Reviewerly/exHarmony huggingface dataset:
Blockchain based Milk Delivery Platform for Stallholder Dairy Farmers in Kenya: Enforcing Transparency and Fair Payment

The dairy industry in Kenya accounts for 4% of the country's GDP and provides a source of income and livelihood to a significant proportion of Kenya's population. The sector currently provides income and employment to over 2 million people across the dairy value chain. The dairy farmers, who are mostly smallholder farmers, rely on local milk collection centres that manually records milk delivery transaction in some hardcopy inventory files, which are stored in their offices. These records have been susceptible to modifications and deletions by these centres to cut down on their payments to farmers. Thus, this paper explores the potential use of blockchain technology in milk delivery among smallholder farmers in the rural areas in developing nations towards creating transparency, trustworthiness and fairness in payment to these farmers. We seek to design a farmer centric blockchain based platform that ensures that farmers are protected from unscrupulous and predatory middlemen in the milk delivery chain that exploit the illiterate and the unsuspecting farmers.
Comparison of SVM method for picking up the microseismic events collected by fiber-optic and electronic monitoring system

Based on three typical features, i.e. energy entropy, histogram distribution percentage and zero-crossing rate, we compared the classification accuracy of microseismic events between fiber-optic and electronic monitoring system using support vector machine.
The influence of electronic games in physical health among School of Technology Management and Logistic (STML) Students

Electronic games is the most popular entertainment in modern societies especially students. Despite the factors that influence the impact of electronic games on students is still unclear. The purpose of this study was to develop addictive criteria and physical health effects for electronics games using a qualitative contents analysis. Besides, the purpose of this study to test the developed approach in School Technology Management and Logistics (STML) and to propose solutions or recommendations to overcome the problems by using qualitative research method, the data collected through questionnaire and open-ended questions. The respondents involved 340 students from STML. This questionnaire was analyzed by using multiple choice and Likert-Scale. Data was analyzed using IBM SPSS Statistic.
Prediction Of The Accuracy Of Student Boarding House Prices Around Widyatama University Using Neural Network Backpropagation Algorithm

Boarding room rental services are a business that has been around for a long time. One of the target consumers of this business is from students who plan to stay temporarily during the lecture period so that they are close to the campus location where the student is studying. A strategic location such as in the area of ​​one of the campuses in Bandung, namely the Widyatama University Campus, provides added value for boarding owners and also consumers who will rent, but there are several problems in this boarding rental business, namely the lack of knowledge of information on boarding rental prices around the area. campus due to the lack of effort made by boarding house owners in surveying boarding rental price data. In addition, boarding house owners find it difficult to determine the range of rental prices in accordance with boarding facilities. This problem is also experienced by prospective boarding house tenants in estimating whether the rental price offered is ideal and comparable to the facilities that will be obtained. This research conducts trials and predicts the range of rental prices in accordance with the parameters of the facilities that have been defined based on a survey of boarding price data via the internet and predicts how accurate the model used is. The method used is an artificial neural network with a backpropagation algorithm. The results of this study indicate that the 14-12-10-2-1 model with an epoch of 100 produces an accuracy rate of 87.18% and an MSE of 0.0033.
Effects of political institutions on the external debt-economic growth nexus in Africa

The main contribution of this study is the determination of an endogenous threshold of institutional quality, beyond which external debt would affect economic growth differently. The focus is on 14 countries of the African Franc zone over the period 1985-2015. Based on the panel Smooth Threshold Regression model, the results reveal that the relationship between external debt and economic growth is based on institutional quality. It is found that the level of indebtedness at which the effect of external debt on economic growth becomes negative is higher in countries with lower levels of corruption and high levels of democracy. This means that poor institutional quality prevents a country from taking full advantage of its credit opportunities. Thus, the more countries become democratic, the more debt helps finance economic growth. These results are robust to sensitivity analysis and Generalized Method of Moments estimation.
Automatic segmentation of texts into units of meaning for reading assistance.

