Reviewerly/exHarmony huggingface dataset:
Understanding Coarsening for Embedding Large-Scale Graphs

A significant portion of the data today, e.g, social networks, web connections, etc., can be modeled by graphs. A proper analysis of graphs with Machine Learning (ML) algorithms has the potential to yield far-reaching insights into many areas of research and industry. However, the irregular structure of graph data constitutes an obstacle for running ML tasks on graphs such as link prediction, node classification, and anomaly detection. Graph embedding is a compute-intensive process of representing graphs as a set of vectors in a d-dimensional space, which in turn makes it amenable to ML tasks. Many approaches have been proposed in the literature to improve the performance of graph embedding, e.g., using distributed algorithms, accelerators, and pre-processing techniques. Graph coarsening, which can be considered a pre-processing step, is a structural approximation of a given, large graph with a smaller one. As the literature suggests, the cost of embedding significantly decreases when coarsening is employed. In this work, we thoroughly analyze the impact of the coarsening quality on the embedding performance both in terms of speed and accuracy. Our experiments with a state-of-the-art, fast graph embedding tool show that there is an interplay between the coarsening decisions taken and the embedding quality.
Covid-19 The Earliest for Digital Learning in Mathematics: an Overview from Technology Literacy

Abstract The Covid-19 Pandemic has caused the learning system in Indonesia to change from conventional learning to digital learning. Various efforts have been made by policy makers and educators. Some of the efforts that have been made include the provision of Free Internet Quota from the Government to educators and students; as well as providing virtual conference facilities used in learning. The purpose of this study was to describe the technological literacy of students during Covid-19 period. The type of research used is qualitative research with a narrative approach. The data collection technique used a questionnaire. The target of this study were students junior high school and senior high school in the East Priangan region Indonesia who were asked to fill out a questionnaire about technological literacy in online learning as a result of this Covid-19. The data on technological literacy capabilities contains four indicators according to the Ministry of Education and Culture’s Guidelines for “School Literacy movement”, namely indicators of content, process, context, and ethics. The four indicators contain 19 questions. The results show that students need to improve their technological literacy skills by adopting technology in line with global trends and realities that arise in learning in schools.
Automatic Design of Deep Neural Networks Applied to Image Segmentation Problems

A U-Net is a convolutional neural network mainly used for image segmentation domains such as medical image analysis. As other deep neural networks, the U-Net architecture influences the efficiency and accuracy of the network. We propose the use of a grammar-based evolutionary algorithm for the automatic design of deep neural networks for image segmentation tasks. The approach used is called Dynamic Structured Grammatical Evolution (DSGE), which employs a grammar to define the building blocks that are used to compose the networks, as well as the rules that help build them. We perform a set of experiments on the BSDS500 and ISBI12 datasets, designing networks tuned to image segmentation and edge detection. Subsequently, by using image similarity metrics, the results of our best performing networks are compared with the original U-Net. The results show that the proposed approach is able to design a network that is less complex in the number of trainable parameters, while also achieving slightly better results than the U-Net with a more consistent training.
The Impact of Knowledge Management Environment on Knowledge Management Effectiveness: Through Mediating Role of Knowledge Sharing Process in Branch Banking of Pakistan

The purpose of this study is to examine the effect of knowledge management environment on knowledge management effectiveness through the mediating role of knowledge sharing processes in public and private, conventional and Islamic branch banking of Pakistan.Survey technique was used for collection of data from the staff of branch banking.A sample of 290 was used for data analysis.The hypothesized relationships were tested using Smart PLS software.The results shows that knowledge management environment has a directly and indirectly positive and significant effect on knowledge management effectiveness through the mediating role of knowledge sharing process in branch banking of Pakistan.Findings of this study reveal that management of the branch banking can enhance their knowledge management effectiveness in terms of innovation, efficiency and adaptability by managing the knowledge management environment (Tacit and Explicit) through a mediating role of knowledge sharing process (Internal and External).
A Reference Model for IoT Embodied Agents Controlled by Neural Networks

