Reviewerly/exHarmony huggingface dataset:
Foreign investment &amp; growth in emerging economies: panel ARDL analysis

Recently, the contributory role of foreign investment growth in Africa has been considered by researchers and policymakers. Studies in this area are not yet clear. Besides, foreign direct investment has emerged as a determining factor of economic growth. Concerning this evidence, the current study tries to investigate the contributions of foreign direct investment on economic growth or for developing the economy of Africa. This study used yearly panel data for the period 1995-2015 for 5 developing the economy of Africa. The results of Panel ARDL indicate that foreign direct investment has a positive effect on economic growth as well as a positive sign of trade openness, inflation, and labor. The study also stresses that, for increasing economic growth, there is a need to seek more foreign investments, increase trade openness and inflation, and at the same time, to improve the employment condition in the selected African developing countries.
The Current Practice of Corporate Social Responsibility in Ethiopia

The responsibility of the organization for the impacts of its decisions and activities on the society and the environment, called Corporate Social Responsibility is evolving investment approach aimed mainly to contribute for sustainable development and welfare of the society. CSR is being integrated with economic and investment policy of different countries and practiced in different modalities. In Ethiopia, literatures indicates that only limited local as well as international or multinational organizations have been practicing CSR in a fragmented ways. Accordingly, this research was aimed to examine the practice of corporate social responsibility in Ethiopia, specifically in Hilton Hotel located at Addis Ababa. Up on the nature and purpose, the research is led by qualitative approach. Key informant and in-depth interviews were used as main data collection techniques. In addition, document review and systematic observation were also used to get supportive data. In-depth interviews were conducted with beneficiaries whereas key informant interviews were employed with managers of organizations. Totally 20 research participants: four employees, four key informant interviewees, and twelve beneficiaries were participated in this study. Purposive sampling technique was used to select the research participants based on the predetermined objectives of the research. The finding of the study shows that Hilton Hotel has several corporate social responsibility programs (CSR) in relation to employees support, community service, environmental protection, and customer handling. Provision of health insurance, freedom of associations for labor, facilitating continues skill based training, support to local NGOs, offering scholarship opportunities, and providing charity and other emergency support are some of the involvements Hilton Hotel has made through its CSR program. This research concluded that though there are several CSR made by the hotel but these have been made without regular budget as well as working procedures.
ReELFA: A Scene Text Recognizer with Encoded Location and Focused Attention

LSTM and attention mechanism have been widely used for scene text recognition.However, existing LSTM-based recognizers usually convert 2D feature maps into 1D space by flattening or pooling operations, resulting in the neglect of spatial information of text images.Additionally, the attention drift problem, where models fail to align targets at proper feature regions, has a serious impact on the recognition performance of existing models.To tackle the above problems, in this paper, we propose a scene text Recognizer with Encoded Location and Focused Attention, i.e., ReELFA.Our ReELFA utilizes one-hot encoded coordinates to indicate the spatial relationship of pixels and character center masks to help focus attention on the right feature areas.Experiments conducted on benchmark datasets IIIT5K, SVT, CUTE and IC15 demonstrate that the proposed method achieves comparable performance on the regular, lowresolution and noisy text images, and outperforms state-of-the-art approaches on the more challenging curved text images.
Modeling Sentiment Evolution for Social Incidents

Modeling sentiment evolution for social incidents in Microblogs is of vital importance for both enterprises and government officials. Existing works on sentiment tracking are not satisfying, due to the lack of entity-level sentiment extraction and accurate sentiment shift detection. Identifying entity-level sentiment is challenging as Microbloggers often use multiple opinion expressions in a sentence which targets different entities. Moreover, the evolution of the background sentiment, which is essential to shift detection, is ignored in the previous study. To address these issues, we leverage the proximity information to obtain more precise entity-level sentiment extraction. Based on it, we propose to simultaneously model the evolution of background opinion and the sentiment shift using a state space model on the time series of sentiment polarities. Experiments on real data sets demonstrate that our proposed approaches outperform state-of-the-art methods on the task of modeling sentiment evolution for social incidents.
Investigating Terminology Translation in Statistical and Neural Machine Translation: A Case Study on English-to-Hindi and Hindi-to-English