The emergence of the digital book is a major step forward in providing access to reading, and therefore often to the common culture and the labour market. By allowing the enrichment of texts with cognitive crutches, EPub 3 compatible accessibility formats such as FROG have proven their effectiveness in alleviating but also reducing dyslexic disorders. In this paper, we show how Artificial Intelligence and particularly Transfer Learning with Google BERT can automate the division into units of meaning, and thus facilitate the creation of enriched digital books at a moderate cost.
"Deepfake" Portrait Image Generation

With the prevailing of deep learning technology, especially generative adversarial networks (GAN), generating photo-realistic facial images has made huge progress. Image generation techniques have many good applications such as data augmentation, entertainment, augmented/virtual reality as well as bad applications like Deepfake, which has caused huge concerns in the society. In this talk, we mainly review general image generation techniques, particularly describing a few of our recent work on high-quality portrait/facial image generation. Our work can be divided into 3D based approaches and GAN based 2D approaches. For 3D based approaches, we will explain how to model facial geometry, reflectance and lighting explicitly, and then show how such 3D modelling knowledge can be used in portrait manipulation. For 2D GAN based approaches, we will present a framework for pluralistic facial image generation from a masked facial input, where all the previous approaches only aim to produce one output. At the end, some suggestions will be provided for detecting deepfake from a generation point of view.
MDP Abstraction with Successor Features.

Abstraction plays an important role for generalisation of knowledge and skills, and is key to sample efficient learning and planning. For many complex problems an abstract plan can be formed first, which is then instantiated by filling in the necessary low-level details. Often, such abstract plans generalize well to related new problems. We study abstraction in the context of reinforcement learning, in which agents may perform state or temporal abstractions. Temporal abstractions aka options represent temporally-extended actions in the form of option policies. However, typically acquired option policies cannot be directly transferred to new environments due to changes in the state space or transition dynamics. Furthermore, many existing state abstraction schemes ignore the correlation between state and temporal abstraction. In this work, we propose successor abstraction, a novel abstraction scheme building on successor features. This includes an algorithm for encoding and instantiation of abstract options across different environments, and a state abstraction mechanism based on the abstract options. Our successor abstraction allows us to learn abstract environment models with semantics that are transferable across different environments through encoding and instantiation of abstract options. Empirically, we achieve better transfer and improved performance on a set of benchmark tasks as compared to relevant state of the art baselines.
Paragenesis of gold mineralization at the Kiyuk Lake Project, Kivalliq Region, Nunavut, Canada

ABSTRACT Exploration for gold in Nunavut has been primarily focused on Archean greenstone belts in the north and coastal regions of the territory, resulting in large areas of underexplored terrain in the south. The Kiyuk Lake property is located in the underexplored southwest corner of the Kivalliq Region of Nunavut within the Hearne domain of the ∼1.9 Ga western Churchill Province. The property is hosted by Proterozoic calc-silicate and clastic sedimentary units of the Hurwitz Group (&amp;lt;2.4–1.9 Ga) and the unconformably overlying Kiyuk Group (1.9–1.83 Ga). Gold mineralization in Proterozoic sedimentary rocks is rare in the Canadian Shield, so the Rusty Zone at Kiyuk Lake presents a unique opportunity to study the enigmatic gold mineralization hosted in such sedimentary rocks. Mineralization at the Rusty Zone is hosted by an immature lithic wacke cut by thin intermediate dikes that are associated with hydrothermal breccias composed of Fe-carbonate, calcite, calcic-amphibole, Fe-sulfide, Fe-oxide minerals, and gold. Textural and timing relationships suggest that the gold mineralization is post-sedimentary and syn- to post-intrusion of intermediate dikes. Stable isotope thermometry suggests that mineralization took place between 450 and 600 °C, and geochronological studies indicate that the intrusion and mineralization occurred before or about 1.83 Ga. Using basement breaching thrusts faults as conduits to the surface, over-pressurization along a later normal fault is thought to be the primary cause for the localized breccia pipe that controls gold mineralization. The hydrothermal fluids are postulated to be volatile-rich aqueous solutions exsolved from a source of cooling magmas at depth. Although sub-economic at present, the occurrence of high-grade gold in a Paleoproterozoic basin such as Kiyuk Lake could signal a new opportunity for exploration for gold in the Canadian Shield.
Safe Learning of Lifted Action Models

Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a domain model from observations. However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects. This may result in generating plans that will fail when executed. In some domains such failures are not acceptable, due to the cost of failure or inability to replan online after failure. In such settings, all learning must be done offline, based on some observations collected, e.g., by some other agents or a human. Through this learning, the task is to generate a plan that is guaranteed to be successful. This is called the model-free planning problem. Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale. We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains. We prove the correctness of our approach, and provide a statistical analysis showing that the number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model. We also present experiments on twelve IPC domains showing that our approach is able to learn the real action model in all cases with at most two trajectories.
N-subalgebras of BCK=BCI-Algebras which are Induced from Hyperfuzzy Structures

