Reviewerly/exHarmony huggingface dataset:
An Analysis of Students’ Problems in Translating Report Text from English into Indonesian

The purpose of this study is to describe the translation problem and find the dominant issues in translating the report text from English to Indonesian faced by the second-grade beauty students of SMKN 3 Metro in the 2021/2022 academic year. The researcher used a qualitative descriptive approach. Researchers obtained data from students' translation tests. To collect data, the researcher needed several steps; the researcher gave the students a report text translation test, collected data on students' translation problems, and analyzed the students' issues in translating the report text from English into Indonesian. Then the researchers drew conclusions based on data analysis. The results of the data analysis showed that the most frequent problem made by students was vocabulary. The highest number of student problems is vocabulary (53%). Student problem number two is incomplete sentences in the target language text (24.47%). The third problem is that messages from the source language are not transmitted into the target language (11.88%). The fourth is an addition (6.29%), and the last is grammar (3.49%). Students face several problems translating English-Indonesian, such as vocabulary, incomplete sentences in the target language text, not sending messages from the source language into the target language, addition, and grammar. The most dominant problem in translating the report text from English to Indonesian the second-class beauty.
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion

Knowledge base completion (KBC) aims to predict the missing links in knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting where all test entities and relations have appeared in the training set. However, there has been limited research on the zero-shot KBC settings, where we need to deal with unseen entities and relations that emerge in a constantly growing knowledge base. In this work, we systematically examine different possible scenarios of zero-shot KBC and develop a comprehensive benchmark, ZeroKBC, that covers these scenarios with diverse types of knowledge sources. Our systematic analysis reveals several missing yet important zero-shot KBC settings. Experimental results show that canonical and state-of-the-art KBC systems cannot achieve satisfactory performance on this challenging benchmark. By analyzing the strength and weaknesses of these systems on solving ZeroKBC, we further present several important observations and promising future directions. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Work was done during the internship at Tencent AI lab. The data and code are available at: https://github.com/brickee/ZeroKBC
EnD: Enhanced Dedensification for Graph Compressing and Embedding

Graph representation learning is essential in applying machine learning methods on large-scale networks. Several embedding approaches have shown promising outcomes in recent years. Nonetheless, on massive graphs, it may be time-consuming and space inefficient for direct applications of existing embedding methods. This paper presents a novel graph compression approach based on dedensification called Enhanced Dedensification with degree-based compression (EnD). The principal goal of our system is to assure decent compression of large graphs that eloquently favor their representation learning. For this purpose, we first compress the low-degree nodes and dedensify them to reduce the high-degree nodes' loads. Then, we embed the compressed graph instead of the original graph to decrease the representation learning cost. Our approach is a general meta-strategy that attains time and space efficiency over the original graph by applying the state-of-the-art graph embedding methods: Node2vec, DeepWalk, RiWalk, and xNetMf. Comprehensive ex-periments on large-scale real-world graphs validate the viability of our method, which shows sound performance on single and multi-label node classification tasks without losing accuracy.
How Drones Can Mitigate Unknown-Unknown Risks Case of Covid-19

This paper shows the importance of technology in mitigating the spread of Covid-19 which has significantly changed the lives of people, affecting health, livelihood, and the global economy due to the closure of international borders. It has promoted social distancing and increased the need for measures that can eliminate the impact of COVID-19 related issues and outcomes. The research investigates the ways technology can benefit to support teams in preventing Covid-19. It also aims to examine the main advantages of drones during Covid-19. In addition to suggesting the use of drones to fight the Covid-19 pandemic. The inability of some countries to cope with the virus and the effective use of drones to deal with it highlights its importance. However, there is a need to adjust some cultures to the use of drones during the coronavirus crisis. Based on the review of literature, it indicated that people couldn’t eliminate the virus impact yet, but they could alleviate the damage they caused. The current research has used interviews to collect data from five organizations involved in the mitigation of unknown-unknown risks, including the Ministry of Health & Community Protection, Dubai Executive Council, DEWA, Melaaha Drones, and ArabDrones. Moreover, a face-to-face interview was conducted by phones and e-mail conversations. As well as a literature review has helped in exploring the unknown-unknown risk method along with the use of Artificial Intelligence such as drones to assist in combating the Covid-19 pandemic. The descriptive qualitative research design has helped to test the hypothesis regarding its ability of drones to mitigate the unknown-unknown risk of Covid-19. The research results part indicated that drones are vital constituents of contingency planning. They have proven their value by going for the extra mile in saving costs and lives by incorporating drones technology in minimizing the effects of the crisis which was illustrated with some real examples. Also, the paper has proven the hypothesis by showing another real example on a drone company like ArbaDrone and how they assess, plan and mitigate unknown-unknown risks by following such a specialized program called RIO Project Program to measure and minimize the crisis impacts in the running company. Thus, drones have significantly helped countries such as the UAE to deal with the risks against Covid-19 through the elimination of infection risks with about 96% accuracy via computerized tomography scans. Finally, since the drones have contributed to international success in mitigating the Covid-19 pandemic and having an essential role in fighting the spread of the virus, so, it is very necessary to re-enforce the drone’s experience to enhance the fight against coronavirus attracting other tools. Furthermore, extra studies still needed to give more bigger pictures about the impact of this coronavirus statistically to have more information about its influences on the global market.
Extracting Entities and Events from Cyber-Physical Security Incident Reports

Cyber- physical systems are an important part of many industries such as the chemical process industry, manufac- turing industry, automobiles, and even sophisticated weaponry. Given the economic importance and influence of these systems, they have increasingly faced the cybersecurity attacks. In this paper, we provide a dataset of real-life security incident reports on cyber-physical systems annotated with entities and events that are important for analysing such security incidents. We analyze and identify the limitations of the 'Domain Objects' in Structured Threat Information Expression (STIX) standard as well as recent research literature for the entity type clas- sification schemes in cybersecurity domain. We propose an updated classification scheme for entity types in the cybersecurity domain. The enhanced coverage provided by the entity scheme is important for automated information extraction and natural language understanding of textual reports containing details of the cybersecurity incident reports. We use deep-learning based sequence labelling techniques and cybersecurity domain specific word embed dings to set up a benchmark for entity and event extraction for cyber- physical security incident report analysis. The annotated dataset of real-life industrial security incidents will be made available for research purpose.
On Robust Incremental Learning over Many Multilingual Steps

Recent work in incremental learning has introduced diverse approaches to tackle catastrophic forgetting from data augmentation to optimized training regimes. However, most of them focus on very few training steps. We propose a method for robust incremental learning over dozens of fine-tuning steps using data from a variety of languages. We show that a combination of data-augmentation and an optimized training regime allows us to continue improving the model even for as many as fifty training steps. Crucially, our augmentation strategy does not require retaining access to previous training data and is suitable in scenarios with privacy constraints.
End-to-end Oriental Language Speech Recognition with Integrated Language Identification

In recent years, with the rise of human-computer interaction and the successful application of end-to-end models in the field of speech recognition, the construction of end-to-end speech recognition models has received extensive attention. Relying on the multi-task learning method and the connection between language identification and speech recognition, we proposed an end-to-end Transformer model, which is a multilingual speech recognition model integrating language identification. The model takes the speech recognition task as the main task and the language identification task as the auxiliary task. In this paper, the validity of the model is verified by using the datasets of 13 languages in the 2021 Oriental Language Recognition challenge (OLR). The experimental results show that the model constructed in this paper has a relative improvement of 37.46% in the speech recognition task compared with the baseline system proposed by the OLR organizer. The accuracy of language identification reaches 89.70 %. The results can get the fifth place in the 2021 OLR constraint track of speech recognition equally.
SGKD: A Scalable and Effective Knowledge Distillation Framework for Graph Representation Learning

As Graph Neural Networks (GNNs) are widely used in various fields, there is a growing demand for improving their efficiency and scalablity. Knowledge Distillation (KD), a classical methods for model compression and acceleration, has been gradually introduced into the field of graph learning. More recently, it has been shown that, through knowledge distillation, the predictive capability of a well-trained GNN model can be transferred to lightweight and easy-to-deploy MLP models. Such distilled MLPs are able to achieve comparable performance as their corresponding G NN teachers while being significantly more efficient in terms of both space and time. However, the research of KD for graph learning is still in its early stage and there exist several limitations in the existing KD framework. The major issues lie in distilled MLPs lack useful information about the graph structure and logits of teacher are not always reliable. In this paper, we propose a Scalable and effective graph neural network Knowledge Distillation framework (SGKD) to address these issues. Specifically, to include the graph, we use feature propagation as preprocessing to provide MLPs with graph structure-aware features in the original feature space; to address unreliable logits of teacher, we introduce simple yet effective training strategies such as masking and temperature. With these innovations, our framework is able to be more effective while remaining scalable and efficient in training and inference. We conducted comprehensive experiments on eight datasets of different sizes - up to 100 million nodes - under various settings. The results demonstrated that SG KD is able to significantly outperform existing KD methods and even achieve comparable performance with their state-of-the-art GNN teachers.
Few-shot Edge Classification in Graph Meta-learning

Recent few-shot learning methods based on graph neural networks (GNN) over-focus on the connections between nodes while ignoring the pair-wise relations between nodes. Meta-learning aims at model parameter initialization, enabling the model to gain the generalization capability, which assists GNN to pay more attention to nodes. There are rare methods to apply meta-learning to non-Euclidean spaces (such as graph structures). Thus, we propose a graph meta-learning framework, Meta Edge-labeling Graph Neural Network (Meta-EGNN), to solve image classification in few-shot learning. Meta-EGNN can learn a better parameter initialization for GNN with the prediction of edge labels, which can enhance the generalization of the model on unseen tasks. We also introduce the first-order gradient model-agnostic meta-learning into meta-EGNN, which can not only reduce the computational costs, but also help meta-EGNN extend to the transductive inference. The experimental results on two benchmarks prove that Meta-EGNN is competitive in both supervised and semi-supervised image classification.
A Systematic Review on the Influence of Entrepreneurial Leadership on Social Capital and Change Propensity

The last three decades have been characterized by an increased number of research studies examining how the management of organizations has become a critical area of concern in modern literature. This paper presents a systematic literature review of the influence of entrepreneurial leadership on social capital and change propensity. The work clearly states the research question on how these variables affect a firm's success. The methodology involved drawing ideas from recent studies that have explored the association between leadership styles and an organization's ability to achieve consistent social capital and effective change management. Relevant literature was accessed on Google scholar search and used to examine the relationships between the variables. This study seeks to fill a gap in the literature by presenting a theoretically-grounded methodology that helps leaders design and adopt approaches that focus on achieving and integrating social capital in managing change in organizations.
Impact of Knowledge Management on Total Quality Management at Private Universities in Jordan

This study aims to know the impact of knowledge management on total quality management in Jordanian private universities. To achieve the objectives of the study, descriptive and analytical approach was adopted, where the researcher developed a questionnaire consisting of (35) items. The study population consisted of all (directors of departments, deans of colleges, department heads, administrative staff, and faculty members), where the study followed the simple random method. The results showed a statistically significant effect of knowledge management on total quality management. The study recommended the necessity for universities to organize meetings to exchange knowledge, opinions, experiences and ideas, address problems facing them at work, and promote a culture of openness, sharing, teamwork and dialogue, in a manner that ensures benefit and learning from each other to improve and implement quality programs.
Multilingual Zero Resource Speech Recognition Base on Self-Supervise Pre-Trained Acoustic Models

Labeled audio data is insufficient to build satisfying speech recognition systems for most of the languages in the world. There have been some zero-resource methods trying to perform phoneme or word-level speech recognition without labeled audio data of the target language, but the error rate of these methods is usually too high to be applied in real-world scenarios. Recently, the representation ability of self-supervise pre-trained models has been found to be extremely beneficial in zero-resource phoneme recognition. As far as we are concerned, this paper is the first attempt to extend the use of pretrained models into word-level zero-resource speech recognition. This is done by fine-tuning the pre-trained models on IPA phoneme transcriptions and decoding with a language model trained on extra texts. Experiments on Wav2vec2.0 and HuBERT models show that this method can achieve less than 20% word error rate on some languages, and the average error rate on 8 languages is 33.77%.
The Impact of Total Quality Management on the Organizational Reputation

The major aim of the study was to examine the impact of total quality management (TQM) on organizational reputation (OR). Therefore, it focused on the faculty members in five public universities, whose number is (5477) faculty members. As for the study sample, it consisted of a proportional stratified sample of (390) individuals, and after the questionnaire was distributed with (410), 390 were recovered, valid for the purposes of statistical analysis. Structural equation modeling (SEM) was conducted to test hypotheses. The results showed that all total quality management dimensions had a positive impact relationship on organizational reputation except focus on beneficiary. Based on the results of the study, researchers recommend managers and decision-makers in public universities to pay attention to the university management standard (higher leadership), which has the largest role in supporting and activating the application of total quality management.
Feature Extraction and Prediction of Combined Text and Survey Data using Two-Staged Modeling

