Reviewerly/exHarmony huggingface dataset:
A Sequential and Intensive Weighted Language Modeling Scheme for Multi-Task Learning-Based Natural Language Understanding

Multi-task learning (MTL) approaches are actively used for various natural language processing (NLP) tasks. The Multi-Task Deep Neural Network (MT-DNN) has contributed significantly to improving the performance of natural language understanding (NLU) tasks. However, one drawback is that confusion about the language representation of various tasks arises during the training of the MT-DNN model. Inspired by the internal-transfer weighting of MTL in medical imaging, we introduce a Sequential and Intensive Weighted Language Modeling (SIWLM) scheme. The SIWLM consists of two stages: (1) Sequential weighted learning (SWL), which trains a model to learn entire tasks sequentially and concentrically, and (2) Intensive weighted learning (IWL), which enables the model to focus on the central task. We apply this scheme to the MT-DNN model and call this model the MTDNN-SIWLM. Our model achieves higher performance than the existing reference algorithms on six out of the eight GLUE benchmark tasks. Moreover, our model outperforms MT-DNN by 0.77 on average on the overall task. Finally, we conducted a thorough empirical investigation to determine the optimal weight for each GLUE task.
Digital text and digital image encryption and steganography method based on SIYu map and least significant bit

Abstract This paper focus on keeping data and information confidentiality on digital image based on chaotic system. In this paper, we use the unmodified SIYu map for the encryption and embedding process and use 3-3-2 LSB insertion method to embed data to cover image. This paper will show how is the result of encryption process and embedding process using SIYu map. Furthermore, we will show our algorithm performance based on randomness of sequence generated by SIYu map with NIST test and entropy test, histogram analysis, goodness of fit test, quality of the stego image, quality of decrypted data, initial value sensitivity, and key space. The results show encrypted data is uniformly distributed; sequence generated by SIYu map passes all NIST test and entropy test, which means the generated sequence is random; stego image have a good quality, it can be seen from the correlation coefficient and PSNR value more than 40 dB; decrypted data has the same quality as the original data; initial value sensitivity reaches 10 −16 ; key space reaches 2.56 × 10 94 ; and add noises can be optional to increase data confidentiality. For the conclusion, our algorithm can be used to keeping data and information confidentiality on digital image.
A Lightweight and Secure Energy Trading Framework for Electric Vehicles

Recent advancements in smart grids (SGs) have introduced Vehicle-to-Grid (V2G) networks as an emerging technology for electric power distribution networks. V2G provides more sophisticated energy trading by enabling bi-directional flow of electricity as well as communication channels between SGs and the electric vehicles (EVs). However, due to the huge daily volumes of trading data, the underlying V2G infrastructure suffers from scalability and security challenges. Therefore, new design principles are favorable to realize next generation of V2G networks. As distributed energy information networks are amongst the key desiderata of future power grids, the blockchain technology shows promising potential to facilitate decentralized power generation and local energy trading. Thus, we propose a framework that uses the distributed ledger and Public Key Infrastructure (PKI) of a blockchain with a dynamic Proof-of-Work (dPoW) consensus for secure V2G energy trading. The ledger functions to execute transactions and thereafter, log them in the blockchain. With PKI, an authentication mechanism for EVs is established, whereas dPoW supports varying mining difficulty levels for a high throughput rate. To demonstrate the feasibility of our framework, security and performance analyses are presented, where the designed framework has been realized to preserve the privacy of EVs, support minimal computational overhead, and facilitate scalability in V2G networks.
Adaptive graph-based discriminative nonnegative matrix factorization for image clustering

Nonnegative matrix factorization(NMF) is an effective dimension reduction method, which is widely used in image clustering and other fields. Some NMF variants preserve the manifold structure of the original data. However, the construction of the traditional neighbor graph depends on the original data, so it may be affected by noise and outliers. Moreover, these methods are unsupervised and do not use available label information. Therefore, this paper presents an adaptive graph-based discriminative nonnegative matrix factorization(AGDNMF). AGDNMF uses the available label to construct the label matrix, such that the new representations with the same label data are aligned to the same axis. And the neighbor graph in AGDNMF is obtained by adaptive iterations. A number of experiments on many image data sets verify that AGDNMF is effective compared with the other state-of-the-art methods.
Pretrained language models

