate the arrival time on the path planning algorithms (A* and Dijkstra algorithms) for the UAVs.As a result, real-time tests were performed with UAVs
Psycholinguistics Meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering

We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA).Motivated by evidence from psycholinguistics, we devise a set of linguistically-informed VQA tasks, which differ by the types of questions involved (Wh-questions and polar questions).We test what impact task difficulty has on continual learning, and whether the order in which a child acquires question types facilitates computational models.Our results show that dramatic forgetting is at play and that task difficulty and order matter.Two well-known current continual learning methods mitigate the problem only to a limiting degree.
NeuralClassifier: An Open-source Neural Hierarchical Multi-label Text Classification Toolkit

In this paper, we introduce NeuralClassifier, a toolkit for neural hierarchical multi-label text classification.NeuralClassifier is designed for quick implementation of neural models for hierarchical multi-label classification task, which is more challenging and common in real-world scenarios.A salient feature is that NeuralClassifier currently provides a variety of text encoders, such as FastText, TextCNN, TextRNN, RCNN, VDCNN, DPCNN, DRNN, AttentiveConvNet and Transformer encoder, etc.It also supports other text classification scenarios, including binary-class and multiclass classification.Built on PyTorch 1 , the core operations are calculated in batch, making the toolkit efficient with the acceleration of GPU.Experiments show that models built in our toolkit achieve comparable performance with reported results in the literature.
New Generation Model of Word Vector Representation Based on CBOW or Skip-Gram

Word vector representation is widely used in natural language processing tasks. Most word vectors are generated based on probability model, its bag-of-words features have two major weaknesses: they lose the ordering of the words and they also ignore semantics of the words. Recently, neural-network language models CBOW and Skip-Gram are developed as continuous-space language models for words representation in high dimensional real-valued vectors. These vector representations have recently demonstrated promising results in various NLP tasks because of their superiority in capturing syntactic and contextual regularities in language. In this paper, we propose a new strategy based on optimization in contiguous subset of documents and regression method in combination of vectors, two of new models CBOW-OR and SkipGram-OR for word vector learning are established. Experimental results show that for some words-pair, the cosine distance obtained by the CBOW-OR (or SkipGram-OR) model is generally larger and is more reasonable than CBOW (or Skip-Gram), the vector space for Skip-Gram and SkipGram-OR keep the same structure property in Euclidean distance, and the model SkipGram-OR keeps higher performance for retrieval the relative words-pair as a whole. Both CBOW-OR and SkipGram-OR model are inherent parallel models and can be expected to apply in large-scale information processing.
Linking Code Readability, Structure, and Comprehension Among Novices: It's Complicated

Novices' functionally-correct code is often redundant, verbose, or un-idiomatic. Such code could indicate shallow understanding of the programming language, or unfamiliarity with experts' preferences for code structure. Understanding why novices write poorly is important for designing instruction and tools to help novices write elegantly. 231 novices judged style and readability for sets of code snippets targeting seven topics. Within each set, functionality was the same, but the writing followed either common novice patterns or a more elegant, "expert" pattern. Overall, 76% of novices thought the "expert" snippets had the best style, but only 64% said those snippets were most readable. However, comprehension was similar for both "expert" and novice patterns, regardless of readability preferences. This suggests that students who prefer novice patterns do not necessarily have deep misunderstandings about the programming language. One topic included a code-writing task, and students' readability preferences were predictive of their code-writing patterns, suggesting that readability preferences reflect writing choices rather than comprehension. Thus, novices may benefit from lightweight tools that identify common patterns and suggest an "expert" solution, while helping them see that the "expert" solution is more readable than they think.
Preserving Location Privacy in Mobile Edge Computing

The burgeoning technology of Mobile Edge Computing (MEC) is attracting the traditional Location-Based Service (LBS) and Location Service (LS) to deploy due to its nature characters such as low latency and location awareness. Although this transplant will avoid the location privacy threat from the central cloud provider, there still exist the privacy concerns in the LS of MEC scenario. Location privacy threat arises during the procedure of the fingerprint localization, and the previous studies on location privacy are ineffective because of the different threat model and information semantic. To address the location privacy in MEC environment, we designed LoPEC, a novel and effective scheme for protecting location privacy for the MEC devices. By the proper model of the Radio Access Network (RAN) access points, we proposed the noise-addition method for the fingerprint data, and successfully induce the attacker from recognizing the real location. Our evaluation proves that LoPEC effectively prevents the attacker from obtaining the user's location precisely in both single-point and trajectory scenarios.
The role of creative self-efficacy, transformational leadership, and digital literacy in supporting performance through innovative work behavior: Evidence from telecommunications industry

