Reviewerly/exHarmony huggingface dataset:
Participatory Communication, COVID-19 and People with Disabilities in Nigeria

This paper sets out to investigate access to COVID-19 updates by people with disabilities in Nigeria, and the instrumentality of participatory communication in the global pandemic.The global pandemic heightened inequality and the marginalization of people living with disabilities in Nigeria. Largely unnoticed in the general media, disabled individuals face increased healthcare social, and economic risks thus, this paper was born out of the need to shed light on the implications and the importance of an all-inclusive response through participatory communication to limit the impact of coronavirus disease on people with disabilities in Nigeria. The paper utilized mixed methods in achieving its objective. The area of study was restricted to Anambra state, Nigeria.This work revealed that since the outbreak of the novel global pandemic, people with disabilities in Anambra state, Nigeria have been dealt a great blow; as they are the most vulnerable group in the state. In addition to the extreme hardship people living with disabilities face, the paper also revealed that while a few of the people with disabilities are poorly informed about COVID-19, others cannot access information sources; thereby increasing their susceptibility to the pandemic in state. This paper ascertains the need for access to information by people with disabilities through a focus group discussion. The paper concludes by emphasizing that in the bid to manage disease outbreaks, adopting an all- inclusive communication approach which is the core principle of participatory communication is instrumental in protecting the most vulnerable.
CRank: Reusable Word Importance Ranking for Text Adversarial Attack

Deep learning models have been widely used in natural language processing tasks, yet researchers have recently proposed several methods to fool the state-of-the-art neural network models. Among these methods, word importance ranking is an essential part that generates text adversarial examples, but suffers from low efficiency for practical attacks. To address this issue, we aim to improve the efficiency of word importance ranking, making steps towards realistic text adversarial attacks. In this paper, we propose CRank, a black box method utilized by our innovated masking and ranking strategy. CRank improves efficiency by 75% at the ’cost’ of only a 1% drop of the success rate when compared to the classic method. Moreover, we explore a new greedy search strategy and Unicode perturbation methods.
Five-dimensional modelling of direction-dependent geotechnical variables

Together with geological and geometallurgical modelling, geotechnical modelling is one of the essential components for the planning and development of open pit and underground mining projects. A particular characteristic of many geotechnical variables is to be direction-dependent, i.e. the measurement of a core sample not only depends on the geographical position of this sample, but also its orientation. To account for this characteristic, it is proposed to regionalise such variables in a five-dimensional space corresponding to the product on the three-dimensional geographical space and the two-dimensional sphere, so that each measurement is indexed by its easting, northing, elevation, azimuth, and dip. Instead of making predictions and simulations conditioned to a particular direction, this new paradigm allows geotechnical variables to be interpolated at any place in the geographical space, for any direction. The spatial correlation structure can be inferred and modelled by using separable covariances or combinations of separable covariances, under an assumption of stationarity in the geographical space and isotropy on the sphere. Also, conditional simulation can be performed by turning bands or spectral methods, based on products of basic stationary random fields in the geographical space and isotropic random fields on the sphere. The proposed methodology is illustrated with the modelling of the linear discontinuity frequency (P10) and the rock quality designation in two copper deposits.
Multi-Sentence Hierarchical Generative Adversarial Network GAN (MSH-GAN) for Automatic Text-to-Image Generation

This research is related to the development of technology in the field of automatic text to image generation. In this regard, two main goals are pursued; first, the generated image should look as real as possible; and second, the generated image should be a meaningful description of the input text. our proposed method is a Multi Sentences Hierarchical GAN (MSH-GAN) for text to image generation. In this research project, we have considered two main strategies: 1) produce a higher quality image in the first step, and 2) use two additional descriptions to improve the original image in the next steps. Our goal is to focus on using more information to generate images with higher resolution by using more than one sentence input text. We have proposed different models based on GANs and Memory Networks. We have also used more challenging dataset called ids-ade. This is the first time; this dataset has been used in this area. We have evaluated our models based on IS, FID and, R-precision evaluation metrics. Experimental results demonstrate that our best model performs favorably against the basic state-of-the-art approaches like StackGAN and AttGAN.
使用遠端直接記憶體存取加速 Kubernetes 中 Knative 函式間的資料交換

