Reviewerly/exHarmony huggingface dataset:
Penerapan Algoritma Decision Tree C4.5 Untuk Memprediksi Kelayakan Calon Pendonor Melakukan Donor Darah Dengan Klasifikasi Data Mining

Based on data from UDD PMI Kampar Regency, many donors must have provisions to become blood donors. So far, blood donor selection has been made manually to determine whether potential donors can donate blood or not. Meanwhile, today's information system has not yet explored further information from the large amount of data stored as knowledge. There is a need for organizational consolidation and continuous evaluation of the performance that has been carried out by PMI in dealing with social and humanitarian problems. By making a data mining application with a classification method using the Decision Tree C4.5 Algorithm in predicting someone worthy or not to donate blood, it can be calculated from the results of variables that are continuous or critical, such as variables of age, body weight, hemoglobin (HB) levels, blood pressure. (systolic and diastolic), The data that enters the information system is calculated using the Decision Tree C4.5 Algorithm formula, which results in detailed results and can produce valid and more accurate values. With the data mining application using the Decision Tree Algorithm C4.5 method, potential blood donors' eligibility can be classified based on age, body weight, hemoglobin, and blood pressure. Hemoglobin with the highest gain value (0.861212618) is the variable that most determines blood donation success.
A Proposed Ontology-Based Generic Context Model for Ubiquitous Learning

Context modeling is the keystone to enable the intelligent system to adapt its functionalities properly to different situations. As such, a representation mechanism that allows an adequate manipulation of this kind of information is required, and diverse approaches have been introduced; however, what takes more value and is being positioned as a standard is the ontology-based context modeling because it presents a common understanding vocabulary for a specific domain. Hence, it might be beneficial to have a generic ontology to model context in this area. However, according to diverse works, there is no proposal of a generic context model for context-aware learning. For addressing this problem, several existing context models are studied to identify the essentials of context modeling, whereby an ontology-based generic context model is presented. The proposed ontology is evaluated in two ways. Firstly, scenarios are used to justify the feasibility of the model; then a comparative study and evaluation metrics are applied to assess the proposal.
Introduction to the Minitrack on Decentralized Federated Learning: Applications, Solutions, and Challenges

Machine learning (ML) has exhibited great potential to transform the computing field.Huge interest has developed over the past years in applying machine learning-assisted approaches in the Internet of Things, healthcare, transportation, and security space, to name a few.However, the assumption in many current solutions is that big training data is widely available and transferable to a centralized server without much considering data privacy concerns.A new framework for machine learning has emerged, referred to as Federated Learning (FL), that advocates the AI-on-edge principle.The main objective of federated learning is to provide privacyby-design training with decentralized data among local machines at the edge layer.In federated learning, a central server just coordinates with local clients to aggregate the model's updates without requiring the actual data (i.e., zero-touch).However, given the fresh nature of the FL, it is important to keep improving and design innovative solutions to mitigate its shortcomings and identify its best applications.This is the focus of the 'Decentralized Federated Learning: Applications, Solutions, and Challenges' mini-track.In this introduction article, we will describe the topic and the accepted paper(s) contributed by researchers.
IoT USN system and hybrid architecture application

Based on the "Ministry of Industry and Information Technology-Research on the Internet of Things Technology in the Shipbuilding Process of Waigaoqiao", this article analyzes three IoT systems: USN, SWE, and RFID, and three IoT architectures: REST, centralized, and distributed, integrating centralized architecture and distributed architecture, and propose an IoT network architecture of hybrid manufacturing plant equipment nodes. Combined with the ship segment manufacturing workshop of Shanghai Waigaoqiao Shipbuilding Co., Ltd., the overall topology of the workshop IoT that integrates three network forms such as WSN, and the three-dimensional and hierarchical four-layer IoT architecture of the workshop under the USN system are designed.At the same time, in conjunction with the "China Intelligent Manufacturing 2025" strategy, shipbuilding will be improved to a more advanced level, and it will better support the national marine development strategy.
Provable Rich Observation Reinforcement Learning with Combinatorial Latent States