Embodied agents is a term used to denote intelligent agents, which are a component of devices belonging to the Internet of Things (IoT) domain. Each agent is provided with sensors and actuators to interact with the environment, and with a `controller' that usually contains an artificial neural network (ANN). In previous publications, we introduced three software approaches to design, implement and test IoT embodied agents. In this paper, we propose a reference model based on statecharts that offers abstractions tailored to the development of IoT applications. The model represents embodied agents that are controlled by neural networks. Our model includes the ANN training process, represented as a reconfiguration step such as changing agent features or neural net connections. Our contributions include the identification of the main characteristics of IoT embodied agents, a reference model specification based on statecharts, and an illustrative application of the model to support autonomous street lights. The proposal aims to support the design and implementation of IoT applications by providing high-level design abstractions and models, thus enabling the designer to have a uniform approach to conceiving, designing and explaining such applications.
A Sensitivity Analysis (and Practitioners’ Guide to) of DeepSORT for Low Frame Rate Video

Simple Online and Real-time Tracking with a Deep Association Metric (DeepSORT) has been widely used due to its simplicity and strong empirical performance on Multiple Object Tracking (MOT). However, in real-world applications involving low frame rate (LFR) videos, DeepSORT requires practitioners to tune its hyperparameters to handle abrupt changes in motion and reduce erroneous tracks. Additionally, it is currently unknown how sensitive the DeepSORT's performance is to changes in these hyperparameters for LFR videos. Thus we conduct a sensitivity analysis of DeepSORT to explore the effect of these hyperparameters on the overall performance in LFR videos. Our main aim is to understand the impact of hyperparameter and identify crucial choices for DeepSORT in LFR-MOT. We finally present practical recommendations based on our extensive empirical study for those interested in getting the most out DeepSORT for LFR-MOT in real-world settings.
Supporting Disassembly in Remanufacturing with Augmented Reality

The disassembly of products is a crucial process step in remanufacturing. Due to the high complexity caused by different variants and conditions of the used products to be dismantled, disassembly in remanufacturing has in general a low degree of automation. At present, employees are usually supported by static instructions and descriptions, which are often ambiguous and therefore may lead to errors. Augmented reality (AR) can reduce these disadvantages by guiding employees systematically through the disassembly process, taking the positioning and orientation of employee and product into account. However, SMEs in particular shy away from using AR applications to support disassembly processes, both because of concerns about high costs and insufficient programming expertise.Software Development Kits (SDK) are a possible solution to implement AR applications with rather low effort and costs. Therefore, a suitable SDK was selected, considering costs and functional scope. Using a practical example, the results of different tracking possibilities and illuminance levels are compared. Finally, the applicability to support disassembly processes with an AR application is demonstrated using an example product.
Estimating Risk-Adjusted Hospital Performance

The quality of healthcare provided by hospitals is subject to considerable variability. Consequently, accurate measurements of hospital performance are essential for various decision-makers, including patients, hospital managers and health insurers. Hospital performance is assessed via the health outcomes of their patients. However, as the risk profiles of patients between hospitals vary, measuring hospital performance requires adjustment for patient risk. This task is formalized in the state-of-the-art procedure through a hierarchical generalized linear model, that isolates hospital fixed-effects from the effect of patient risk on health outcomes. Due to the linear nature of this approach, any non-linear relations or interaction terms between risk variables are neglected.In this work, we propose a novel method for measuring hospital performance adjusted for patient risk. This method captures non-linear relationships as well as interactions among patient risk variables, specifically the effect of co-occurring health conditions on health outcomes. For this purpose, we develop a tailored neural network architecture that is partially interpretable: a non-linear part is used to encode risk factors, while a linear structure models hospital fixed-effects, such that the risk-adjusted hospital performance can be estimated. We base our evaluation on more than 13 million patient admissions across almost 1,900 US hospitals as provided by the Nationwide Readmissions Database. Our model improves the ROC-AUC over the state-of-the-art by 4.1 percent. These findings demonstrate that a large portion of the variance in health outcomes can be attributed to non-linear relationships between patient risk variables and implicate that the current approach of measuring hospital performance should be expanded.
Autoencoder-based outlier detection for sparse, high dimensional data

Outlier detection is essential in many data mining tasks. For high-dimensional data, its outlier detection often faces two challenges caused by sparse spatial distribution of data and big difficulties to get enough class labels. Therefore, it is valuable to explore a simpler and more effective approach to unsupervised outlier detection. In this paper, focusing on high-dimensional sparse data, an unsupervised outlier detection approach based on autoencoders and Robust PCA is proposed. Because Robust PAC has greater advantages in feature extraction of high-dimensional data and autoencoder has powerful capabilities in the reconstruction of normal data, the proposed approach can effectively address the two problems concerned above. The proposed approach is compared with some representative approaches, including ABOD, KNN, LOF, SOS and SOD, on eight well-known public datasets. The experiments show that compared with them, the proposed approach has advantages in both precision and recall rate, and can more accurately distinguish between normal data and outliers.
The Impact of Covid-19 Pandemic ‘Lockdown’ on Reading Engagement of Selected Secondary School Students in Nigeria