The rise of serverless architecture enables developers to use unlimited auto-scalable serverless functions on-demand without the worries of operating a massive computer cluster made of many computers. Moreover, developers only need to pay for the resources consumed by serverless functions during execution at millisecond-granularity. As a result, many researchers try to use high parallelism provided by serverless platforms to reduce the execution time of applications. Although serverless architecture can reduce the computation time of applications, it also increases the I/O time because exchanging data among serverless functions relies on remote storage services. To reduce the time for exchanging data, we propose a method that uses Remote Direct Memory Access (RDMA) to speed up accessing remote storage services.We choose Knative as our serverless platform to avoid vendor lock-in because Knative is built on top of Kubernetes. Our approach is based on Apache Crail.Apache Crail is an open-sourced distributed data store designed for RDMA. Before accessing Apache Crail, applications have to create Crail clients and RDMA queue pairs with Apache Crail; this creation is time-consuming. However, every serverless function has to create a Crail client and RDMA queue pairs because serverless functions are stateless and cannot share Crail clients and RDMA queue pairs, causing massive overhead.To solve the above problem, we build a Kubernetes PersistentVolume backed by Apache Crail to share Crail clients and RDMA queue pairs among serverless functions to save time create and destroy Crail clients and RDMA queue pairs repeatedly.
PERENCANAAN PEMBANGKIT LISTRIK TENAGA SURYA (PLTS) SISTEM OFF GRID DENGAN KAPASITAS 2 KWP PADA INSTALASI MENARA SUAR BULUKUMBA

Indonesia is the largest archipelago country in the world. With this geographical condition, the intensity of shipping in Indonesia is very busy. This results in a high possibility of accidents at sea. This is due to the lack of navigation equipment in the form of a lighthouse. The flow of electricity in remote areas is still unstable, resulting in frequent blackouts, even though the lighthouse must always work every night without any technical problems. Therefore, we need an alternative energy source that is easily available and in accordance with the geographical conditions of the area, namely the Solar Power Plant (PLTS). This study uses technical and economic analysis methods to plan the PLTS system in the beacon tower installation. This type of research is applied research, which means that the results of this research can be applied directly. This PLTS is designed to supply electrical energy with an estimated energy of 6360 Wh per day by using 6 solar panels with a capacity of 330 Wp which are assembled in 3 series-2 parallel and placed on the roof of the building as a solution to unavailability of vacant land. Other components needed are SCC 1 piece, inverter 1 piece, battery 12 pieces and 1 piece combiner box containing protection equipment. The results of the off-grid PLTS planning are in the feasible category with a performance ratio of 81%. The total cost required in this plan is Rp.87.400.000. Feasibility analysis results obtained a positive NPV value of Rp.20.992,82, an IRR value of 10,51%, a BCR value of 1,0002 and a return on investment in the 8th year of the project life.
Attention Based Reinforcement Learning with Reward Shaping for Knowledge Graph Reasoning

Knowledge graph reasoning aims at solving certain tasks by finding reasoning paths, which has aroused extensive attention. Recently, a solution for path reasoning that combines reinforcement learning has achieved successful progress. But these researches mainly focus on the agent's choice of relation and ignore the importance of entity, which will cause the random selection by the agent if 1-N/N-N relations occur. Thus, we propose a reinforcement learning based path reasoning model, which solves this problem from the topological and semantic levels. First, the attention mechanism is introduced in our model, which can extract the hidden feature from neighbor entities and helps the policy network to make a suitable choice instead of random for the actions with the same relation. Then, we introduce a convolutional neural network into our model to distinguish the rationality of the path by the semantic feature. To mitigate the negative impact of terminal rewards, we use a potential-based reward shaping function, which considers the potential gap between agent states as the reward and without any pre-training. Finally, we compare our model with the state-of-the-art baselines on two benchmark datasets, the results of extensive comparison experiments validate the effectiveness of the proposed method.
Serial management in special libraries in Nigeria: A study of selected ministry libraries in Imo State, Nigeria

This study investigated serial management in special libraries in Nigeria: a case study of five selected ministry libraries in Imo state – Nigeria. The purpose of the study was to ascertain how serials are managed for effective use by users in the selected ministry libraries. Survey research design method was used to conduct the study. The population of the study112, this comprised of the entire staff and users of the five ministry libraries studied. The sample size of the study is 112. Questionnaire and direct observation were used as instruments for data collection. Data collected for the study were analyzed using descriptive statistics and pie chart. Findings of the study showed that the  five ministry libraries manage their serials by preserving and displaying them on the tables, placing them on the wood shelves, tying the serials into heaps as well as shelving some of the serials on the shelf. The study recommended that adequate fund, functional air conditions, modern reading desks and chairs should be provided to the ministry libraries by the Imo State Government in order to attract more readers.Key Words: Management, Special, Library, Serials, Ministry Organizational, Users, Development, Economic, Growth, Information Resources.
Performance evaluation of semantic segmentation using efficient neural network (ENet) on various traffic scene conditions / Miza Fatini Shamsul Azmi, Fadhlan Hafizhelmi Kamaru Zaman and Husna Zainol Abidin

