Reviewerly/exHarmony huggingface dataset:
The smart village program challenges in supporting national food security through the implementation of agriculture 4.0

Abstract The purpose of this study is to: (1) look at the possible concept of agriculture 4.0 which can be an important part of the smart village program; (2) evaluate the relationship between the concept of agriculture 4.0 and the principles of sustainable agriculture in line with the achievement of the Village Sustainable Development Goals; (3) analyze the opportunities of the agriculture-based smart village program 4.0 to be able to contribute to food security. The method used in this study is a qualitative approach with case study methods and is analyzed descriptively. Data is collected from written and audio-visual materials, in the form of policies, field data, opinions and experiences of experts and practitioners, and others. The results of this study show several conclusions, including; (a) The concept of agriculture 4.0 is very likely to be part of the implementation of the Smart Village program, considering that Indonesian villages in general are agricultural villages that still need digitalization in agricultural activities. (b) Agriculture 4.0 technology has characteristics that are in line with the principles of sustainable agriculture, including minimizing the use of inputs, minimizing the use of water in agricultural cultivation, and the ability to make various predictions that can provide benefits for farmers, (c) thus the opportunity to develop smart villages based on agriculture 4.0, can provide a great opportunity to be able to contribute to food security in Indonesia and can improve the welfare of rural communities.
Libra: High-Utility Anonymization of Event Logs for Process Mining via Subsampling

Process mining techniques enable analysts to identify and assess process improvement opportunities based on event logs. A common roadblock to process mining is that event logs may contain private information that cannot be used for analysis without consent. An approach to overcome this roadblock is to anonymize the event log so that no individual represented in the original log can be singled out based on the anonymized one. Differential privacy is an anonymization approach that provides this guarantee. A differentially private event log anonymization technique seeks to produce an anonymized log that is as similar as possible to the original one (high utility) while providing a required privacy guarantee. Existing event log anonymization techniques operate by injecting noise into the traces in the log (e.g., duplicating, perturbing, or filtering out some traces). Recent work on differential privacy has shown that a better privacy-utility tradeoff can be achieved by applying subsampling prior to noise injection. In other words, subsampling amplifies privacy. This paper proposes an event log anonymization approach called Libra that exploits this observation. Libra extracts multiple samples of traces from a log, independently injects noise, retains statistically relevant traces from each sample, and composes the samples to produce a differentially private log. An empirical evaluation shows that the proposed approach leads to a considerably higher utility for equivalent privacy guarantees relative to existing baselines.
Cognitive Science of Augmented Intelligence

Cognitive science has been traditionally organized around the individual as the basic unit of cognition. Despite developments in areas such as communication, human-machine interaction, group behavior, and community organization, the individual-centric approach heavily dominates both cognitive research and its application. A promising direction for cognitive science is the study of augmented intelligence, or the way social and technological systems interact with and extend individual cognition. The cognitive science of augmented intelligence holds promise in helping society tackle major real-world challenges that can only be discovered and solved by teams made of individuals and machines with complementary skills who can productively collaborate with each other.
Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications

Global solar irradiation is an important variable that can be used to determine the suitability of an area to install solar systems; nevertheless, due to the limitations of requiring measurement stations around the entire world, it can be correlated with different meteorological parameters. To confront this issue, different locations in Rias Baixas (Autonomous Community of Galicia, Spain) and combinations of parameters (month and average temperature, among others) were used to develop various machine learning models (random forest -RF-, support vector machine -SVM- and artificial neural network -ANN-). These three approaches were used to model and predict (one month ahead) monthly global solar irradiation using the data from six measurement stations. Afterwards, these models were applied to seven different measurement stations to check if the knowledge acquired could be extrapolated to other locations. In general, the ANN models offered the best results for the development and testing phases of the model, as well as for the phase of knowledge extrapolation to other locations. In this sense, the selected ANNs obtained a mean absolute percentage error (MAPE) value between 3.9 and 13.8% for the model development and an overall MAPE between 4.1 and 12.5% for the other seven locations. ANNs can be a capable tool for modelling and predicting monthly global solar irradiation in areas where data are available and for extrapolating this knowledge to nearby areas.
Arbitrary entanglement of three qubits via linear optics