The pretrained language model has been a hot topic of natural language processing in recent years. The concept of pretraining derives from transfer learning. A model is first pretrained on related tasks and then fine-tuned on the target task to transfer the learned parameters. This methodology is particularly useful when the target task [e.g., machine reading comprehension (MRC)] has insufficient training data, while the related tasks have ample training data. In this chapter, we will first introduce the concepts and history of pretrained language models, and then analyze several pretrained language models which have been successfully applied to the MRC task. In particular, we will focus on the phenomenal pretrained language model Bidirectional Encoder Representations from Transformers and its revolutionary impact on MRC.
The Integration of Augmented Reality (AR) in Learning Environment

In the uncertainty environment of today's world due to the Covid-19 pandemic, the education sector also shifting the teaching and learning technique to cater all the student learning needs. The Ministry of Education (MOE), Malaysia recommends all schools under MOE to use Open Distance Learning (ODL) which is part of the e-learning method during this critical period of time. With the advancement of the Internet of Things (IoT) and the integration of technology know-how, it will make every aspect of learning possible. This study goes some way to bridging the gap by exploring students' engagement and motivation towards Augmented Reality (AR). Findings showed encouraging learning outcomes as quantified by data analysis. This unique feature of Augmented Reality had led it to be implemented in education as it is able to convey abstract knowledge to a comprehensible context. Augmented Reality provides an exciting and fascinating learning environment as it increases the motivation and attractiveness of teaching and learning in real-life scenarios for students.
Supplier recommendation based on knowledge graph embedding

Selection optimal suppliers is an important issue for supply chain management. Cloud manufacturing and other new digital manufacturing paradigms pose challenges to supplier selection due to high dynamic characteristics, but also in turn provide new opportunities for improving supplier selection by usage of data. Knowledge graph has been widely researched in recommendation system and achieved remarkable results. And knowledge graph will also play an important role in improving supply chain management. In this work, a novel approach is proposed to learning purchase demand-procurement records property specific and global relatedness from supply knowledge graph based on knowledge graph embedding. And then we use the relatedness features to predict top-N procurement records which are most related to purchase demand. Finally, we conduct a numerical example to demonstrate the practicality and effectiveness of our approach.
A framework for elders fall detection using deep learning

Elders are one of the huge and fast-growing populations, the majority of them suffers from several daily accidents like fall, and according to the World Health Organization, fall is the second leading cause of accidental injury deaths, for this reason, we proposed a dynamic video classification system in order detect fall with one surveillance camera. Our proposed framework consists of two steps, the first step is the extraction of the human body silhouette from the video frames with the Mask R-CNN, in the second step, we used a combination between the Convolutions Neural Network CNN, and the LSTM long short-time memory to learn long-term dependencies between the successive frames. The best result of our system was achieved when we used 10 frames per video with accuracy, precision, recall, and F1-score equals to 100%.
The effect of using theodolite 3D AR in teaching measurement error on learning outcomes and satisfaction of civil engineering students with different spatial ability

The purpose of this study was to investigate the effect of augmented reality (AR) using a 3D app in a smartphone on students' learning outcomes and satisfaction in teaching angle measurement error to vocational high school students with different spatial ability. A quasi-experimental pretest/posttest was employed. There were 197 students from three vocational high schools in the southern, northern, and eastern part of Taiwan participated in this study. To minimize the biased result, we established one control group and one experimental group in each school. The experimental group used multimedia teaching tools and AR technology as a supplement, while the control group was taught by multimedia teaching tools only. We assessed some factors that possibly affect the learning outcomes and learning satisfaction, including different spatial abilities and different digital materials as teaching tools. Our finding highlights that different teaching materials influence students' learning outcomes and satisfaction, whereas spatial ability does not affect. Further discussion and implementation are also identified in this study.
Online Area Covering Robot in Unknown Dynamic Environments

Autonomous area covering robots are being increasingly deployed in residential and commercial settings for a variety of purposes. These robots usually employ universal area covering algorithms to cover indoor environments. The performance of such algorithms heavily depends on room geometry as well as obstacle location, and often suffers from significant overlap leading to inordinately long coverage time, especially in realistic unknown environments with dynamic obstacles. Hence, deploying smarter algorithms that adapt to the environment can improve the performance significantly. In this study, we explore deep reinforcement learning (RL) algorithms for efficient coverage in unknown environments with multiple dynamic obstacles. Through experiments in grid-based environments and Gazebo simulator, we demonstrate the superior performance of RL based coverage algorithms in environments with dynamic obstacles. The performance of RL based algorithm is compared with the BA* algorithm with dynamic re-planning to demonstrate the advantages of the former over one-shot algorithms. Further, by employing transfer learning the trained RL agent learns to cover unseen unknown environments with minimal additional sample complexity. Importantly, we show that RL agents trained in smaller environments can be deployed for coverage in larger unknown environments with marginal additional sample complexity.
CoveR: An Information Diffusion Aware Approach for Efficient Recommendations Under User Coverage Constraints