Object recognition, object detection, and semantic segmentation are fundamental components of the intelligent vehicle. Recently, there have been various methods proposed to create a reliable and accurate model to provide intelligent assistance to drivers. However, a reliable and accurate model in adverse conditions such as snow, rain, and fog remain a problem for advance driving assistance systems. The methods proposed only effectively solve the problem in a specific condition. Therefore, in this work, we focus on performing semantic segmentation in normal, rainy, foggy, and low light conditions using Efficient Neural Network (ENet) and ResNet18 and subsequently evaluating the trained model’s performance in these conditions. In the experiment, we used a daytime data set from CamVid and synthetically transformed the daytime data set into rainy, foggy, and low light conditions. To verify the accuracy of the proposed method, the Intersection over Union (IoU) is used, and the result is elaborated in the section result and discussion. This approach only performs accurately during daylight. From the experiments, both methods do suffer from various conditions, but the ENet method performs better in certain conditions compared to ResNet18.
ANALYZING HOTS OF THE THIRD SEMESTER ENGLISH STUDENTS AT TIMOR UNIVERSITY ON WRITING THESIS STATEMENTS OF PARAGRAPHS

The purpose of this is study is to analyze HOTS of the third semester English Students at Timor University on writing thesis statements of paragraphs. The population of the research is 63 and the sample was taken by cluster technique sampling. The sample is 35 students of the third semester English students at Timor University. The research instrument used were the test which consists of three types: Analyzing, Evaluating, and Creating. The data were analyzed by calculating the students’ score and performed the item analysis to identify the HOTS levels of the students’ score. Based on the analysis, the most dominant score is 51-75, and there are 26 students in that level with 74%. The data also showed that there were only a few students, 2 students with the percentage 6% who could achieve the scores of 100-76 and 7 students with the percentage 20% who could achieve the scores of 26-50. The findings of this study indicate that the student’s ability of third semester in Timor University is at good level.
경제성장에서 부패, FDI 및 ODA의 효과: 아시아와 라틴아메리카 개발도상국 비교연구

Purpose – This study compares the impact of corruption on economic growth in developing countries located in Asia and Latin America. Design/methodology – To identify the effects of corruption, FDI, and ODA on economic growth. The estimated model is established based on two models in this study. First, the most basic model, the pooled OLS, was used. In addition, a random effect model was used according to Hausman Test result. The CPI data is provided by Transparency International. Data of FDI and ODA are provided by World Bank. Findings– The empirical analysis shows no difference between regions. But the findings show some facts. The CPIs in both Latin and Asian countries have a positive impact on economic growth. This means that corruption should be reduced because it negatively affects economic growth. The FDI also has a positive effect on economic growth while the ODA has a negative effect on economic growth. These results shows that FDI should be increased, and ODA should be used efficiently for economic growth. Research implication or Originaltiy– There is no more lubricant effects occur. And then in the case of FDI, it must be invested in an efficient and constructive industries. The case of ODA, it will have to be invested in education and research projects, not just consumption.
How students of Universidad Pedagógica Veracruzana are going through the pandemic: a case study

This article describes who the students of the Universidad Pedagogica Veracruzana are and how they are going through the health emergency derived from COVID-19, in order to account for their strategies, motivations and practices during the lockdown. We conducted a series of surveys, observation records and semi-structured interviews with a sample of a total of 82 students from the institution at the bachelor’s degree, master’s and doctoral levels, using a multi-method approach. Based on the data obtained, we carried out a post-decision type policy analysis with the aim of pointing out the university’s performance as a result of our findings with its students, one of which was that, although the university’s response was immediate, it did not take into account the students’ personal and professional contexts: apparently, there was only a sort of overlapping of the curricular contents to be addressed, since due to the lack of an active institutional platform the teachers had to resort to the use of technological tools such as Zoom, Meet, Skype, and edmodo, among others.
Language Laboratory to Overcome the Barrier of Classroom English Learning: Does it Exist and Is it Used in Islamic Schools of Majene?

A staple issue in English language teaching is the fact that individual students in formal classrooms usually have minimal opportunity to speak due to large class sizes. Recent studies have vouched language laboratories to be capable of overcoming this barrier. As far as the researchers know, no study has launched a wide investigation whether a language laboratory exists and is used to optimise the English learning in the Islamic schools in Majene, Indonesia. This research applies descriptive quantitative design, involving 22 Islamic schools in the level of junior high school. 22 English teachers that represents all the schools filled a questionnaire to provide the data. It is found that the Islamic schools at Majene do not yet have a language laboratory even though the English teachers consider it important. Its absence is influenced by the lack of support from leaders. As a result, listening practice activities are rarely carried out. English teachers only carry out practical activities in the classroom with limited tools and materials. This research illuminates a concern for policy makers in the Ministry of Religious Affairs in the field of Islamic school education and the principals to hold a language laboratory at Islamic Schools. To improve English learning activities, the existence of a language laboratory in Islamic schools is matter, so that students at Islamic schools can show their quality in foreign language mastery and compete with others.
Diagnostics of the readiness for self-improvement of the future teacher in the conditions of COVID-19

The purpose of the article is to study a system for diagnosing a future teacher's readiness for self-improvement in the context of COVID-19. The high requirements of modern society for the personality, the level of professional competence and activities of educators, and as a result, new requirements for the training of pedagogical personnel and advanced training of teachers in the vocational education system imply the development of not only their professionally significant qualities, but the personality as a whole as a subject of life and self-development. The activity-value paradigm of modern education presupposes the presence of the teacher's subjective position in relation to his own development. As a result, the key aspects of the system for diagnosing the readiness for self-improvement of the future teacher in the context of COVID-19 were characterized.
ANALISA INTENSITAS HUJAN DI SUNGAI SUNTER JAKARTA UTARA

AbstractThe amount of rainfall intensity in an area is very important to be known, studied, discussed and studied in civil engineering. Analysis of rainfall intensity in a watershed can be calculated by several methods, for daily rainfall data the intensity can be calculated using the Mononobe method. This study aims to calculate the intensity of rainfall in the Sunter River using the Mononobe method. Rain data in this study uses daily rain data from the Tj. North Jakarta Priok. The calculation of the data in this study was carried out with the help of Microsoft Excel program. The results of this study indicate that the Mononobe method is able to calculate the amount of rain intensity in the Sunter River based only on daily rainfall data. Keywords: Rainfall Intensity, Sunter River, Mononobe Methode
Figshare research data repository pilot: a case of NWU

Presentation at 2018 NWU open access week. Reporting on Figshare data repository pilot at NWU.
SafeDNN: Understanding and Verifying Neural Networks

We present NNrepair, a constraint-based technique for repairing neural network classifiers. The technique aims to fix the logic of the network at an intermediate layer or at the last layer. NNrepair first uses fault localization to find potentially faulty network parameters (such as the weights) and then performs repair using constraint solving to apply small modifications to the parameters to remedy the defects. We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class. We demonstrate the technique in the context of three different scenarios: (1) Improving the overall accuracy of a model, (2) Fixing security vulnerabilities caused by poisoning of training data and (3) Improving the robustness of the network against adversarial attacks. Our evaluation on MNIST and CIFAR-10 models shows that NNrepair can improve the accuracy by 45.56 percentage points on poisoned data and 10.40 percentage points on adversarial data. NNrepair also provides small improvement in the overall accuracy of models, without requiring new data or re-training.
A General Method for Transferring Explicit Knowledge into Language Model Pretraining

Recently, pretrained language models, such as Bert and XLNet, have rapidly advanced the state of the art on many NLP tasks. They can model implicit semantic information between words in the text. However, it is solely at the token level without considering the background knowledge. Intuitively, background knowledge influences the efficacy of text understanding. Inspired by this, we focus on improving model pretraining by leveraging external knowledge. Different from recent research that optimizes pretraining models by knowledge masking strategies, we propose a simple but general method to transfer explicit knowledge with pretraining. To be specific, we first match knowledge facts from a knowledge base (KB) and then add a knowledge injunction layer to a transformer directly without changing its architecture. This study seeks to find the direct impact of explicit knowledge on model pretraining. We conduct experiments on 7 datasets using 5 knowledge bases in different downstream tasks. Our investigation reveals promising results in all the tasks. The experiment also verifies that domain-specific knowledge is superior to open-domain knowledge in domain-specific task, and different knowledge bases have different performances in different tasks.