Several studies have decried the non-existence of functional school libraries in many primary and secondary schools in Nigeria. This inadequacy in school libraries made it extremely difficult for school librarians to foster the culture of reading in students through school library use and reading promotional activities. Moreover, library hours are not included in the school timetable in many secondary schools in Nigeria. Thus, reading engagement has been on the decline among secondary school students in the country. Despite these challenges, some studies suggested that students will spend more time reading if they have more spare time. Therefore, this study investigated selected secondary school students’ reading engagement during the COVID-19 pandemic school lockdown. It was assumed that the availability of time during the period would motivate students to read since schools were closed and movement was restricted. The study adopted an online survey instrument (Questionnaire) administered to the students over 3-month period (April - June 2020). This instrument was adopted because there was total closure of schools throughout the country, thus the only means students could be reached at the time was through online. The questionnaire was sent to individual students and school online groups. Thus, the study was limited to students with information technology gadgets and online presence. The result revealed that there was no significant difference in the duration of time students spent reading during the lockdown and when school was in session. It also revealed that students read textbooks to prepare for examinations and to keep sound academically.
Combining bio-inspiration and ecodesign

Prescriptive ecodesign tools aim to aid designers in creating more sustainable designs by providing them with general recommendations, relying on the designer to adapt and convert such recommendations into a creative concept. This class of ecodesign methods relies on - and is limited by - the creativity and previous knowledge of the designer. In contrast, bio-inspired design creates new concepts using analogical transfer from existing solutions found in nature. The resulting designs however do not necessarily have a lower environmental impact. This paper proposes to join ecodesign and bio-inspired design, combining the strengths of both and making them more accessible. The general recommendations resulting from ecodesign are used to steer the selection of relevant biological strategies, resulting in more tailored inspiration with the clear direction of improving the environmental impact. Starting from a single ecodesign recommendation, a number of diverse biological strategies are presented. Both the principles used by termites to passively regulate the temperature of their mound and the principles used by sharks and fish to reduce the drag across their skin can be used as an embodiment of the guideline to reduce energy consumption during use.
Super-Resolving Cross-Domain Face Miniatures by Peeking at One-Shot Exemplar

Conventional face super-resolution methods usually assume testing low-resolution (LR) images lie in the same domain as the training ones. Due to different lighting conditions and imaging hardware, domain gaps between training and testing images inevitably occur in many real-world scenarios. Neglecting those domain gaps would lead to inferior face super-resolution (FSR) performance. However, how to transfer a trained FSR model to a target domain efficiently and effectively has not been investigated. To tackle this problem, we develop a Domain-Aware Pyramid-based Face Super-Resolution network, named DAP-FSR network. Our DAP-FSR makes the first attempt to super-resolve LR faces from a target domain by exploiting only a pair of high-resolution (HR) and LR exemplar in the target domain. To be specific, our DAP-FSR firstly employs its encoder to extract the multi-scale latent representations of the input LR face. Considering only one target domain example is available, we propose to augment the target domain data by mixing the latent representations of the target domain face and source domain ones, and then feed the mixed representations to the decoder of our DAP-FSR. The decoder will generate new face images resembling the target domain image style. The generated HR faces in turn are used to optimize our decoder to reduce the domain gap. By iteratively updating the latent representations and our decoder, our DAP-FSR will be adapted to the target domain, thus achieving authentic and high-quality upsampled HR faces. Extensive experiments on three benchmarks validate the effectiveness and superior performance of our DAP-FSR compared to the state-of-the-art methods.
Instance-level salient object segmentation

Image saliency detection has recently achieved great success due to the development of deep convolutional neural networks. However, most of the existing salient object detection methods cannot identify individual object instances in the detected salient region. In this paper, we present a salient instance segmentation method that produces a saliency map with distinct object instance labels for an input image. Our method consists of three primary steps, i.e., salient region inference, salient object contours detection, and salient object instances identification. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. For the last step, we propose a morphology algorithm that incorporates detected salient regions and salient object contours to generate promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database (ILSO-2K) of 2,000 images with pixel-wise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving satisfactory performance over six public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation. The source code and proposed dataset will be public available at https://github.com/Kinpzz/MSRNet-CVIU.
Language Identification on Massive Datasets of Short Messages using an Attention Mechanism CNN