We present a linear-optical scheme for generating an arbitrary state of three qubits. It requires only three independent particles in the input and post-selection of the coincidence type at the output. The success probability of the protocol is equal for any desired state. Furthermore, the optical design remains insensitive to particle statistics (bosons, fermions or anyons). This approach builds upon the no-touching paradigm, which demonstrates the utility of particle indistinguishability as a resource of entanglement for practical applications.
A two-stage anomaly detection framework: Towards low omission rate in industrial vision applications

In industrial manufacturing, there are many types of defective samples that are difficult to obtain. Practical industrial vision anomaly detection has proven to be a challenging task because techniques use only normal (non-defective) samples to train a model to detect anomalies. Currently, some reasonably effective models do not perform very well once differences between samples are large, and they ignore the fact that the cost of missing a defect is much higher than the cost of misidentifying a normal sample. To that end, in this paper, we propose a two-stage framework to construct an anomaly detector. We first train a classification network and then build a one-class classifier on learned representations using another pre-trained network. This paper innovatively proposes using the theoretical quantile as the discriminant threshold. We conduct experiments on the Nut and Motor Brush Holder datasets from real industrial production lines. The results show that our method greatly reduces missed detection of anomalous samples, achieving state-of-the-art AUROC scores of 99.3 % and 96.2 %. We also conduct experiments on the publicly available dataset Rd-MVTec AD, showing that our model has good generalizability and fast testing speed while maintaining high AUROC scores. Our model gives excellent results for nonaligned and defective data with diverse anomalous patterns, and it is easy to optimize. Therefore, not only does our technique handle industrial cold starts well, but it also meets the requirement of online updating, which indicates that our solution is highly suitable for industrial manufacturing scenarios.
AI shapes the future of web conferencing platforms

In the last couple of years, videoconferencing platforms have become very popular and have captured the attention of researchers and the public, particularly due to the Covid-19 pandemic. Immediately after the pandemic started in the spring of 2020, demand for video conferencing apps has grown exponentially. This demand, and the emergence of new needs, have forced manufacturers to adapt to the new context by improving the services offered and adding new features to existing applications. Many new video conferencing applications have also emerged with this demand. This paper presents a series of statistics on the evolution during the pandemic and the current status of the main video conferencing systems. The different ways in which these systems have integrated artificial intelligence technologies to address different identified problems and user needs are also presented.
Cryptocurrencies and traditional assets: Decoding the analogy from emerging economies with crypto usage

This paper investigates the relationship of cryptocurrencies with four traditional assets: equity, fiat currencies, crude oil, and gold in Nigeria, Vietnam, the Philippines, Turkey, and Peru. According to Statista’s 2020 Cryptocurrency Adoption Survey, these five countries showed high levels of crypto usage and ownership. Emerging economies attract the attention of portfolio managers due to the high returns associated with assets originating from these countries. The paper explores the possibility of creating a multi-asset portfolio, including cryptocurrencies. Vector Autoregression Granger causality and Johansen Cointegration tests are conducted to study the relationship between each traditional asset and cryptocurrencies. The study period is from October 2017 to June 2021. The composite Crypto Index was created using the top seven cryptocurrencies based on market capitalization. The Granger Causality test results reveal no causality between Nigeria’s chosen traditional assets and the cryptocurrency index. In the case of the Philippines, there is a unidirectional causality relationship from crypto returns to currency returns; and gold returns to crypto index returns. In Vietnam, stock index returns cause crypto returns; in Peru, gold returns cause crypto returns; and in Turkey, crypto returns cause currency returns. None of the countries has exhibited a bidirectional relationship between traditional assets and the crypto index. The robustness of the causality relations is checked using the Johansen Cointegration test. All the assets taken under study, country-wise, are cointegrated with one another. Hence, when building a multi-asset portfolio covering these five emerging nations, cryptocurrencies do not offer investors diversification, hedging, or haven benefits.
PROFIL KEMAMPUAN LITERASI DIGITAL SISWA SEKOLAH DASAR DALAM PENGGUNAAN MEDIA PEMBELAJARAN JARAK JAUH

