Reviewerly/exHarmony huggingface dataset:
Learning using real science mask with qr code to increase students’ digital literacy

Abstract The Industrial Revolution 4.0 demands the use of technology in every aspect, including in education. One of the efforts to prepare human resources in the field of education is the government policy regarding “Independent Learning: Independent Campus”. Higher education graduates will be required to master various fields of science and be flexible in developing independence when seeking and finding knowledge. This requires good digital literacy skills. Real Science Mask with QR Code is a learning media which connected to Google classroom and make a concrete science concept. The research method was carried out by developing a product in Real Science Mask with a QR Code which will be tested for its validity and the effectiveness of the product in learning. The final data analysis was carried out by analyzing the students’ digital literacy scores. Students’ digital literacy skills using Real Science Mask with QR Code were in the high category of 71.43% and the medium category of 28.57%. The results of the study were also supported by a good response from students of 57.14%. It concluded that learning using the Real Science Mask with the QR Code model is effective in improving the students’ digital literacy skills.
DLFusion: An Auto-Tuning Compiler for Layer Fusion on Deep Neural Network Accelerator

Many hardware vendors have introduced specialized deep neural networks (DNN) accelerators owing to their superior performance and efficiency. As such, how to generate and optimize the code for the hardware accelerator becomes an important yet less explored problem. In this paper, we perform the compiler-stage optimization study using a novel and representative Cambricon DNN accelerator and demonstrate that the code optimization knobs play an important role in unleashing the potential of hardware computational horsepower. However, even only two studied code optimization knobs, namely the number of cores and layer fusion scheme, present an enormous search space that prevents the naive brute-force search. This work introduces a joint, auto-tuning optimization framework to address this challenge. We first use a set of synthesized DNN layers to study the interplay between the hardware performance and layer characteristics. Based on the insights, we extract the operation count and feature map channel size as each layer's characteristics and derive a joint optimization strategy to decide the performance-optimal core number and fusion scheme. We evaluate the performance of the proposed approach using a set of representative DNN models and show that it achieves the minimal of 3.6x and the maximal of 7.9x performance speedup compared to no optimization baseline. We also show that the achieved speedup is close to the oracle case that is based on a reduced brute-force search but with much less search time.
A Gesture Segmentation Method Based on Domain Adaptation and Channel Attention Mechanism

Aiming at solving semantic inconsistency of segmentation results of HGR-Net network, a new Channel Attention Block (newCAB) is proposed in this paper. The original CAB is modified by embedding the global max pooling on lower stage to enhance the spatial context. The newCAB not only supplements the spatial information but also enhances the semantic information. In order to improve the generalization performance of our hand gesture segmentation model, an adaptive domain adaptation method based on a style transfer is proposed. VGG model is employed to reconstruct the source domain image according to the total loss function which is calculated by content loss and style loss. The stylization degree parameter is adjusted according to the complexity and the illumination conditions of the source image. The experiments on the OUHANDS dataset show that the value of mIoU and MPA of our network are 0.9324 and 0.9667 respectively, which are 5.6 and 5.2 percentage points higher than those of HGR-Net. The experiments on the self-collected dataset show that the mIoU and MPA of those images after applying our domain adaptation method are 0.8031 and 0.8899 respectively, and have been increased by 33.42% and 35% averagely. The proposed method based on domain adaptation and channel attention mechanism can improve the intra-class inconsistency and generalization performance.
Citations and the readers’ information-extracting costs of finance articles

This paper focuses on the relationship between the reader's information-extracting costs of finance articles and the article's number of citations. The reader's information-extracting costs are measured using three metrics: (i) the Flesch-Kincaid readability score, (ii) the article's length, and (iii) the number of complex words. Based on a sample of more than 14,000 full text articles published between 2000 and 2016 in 16 finance journals, we show that the information-extracting costs of finance journals have significantly increased over time, while the topics of these articles, determined by machine-learning topic modeling, remained relatively constant. We find a positive correlation between the reader's information-extracting costs and the number of citations achieved by a paper for articles that are published in the top-three finance journals (JF, JFE, RFS), but do not observe this pattern for articles published in other major finance journals.
ROBY: a Tool for Robustness Analysis of Neural Network Classifiers