Language Identification (LID) is a challenging task, especially when the input texts are short and noisy such as microblog posts on social media or chat logs on gaming forums. The task has been tackled by either designing a feature set for a traditional classifier (e.g. Naive Bayes) or applying a deep neural network classifier (e.g. Bi-directional GRU, Encoder-Decoder). These methods are usually trained and tested on a private corpus, then used as off-the-shelf packages by other researchers on their own datasets, and consequently the various results published are not directly comparable. In this paper, we first create a new massive labeled dataset based on one year of Twitter data. We use this dataset to test several existing LID systems, in order to obtain a set of coherent benchmarks, and we make our dataset publicly available so that others can add to this set of benchmarks. Finally, we propose a shallow but efficient neural LID system, which is a ngram-regional convolution neural network enhanced with an attention mechanism. Experimental results show that our architecture is able to predict tens of thousands of samples per second and surpasses all state-of-the-art systems in accuracy and F1 score, including outperforming the popular langid system by 5%.
Usability of a Co-designed eHealth Prototype for Caregivers: Combination Study of Three Frameworks

Background Co-design (or the participation of users) has shown great potential in the eHealth domain, demonstrating positive results. Nevertheless, the co-design approach cannot guarantee the usability of the system designed, and usability assessment is a complex analysis to perform, as evaluation criteria will differ depending on the usability framework (or set of criteria) used. ISO (International Organization for Standardization) on usability (ISO 9241-210), Nielsen heuristic, and Garrett element of user experience inform different yet complementary aspects of usability. Objective This study aims to assess the usability and user experience of a co-design prototype by combining 3 complementary frameworks. Methods To help caregivers provide care for functionally impaired older people, an eHealth tool was co-designed with caregivers, health and social service professionals, and community workers assisting caregivers. The prototype was a website that aims to support the help-seeking process for caregivers (finding resources) and allow service providers to advertise their services (offering resources). We chose an exploratory study method to assess usability in terms of each objective. The first step was to assess users’ first impressions of the website. The second was a task scenario with a think-aloud protocol. The final step was a semistructured interview. All steps were performed individually (with a moderator) in a single session. The data were analyzed using 3 frameworks. Results A total of 10 participants were recruited, 5 for each objective of the website. We were able to identify several usability problems, most of which were located in the information design and interface design dimensions (Garrett framework). Problems in both dimensions were mainly coded as effectiveness and efficiency (ISO framework) and error prevention and match between the systemand the real world (Nielsen heuristic). Conclusions Our study provided a novel contribution about usability analysis by combining the 3 different models to classify the problems found. This combination provided a holistic understanding of the usability improvements needed. It can also be used to analyze other eHealth products. International Registered Report Identifier (IRRID) RR2-10.2196/11634
SMDSB: Efficient Off-Chain Storage Model for Data Sharing in Blockchain Environment

Blockchain technologyKumar, Randhir has been gaining great attention in recent years.Marchang, Ningrinla Owing to its feature of immutability, the data volume of the blockchain network is continuously growing in size.Tripathi, Rakesh As of now, the size of Bitcoin distributed ledger has reached about 200 GB. Consequently, the increasing size of the blockchain network ledger prevents many peers from joining the network. Hence, the growing size not only limits the expansion of network but also the development of the blockchain ledger. This calls for development of efficient mechanisms for storage and bandwidth synchronization. As a step toward this goal, we propose an IPFS-based decentralized off-chain storage called storage model for data sharing in blockchain (SMDSB), to store the data volume of the blockchain network. In the proposed model, the miners validate and deposit the transactions into an IPFS-based decentralized storage, whereas they store the hash of the transactions in a blockchain network. Thus, by utilizing the characteristics of IPFS-based off-chain storage and its feature of hash creation, the blockchain ledger size can be significantly reduced. Implementation of SMDSB results in reduction of Bitcoin storage by 81.54%, Ethereum by 53.86%, and Hyperledger by 62.59%. Additionally, the experimental results also highlight the impact on storage cost for 1 transaction per second (TPS) in a year.