We propose a novel setting for reinforcement learning that combines two common real-world difficulties: presence of observations (such as camera images) and factored states (such as location of objects). In our setting, the agent receives observations generated stochastically from a factored state. These observations are rich enough to enable decoding of the latent state and remove partial observability concerns. Since the latent state is combinatorial, the size of state space is exponential in the number of latent factors. We create a learning algorithm FactoRL (Fact-o-Rel) for this setting, which uses noise-contrastive learning to identify latent structures in emission processes and discover a factorized state space. We derive polynomial sample complexity guarantees for FactoRL which polynomially depend upon the number factors, and very weakly depend on the size of the observation space. We also provide a guarantee of polynomial time complexity when given access to an efficient planning algorithm.
Weakly-supervised Salient Instance Detection

Existing salient instance detection (SID) methods typically learn from pixel-level annotated datasets. In this paper, we present the first weakly-supervised approach to the SID problem. Although weak supervision has been considered in general saliency detection, it is mainly based on using class labels for object localization. However, it is non-trivial to use only class labels to learn instance-aware saliency information, as salient instances with high semantic affinities may not be easily separated by the labels. We note that subitizing information provides an instant judgement on the number of salient items, which naturally relates to detecting salient instances and may help separate instances of the same class while grouping different parts of the same instance. Inspired by this insight, we propose to use class and subitizing labels as weak supervision for the SID problem. We propose a novel weakly-supervised network with three branches: a Saliency Detection Branch leveraging class consistency information to locate candidate objects; a Boundary Detection Branch exploiting class discrepancy information to delineate object boundaries; and a Centroid Detection Branch using subitizing information to detect salient instance centroids. This complementary information is further fused to produce salient instance maps. We conduct extensive experiments to demonstrate that the proposed method plays favorably against carefully designed baseline methods adapted from related tasks.
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization

Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs). At the same time, there are concerns that these visualizations might not accurately represent CNNs' inner workings. Here, we measure how much extremely activating images help humans to predict CNN activations. Using a well-controlled psychophysical paradigm, we compare the informativeness of synthetic images by Olah et al. (2017) with a simple baseline visualization, namely exemplary natural images that also strongly activate a specific feature map. Given either synthetic or natural reference images, human participants choose which of two query images leads to strong positive activation. The experiment is designed to maximize participants' performance, and is the first to probe \emph{intermediate} instead of final layer representations. We find that synthetic images indeed provide helpful information about feature map activations (82±4% accuracy; chance would be 50%). However, natural images---originally intended to be a baseline---outperform these synthetic images by a wide margin (92±2% accuracy). Additionally, participants are faster and more confident for natural images, whereas subjective impressions about the interpretability of the feature visualizations by Olah et al. (2017) are mixed. The higher informativeness of natural images holds across most layers, for both expert and lay participants as well as for hand- and randomly-picked feature visualizations. Even if only a single reference image is given, synthetic images provide less information than natural images (65±5% vs. 73±4%). In summary, synthetic images from a popular feature visualization method are significantly less informative for assessing CNN activations than natural images. We argue that visualization methods should improve over this simple baseline.
Understanding factors and barriers to alternative media development in emerging economies: Learning from the Check Global project

Check Global is a journalism and digital literacy development project (2019–2021) supporting countries and regions affected by conflict or state controls. In such contexts, expectations are set high for alternative journalism to accurately counter mainstream media narratives, controlled as they often are by the state; this article presents factors to be taken into consideration as a starting point for better understanding the challenges involved in developing journalism, e.g., through funded training initiatives. The article draws on interviews with prominent alternative and independent media outlets (some of them Check Global partners) from India, Latin America, Egypt and Lebanon, who therefore have operational experience of these issues. By viewing digital and social media through an anti-determinist lens, we challenge assumptions — especially prevalent following the 2011 Arab uprisings — that ‘open access’ and social media platforms can easily provide solutions to media plurality concerns. We explore factors such as the role of technology in alternative media, but also the main barriers faced by alternative media projects and outlets. This article therefore opens up a more honest discussion about the nature of alternative media projects in such contexts, and the ways in which digital literacy projects such as Check Global could support them.
Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center -- Preliminary Considerations