In the paper [J. Ghosh and T.K. Samanta, Hyperfuzzy setsand hyperfuzzy group, Int. J. Advanced Sci Tech. 41 (2012), 27–37],Ghosh and Samanta introduced the concept of hyperfuzzy sets as a generalizationof fuzzy sets and interval-valued fuzzy sets and applied it togroup theory. The aim of this manuscript is to study N-structures inBCK\BCI-algebras induced from hyperfuzzy structures.
INFORMATION SECURITY POLICY: A CRITICAL STUDY OF THE CONTENT OF UNIVERSITY POLICY

The article is relevant, as it provides an assessment of the information security of universities. Ensuring the security of corporate information, which is increasingly stored, processed and disseminated using information and communication technologies (ICT). This is a particularly important problem for knowledge-intensive organizations such as universal ones; the effective conduct of their main educational activities and research activities increasingly depends on the availability, integrity and accuracy of computer information resources. One of the more important mechanisms to reduce the number of security breaches, and thus corporate information, is the development and implementation of a formal information security policy (ISP). Although much has now been written about the importance and role of information security policies and approaches to formulating them, there is relatively little empirical material that is incorporated into the structure or content of security policies. The purpose of the article is to fill this gap in the literature through this method of using the structure and methods of authentic information security policies. Having established the parameters and key features of university policies, the article critically examines the concept of information security embedded in the policy. Two important conclusions can be drawn from this study: 1) the wide variety of disparate policies and standards used, whether there will be a consistent approach to security management; and 2) the range of specific issues explicitly covered by university policy, a surprisingly low and highly technocentric view of information security management. This article is one of the first to objectively, rigorously and independently assess the content of authentic information security policies and information security documentation frameworks in a well-organized organizational environment. The article notes that there are four different levels of information policy: “system security policy, product security policy, community security policy, and corporate information security policy.” All policies involve: personal use of information systems, information disclosure, physical security, breaches and hacks, viruses, system access control, mobile computing, internet access, software development, encryption and contingency planning
Needs Analysis of Agricultural English Teaching in an Indonesian Islamic Higher Education Context

The research is the development of Agricultural English teaching materials at Universitas Muslim Indonesia. The purposes of this research are to (1) produce a type of Agricultural English teaching material suitable to be used by the students of Faculty of Agriculture UMI and to (2) discover why the students are learning English. The method used is research and development. The research site was at the Faculty of Agriculture UMI with the number of 50 respondents. The results showed that based on the student responses, 12 out of 42 topics occupy the highest percentage needed for Agricultural English teaching materials. Those 12 topics are as follows: rice, tomatoes, chili, shallots, eggplant, papaya, rambutan, coconut, coffee, land, disease pest, and seeds. Then, the purposes of the students learning English can be categorized into two major groups, namely to anticipate competition in the workplace where the ability to communicate both oral and written is needed and to prepare themselves in the academic field both to complete S1 (undergraduate degree) and to continue their studies S2 (master degree).
FDI, Financial Development and Growth in Host Countries: The Case of North African Countries (NACs)

This paper investigates the long-run relationship between financial depth, FDI, and economic growth in a sample of 5 FDI-receiving NACs over the period 1980-2018. Standard growth models are estimated using both fixed-effects and random-effects models. In addition panel unit root and panel, cointegration tests are employed to check for the efficiency of the data. The long-run relationship is estimated using fully modified OLS and: Panel Dynamic Least Squares (DOLS) methods.The empirical results show the support of the fixed –effects method as the random-effects model is rejected based on the Hausman test result. The results of the fixed effect show positive and statistically significant measures of the interaction terms of financial market development indicators with FDI. These results confirm the complementarity between the different components of financial intermediary and FDI and their effect on economic growth. The panel cointegration tests confirm the fact that there is a positive, significant, and long period relationship among financial depth indicators, FDI, and economic growth..This study concludes that for countries in the NACs region, to utilize the benefits from FDI more emphasis and concern should be directed to the pre-country conditions for attracting FDI, mainly financial sector development.
A Multi-Integrated Optimization Technique for Classification of Breast Cancer Disease Using an Artificial Neural Network

Classification in data mining plays an important role in biomedical analysis that gives a better way to predict or diagnose any disease at an early stage. As the data size increases, the methodologies and techniques for selecting and optimizing the features also increases. It plays a challenging task for developing a computationally efficient and accurate predictive model. The proposed work presented the multi-integrated optimization method using Genetic algorithm (GA) and particle swarm optimization (PSO) and classifies using the artificial neural network (ANN). It presents an efficient approach for predicting cancer disease and also proved their efficiency and accuracy over other single optimization techniques.  This system takes the union of both the optimal relevant features generating from PSO and GA.Thus, a new subset of data formed after two ways integrated feature selection reduces the complexity and enhances the performances when classifying with the ANN. The comparison of performance is also evaluated with different subsets of features after and before feature optimization. This experiment provides a better approach for data selection and gives an accuracy of 98.24% respectively.
Political Instability and Political Terror: Global Evidence on Persistence