Deep learning (DL) based natural language processing (NLP) has recently grown as one the fastest research domain and retained remarkable improvement in many applications. Due to the significant amount of data, the adaptation of feature learning and symmetric data efficiency is a critical underlying task in such applications. However, their ability to extract features is limited due to a lack of proper model formation. Moreover, the use of these methods on smaller datasets is unexplored and underdeveloped compared to more popular research areas. This work introduces a two-stage modeling approach to combine classical statistical analysis with NLP problems in a real-world dataset. We effectively layout a combination of the classical statistical model incorporating a stacked ensemble classifier and a DL framework of convolutional neural network (CNN) and Bidirectional Recurrent Neural Networks (Bi-RNN) to structure a more decomposed architecture with lower computational complexity. Additionally, the experimental results illustrating 96.69 % training and 70.56 % testing accuracy and hypothesis testing from our DL models followed by an ablation study empirically demonstrate the validation of our proposed combined modeling technique.
Data-driven Kernel Subspace Clustering with Local Manifold Preservation

Kernel-based subspace clustering methods that can reveal the nonlinear structure of data are an emerging research topic. While advances have been made, existing methods suffer from one or both of the following shortcomings: (1) the predefined kernel determines their performance; (2) they may be vulnerable in arbitrary manifold subspace. In this paper, we propose a novel data-driven kernel subspace clustering model with local manifold preservation, named DKLM. Specifically, DKLM provides an explicit data-driven kernel learning strategy for learning kernel directly from the self-representation of data while satisfying the adaptive-weighting. Based on the kernel, DKLM allows preserving the local manifold structure of data through a kernel local manifold term in nonlinear space and encourages acquiring an affinity matrix with the optimal block diagonal. Various experiments on both synthetic data and real-world data demonstrate the effectiveness of our method.
The Quality Evaluation of Academic Papers in Carbon Capture, Utilization, and Storage

Carbon capture, utilization, and storage (CCUS) technology is a research hotspot worldwide owing to global climate change and warm gas control. CCUS papers from the last twenty years, derived from the Web of Science database, were analyzed to investigate the impact and citation of academic papers using machine learning methods. Firstly, nine author characteristics, keywords, and three other categories were extracted from the literature. Then, the natural logarithm distribution of the annual average number of citations was obtained, and five evaluation grades were decided based on the citations. Next, the keyword evolution of papers in the CCUS field is analyzed, and a model on the keyword evolution trend index is proposed. Data integration and imbalance processing were conducted after random sampling. A citation prediction model was proposed to evaluate the quality of CCUS academic papers and was verified on the recent five years' CCUS papers by comparing it with nine other mainstream machine learning models. The results indicate that the random forest model exhibits the best performance, with an accuracy rate reaching 88.7%; an ablation experiment was also conducted using the same algorithm. The results prove the effectiveness of our proposed method, shedding light on the quality of academic papers and technology development in CCUS.
Identification of Safe Air Quality for Activity Using Long Short-Term Memory

Air pollution is one of the problems that is often occurred in the cities with high industrial and transportation levels. Several informations are needed by the community so that they can be use it for people’s health concerns. Air Quality Index is a standard for determining air quality and pollution based on the main parameters, namely: Carbon Dioxide (CO2), Sulfur Dioxide (SO2), Ozone (O3), and Particulate Dust (PM10 and PM2.5). While, for the methods, we suggest Long Short-Term Memory model which have good performance in identifying safe air quality for activities. The model conducts training with a parameter of 100 epoch, learning rate = 0.001, batch size = 32. Testing uses the optimizer, activation function, and the right function to measure air quality with an accuracy of up to 98%. Thus, we adjust LSTM model with Adamax optimizer and Sigmoid activation function is the best parameter to get the highest accuracy.
Holder and Target Identification on Opinion Text using Convolutional Neural Network

The development of social media platforms has made it possible for everyone to be able to express their opinions online. Therefore, various techniques have been developed to extract the information in opinion texts. Opinion Role Labeling (ORL) aims to identify opinion holder and opinion target within documents. We propose the deep learning models to identify opinion holder and opinion target given opinion text. The different between our study and other regardless to holder and target identification is that we used different architecture by combining the character level feature using Convolutional Neural Network (CNN) with BERT-BiLSTM-CRF. Based on the experiments using MPQA as training data, we report that the use of CNN architecture for character level feature extraction can increase the F1-Score of the BERT-BiLSTM-CRF base as baseline model by 3 %. In addition of an opinion expression feature on the model can significantly increase the F1-Score of the baseline model by 20%.