Classification using Artificial Neural Networks (ANNs) is widely applied in critical domains, such as autonomous driving and in the medical practice; therefore, their validation is extremely important. A common approach consists in assessing the network robustness, i.e., its ability to correctly classify input data that is particularly challenging for classification. We recently proposed a robustness definition that considers input data degraded by alterations that may occur in reality; the approach was originally devised for image classification in the medical domain. In this paper, we extend the definition of robustness to any type of input for which some alterations can be defined. Then, we present ROBY, a tool for ROBustness analYsis of ANNs. The tool accepts different types of data (images, sounds, text, etc.) stored either locally or on Google Drive. The user can use some alterations provided by the tool, or define their own. The robustness computation can be performed either locally or remotely on Google Colab. The tool has been experimented for robustness computation of image and sound classifiers, used in the medical and automotive domains.
Domain-Adaptive Fall Detection Using Deep Adversarial Training

Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-scale annotated fall events with various specifications of sensors or sensor positions during the implementation of accurate FD systems. Moreover, the knowledge obtained through machine learning has been restricted to tasks in the same domain. The mismatch between different domains might hinder the performance of FD systems. Cross-domain knowledge transfer is very beneficial for machine-learning based FD systems to train a reliable FD model with well-labeled data in new environments. In this study, we propose domain-adaptive fall detection (DAFD) using deep adversarial training (DAT) to tackle cross-domain problems, such as cross-position and cross-configuration. The proposed DAFD can transfer knowledge from the source domain to the target domain by minimizing the domain discrepancy to avoid mismatch problems. The experimental results show that the average F1-score improvement when using DAFD ranges from 1.5% to 7% in the cross-position scenario, and from 3.5% to 12% in the cross-configuration scenario, compared to using the conventional FD model without domain adaptation training. The results demonstrate that the proposed DAFD successfully helps to deal with cross-domain problems and to achieve better detection performance.
Controlling Costs in Feature Selection: Information Theoretic Approach

Feature selection in supervised classification is a crucial task in many biomedical applications. Most of the existing approaches assume that all features have the same cost. However, in many medical applications, this assumption may be inappropriate, as the acquisition of the value of some features can be costly. For example, in a medical diagnosis, each diagnostic value extracted by a clinical test is associated with its own cost. Costs can also refer to non-financial aspects, for example, the decision between an invasive exploratory surgery and a simple blood test. In such cases, the goal is to select a subset of features associated with the class variable (e.g., the occurrence of disease) within the assumed user-specified budget. We consider a general information theoretic framework that allows controlling the costs of features. The proposed criterion consists of two components: the first one describes the feature relevance and the second one is a penalty for its cost. We introduce a cost factor that controls the trade-off between these two components. We propose a procedure in which the optimal value of the cost factor is chosen in a data-driven way. The experiments on artificial and real medical datasets indicate that, when the budget is limited, the proposed approach is superior to existing traditional feature selection methods. The proposed framework has been implemented in an open source library (Python package: https://github.com/kaketo/bcselector).
Width transfer: on the (in)variance of width optimization

Optimizing the channel counts for different layers of a CNN has shown great promise in improving the efficiency of CNNs at test-time. However, these methods often introduce large computational overhead (e.g., an additional 2× FLOPs of standard training). Minimizing this overhead could therefore significantly speed up training. In this work, we propose width transfer, a technique that harnesses the assumptions that the optimized widths (or channel counts) are regular across sizes and depths. We show that width transfer works well across various width optimization algorithms and networks. Specifically, we can achieve up to 320× reduction in width optimization overhead without compromising the top-1 accuracy on ImageNet, making the additional cost of width optimization negligible relative to initial training. Our findings not only suggest an efficient way to conduct width optimization, but also highlight that the widths that lead to better accuracy are invariant to various aspects of network architectures and training data.
JomMedicMinder: Live or Dead