Today, data centers deal with fast-growing data volumes.To deliver services, they deploy a growing amount of heterogeneous hardware.As a result, it becomes practically impossible to apply human-based data center management.For instance, in a real-world data center with 500+ computersdelivering data, computational, and network services, it becomes impossible to visualize and understand causal relationships among variables describing the performance of monitored resources.However, it is possible to collect data describing the behavior of individual nodes.Hence, such data may be used to analyze/model system performance.In particular, it may be applied to recognize and predict anomalies in system behavior.Furthermore, collected data should allow finding the cause(s) of anomalies.Therefore, "data-driven approaches" have been applied to the real-world data, to find Root Cause of anomalies.
Artificial Intelligence as Solution in Facing the Age of Digital Disruption 4.0

Artificial Intelligence is part of the Industrial Revolution 4.0 and already exists today. This shows that the future has come and everyone must prepare for the implementation of Artificial Intelligence to face the transformation of the digital era, especially the world of education. The community service workshop was attended by 66 participants, namely students, teachers, and structural officials of SMK Negeri 2 Singkawang. The workshop was held using demonstration methods, lectures, discussions and question and answer. This workshop provides information to teachers and students about the importance of Artificial Intelligence (AI) in the digital transformation process. For teachers and students at SMKN 2 Singkawang it was introduced that algorithms or artificial intelligence methods could be given simply by representing problems into simple solutions with several examples of implementing artificial intelligence using Microsoft Excel and utilizing VBA macros.
Single Use Goes Circular–An ICT Proto-Practice for a Sustainable Circular Economy Future

Design research within information and communication technology and human-computer interaction is well poised to link relevant artefactsâ lifecycle phases, such as the end-of-life with design. From a lifecycle thinking perspective, this paper investigates aspects of product longevity, interrogating what sustainable product lifetimes in a sustainable Circular Economy mean. The potential of the latter concepts has not yet been fully exploited. Also, the power of stakeholders, e.g., of designers and consumers, has not been synergistically combined. However, fulfilling this potential might facilitate a transition towards more sustainable future societies. The present work draws inspiration from an extreme case of âsingle useâ cameras. In particular, it uses the notion of âpracticesâ as a basic unit of design to articulate the desired linkages in lifecycles. âSingle useâ practices then serve as an epitome of a âborrowed for useâ scenario, whichâtransferred to the mobile phoneâresults in a proto-practice. As outlined and argued in this paper, the proposed proto-practice might exact a more profound change compared to previous concepts or lived practices. It is a specific example of designing for the Circular Economy using the mobile phone, which also epitomises how designers and consumers collectively can address temporalities, rebound-effects and design trade-offs in general. Developing proto-practices and with them setting goals that might have been out of reach previously, is proposed as a central component for future design research. Proto-practices thus promote more provocative visions of transition towards sustainable societies.
Predicting Unplanned Hospital Readmissions using Patient Level Data

The rate of unplanned hospital readmissions in the US is likely to face a steady rise after 2020.Hence, this issue has received considerable critical attention with the policy makers.Majority of hospitals in the US pay millions of dollars as penalty for readmitting patients within 30 days due to strict norms imposed by the Hospital Readmission Reduction Program.In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days.Both these models are hybrid sequence models that leverage both sequential events (history of events) and static features (like gender, blood pressure) of the patients to mine patterns in the data.Our results are promising, and they benchmark previous results in predicting hospital readmissions.The contributions of this study add to existing literature on healthcare analytics.
Strategic Realignments: Reorganizing Technical Services and E-Resources Management. A Report from the Electronic Resources &amp; Libraries Conference, Austin, TX, March 2020

The presentation, “Strategic Realignments: Reorganizing Technical Services and E-Resources Management”, took place at the Electronic Resources & Libraries Conference on March 10th, 2020. The presen...
A Brief Review on Image Steganography Techniques

