Reviewerly/exHarmony huggingface dataset:
Matching Method of Cultural Relic Fragments Constrained by Thickness and Contour Feature

The virtual restoration of cultural relics by adopting computer technology is the trend of cultural relics protection and value mining.In order to solve the problem of large error in the existing matching methods of non-thin-walled cultural relic fragments, in this paper, a matching method of cultural relic fragments is proposed, when the thickness feature and the contour feature are combined, and the strategy of coarse matching followed by fine matching is adopted.Firstly, the broken contour of cultural relic fragments is extracted, and the upper and lower contour lines are identified; secondly, the thickness feature is calculated and the thickness histogram is constructed.After that, the initial matching of fragments is completed according to the similarity of the histogram.Thirdly, the fracture contour is segmented, and the fragments of the mismatching relationship in the initial matching are also eliminated by the modified Hausdorff distance to optimize the coarse matching results.Finally, the SVD method is adopted to solve the rigid body transformation parameters, and the ICP algorithm is employed to accurately align the fine matching.The experimental results show that with the method in this paper, excellent matching results have been obtained regarding the fragments of the Terracotta Warriors.Compared with matching methods of other cultural relic fragments, the accuracy and algorithm cost of this method are better.
Goal-Directed Behavior under Variational Predictive Coding: Dynamic organization of Visual Attention and Working Memory

Mental simulation is a critical cognitive function for goal-directed behavior because it is essential for assessing actions and their consequences.When a self-generated or externally specified goal is given, a sequence of actions that is most likely to attain that goal is selected among other candidates via mental simulation.Therefore, better mental simulation leads to better goal-directed action planning.However, developing a mental simulation model is challenging because it requires knowledge of self and the environment.The current paper studies how adequate goal-directed action plans of robots can be mentally generated by dynamically organizing top-down visual attention and visual working memory.For this purpose, we propose a neural network model based on variational Bayes predictive coding, where goal-directed action planning is formulated by Bayesian inference of latent intentional space.Our experimental results showed that cognitively meaningful competencies, such as autonomous top-down attention to the robot end effector (its hand) as well as dynamic organization of occlusion-free visual working memory, emerged.Furthermore, our analysis of comparative experiments indicated that the introduction of visual working memory and the inference mechanism using variational Bayes predictive coding significantly improved the performance in planning adequate goal-directed actions.
Real-Time Compliant Stream Processing Agents for Physical Rehabilitation

Digital rehabilitation is a novel concept that integrates state-of-the-art technologies for motion sensing and monitoring, with personalized patient-centric methodologies emerging from the field of physiotherapy. Thanks to the advances in wearable and portable sensing technologies, it is possible to provide patients with accurate monitoring devices, which simplifies the tracking of performance and effectiveness of physical exercises and treatments. Employing these approaches in everyday practice has enormous potential. Besides facilitating and improving the quality of care provided by physiotherapists, the usage of these technologies also promotes the personalization of treatments, thanks to data analytics and patient profiling (e.g., performance and behavior). However, achieving such goals implies tackling both technical and methodological challenges. In particular, (i) the capability of undertaking autonomous behaviors must comply with strict real-time constraints (e.g., scheduling, communication, and negotiation), (ii) plug-and-play sensors must seamlessly manage data and functional heterogeneity, and finally (iii) multi-device coordination must enable flexible and scalable sensor interactions. Beyond traditional top-down and best-effort solutions, unsuitable for safety-critical scenarios, we propose a novel approach for decentralized real-time compliant semantic agents. In particular, these agents can autonomously coordinate with each other, schedule sensing and data delivery tasks (complying with strict real-time constraints), while relying on ontology-based models to cope with data heterogeneity. Moreover, we present a model that represents sensors as autonomous agents able to schedule tasks and ensure interactions and negotiations compliant with strict timing constraints. Furthermore, to show the feasibility of the proposal, we present a practical study on upper and lower-limb digital rehabilitation scenarios, simulated on the MAXIM-GPRT environment for real-time compliance. Finally, we conduct an extensive evaluation of the implementation of the stream processing multi-agent architecture, which relies on existing RDF stream processing engines.
Quality standards for digital forensics: Learning from experience in England &amp; Wales

The Forensic Science Regulator has the role of setting quality standards for forensic science in the Criminal Justice System (CJS) in England and Wales. The current requirement is for organisations carrying out digital forensics to gain accreditation to the international standard ISO/IEC 17025 and the Forensic Science Regulator's Codes of Practice and Conduct. The aim of this requirement is to embed a systematic approach to quality, including understanding methods, validating software and systems, understanding risks, ensuring that all involved in the crime scene to court process have the skills and competence they need and the appropriate equipment and environment for the work, and providing ongoing assurance of quality through audit and proficiency tests. However, the challenge of implementing the standards in digital forensics should not be underestimated, particularly in an environment where there is insufficient capacity to meet a growing demand for services in an area of increasing complexity and fragmented delivery. It is therefore timely to review available data to determine the extent to which accreditation to ISO/IEC 17025 is addressing quality issues in digital forensics and consider what changes and resources could be made available to assist with implementation of quality systems.
Voice Control Intelligent Wheelchair Movement Using CNNs

In this paper, we introduced a voice control intelligent wheelchair movement using Convolutional Neural Networks (CNNs). The intelligent wheelchair used four voice commands such as stop, go, left and right to assist disable people to move. Data are collected from google in the wav format. Mel-Frequency Cepstral Coefficient (MFCC) is applied to extract the command voice. The hardware used to deploy the system is Raspberry PI 3B+. The proposed method is using CNNs to classify the voice command and achieved excellent result with 95.30% accuracy. Therefore, the method can be commercialized and hopefully can give benefit to the disable society.
Server‐aided revocable attribute‐based encryption for cloud computing services

Summary Attribute‐based encryption (ABE) has been regarded as a promising solution in cloud computing services to enable scalable access control without compromising the security. Despite of the advantages, efficient user revocation has been a challenge in ABE. One suggestion for user revocation is using the binary tree in the key generation phase of an ABE scheme, which enables a trusted key generation center to periodically distribute the key update information to all nonrevoked users over a public channel. This revocation approach reduces the size of key updates from linear to logarithmic in the number of users. But it requires each user to keep a private key of the logarithmic size, and asks each nonrevoked user to periodically update his/her decryption key for each new time period. To further optimize user revocation in ABE, a server‐aided revocable ABE (SR‐ABE) scheme has been proposed, in which almost all workloads of users incurred by the user revocation are outsourced to an untrusted server, and each user only needs to store a private key of the constant size. In addition, SR‐ABE does not require any secure channel for the key transmission, and a user only needs to perform a small amount of calculations to decrypt a ciphertext. In this paper, we revisit the notion of SR‐ABE, and present a generic construction of SR‐ABE, which can transform a revocable ABE (RABE) scheme to an SR‐ABE scheme. In addition, we give an instantiation of SR‐ABE by applying the generic construction on a concrete RABE scheme, and implement an instantiation of SR‐ABE and an RABE scheme to evaluate the performance of SR‐ABE.
A Decentralized Way to Store and Authenticate Educational Documents on Private Blockchain

A decentralized private Blockchain implementation for academic document storage and document verification can add self-sovereignty to the process. It can dramatically minimize the time and cost of verification at various layers of verification. Blockchain would remove all the layers at all and it will provide immediate auditing of any document. So, the request and response will be truly real time in our case.Apart from speeding up the verification process, it will also increase the security of personal education data and will check all kind of misuse also. Putting documents on Blockchain would increase the security, because all the data would only be accessible by private key and proper authentication to that private key.We are proposing a private Blockchain that would be managed by some private vendors and only those vendors will take part in consensus. So, we are using proof of stake consensus in our case. We are using private IPFS database server for storing our documents over Blockchain. We are considering Ethereum Blockchain ecosystem in our case. Alternatively we can also use Hyperledger Fabric for implementation but that discussion is out of the scope for current document.
Robust prediction of complex spatiotemporal states through machine learning with sparse sensing

Complex spatiotemporal states arise frequently in material as well as biological systems consisting of multiple interacting units. A specific, but rather ubiquitous and interesting example is that of "chimeras", existing in the edge between order and chaos. We use Machine Learning methods involving "observers" to predict the evolution of a system of coupled lasers, comprising turbulent chimera states and of a less chaotic biological one, of modular neuronal networks containing states that are synchronized across the networks. We demonstrated the necessity of using "observers" to improve the performance of Feed-Forward Networks in such complex systems. The robustness of the forecasting capabilities of the "Observer Feed-Forward Networks" versus the distribution of the observers, including equidistant and random, and the motion of them, including stationary and moving was also investigated. We conclude that the method has broader applicability in dynamical system context when partial dynamical information about the system is available.
Local Blurred Natural Image Restoration Based on Self-Reference Deblurring Generative Adversarial Networks

This paper solves the deblurring problem of local blurred natural images by the proposed self-reference deblurring generative adversarial networks, that is SR-Deblur GAN. The method proposed divides the clear part and the blurred part of picture, and guides the deblurring process of the blurred part with the former. By the novel network architecture and a multi-component loss function, the method achieves better deblurring results both on artificial local blurred images and natural local blurred images than the recently most advanced deblurring generative adversarial networks, i.e. Deblur GAN, obtaining the state-of-the-art. The network can also have application to local inpainting and local translation for images.
A Handwritten Chinese Text Recognizer Applying Multi-level Multimodal Fusion Network

Handwritten Chinese text recognition (HCTR) has received extensive attention from the community of pattern recognition in the past decades. Most existing deep learning methods consist of two stages, i.e., training a text recognition network on the base of visual information, followed by incorporating language constrains with various language models. Therefore, the inherent linguistic semantic information is often neglected when designing the recognition network. To tackle this problem, in this work, we propose a novel multi-level multimodal fusion network and properly embed it into an attention-based LSTM so that both the visual information and the linguistic semantic information can be fully leveraged when predicting sequential outputs from the feature vectors. Experimental results on the ICDAR-2013 competition dataset demonstrate a comparable result with the state-of-the-art approaches.
Comparison of Object Recognition Approaches using Traditional Machine Vision and Modern Deep Learning Techniques for Mobile Robot

In this paper, we consider the problem of object recognition for a mobile robot in an indoor environment using two different vision approaches. Our first approach uses HOG descriptor with SVM classifier as traditional machine vision model while the second approach uses Tiny-YOLOv3 as modern deep learning model. The purpose of this study is to gain intuitive insight of both approaches for understanding the principles behind these techniques through their practical implementation in real world. We train both approaches with our own dataset for doors. The proposed work is assessed through the real-world implementation of both approaches using mobile robot with Zed camera in real world indoor environment and the robustness has been evaluated by comparing and analyzing the experimental results of both models on same dataset.
Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier

The development of smartphones with a geopositioning system (GPS) embedded on it, paves a way to the online transportation service application. Since then, online transportation platforms such as Gojek and Grab gain its popularity. Transportation network companies such as Gojek and Grab use social media as advertisement media and source of user reviews. Public communicate their opinion about their experience of using an online transportation service through social media. Sentiment analysis helps to extract people's opinions, sentiments, evaluations, and emotions concerning user experience of certain services. This research considers sentiment in level which is positive, neutral and negative with uses twitter data as the data source. A preprocessing task ensures the quality of the data, cleansing, filtering, tokenizing and stemming were performed before the classification task. From the evaluation result, classification using Bayes Naive algorithm succeeded get accuracy of 72.33% with average recall and precision of 73.95% and 73.24%.
Predicting Damage to Buildings Caused by Earthquakes Using Machine Learning Techniques

This paper presents the level of damage prediction to buildings caused by Gorkha Earthquake in Nepal using machine learning techniques. The predictions have been made based on mathematically calculated eight tectonic indicators and past vibrational activity records. In this research the objective is to predict earthquake damage on existing data set of seismic activity by using machine learning techniques. In this study, two well-known approaches of machine learning viz. Neural Network (NN) and Random Forest (RF) have been implemented and optimal parameters for accurate prediction are investigated. The analysis reveals that Random forest method has outperformed the neural network approach for building damage prediction. The F1 score using the random forest classification has been obtained as 74.32%.
Recurrent YOLO and LSTM-based IR single pedestrian tracking

In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for thermal infrared (TIR) visual pedestrian tracking. Our method extends the YOLO deep convolutional neural network into the spatiotemporal domain using Long Short-Term Memory (LSTM). In particular, we propose a new CNN model that satisfies both accurate and robust while maintaing low computational cost in the TIR-VOT. Our experimental results and performance comparison with state-of-the-art tracking methods on challenging benchmark video tracking datasets. The proposed TIR-ROLO method processes images in real-time at 45 fps and achieves the best tracking performance in occlusion scenario.
Collaborative Approaches to Problem-Solving on Lines and Angles Using Augmented Reality

The concepts of 2D geometry are often taught in classrooms without the analysis of the learners' understanding and interpretation of the existence of the concepts in natural surroundings. To bring in active participation of the students while they realize the practical application of the taught concepts, we have designed a module on Lines and Angles of an Augmented Reality (AR) intervention named ScholAR. Using ScholAR, the students can interact with an augmented 3D object. They can recall, visualize, identify the type of angle and then mark it by drawing on that 3D object. We performed a comparative study with 21 participants (6 dyads and 9 individuals) of 8th grade. In this paper, we report: 1) the perspectives of the students on their experience of performing the AR learning activities individually and in dyads, 2) the students' approaches while solving the AR learning activities, and 3) their motivation of using ScholAR. We found that 90.4% of the total participants preferred to perform the AR learning activities in collaboration. At α=0.05 (t=2.21, p=0.048), the performance of the dyads was significantly higher after using ScholAR. The usability and motivation level scores, however, were higher for the individuals (70.28; M=4.07) as compared to the dyads (65.23; M=3.94).
Recovering variability information from source code of clone-and-own software systems

Clone-and-own prevails as an ad-hoc reuse strategy that addresses changing requirements by copying and modifying existing system variants. Proper documentation is typically not cherished and knowledge about common and varying parts between individual variants, denoted their variability information, is lost with a growing system family. With overall maintainability impaired in the longrun, software product lines (SPLs) or concepts thereof, can be a remedy. However, migrating a system family towards structured reuse requires a prior recovery of the systems' variability information. For software systems resulting from clone-and-own, this information is not explicitly available and recovering it remains an open challenge.
Self-Adaptive Attribute Value Weighting for Averaged One-Dependence Estimators

Of numerous proposals for weakening the attribute independence assumption of Naive Bayes, averaged one-dependence estimators (AODE) learns by extrapolation from marginal to full-multivariate probability distributions, and has demonstrated reasonable improvement in terms of classification performance. However, all the one-dependence estimators in AODE are assigned with the same weight, and their probability estimates are combined linearly. This work presents an efficient and effective attribute value weighting approach that assigns discriminative weights to different super-parent one-dependence estimators for different instances by identifying the differences among these one-dependence estimators in terms of log likelihood. The proposed approach is validated on widely used benchmark datasets from UCI machine learning repository. Experimental results show that the proposed approach achieves bias-variance trade-off and is a competitive alternative to state-of-the-art Bayesian and non-Bayesian learners (e.g., tree augmented Naive Bayes and logistic regression).
A Supply Chain Model with Blockchain-Enabled Reverse Auction Bidding Process for Transparency and Efficiency

Blockchain technology as a foundation of distributed ledger offers an innovative platform for transparent and efficient transaction in Reverse Auction Bidding process in a supply chain for procuring carriers. This research work provides background and motivation for the use of Blockchain in such domains. A supply chain model is realized by deploying a smart contract in Blockchain to procure carrier. The model considers multi-attribute of the carriers while procuring one through the reverse auction bidding process. This research work validates the Blockchain-enabled supply chain model by simulating a supply chain proposed for a Dairy Company. Data to calibrate the simulation was taken from a published case study on Reverse Auctions in the supply chain. The result shows that the model is a feasible scheme and its features will offset the challenges of current RAB process making it more efficient and transparent.
PSNR of Highest Distortion Region: An Effective Image Quality Assessment Method

Due to various reasons an image may get distorted. To measure the perceptual distortions, image quality assessment (IQA) metrics are required. Peak-signal-to-noise-ratio (PSNR) is one of the popular available IQA metric. It is usually measured by computing distortion in terms of mean-squared-error (MSE) between the reference image and its distorted version. Mean-squared-error (MSE) is measured over the entire image by giving equal weight to each corresponding pixel difference of the reference and distorted image. However, as per investigation done in this paper, it is found that instead of calculating MSE/PSNR on overall image, PSNR measured over the region of maximum distortion only is a better choice. The PSNR computed over the most distorted region (PSNR-MDR) reflects the image quality better than it computed over entire image. The PSNR-MDR is not only superior to the PSNR but also competitive to celebrated metric, SSIM (Structural Similarity Index).
The Development and Regulation of Cryptoassets: Hong Kong Experiences and a Comparative Analysis

Cryptoassets have emerged as a new category of financial products in recent years and have attracted a great deal of attention from market participants and regulators. While the characteristics of cryptoassets, such as anonymity and disintermediation in transactions, bring significant benefits, they come with a range of significant risks concerning investor protection and market integrity. Due to the difficulties in regulating cryptoassets under the traditional framework, Hong Kong has set up its first comprehensive regulatory regime on cryptoassets in November 2018, imposing new standards on cryptoasset fund managers, distributors and platform operators. By means of a comparison with four major jurisdictions overseas, including Mainland China, the US, the UK, and Singapore, the strengths and potential concerns of Hong Kong’s new regime are analysed. Overall, the new regulatory regime for cryptoassets in Hong Kong is a significant development, addressing the issues of regulatory gaps and regulatory arbitrage that existed under the previous framework as well as introducing enhanced regulatory standards. This has the effect of improving investor protection, but there are some remaining concerns. Chief amongst them are the problems with regulatory scope, the application of traditional regulatory standards to cryptoassets that do not fall within the definition of securities or futures, problems with the sandbox mechanism, and ultimately as a matter of regulatory philosophy, the need for a better balance between investor protection and market development.