In the current technology shifting era, technology plays an essential function that determines the continuity of the industry.The current technology advancements empower companies to produce creativity and innovation to serve customer needs and expectation.To deliver this challenge, it requires the continuous breakthrough and innovation.To create organization innovation, employee plays the main key, as the main source and the executor of innovation.The organizational environment in knowledge-based industries, especially in the telecommunication industry, it is very dynamic and work activities tend to involve non-standard tasks and complicated non-routines.Routines carried out by organizations may not be able to respond quickly to rapid technological changes, or often experience technical problems that require fast and creative solutions.This study, focused on the innovative work behavior on employee perspective (creative self-efficacy and transformational leadership) and tested the digital literacy role on moderating the relationship between innovative work and employee performance.This study was made based on the survey conducted at various telecommunication companies across Indonesia comprising of 235 participants with managerial level and above.Structural equation modeling (LISREL) was used for data analysis in this quantitative research.The result proved that there was a positive and significant relationship between creative self-efficacy, transformational leadership and innovative work behavior, innovative work behavior was positively related to performance, digital literacy gave significant moderating influences on the relationship between innovative work behavior and performance.
Learning to Interpret Satellite Images using Wikipedia

Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitation, we construct a novel dataset called WikiSatNet by pairing geo-referenced Wikipedia articles with satellite imagery of their corresponding locations. We then propose two strategies to learn representations of satellite images by predicting properties of the corresponding articles from the images. Leveraging this new multi-modal dataset, we can drastically reduce the quantity of human-annotated labels and time required for downstream tasks. On the recently released fMoW dataset, our pre-training strategies can boost the performance of a model pre-trained on ImageNet by up to 4.5% in F1 score.
Large-Scale Evolution Strategy Based on Search Direction Adaptation

The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful evolutionary algorithm for single-objective real-valued optimization. However, the time and space complexity may preclude its use in high-dimensional decision space. Recent studies suggest that putting sparse or low-rank constraints on the structure of the covariance matrix can improve the efficiency of CMA-ES in handling large-scale problems. Following this idea, this paper proposes a search direction adaptation evolution strategy (SDA-ES) which achieves linear time and space complexity. SDA-ES models the covariance matrix with an identity matrix and multiple search directions, and uses a heuristic to update the search directions in a way similar to the principal component analysis. We also generalize the traditional 1/5th success rule to adapt the mutation strength which exhibits the derandomization property. Numerical comparisons with nine state-of-the-art algorithms are carried out on 31 test problems. The experimental results have shown that SDA-ES is invariant under search-space rotational transformations, and is scalable with respect to the number of variables. It also achieves competitive performance on generic black-box problems, demonstrating its effectiveness in keeping a good tradeoff between solution quality and computational efficiency.
A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

Precise solar radiation forecasting is of great importance for solar energy utilization and its integration into the grid, but because of the daily solar radiation’s intrinsic non-stationary and nonlinearity, which is influenced by a lot of elements, single predicting models may have difficulty obtaining results with high accuracy. Therefore, this paper innovatively puts forward an original hybrid model that predicts solar radiation through extreme learning machine (ELM) optimized by the bat algorithm (BA) based on wavelet transform (WT) and principal component analysis (PCA). First, choose the meteorological variables on the basis of Pearson coefficient test, and WT will decompose historical solar radiation into two time series, which are de-noised signal and noise signal. In the approximate series, the lag phase of historical radiation is obtained by partial autocorrelation function (PACF). After that, use PCA to reduce the dimensions of the influencing factors, including meteorological variables and historical radiation. Finally, ELM is established to predict daily solar radiation, whose input weight and deviation thresholds gained optimization by BA, thus it is called BA-ELM henceforth. In view of the four distinct solar radiation series obtained by NASA, the empirical simulation explained the hybrid model’s validity and effectiveness compared to other primary methods.
Select and Attend: Towards Controllable Content Selection in Text Generation

Xiaoyu Shen, Jun Suzuki, Kentaro Inui, Hui Su, Dietrich Klakow, Satoshi Sekine. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
A Method of Short Text Representation Based on the Feature Probability Embedded Vector

Text representation is one of the key tasks in the field of natural language processing (NLP). Traditional feature extraction and weighting methods often use the bag-of-words (BoW) model, which may lead to a lack of semantic information as well as the problems of high dimensionality and high sparsity. At present, to solve these problems, a popular idea is to utilize deep learning methods. In this paper, feature weighting, word embedding, and topic models are combined to propose an unsupervised text representation method named the feature, probability, and word embedding method. The main idea is to use the word embedding technology Word2Vec to obtain the word vector, and then combine this with the feature weighted TF-IDF and the topic model LDA. Compared with traditional feature engineering, the proposed method not only increases the expressive ability of the vector space model, but also reduces the dimensions of the document vector. Besides this, it can be used to solve the problems of the insufficient information, high dimensions, and high sparsity of BoW. We use the proposed method for the task of text categorization and verify the validity of the method.
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training

The objective of non-parallel text style transfer is to alter specific attributes (e.g.sentiment, mood, tense, politeness, etc) of a given text while preserving unrelated content.Adversarial training is a popular method to ensure the transferred sentences have the desired target styles.However, previous works often suffer from content leaking problem.In this paper, we propose a new adversarial training model with a word-level conditional architecture and a two-phase training procedure.By using a style-related condition architecture before generating a word, our model is able to maintain style-unrelated words while changing the others.By separating the training procedure into reconstruction and transfer phases, our model is able to balance the reconstruction and adversarial losses.We test our model on polarity sentiment transfer and multiple-attribute transfer tasks.The empirical results show that our model achieves comparable evaluation scores in both transfer accuracy and fluency but significantly outperforms other state-of-the-art models in content compatibility on three real-world datasets.