Nonadherence medication refers to the misuse or forgetting to take medication on time. It is a fatal issue that causes a huge number of health issues and complex failure in the body and unfortunately, it is responsible for a great number of death cases, especially heart attack and heart diseases. This paper proposes an advanced innovative medication reminder device that consists of the software module presented by the mobile application and it is connected wirelessly using IoT to the hardware part as a special medication container in a shape of a smartphone cover or chain. To evaluate the device a survey was distributed to different population samples (students and patients) of the different age groups from both local and private sectors followed by an intense interview. This device has shown a significant increase in medication adherence and 58% of patients and 50% of students strongly agreed to use the device for hospital cost reduction yet 95% of the respondents insisting on implementing security measurements. Furthermore, it maintains and improves the financial level of users. Therefore, this medication device is the perfect and most convenient method to stick to medication time and the number of pills to help in fast recovery at all ages without complications and most importantly to save money.
RatioRF: a novel measure for Random Forest clustering based on the Tversky's Ratio model

In this paper we propose RatioRF, a novel Random Forest-based similarity measure for clustering. We build upon Tversky's ratio model definition of similarity and specialize it to the Random Forest case. We study some properties of the proposed axiomatic similarity measure and present an extensive experimental clustering analysis involving different datasets and configurations. Results confirm that RatioRF represents a good alternative to other similar measures for clustering recently studied in the literature.
Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection

In this paper, we explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system. Building on a reservoir computing (RC)-based approach towards symbol detection, we introduce a symmetric and decomposed binary decision neural network to take advantage of the structure knowledge inherent in the MIMO-OFDM system. To be specific, the binary decision neural network is added in the frequency domain utilizing the knowledge of the constellation. We show that the introduced symmetric neural network can decompose the original M-ary detection problem into a series of binary classification tasks , thus significantly reducing the neural network detector complexity while offering good generalization performance with limited training overhead. Numerical evaluations demonstrate that the introduced hybrid RC-binary decision detection framework performs close to maximum likelihood model-based symbol detection methods in terms of symbol error rate in the low SNR regime with imperfect channel state information (CSI).
The correlation between statistical descriptors of heterogeneous materials

Heterogeneous materials such as rocks and composites are comprised of multiple material phases of different sizes and shapes that are randomly distributed through the medium. The random microstructure is typically described by using various statistical descriptors, which include volume fraction, two-point correlation function, and tortuosity, to name a few. Capturing different morphological features, a large number of statistical descriptors are proposed in different research fields, such as material science, geoscience and computational engineering. It is well known that these statistical descriptors are not independent from each other, but until recently it remains unclear what descriptors are more similar or more different. In particular, it is extremely difficult to look for quantified relations between various descriptors, since they are often defined in very different formats. The lack of quantified understanding of descriptors’ relations can cause uncertainties or even systematic errors in heterogeneous materials studies. To address this issue, we propose a novel and generic correlation analysis strategy and establish, for the first time, the quantified relations between various statistical descriptors for heterogeneous materials. Based on data science techniques, our approach consists of three operational steps: data regularization, dimension reduction and correlation analysis. A total of 41 statistical descriptors are collected and analysed in this study, which is readily extensible to include other new descriptors. The generic and quantified correlation results are compared with other established descriptor relations that are obtained from analytical analysis or physical intuition, and good agreements are observed in all cases. The quantified relations between various descriptors are summarized in a single correlation graph, which provides useful guiding information for the characterization, reconstruction and property prediction of heterogeneous materials.
Detection of the Key Actor of Issues Spreading Based on Social Network Analysis in Twitter Social Media