In this article, we consider the problem of recommendations to the users of an online social network (OSN), through an information diffusion aware recommender system (IDARS). We map the assignment of recommendations in influence networks to a problem of selecting an <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> -cover of the minimum total cost, which is defined to be a set of assignments such that each user in the OSN is recommended of at least <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> different items at the minimum defined cost. This corresponds to a special case of the minimum weighted partition set cover problem, which is a generalization of the minimum weighted set cover problem, both of which are proven to be NP-hard. We formulate a corresponding integer programming problem and we apply a linear programming (LP)-based branch and bound (BnB) methodology for its solution. We also propose a greedy algorithm, denoted as CoveR, which we show to be an O((Δ/δ)·H(Δ))-approximation for the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> -coverage problem, where Δ and δ are the maximum and minimum degree of an influence network, respectively, and H(Δ) is the Δ th harmonic number. We investigate CoveR's performance through extensive simulations on both synthetic and real networks, which indicate that the quality of its solution is comparable to the one obtained by the BnB method, while at the same time outperforms other information diffusion-aware recommendation heuristics.
DDGAN: Double Discriminators GAN for Accurate Image Colorization

The purpose of image colorization is to map reasonable colors for grayscale images. Although more and more deep neural networks have been proposed and shown good performance, the color images generated usually have the phenomenon of color crossing. In this paper, we propose DDGAN, a Generative Adversarial Networks (GANs) with double auto-encoding discriminators to solve this problem. The purpose of adding a discriminator is to use the complementary statistical properties of KL divergence and reverse KL divergence to avoid mode collapse and improve training stability. The auto-encoding structure is used for pixel level adversarial training to obtain color images with accurate edges. Experiments show that DDGAN has a better performance in the correctness of color migration and the suppression of excessive smoothing of color at the boundary.
Optimal Knots Point and Bandwidth Selection in Modeling Mixed Estimator Nonparametric Regression

Abstract The nonparametric regression model that is currently developing is limited to only one estimator form that is used. This happens, due to the assumption from the researcher that each of these predictors has the same data pattern. However, when we model the predictor variable with the response variable, what happens is that each predictor may have a different pattern, so it is necessary to develop a mixed estimator. The mixed estimator used in this study is the truncated spline and the Gaussian Kernel. Furthermore, it becomes a separate problem, when combining the truncated spline with the Gaussian Kernel, so it must determine the optimal knot point and bandwidth. Determine the optimal knot point and bandwidth is very important. The methods used are Cross-Validation (CV), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR). In this study, the nonparametric regression model was applied to the data on the percentage of poor people in Districts/Cities on Borneo Island in 2019. Based on analysis, the modeling of the nonparametric regression approach with a mixed estimator of the truncated spline and the Gaussian Kernel uses the GCV method which gives the best results. This is supported by the coefficient of determination obtained by 90.52% and the MSE value of 1.10. This means that the nonparametric regression model built can explain the effect of the predictor variable on the response variable, namely the percentage of poor people by 90.52%.
Environmental geochemical signature of shale in pollution assessment of trace metals in soil and water in parts of southern Benue trough, southeastern Nigeria