The COVID-19 pandemic has changed the online learning process from offline to online. One of the references for measuring digital literacy levels is to directly observe the learning process that occurs using platforms that support distance learning. The purpose of this study was to analyze the digital literacy skills of elementary school students in the use of distance learning media and analyze the understanding of digital media of elementary school students in the use of distance learning media. The method used is qualitative with a phenomenological design. Data collection techniques using interview techniques, field observations, and documentation. The data validity technique uses source and technique triangulation. At the same time, the data analysis technique uses an inductive step. The results showed that the students of SD N 1 Brajan had a good level of digital literacy; students knew the benefits of digital media used as learning media, such as understanding the help of the media features used for practical benefits. Meanwhile, in applying digital media, students can also use it as a learning medium. This is because students are used to using these media in everyday life. In contrast, some students have difficulty applying digital media because they have the reason that the cellphones they use are still alternating with their parents, so the infrequent use of digital media will affect students' digital literacy skills. Teachers can make efforts to improve students' digital skills by providing learning media that can provide additional learning in the use of learning media that are foreign to students.
An adaptive preference retention collaborative filtering algorithm based on graph convolutional method

&lt;abstract&gt; &lt;p&gt;Collaborative filtering is one of the most widely used methods in recommender systems. In recent years, Graph Neural Networks (GNN) were naturally applied to collaborative filtering methods to model users' preference representation. However, empirical research has ignored the effects of different items on user representation, which prevented them from capturing fine-grained users' preferences. Besides, due to the problem of data sparsity in collaborative filtering, most GNN-based models conduct a large number of graph convolution operations in the user-item graph, resulting in an over-smoothing effect. To tackle these problems, Adaptive Preference Retention Graph Convolutional Collaborative Filtering Method (APR-GCCF) was proposed to distinguish the difference among the items and capture the fine-grained users' preferences. Specifically, the graph convolutional method was applied to model the high-order relationship on the user-item graph and an adaptive preference retention mechanism was used to capture the difference between items adaptively. To obtain a unified users' preferences representation and alleviate the over-smoothing effect, we employed a residual preference prediction mechanism to concatenate the representation of users' preferences generated by each layer of the graph neural network. Extensive experiments were conducted based on three real datasets and the experimental results demonstrate the effectiveness of the model.&lt;/p&gt; &lt;/abstract&gt;
Solving digital image correlation with neural networks constrained by strain-displacement relations

The use of supervised neural networks is a new approach to solving digital image correlation (DIC) problems, but the existing methods solely adopt the black-box neural network, i.e., the mapping from speckle image pair (reference image and deformed image) to multiple deformation fields (displacement fields and strain fields) is directly established without considering the physical constraints between the fields, causing a low level of accuracy that is even inferior to that of Subset-DIC. In this work, we proposed a deep learning model by introducing strain-displacement relations into a neural network, in which the effect of errors both in displacement and strain are considered in the network training. The back-propagation process of the proposed model is derived, and the solution scheme is implemented by Python. The performance of the proposed model is evaluated by simulation and real DIC experiments, and the results show that adding physical constraints to the neural network can significantly improve prediction accuracy.
Flipbook Maker-Based Teaching Materials of thematic Learning for grade II Elementary School Students

Currently, there are still many schools that only focus on textbooks without the innovation and creativity of teachers in preparing teaching materials, so that it is very influential for the learning process. The purpose of this study was to develop design and feasibility of teaching materials based on Flipbook Maker application. The test subjects in this study were content experts, designs, instructional medias, and second grade elementary school students. This study uses the ADDIE development model (Analyze, Design, Development, Implementation, Evaluation). Data were collected using observation, interview and questionnaire methods. The data analysis technique used is descriptive qualitative and quantitative descriptive analysis techniques. However the results of data analysis as follows. (1) Design of Flipbook Maker-based e-teaching materials which consists of five stages, namely the analysis stage (Analyze), the design stage (Design), the development stage (Development), the implementation stage (Implementation), and the evaluation stage (Evaluations). (2) teaching materials which are based on the Flipbook Maker have a good quality based on the results of the review of subject matter experts showing a score of 96%, the results of the learning design expert review showing a score of 100%, the results of the media expert review showing a score of 95%, the results of the individual test showing a score of 97.6% and the results of the small group trial showed a score of 99% so that the overall score showed a very good category. It is concluded that flipbook maker-based teaching materials are suitable used for learning.
Socio-Technical and Political Complexities: Findings from Two Case Studies of Large IT Project-Based Organizations