We test the hypotheses that fundamental characteristics in regional proximity, landlockedness, religious-domination, legal origin, and income levels affect cross-country differences in the persistence in political terror and political instability in 163 countries for the period 2010 to 2015. The empirical evidence is based on Generalised Method of Moments. The hypotheses are that the following are associated with comparatively higher levels of persistence in political terror and political instability: regions with predominantly low income countries (Hypothesis 1); landlockedness (Hypothesis 2); Christian-orientation (Hypothesis 3); French civil law (Hypothesis 4) and Low income (Hypothesis 5). The tested hypotheses are largely invalid. Only Hypothesis 5 and Hypothesis 2 are robustly investigated in the light of concerns about instrument proliferation. Hypothesis 2 is valid for political terror but not for political instability while Hypothesis 5 is neither valid for political instability nor for political terror.
Alat Pemberi Pakan Udang dan Pemantau Kekeruhan Air yang Dilengkapi dengan Filter Air dan Sistem Penggantian Air Menggunakan WeMos D1 R1 Berbasis Internet of Things

In a shrimp farming area, several things appear. Where shrimp farming jobs still use traditional methods or work manually so they need a lot of time. Shrimp farming is an entrepreneur with long production results, so hereby collect some of the main problems experienced by shrimp farmers. The author conducts research to support businesses to operate effectively, efficiently and practically. It is known that shrimp farmers have little difficulty in maintaining feed. Therefore, the authors designed a feeder and water turbidity monitor that can work automatically based on the feeding schedule and the level of water turbidity. This shrimp feeder and water turbidity monitor uses the WeMos D1 R1 microcontroller which is the main control center, uses the Blynk application to control the tool, and uses servo motors, sensors, filters and other components. By using these components and some software that supports the tool, shrimp feeders and water turbidity monitors can work automatically according to a predetermined schedule, and are able to send notifications to Blynk when the tool finishes the work process
Memory and Knowledge Augmented Language Models for Inferring Salience in Long-Form Stories

Measuring event salience is essential in the understanding of stories. This paper takes a recent unsupervised method for salience detection derived from Barthes Cardinal Functions and theories of surprise and applies it to longer narrative forms. We improve the standard transformer language model by incorporating an external knowledgebase (derived from Retrieval Augmented Generation) and adding a memory mechanism to enhance performance on longer works. We use a novel approach to derive salience annotation using chapter-aligned summaries from the Shmoop corpus for classic literary works. Our evaluation against this data demonstrates that our salience detection model improves performance over and above a non-knowledgebase and memory augmented language model, both of which are crucial to this improvement.
Meningkatkan Hasil Belajar Tema 4 Subtema1 Dengan Media Audio Visual Kelas II SDN 1 Selopuro

This study aims to determine whether using Audio Visual media can improve student learning outcomes in grade 2 SD Negeri 1 Selopuro. The problem studied in this research is how much improvement in learning outcomes with audio-visual media in Class 2 SD Negeri 1 Selopuro? The research uses Classroom Action Research, namely by research in the form of collaboration or collaboration between researchers with teachers and students. The results showed that learning theme 4 with audio visual media in grade 2 students of SDN 1 Selopuro improved better, student activity in learning and sending LKPD assignments on time. Negeri 1 Selopuro, it can be seen where in the pre-cycle the average value of student learning outcomes is 68, 33 with class completeness reaching 50% to 95 during the first cycle with 100% class completeness.
Large-Scale Social Network Privacy Protection Method for Protecting K-Core

Social network analysis has many important applications and methods which depend on the sharing and publishing of graphs. For example, link privacy requires limiting the probability of an adversary identifying a target sensitive link between two individuals in the published social network graph. However, the existing link privacy protection methods have low processing power for large-scale graph data and less consideration of community protection in the publishing graphs. Therefore, aiming at sensitive link privacy protection, a large-scale social network privacy protection model to protect K-Core (PPMPK) was proposed. The large-scale social network graph was processed to ensure that the core number and the community structure of the nodes were unchanged based on the Pregel parallel graph processing model. Extensive experiments on the real data sets showed that the proposed method could effectively process the large-scale graph data and protect the data availability of the published graphs, especially in community protection.