With the development of today's society's communication facilities, social media becomes the most effective and efficient means of conveying information to other parties. Social media's advantages ultimately contribute to social media misuse and contribute to the emergence and development of hoaxes and hate speech. Online social media such as Twitter is the most widely used means of communication in cyberspace. The important issue with the spread of news on Twitter is the presence of key actors who often spread the issue and are accounts that influence social media. These accounts usually have a lot of followers. Detection of the key actors is one of the obstacles to handling hate speech and fake news on Twitter. It can be solved using a centrality analysis algorithm with degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality method to detect the key actor. Also, sentiment value is used to determine the positive or negative value of the comments' comments in the account post. The analysis of degree centrality algorithms, betweenness centrality, and eigenvector centrality has shown that the user who has the most influence and becomes a key actor in the spread of the issue is the user with user_id 150589950. The sentiment analysis algorithm obtained the sentiment calculation results shown by the tweet amount. The most influential users in the spread of tweets can be seen from the number of tweets that can be found from the tweet amount.
Collaborative Reflectance-And-Illumination Learning For High-Efficient Low-Light Image Enhancement

In this paper, we settle the low-light image enhancement problem by developing a collaborative learning framework, which not only improves lightness and suppresses noises simultaneously but also with fast speed and requires few computational resources. The approach is inspired by the fact that reflectance and illumination are highly correlated to satisfy the well-known Retinex decomposition principle. With this in mind, we establish a Reflectance-and-Illumination Collaborative (RIC) block to depict the compact physical relationship between reflectance and illumination. By cascading multiple RIC blocks, we obtain an end-to-end RICNet to interactively optimize these two components in a collaborative manner. Benefiting from the RIC block that integrates powerful task cues, RICNet just needs few parameters to simultaneously improve brightness and remove noises. Extensive experiments demonstrate our superiority against existing state-of-the-art methods. We also make meticulous analysis for the RIC block. The results reveal the rationality and effectiveness of our built mechanism.
Multimodal Data Fusion Using Non-Sparse Multi-Kernel Learning With Regularized Label Softening

Due to the need of practical application, multiple sensors are often used for data acquisition, so as to realize the multimodal description of the same object. How to effectively fuse multimodal data has become a challenge problem in different scenarios including remote sensing. Nonsparse multi-Kernel learning has won many successful applications in multimodal data fusion due to the full utilization of multiple Kernels. Most existing models assume that the nonsparse combination of multiple Kernels is infinitely close to a strict binary label matrix during the training process. However, this assumption is very strict so that label fitting has very little freedom. To address this issue, in this article, we develop a novel nonsparse multi-Kernel model for multimodal data fusion. To be specific, we introduce a label softening strategy to soften the binary label matrix which provides more freedom for label fitting. Additionally, we introduce a regularized term based on manifold learning to anti over fitting problems caused by label softening. Experimental results on one synthetic dataset, several UCI multimodal datasets and one multimodal remoting sensor dataset demonstrate the promising performance of the proposed model.
Using ISO and Semantic Web standard for building a multilingual terminology e-Dictionary: A use case of Chinese ceramic vases

Cultural heritage is the legacy of physical artefacts and intangible attributes of a group or society that is inherited from past generations. Terminology is a tool for the dissemination and communication of cultural heritage. The lack of clearly identified terminologies is an obstacle to communication and knowledge sharing. Especially, for experts with different languages, it is difficult to understand what the term refers to only through terms. Our work aims to respond to this issue by implementing practices drawn from the Semantic Web and ISO Terminology standards (ISO 704 and ISO 1087-1) and more particularly, by building in a W3C format ontology as knowledge infrastructure to construct a multilingual terminology e-Dictionary. The Chinese ceramic vases of the Ming and Qing dynasties are the application cases of our work. The method of building ontology is the ‘term-and-characteristic guided method’, which follows the ISO principles of Terminology. The main result of this work is an online terminology e-Dictionary. The terminology e-Dictionary could help archaeologists communicate and understand the concepts denoted by terms in different languages and provide a new perspective based on ontology for the digital protection of cultural heritage. The e-Dictionary was published at http://www.dh.ketrc.com/e-dictionary.html .
Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots

Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases.
Time-aware Neural Collaborative Filtering with Multi-dimensional Features on Academic Paper Recommendation

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.