Terminology translation plays a critical role in domain-specific machine translation (MT).In this paper, we conduct a comparative qualitative evaluation on terminology translation in phrasebased statistical MT (PB-SMT) and neural MT (NMT) in two translation directions: English-to-Hindi and Hindi-to-English.For this, we select a test set from a legal domain corpus and create a gold standard for evaluating terminology translation in MT.We also propose an error typology taking the terminology translation errors into consideration.We evaluate the MT systems' performance on terminology translation, and demonstrate our findings, unraveling strengths, weaknesses, and similarities of PB-SMT and NMT in the area of term translation.
Link Prediction on Dynamic Heterogeneous Information Networks

This work develops a broad learning method for link prediction on dynamic heterogeneous information networks. While existing works have primarily focused on dynamic homogeneous networks or static heterogeneous networks. As such, the existing methods can be suboptimal for link prediction on dynamic heterogeneous information networks. In this paper, we try to study the problem of link prediction combining dynamic networks and heterogeneous networks. However, none of the existing works has paid special attention to connect these two kinds of network data. To tackle this challenge, we propose a new broad learning-based method named HA-LSTM, short for Hierarchical Attention Long-Short Time Memory to address this problem on dynamic heterogeneous information networks. Firstly, we employ the Graph Convolutional Network (GCN) to extract the feature from Heterogeneous Information Networks (HINs). Then, we utilize a broad learning and attention based framework to fuse and extract the information among HINs broadly over timestamps. Finally, the link prediction in time-dimension by employing LSTM could be performed. We conduct extensive experiments on several real dynamic heterogeneous information networks covering the task of link prediction. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our HA-LSTM method.
Students’ Success in English Vocabulary Acquisition through Multimedia Annotations Sent via Whatsapp

The purpose of this study is to determine the effects of the multimedia annotations which were sent through instant messaging application WhatsApp on students’ success in English vocabulary acquisition together with their opinions on the educational use of WhatsApp and the multimedia annotations. In the study, mixed method model, which combines both quantitative and qualitative data, was employed. Results of the variance analysis of the quatitative data indicated that the multimedia annotations sent through WhatsApp yielded a meaningful increase in students’ vocabulary acquisition. Multiple comparison test to identify the reason of this increase revealed that ‘Text+Picture+Audio’ and ‘Text+Picture’ multimedia annotations increased students’ vocabulary acquisition meaningfully when compared to other learning multimedia annotations. Although ‘Text+Audio’ multimedia annotation contributed to the success of students more than the students who received only ‘texts’, it wasn’t found meaningful. In the qualitative aspect of the study, students expressed their willingness to go on the treatment. They stated that similar researches in their other courses could be useful. They also added that they could learn unconsciously and expressed positive opinions towards the use of the pictures and audio annotations in particular. However, a few students reacted negatively againts the irrelevant posts and stated that the posts occupied a lot of space on their phones.
Expressive Authoring of Node-Link Diagrams With Graphies

Expressive design environments enable visualization designers not only to specify chart types and visual mappings, but also to customize individual graphical marks, as they would in a vector graphics drawing tool. Prior work has mainly investigated how to support the expressive design of a wide range of charts generated from tabular data: bar charts, scatterplots, maps, etc. We focus here on an expressive design environment for node-link diagrams generated from multivariate networks. Such data structures raise specific challenges and opportunities in terms of visual design and interactive authoring. We discuss those specificities and describe the user-centered design process that led to Graphies, a prototype environment for expressive node-link diagram authoring. We then report on a study in which participants successfully reproduced several expressive designs, and created their own designs as well.
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews

Due to the nature of online user reviews, sentiment analysis on such data requires a deep semantic understanding of the text.Many online reviews are sarcastic, humorous, or hateful.Signals from such language nuances may reinforce or completely alter the sentiment of a review as predicted by a machine learning model that attempts to detect sentiment alone.Thus, having a model that is explicitly aware of these features should help it perform better on reviews that are characterized by them.We propose a composite two-step model that extracts features pertaining to sarcasm, humour, hate speech, as well as sentiment, in the first step, feeding them in conjunction to inform sentiment classification in the second step.We show that this multi-step approach leads to a better empirical performance for sentiment classification than a model that predicts sentiment alone.A qualitative analysis reveals that the conjunctive approach can better capture the nuances of sentiment as expressed in online reviews.
Pengaruh kecerdasan adversitas dan kecerdasan emosional melalui model inkuiri sosial terhadap keterampilan sosial siswa

The research purpose is to know the effect of adversity intelligence and emotional intelligence through social inquiry models against social skills. The research method used ex post facto — the collection data technique using questionnaires. The taking sample technique is approached by simple random sampling. The amount of sample is 104 students on SMP N 10 Bandar Lampung. The data analysis using Structural Equation Modeling (SEM). The research result shows that there are a definite relation linkages between endogenous variable, exogenous variable, and intervening variable. Through the social inquiry model, intelligence and emotional intelligence adversity will play a role effective in improved the skills of social. It would be useful for a student to are learning success in classes and the future. It is expected that schools can optimize emotional adversity intelligence and intelligence to enhance their social skills and skills students academics. It can be concluded that if adversity intelligence and emotional intelligence through social inquiry model against social skill. DOI: 10.17977/um022v4i22019p066
Risk mitigation in electric power systems: Where to start?

Abstract Power grids are becoming increasingly intelligent. In this regard, they benefit considerably from the information technology (IT) networks coupled with their underlying operational technology (OT) networks. While IT networks provide sufficient controllability and observability of power grid assets such as voltage and reactive power controllers, distributed energy resources, among others, they make those critical assets vulnerable to cyber threats and risks. In such systems, however, several technical and economic factors can significantly affect the patching and upgrading decisions of their components including, but not limited to, limited time and budget as well as legal constraints. Thus, resolving all vulnerabilities at once could seem like an insuperable hurdle. To figure out where to start, an involved decision maker (e.g. a security team) has to prudently prioritize the possible vulnerability remediation actions. The key objective of prioritization is to efficiently reduce the inherent security risk to which the system in question is exposed. Due to the critical role of power systems, their decision makers tend to enhance the system resilience against extreme events. Thus, they seek to avoid decision options associated with likely severe risks. Practically, this risk attitude guides the decision-making process in such critical organizations and hence the sought-after prioritization as well.Therefore, the contribution of this work is to provide an integrated risk-based decision-support methodology for prioritizing possible remediation activities. It leverages the Time-To-Compromise security metric to quantitatively assess the risk of compromise. The developed risk estimator considers several factors including: i) the inherent assessment uncertainty, ii) interdependencies between the network components, iii) different adversary skill levels, and iv) public vulnerability and exploit information. Additionally, our methodology employs game theory principles to support the strategic decision-making process by constructing a chain of security games. Technically, the remediation actions are prioritized through successively playing a set of dependent zero-sum games. The underlying game-theoretical model considers carefully the stochastic nature of risk assessments and the specific risk attitude of the decision makers involved in the patch management process across electric power organizations.
Window-Based Neural Tagging for Shallow Discourse Argument Labeling

This paper describes a novel approach for the task of end-to-end argument labeling in shallow discourse parsing.Our method describes a decomposition of the overall labeling task into subtasks and a general distance-based aggregation procedure.For learning these subtasks, we train a recurrent neural network and gradually replace existing components of our baseline by our model.The model is trained and evaluated on the Penn Discourse Treebank 2 corpus.While it is not as good as knowledge-intensive approaches, it clearly outperforms other models that are also trained without additional linguistic features.
Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis

To improve the accuracy of predicateargument structure (PAS) analysis, large-scale training data and knowledge for PAS analysis are indispensable.We focus on a specific domain, specifically Japanese blogs on driving, and construct two wide-coverage datasets as a form of QA using crowdsourcing: a PAS-QA dataset and a reading comprehension QA (RC-QA) dataset.We train a machine comprehension (MC) model based on these datasets to perform PAS analysis.Our experiments show that a stepwise training method is the most effective, which pre-trains an MC model based on the RC-QA dataset to acquire domain knowledge and then fine-tunes based on the PAS-QA dataset.
Big Bidirectional Insertion Representations for Documents

The Insertion Transformer is well suited for long form text generation due to its parallel generation capabilities, requiring O(log 2 n) generation steps to generate n tokens.However, modeling long sequences is difficult, as there is more ambiguity captured in the attention mechanism.This work proposes the Big Bidirectional Insertion Representations for Documents (Big BIRD), an insertionbased model for document-level translation tasks.We scale up the insertion-based models to long form documents.Our key contribution is introducing sentence alignment via sentence-positional embeddings between the source and target document.We show an improvement of +4.3 BLEU on the WMT'19 English→German document-level translation task compared with the Insertion Transformer baseline.
Orometry-based terrain analysis and synthesis