Information technology (IT) projects are becoming more complex due to technological advancements, increased sociopolitical demand, and competition. In recent years, the project complexity field has attracted increasing attention with diverse strategies and methods proposed to identify, evaluate, and respond to various complexities. This study aims to identify and prioritize factors contributing to complexity in IT projects by reporting two case studies conducted on large IT organizations. The literature on project complexity informed and guided this exploratory research. The data were collected through 21 semi-structured interviews and analyzed by applying open and axial coding content analysis. Underpinned by complexity theories, 19 factors contributing to the complexity of IT projects were identified, and their importance was highlighted using the Friedman test. The top five factors contributing to IT project complexity were identified as follows: the diversity of stakeholders; technological newness of the project; conflicting goals of stakeholders; variety of product sub-systems and components; and uncertainty of project objectives. This study’s findings contribute to the project management literature and inform practitioners about how to achieve more effective management of complex IT projects.
Woe is the dark Web: the main challenges that governments of the Commonwealth Caribbean will face in combating dark Web-facilitated criminal activities

Purpose This paper aims to investigate the potential challenges that governments in the Commonwealth Caribbean are likely to face combating crimes facilitated by the dark Web. Design/methodology/approach The “lived experience” methodology guided by a contextual systematic literature review was used to ground the investigation of the research phenomena in the researchers’ collective experiences working in, living in and engaging in research with governments in the Commonwealth Caribbean. Findings The two major findings emerging from the analysis are that jurisdictional and technical challenges are producing major hindrances to the creation of an efficient and authoritative legislative framework and the building of the capacity of governments in the Commonwealth Caribbean to confront the technicalities that affect systematic efforts to manage problems created by the dark Web. Practical implications The findings indicate the urgency that authorities in the Caribbean region must place on reevaluating their administrative, legislative and investment priorities to emphasize cyber-risk management strategies that will enable their seamless and wholesome integration into this digital world. Originality/value The research aids in developing and extending theory and praxis related to the problematization of the dark Web for governments by situating the experiences of Small Island Developing States into the ongoing discourse.
Deep Large-Margin Rank Loss for Multi-Label Image Classification

The large-margin technique has served as the foundation of several successful theoretical and empirical results in multi-label image classification. However, most large-margin techniques are only suitable to shallow multi-label models with preset feature representations and a few large-margin techniques of neural networks only enforce margins at the output layer, which are not well suitable for deep networks. Based on the large-margin technique, a deep large-margin rank loss function suitable for any network structure is proposed, which is able to impose a margin on any chosen set of layers of a deep network, allows choosing any ℓp norm (p≥1) on the metric measuring the margin between labels and is applicable to any network architecture. Although the complete computation of deep large-margin rank loss function has the O(C2) time complexity, where C denotes the size of the label set, which would cause scalability issues when C is large, a negative sampling technique was proposed to make the loss function scale linearly to C. Experimental results on two large-scale datasets, VOC2007 and MS-COCO, show that the deep large-margin ranking function improves the robustness of the model in multi-label image classification tasks while enhancing the model’s anti-noise performance.
Blockchain Systems in Embedded Internet of Things: Systematic Literature Review, Challenges Analysis, and Future Direction Suggestions

Internet of Things (IoT) environments can extensively use embedded devices. Without the participation of consumers; tiny IoT devices will function and interact with one another, but their operations must be reliable and secure from various threats. The introduction of cutting-edge data analytics methods for linked IoT devices, including blockchain, may lower costs and boost the use of cloud platforms. In a peer-to-peer network such as blockchain, no one has to be trusted because each peer is in charge of their task, and there is no central server. Because blockchain is tamper-proof, it is connected to IoT to increase security. However, the technology is still developing and faces many challenges, such as power consumption and execution time. This article discusses blockchain technology and embedded devices in distant areas where IoT devices may encounter network shortages and possible cyber threats. This study aims to examine existing research while also outlining prospective areas for future work to use blockchains in smart settings. Finally, the efficiency of the blockchain is evaluated through performance parameters, such as latency, throughput, storage, and bandwidth. The obtained results showed that blockchain technology provides security and privacy for the IoT.