An investigation of environmental geochemistry of shale rock, soil and water in parts of Abakaliki, southern Benue Trough , Nigeria was carried out in order to evaluate the impact of shale bedrock in the soil and water environment. Shale rock, soil and water samples were collected from ten (10) locations within the study area and were analysed for major and trace elements using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). In situ parameters such as pH, in water were measured in the field. The results show that Pb is highly enriched while As. Cr, Co, Fe, Ni, Sr, V and Zn are slightly enriched in the shales relative to the global shale average. Similar geochemical trend was observed for surface and subsurface soils. Also, the mean concentrations of As, Co. Cu. Mn, Pb and Zn in soils at both depths are higher than the mean concentrations in the shale bedrock in the area of study, implying relative enrichments of these metals in the soils of the study area. In addition, the mean values of Al, Fe, Ti, Cr, Ni, Sr and V in both soil depths are lower than the values recorded in the shale bedrock. Relationship between pH and metal load suggested that the waters are dominantly near neutral to alkaline range suggesting that acidic contaminants do not affect the water quality. The mean concentrations of Al, Fe, Mn and Pb in groundwater and surface water were above the regulatory standards indicating contamination of the water. The calculated enrichment factor, anthropogenic input, pollution index for soil indicated that the soil are highly contaminated through numerous geogenic and anthropogenic sources with Pb being the most toxic metal with the shale bedrock and the mineral load contained in them as the major contributor of these contaminants.
epipack: An infectious disease modeling package for Python

Analyzing the spread of infectious diseases by means of compartmental mathematical models has been an active area of research for almost a century (Anderson & May, 2010;Keeling & Rohani, 2011;Kermack & McKendrick, 1991).Since the emergence of the coronavirus disease 2019 pandemic in early 2020, the field has seen yet another considerable boost in interest.Researchers have since been working on a large number of different models that are used to forecast case numbers, to analyze the implications of different contact structures between individual people, or to discuss the influence of various mitigation and containment strategies, to only name a few applications (Estrada, 2020).
Human posture tracking with flexible sensors for motion recognition

Abstract The integration of conventional clothes with flexible electronics is a promising solution as a future‐generation computing platform. However, the problem of user authentication on this novel platform is still underexplored. This work uses flexible sensors to track human posture and achieves the goal of user authentication. We capture human movement pattern by four stretch sensors around the shoulder and one on the elbow. We introduce the long short‐term memory fully convolutional network (LSTM‐FCN), which directly takes noisy and sparse sensor data as input and verifies its consistency with the user's predefined movement patterns. The method can identify a user by matching movement patterns even if there are large intrapersonal variations. The authentication accuracy of LSTM‐FCN reaches 98.0%, which is 10.7% and 6.5% higher than that of dynamic time warping and dynamic time warping dependent.
Design of Backward Chaining for Identification Palm Oil Diseases Base on Expert System

Abstract The most important oil crop and highest production in the world is oil palm. Crude Palm Oil (CPO) and Palm Kernel Oil (PKO) are the products of the fruit. The benefits of palm oil include being used for food, chemical industry, pharmaceuticals, health supplements and as a source of biofuels. The challenges faced by oil palm growers are the diseases and pests that attack them. This research will design of backward chaining methode for expert system that can identify diseases in oil palm to provide treatment. The designs that have been produced include: knowledge representation about symptoms, diseases and treatment, input output systems and process design which includes data flow diagrams, databases, search for backward chaining using a decision tree. This design can be implemented using various programming languages, especially database or visual programming. The results of this research are very suitable as a reference for novice researchers who are interested in expert systems. The weakness of this research lies in the research domain that only comes from literature, it has not directly involved oil palm experts.
Cost-Sensitive Awareness-Based SAR Automatic Target Recognition for Imbalanced Data

With the maturity of synthetic aperture radar (SAR) technology, the problem of imbalanced data has gradually emerged. This problem makes it difficult for the automatic target recognition (ATR) model to properly learn the classification boundaries of majority and minority category target samples. In this article, we propose an ATR model with new architecture, called the cost-sensitive awareness-based automatic target recognition (CA-ATR) model, which provides an effective way of solving the problem of imbalanced data. Aimed at the two issues caused by imbalanced data on ATR models, the proposed method solves the problems from both the data and algorithm levels. At the data level, CA-ATR avoids adverse correlations among the target samples through different oversampling methods. By making the ATR model cost-sensitive, the proposed method also avoids the empirical risk preference of the ATR model for majority category target samples at the algorithm-level. At the same time, CA-ATR can autonomously learn different cost-sensitive awareness from different imbalanced data sets. The awareness enables the ATR model to more accurately learn the classification boundaries between target samples that belong in different categories. Several experimental results show the superiority of the proposed approach based on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set. Compared with other imbalanced learning methods, the proposed method is able to solve different types of imbalanced data problems.
The INSESS-COVID19 Project. Evaluating the Impact of the COVID19 in Social Vulnerability While Preserving Privacy of Participants from Minority Subpopulations

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized.