Reviewerly/exHarmony huggingface dataset:
University Students’ Experiences of the Use of Mlearning as a Training Resource for the Acquisition of Biomechanical Knowledge

Mobile learning (mLearning) is now being increasingly used in university education, with positive impacts on the motivation and predisposition of students when learning. The aim of this study was to evaluate the impact of the use of mobile applications as a learning resource in applied biomechanics, with regard to attitudinal variables (interest, motivation, applicability and learning experience) and its efficacy in the learning of general physics contents. A quasi-experimental design was proposed, with the random assignment of participants to experimental and control groups, with the post-trial measurement of a sample of 76 university students studying Applied Biomechanics, who were given an ad-hoc questionnaire in order to evaluate attitudinal aspects, as well as a test of knowledge. With regard to attitudinal aspects, higher scores were observed among the group which used the applications as learning instruments (the experimental group) when evaluating their interest in biomechanics (t = 2.79; p &lt; 0.05; d = −0.641), the applicability of the applications in real life (t = 7.34; p &lt; 0.001; d = −1.687) and the general evaluation of the practical sessions (t = 6.45; p &lt; 0.001; d = −1.481). At the conceptual level, significant differences were observed in the contents worked on with the KinematicLab Jump© application. Our results show the positive effect of the use of applications on important attitudinal aspects in the learning of university students, without any relevant effect on the acquisition of conceptual learning.
Evaluating the BPPT Medical Speech Corpus for An ASR Medical Record Transcription System

In a joint research funded by Indonesia Endowment Fund for Education (LPDP), Agency for the Assessment and Application of Technology (BPPT) is collaborating with Solusi247 and Harapan Kita National Heart and Vascular Hospital to develop a speech corpus named BPPT Medical Speech Corpus for building a medical ASR (automatic speech recognition) system. The corpus was produced by recording 100 speakers for 81.68 hours. The corpus has 600 unique sentences containing medical terms. Previously, BPPT has developed a speech corpus for the general named BPPT General Speech Corpus. A third speech corpus named BPPT Combined Speech Corpus was created by combining the medical and general corpus, and each corpus was trained using PyChain to build an ASR model. Based on the experiments we conducted, the ASR model built from the combined speech corpus has the best WER of 6.10%.
Blockchain-enabled Personalized Incentives for Sustainable Behavior in Smart Cities

Smart cities must adopt innovative technologies and strategies to boost existing sustainability solutions in order to fight climate change and reduce greenhouse gas emissions. We provide an overview of the existing work on the application of blockchain technologies in conjunction with other digital technologies such as IoT for incentivizing individuals and organizations to engage in more sustainable behaviors. We focus on three main areas in which these digital technologies can encourage actions aimed at reducing environmental impact: low-carbon transportation, energy efficiency, and waste diversion. Some notable examples are The Plastic Bank, ECO-Coin and SolarCoin. By analyzing case studies in which monetary and nonmonetary incentives have successfully demonstrated behavior change, we seek to understand the key elements for implementing blockchain-based solutions. We also identify key directions for future research in this area.
An adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture‐based clustering

Summary We introduce a new approach to deciding the number of clusters. The approach is applied to Optimally Tuned Robust Improper Maximum Likelihood Estimation (OTRIMLE; Coretto &amp; Hennig, Journal of the American Statistical Association 111 , 1648–1659) of a Gaussian mixture model allowing for observations to be classified as ‘noise’, but it can be applied to other clustering methods as well. The quality of a clustering is assessed by a statistic Q that measures how close the within‐cluster distributions are to elliptical unimodal distributions that have the only mode in the mean. This non‐parametric measure allows for non‐Gaussian clusters as long as they have a good quality according to Q . The simplicity of a model is assessed by a measure S that prefers a smaller number of clusters unless additional clusters can reduce the estimated noise proportion substantially. The simplest model is then chosen that is adequate for the data in the sense that its observed value of Q is not significantly larger than what is expected for data truly generated from the fitted model, as can be assessed by parametric bootstrap. The approach is compared with model‐based clustering using the Bayesian information criterion (BIC) and the integrated complete likelihood (ICL) in a simulation study and on two real data sets.
A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.
ICDAR 2021 Competition on Multimodal Emotion Recognition on Comics Scenes

The paper describes the "Multimodal Emotion Recognition on Comics scenes" competition presented at the ICDAR conference 2021. This competition aims to tackle the problem of emotion recognition of comic scenes (panels). Emotions are assigned manually by multiple annotators for each comic scene of a subset of a public large-scale dataset of golden age American comics. As a multi-modal analysis task, the competition proposes to extract the emotions of comic characters in comic scenes based on visual information, text in speech balloons or captions and the onomatopoeia. Participants were competing on CodaLab.org from December 16\(^{th}\) 2020 to March 31\(^{th}\) 2021. The challenge has attracted 145 registrants, 21 teams have joined the public test phase, and 7 teams have competed in the private test phase. In this paper we present the motivation, dataset preparation, task definition of the competition, the analysis of participant's performance and submitted methods. We believe that the competition have drawn attention from the document analysis community in both fields of computer vision and natural language processing on the task of emotion recognition in documents.
Visible-infrared Person Re-identification with Human Body Parts Assistance

Person re-identification (re-id) has received ever-increasing research focus, because of its important role in video surveillance applications. This paper addresses the re-id problem between visible images of color cameras and infrared images of infrared cameras, which is significant in case that the appearance information is insufficient in poor illumination conditions. In this field, there are two key challenges, i.e., the difficulty to locate the discriminative information to re-identify the same person between visible and infrared images, and the difficulty to learn a robust metric for such large-scale cross-modality retrieval. In this paper, we propose a novel human body parts assistance network (BANet) to tackle the two challenges above. BANet mainly focuses on extracting discriminative information and learning robust features by leveraging the human body part cues. Extensive experiments demonstrate that the proposed approach outperforms the baseline and the state-of-the-art methods.
Real-Time Fraud Prediction On Streaming Customer-Behaviour Data

The field of detection and analysis of fraud situations continues to be an essential topic in the telecom industry. This study proposes a methodology that can predict abnormal behavior situations on real-time streaming customer behavior data in the telecommunication domain. The prototype implementation of the proposed method is designed, developed, and applied to the telecommunications dataset. We perform performance tests for the method's prediction success and scalability metrics on the designed prototype application. The results indicate that the proposed method proves to be a promising approach in the telecommunication sector.
Deep Neural-Network Prediction for Study of Informational Efficiency

In this paper, we attempt to verify a hypothesis of informational efficiency of financial markets, known as “random walk” introduced by Fama. Such hypotheses could be considered in relation to financial crises. In our study the hypothesis is tested on data taken from Warsaw Stock Exchange in 2007–2009 years. The hypothesis is tested by predictive modelling based on Machine Learning (ML). We compare conventional ML techniques and the proposed “deep” neural-network structures grown by Group Method of Data Handling (GMDH). In our experiments a GMDH-type neural-network model has outperformed the conventional ML techniques, which is important for achieving the reliable results of predictive modelling and testing the hypothesis. GMDH-type modelling does not require the knowledge of network structure, as a desired network of near-optimal connectivity is learnt from the data. The experimental results compared in terms of prediction error show that the GMDH-type prediction model has a significantly smaller error than the conventional autoregressive and neural-network models.
Research on CAMshift Algorithm Based on Feature Matching and Prediction Mechanism

Aiming at the problem of target loss caused by background interference and occlusion in the traditional CAMshift algorithm for target tracking, a CAMshift tracking algorithm based on feature matching and prediction mechanism is designed. The algorithm re-locates the size and position of the target by ORB feature matching between the template target and the frame to be tracked, and realizes the accurate tracking of the target under the interference of similar background and complex background. In order to solve the problem of inaccurate tracking and feature matching under occlusion, the Kalman filter is used to predict the position of the occluded target. The experimental results show that the algorithm can accurately track the target under complex background, similar background and occlusion.
Generating linguistically relevant metadata for the Royal Society Corpus

This paper provides an overview of metadata generation and management for the Royal Society Corpus (RSC), aiming to encourage discussion about the specific challenges in building substantial diachronic corpora intended to be used for linguistic and humanistic analysis. We discuss the motivations and goals of building the corpus, describe its composition and present the types of metadata it contains. Specifically, we tackle two challenges: first, integration of original metadata from the data providers (JSTOR and the Royal Society); second, derivation of additional linguistically relevant metadata regarding text structure and situational context (register).
The Design of an Aerial/Ground Dual-modal Mobile Robot for Exploring Complex Environments

Most of the robotic systems are restricted by their single locomotion when exploring the unknown complex environment. The inefficient energy utilization is the restriction to a flying robot, while the inferior capability of traversing rugged terrain limits the ground mobile robots. This paper proposes a novel design of a dual-modal mobile robot, which possesses aerial flight and ground movement abilities. It can traverse uneven terrain by flight mode and achieve efficient energy utilization by driving on the ground. We fabricate a prototype that contains a UAV configuration that suspends mobile chassis based on the above design conception, and verify the prototype's aerial/ground dual-modal movement capability and the hybrid locomotion efficiency by experiments.
An Efficient and Effective Blockchain-based Data Aggregation for Voting System

Blockchain is opening up new avenues for the development of new sorts of digital services. In this article, we'll employ the transparent Blockchain method to propose a system for collecting data from many sources and databases for use in local and national elections. The Blockchain-based system will be safe, trustworthy, and private. It will assist to know the overall count of the candidates who participated and it functions in the same way as people's faith in their governments does. Blockchain technology is the one that handles the actual vote. We use the secure hash algorithm for resolving this problem and tried to bring a solution through the usage of this booming technology. A centralized database in a blockchain system keeps track of the secure electronic interactions of users in a peer-to-peer network.
Adaptive Facial Imagery Clustering via Spectral Clustering and Reinforcement Learning

In an era of big data, face images captured in social media and forensic investigations, etc., generally lack labels, while the number of identities (clusters) may range from a few dozen to thousands. Therefore, it is of practical importance to cluster a large number of unlabeled face images into an efficient range of identities or even the exact identities, which can avoid image labeling by hand. Here, we propose adaptive facial imagery clustering that involves face representations, spectral clustering, and reinforcement learning (Q-learning). First, we use a deep convolutional neural network (DCNN) to generate face representations, and we adopt a spectral clustering model to construct a similarity matrix and achieve clustering partition. Then, we use an internal evaluation measure (the Davies–Bouldin index) to evaluate the clustering quality. Finally, we adopt Q-learning as the feedback module to build a dynamic multiparameter debugging process. The experimental results on the ORL Face Database show the effectiveness of our method in terms of an optimal number of clusters of 39, which is almost the actual number of 40 clusters; our method can achieve 99.2% clustering accuracy. Subsequent studies should focus on reducing the computational complexity of dealing with more face images.
ASAR 2021 Online Arabic Writer Identification Competition

This paper describes the online Arabic writer identification competition held at ICDAR 2021 Workshop on Arabic and derived Script Analysis and Recognition (ASAR 2021, 4th edition). This first competition of online writer identification uses the ADAB database with online Arabic handwritten words which represent the Tunisian town names. Four systems are participating in the competition. The systems are evaluated on test data that are unknown to the participants. The systems are compared based on the writer identification rate. A detailed description of the systems and the results achieved are presented.
c-trie++: A dynamic trie tailored for fast prefix searches

Given a dynamic set K of k strings of total length n whose characters are drawn from an alphabet of size σ, a keyword dictionary is a data structure built on K that provides lookup, prefix search, and update operations on K. Under the assumption that α=w/lg⁡σ characters fit into a single machine word of w bits, we propose a keyword dictionary that represents K in either nlg⁡σ+Θ(klg⁡n) or |T|lg⁡σ+Θ(kw) bits of space, where |T| is the number of nodes of a trie representing K. It supports all operations in O(m/α+lg⁡α) expected time on an input string of length m in the word RAM model. An evaluation of our implementation highlights the practical usefulness of the proposed data structure, especially for prefix searches — one of the most essential keyword dictionary operations.
Infrastructural Constraints of Cloud Computing

The concept of cloud computing isn't new, or even excessively complicated coming from technology resources as well as internetworking perspective. What's new is the development and also the maturity of cloud computing methods, as well as approaches that enable the objectives of business agility. Recalling, the key phrase "energy computing" really did not spellbind or make the stir in the details field as the phrase "cloud computing" invites current years. Nevertheless, the respect of conveniently on call sources has gotten there and the practical or even servicing components are what go to the centre of outsourcing the access of infotech resources and also companies. Within this light, cloud computing exemplifies an adaptable, cost-efficient, as well as tested distribution platform for the company and also individual details solutions over the Internet. This paper provides the information about infrastructural constraints of cloud computing.
An End-to-End Optical Character Recognition Pipeline for Indonesian Identity Card

Optical Character Recognition has been long studied over the past few years. The challenge remains for the specific purpose of extracting information from image documents. The aim of this study is to create an end-to-end pipeline for an Indonesian identity card. The final pipeline uses deep learning approach consist of Faster R-CNN for text detection, YOLOv5 for character detection, and Support Vector Machine for Character Recognition. The proposed pipeline showed a remarkable result for both the identity number and the full name. This provides a powerful tool for the auto-fill form and verification process effectively and efficiently.
Towards Trustworthy IoT: A Blockchain-Edge Computing Hybrid System with Proof-of-Contribution Mechanism

The emerging smart city is driving massive transformations of modern cities, facing the huge influx of sensor data from IoT devices. Edge computing distributes computing tasks to the near-edge end, which greatly enhances the service quality of IoT applications, that is, ultralow latency, large capacity, and high throughput. However, due to the constrained resource of IoT devices, currently, systems with a centralized model are vulnerable to attacks, such as DDoS from IoT botnet and central database failure, which can hardly provide high-confidence services. Recently, blockchain with a high security promise is considered to provide new approaches to enhancing the security of IoT systems. However, blockchain and IoT have obvious incompatibility, and low-capacity IoT devices can hardly be incorporated into blockchain with high computing requirements. In this paper, a blockchain-edge computing hybrid system (BEHS) is presented to make the adaptation of blockchain to edge computing and provide trustworthy IoT management services for a smart city. A novel extensible consensus protocol designed for proof-of-work, named proof-of-contribution (PoC), is proposed to regulate the data upload behaviors of nodes, especially the data upload frequency of IoT device nodes, so as to protect the system from attack about frequency. In order to secure the data privacy and authenticity, a data access control scheme is designed by integrating symmetric encryption with asymmetric encryption algorithm. We implemented a concrete BEHS on Ethereum, realized the function of PoC mechanism via smart contracts, and conducted a case study for smart city. The extensive evaluations and analyses show that the proposed PoC mechanism can effectively detect and automatically manage the behavior of nodes, and the time cost of data access control scheme is within an acceptable range.
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

Abstract Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised and domain-adaptive semantic segmentation, which is enhanced by self-supervised monocular depth estimation (SDE) trained only on unlabeled image sequences. In particular, we utilize SDE as an auxiliary task comprehensively across the entire learning framework: First, we automatically select the most useful samples to be annotated for semantic segmentation based on the correlation of sample diversity and difficulty between SDE and semantic segmentation. Second, we implement a strong data augmentation by mixing images and labels using the geometry of the scene. Third, we transfer knowledge from features learned during SDE to semantic segmentation by means of transfer and multi-task learning. And fourth, we exploit additional labeled synthetic data with Cross-Domain DepthMix and Matching Geometry Sampling to align synthetic and real data. We validate the proposed model on the Cityscapes dataset, where all four contributions demonstrate significant performance gains, and achieve state-of-the-art results for semi-supervised semantic segmentation as well as for semi-supervised domain adaptation. In particular, with only 1/30 of the Cityscapes labels, our method achieves 92% of the fully-supervised baseline performance and even 97% when exploiting additional data from GTA. The source code is available at https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth .
Grid-Edge Blockchain-Based Transactive Energy Platform: Design and implementation

With the upward trend of the active participation of distributed energy resources (DERs) and grid-edge assets in grid and market operations, there is increased interest in transactive energy (TE) systems that support both peer-to-market and peer-to-peer energy exchanges. Peer-to-market transactions generally involve the market-based provision of grid services by prosumers (owners and operators of grid-edge assets, or their agents). Peer-to-peer transactions include bilateral exchanges of energy and energy derivatives among prosumers. Associated with this trend, there is growing interest in the potential use of blockchain technology to facilitate and enhance the security of transactive exchanges and streamline the associated settlement processes. This article presents a blockchain-based transactive energy BCTE (BCTE) platform design that supports peer-to-peer and peer-to-market transactive exchanges from grid-edge assets. An energy-blockchain-as-a-service (EBaaS) platform implementation is discussed, along with an illustrative use case.