Mountainous digital terrains are an important element of many virtual environments and find application in games, film, simulation and training. Unfortunately, while existing synthesis methods produce locally plausible results they often fail to respect global structure. This is exacerbated by a dearth of automated metrics for assessing terrain properties at a macro level. We address these issues by building on techniques from orometry, a field that involves the measurement of mountains and other relief features. First, we construct a sparse metric computed on the peaks and saddles of a mountain range and show that, when used for classification, this is capable of robustly distinguishing between different mountain ranges. Second, we present a synthesis method that takes a coarse elevation map as input and builds a graph of peaks and saddles respecting a given orometric distribution. This is then expanded into a fully continuous elevation function by deriving a consistent river network and shaping the valley slopes. In terms of authoring, users provide various control maps and are also able to edit, reposition, insert and remove terrain features all while retaining the characteristics of a selected mountain range. The result is a terrain analysis and synthesis method that considers and incorporates orometric properties, and is, on the basis of our perceptual study, more visually plausible than existing terrain generation methods.
Deep Transfer Learning for Histopathological Diagnosis of Cervical Cancer Using Convolutional Neural Networks with Visualization Schemes

This study aimed to propose a deep transfer learning framework for histopathological image analysis by using convolutional neural networks (CNNs) with visualization schemes, and to evaluate its usage for automated and interpretable diagnosis of cervical cancer. First, in order to examine the potential of the transfer learning for classifying cervix histopathological images, we pre-trained three state-of-the-art CNN architectures on large-size natural image datasets and then fine-tuned them on small-size histopathological datasets. Second, we investigated the impact of three learning strategies on classification accuracy. Third, we visualized both the multiple-layer convolutional kernels of CNNs and the regions of interest so as to increase the clinical interpretability of the networks. Our method was evaluated on a database of 4993 cervical histological images (2503 benign and 2490 malignant). The experimental results demonstrated that our method achieved 95.88% sensitivity, 98.93% specificity, 97.42% accuracy, 94.81% Youden's index and 99.71% area under the receiver operating characteristic curve. Our method can reduce the cognitive burden on pathologists for cervical disease classification and improve their diagnostic efficiency and accuracy. It may be potentially used in clinical routine for histopathological diagnosis of cervical cancer.
Teleportation of squeezed states in the absence and presence of dissipation

In this paper at first we successfully teleport the unknown quantum state which is a superposition of squeezed vacuum state and squeezed one-photon state using the beam splitter in the absence of dissipation. In the continuation, we try to implement the same teleportation protocol, however, in the presence of dissipation effects. To do this task, we use proper entangled channel to reach to perfect teleportation under the influence of decoherence. Finally, we consider another superposition of two squeezed vacuum states with separation in phase by $ \pi$ and teleport it with a different appropriate entangled channel. In fact, we will observe that one can successfully teleport the considered superposition of squeezed states by choosing proper entangled channels in the presence and absence of dissipation in appropriate chosen conditions.
Analisis K-Medoids Clustering Dalam Pengelompokkan Data Imunisasi Campak Balita di Indonesia

Measles is a contagious infections disease that attacks children caused by a virus. Transmission of measles from people through coughing and sneezing. Measles causes disability and death, so further threatment is needed. Measles immunization program that can inhibit the development of measles is one of the efforts in eradicating the disease. In this study the data used were sourced from the Central Statistics Agency National in 2013-2017. This study uses datamining techniques in data processing with K-Medoids algorithm. The K-Medoids method is a clustering method that functions to break datasets into groups. The advantages of this method are the ability to overcome the weaknesses of the K-Means method which is sensitive to outliers. Another advantage of this algorithm is that the results of the clustering process do not depend on the entry sequence of the dataset. The k-medoids clustering method can be applied to the data on the percentage of measles immunization can be identified based on province, so that the grouping of provinces based on these data. From the data grouping three clusters are obtained: low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) with the percentage of measles immunization in each of these provinces from data grouping in percentage. It is expected this research can provide information to the govermant about the data on grouping measles immunization for toddlers in Indonesia which has an impact on the distribution of immunization against measles toddlers in Indonesia.
Learning Strategies in Reading: The Case of Indonesian Language Education Student Teachers

This study aimed at analyzing the English reading strategies used by Indonesian language education student teachers at an Indonesian public university, particularly the types and frequencies of English reading strategies that the student teachers used.The participants of this study were student teachers from one language study program.The study involved 570 student teachers, 438 females and 132 males.This study used a quantitative design with a survey approach.This study utilized a questionnaire consisting of 23 items.Reading strategies (cognitive and metacognitive) were evaluated under three headings: pre-reading, while-reading, and post-reading.The results of this study indicated that the most strategies used by students in pre-reading, while-reading, and post-reading were cognitive strategies.Hence, the students in this study categorized as the cognitive reading strategy users.The gender-based analysis of the reading strategies category revealed that female students were cognitive users, while male students were metacognitive strategies users.The implications of the findings are also discussed.