This paper presents an overview of image steganography techniques. In the present paper, the main emphasis is given on transform domain techniques including a brief introduction on spatial domain techniques as well. The major advantage with transform domain technique is that the data is concealed in every bit of cover image and almost impossible for an intruder to get unauthorized access to it. The study analyzes different transforms based techniques including many variants of Wavelet Transform with its advantages, challenges and applications.
A CNN Based Approach for the Near-Field Photometric Stereo Problem

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose the first CNN based approach capable of handling these realistic assumptions in Photometric Stereo. We leverage recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to near field setup. We achieve this by employing an iterative procedure for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. To the best of our knowledge this is the first near-field framework which is able to accurately predict 3D shape from highly specular objects. Our method outperforms competing state-of-the-art near-field Photometric Stereo approaches on both synthetic and real experiments.
Shifting Micro-Enterprises into the Cloud: Guidelines for Cloud Service Providers

Cloud computing is a driving force in digitalization, and it promises companies benefits such as lower entry costs, greater flexibility/scalability, and cost savings compared to traditional IT solutions.Such benefits can also be valuable for microenterprises.However, many of these small businesses do not have access to the necessary know-how and resources to select, implement, and operate cloud technologies.As sources of these technologies, cloud service providers play a crucial role in this context as it is their business strategy that can support and convince micro-enterprises to adopt cloud technologies.As the class of micro-enterprises and the role of cloud service providers has been largely ignored in recent research, we conducted a quantitative study on the cloud computing user behavior of micro-enterprises in Germany.Based on our findings, we derived guidelines and recommendations for cloud service providers concerning the adaptation of their business strategies to adequately address the needs of micro-enterprises.
A BERT-Based Semantic Matching Ranker for Open-Domain Question Answering

Open-domain question answering (QA) is a hot topic in recent years. Previous work has shown that an effective ranker can improve the overall QA performance by denoising irrelevant context. There are also some recent works leveraged BERT pre-trained model to tackle with open-domain QA tasks, and achieved significant improvements. Nevertheless, these BERT-based models simply concatenates a paragraph with a question, ignoring the semantic similarity of them. In this paper, we propose a simple but effective BERT-based semantic matching ranker to compute the semantic similarity between the paragraph and given question, in which three different representation aggregation functions are explored. To validate the generalized performance of our ranker, we conduct a series of experiments on two public open-domain QA datasets. Experimental results demonstrate that the proposed ranker contributes significant improvements on both the ranking and the final QA performances.
Moodle Research Software: Emotional Context in Ecuadorian Higher Education

The objective of the study was to develop a theoretical approach between the logical and affective emotional bond that exists in university students in Ecuador when conducting educational research with the Moodle system. The nature of the research was developed under the qualitative approach, based on the humanist-interpretative paradigm. Due to its qualitative scheme, there are 22 universities in the city of Azogues with a population of 700,790 students, of which the selection of a homogeneous sample was applied, obtaining the participation of 618 students. The results were analyzed through the generalized comparison of data and theories through the qualitative data analysis software ATLAS.ti, which concludes with a theoretical approach consisting of three main constructs: cognitive influences of information and communication technologies, emotional logic and emotional bonding, establishing that digital connectivity facilitates the capacity of university research thinking.
Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP)

There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ.&#x0D;
Could you please pay attention?’ Comparing in-person and MTurk Responses on a Computer Code Review Task

The current study examined the differences in data quality across two environments (i.e., in a laboratory and online via Amazon's Mechanical Turk) on a computer code review task.Researchers and practitioners often collect data online for the sake of convenience, as well as for obtaining a more generalizable sample of participants.The lack of social contact between the researchers and participants, however, may result in less effort dedicated to the experimental task resulting in poor quality data.The results of the current study showed that data quality-at least when measuring the individual difference variables-was drastically worsened when the experimental task was presented online.In contrast, we observed little differences in the experimental task perceptions across the two samples.Rather, participants spent significantly less time examining the computer code when completing the experiment online.The current study has implications for the effects of using online platforms (like MTurk) to collect experimental data.