Reviewerly/exHarmony huggingface dataset:
An Ontology-based Metamodel for Capability Descriptions

This paper presents an approach to describe abilities of manufacturing resources by a formal description of capabilities using Semantic Web technologies.A hierarchical ontology architecture is proposed to represent, publish, and extend knowledge on capabilities for different application domains and use cases.Furthermore, the paper describes patterns of how the underlying formal logic can be used in taxonomy modeling and the inference of implicit capability facts.The usability and performance of the approach was validated by formalizing capability knowledge of related work and evaluated in benchmarking a prototypical implemented tool for managing and querying catalogs of resources and their capabilities.The proposed concept is intended to be used as a foundation for a future multi-layered feasibility checking, which evaluates the compatibility of resources and their offered skills with the requirements of manufacturing tasks at symbolic and subsymbolic levels.Extended evaluations might be based on parameters, analytics, simulation, and other means.
CNN-Based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors

Convolutional Neural Network (CNN) provides leverage to extract and fuse features from all layers of its architecture. However, extracting and fusing intermediate features from different layers of CNN structure is still uninvestigated for Human Action Recognition (HAR) using depth and inertial sensors. To get maximum benefit of accessing all the CNN's layers, in this paper, we propose novel Multistage Gated Average Fusion (MGAF) network which extracts and fuses features from all layers of CNN using our novel and computationally efficient Gated Average Fusion (GAF) network, a decisive integral element of MGAF. At the input of the proposed MGAF, we transform the depth and inertial sensor data into depth images called sequential front view images (SFI) and signal images (SI) respectively. These SFI are formed from the front view information generated by depth data. CNN is employed to extract feature maps from both input modalities. GAF network fuses the extracted features effectively while preserving the dimensionality of fused feature as well. The proposed MGAF network has structural extensibility and can be unfolded to more than two modalities. Experiments on three publicly available multimodal HAR datasets demonstrate that the proposed MGAF outperforms the previous state-of-the-art fusion methods for depth-inertial HAR in terms of recognition accuracy while being computationally much more efficient. We increase the accuracy by an average of 1.5% while reducing the computational cost by approximately 50% over the previous state-of-art.
Recent Advances and Application of Metaheuristic Algorithms: A Survey (2014–2020)

Metaheuristic optimization is a higher-level optimization that uses a simple and efficient procedure to solve optimization problems. Metaheuristic can understand higher-level algorithmic framework which is problem independent and equipped with a set of strategies to develop heuristic optimization algorithms. Metaheuristic can be defined as a method to get a solution that is "good enough" in "small enough" computing time. MHA gives a better trade-off between exploration (global optima) as well as exploitation (local optima) along with solution quality and computing time. The characteristic which makes MHA more reliable and efficient as compared to exact methods are (1) adaption according to the need of real-time optimization problems (2) better solution quality in lesser computation time (3) non-problem specific, approximate and non-deterministic. The literature of the last three decades clearly shows that there is an explosion in the field of MHA from different sources of inspiration. This literature review consists of some of the metaheuristic algorithms which come in existence from 2014 to 2020. The review describes algorithms and modifications done so far.
Construction of one-way hash functions with increased key space using adaptive chaotic maps

Chaotic hash functions are a prospective branch of modern cryptography. Being compared with traditional hashing algorithms, an approach based on deterministic chaos allows achieving diffusion and confusion with less computational costs. Most of the recently proposed chaotic hash functions use piecewise maps. Cryptosystems based on such maps are not vulnerable to attack by the reconstruction of phase space but their key spaces depend on maps parameters and therefore can be insufficient. In this paper, we propose an approach for the construction of piecewise hash functions from adaptive chaotic maps. The idea is to match different values of the adaptive coefficient to several sub-domains of the chaotic map. Thus, the adaptive coefficient values are part of the hash function key. Therefore, an increase in the sub-functions number potentially enhances the cryptographic strength of the algorithm. Thus, hash functions based on novel adaptive maps have larger key space compared to conventional piecewise maps. We explicitly show that the proposed hash generation technique allows obtaining digests with the required statistical properties. Moreover, we run a collision test to prove that the collision probability is small. The obtained results can be useful in chaos-based cryptography as well for the various simulations of real processes and phenomena with chaotic behavior, in computer graphics and multimedia.
Style transfer for unsupervised domain-adaptive person re-identification

Person re-identification (Re-ID) is aimed to identify the same person under different cameras with disjoint views. With the development of deep learning, supervised Re-ID has made great progress. However, most methods fail to be generalized to new domains due to the potential image distinctions between different domains, making it difficult to be applied in practical use. We propose an unsupervised domain-adaptive person re-identification method based on style transfer (STReID) to improve model cross-domain capability. In this work, a novel style transfer framework is proposed for Re-ID, which allows us to change the style of images while retaining content information. Specifically, STReID mainly includes two steps: first, each image in the source domain is converted into the target domain style and the identity (ID) information remains unchanged. Then the source domain images and style transferred images are combined to train a style-independent Re-ID model. Experiments show that our method achieves higher performance of unsupervised cross-domain person Re-ID.
Bridging the Web Data and Fine-Grained Visual Recognition via Alleviating Label Noise and Domain Mismatch

To distinguish the subtle differences among fine-grained categories, a large amount of well-labeled images are typically required. However, manual annotations for fine-grained categories is an extremely difficult task as it usually has a high demand for professional knowledge. To this end, we propose to directly leverage web images for fine-grained visual recognition. Our work mainly focuses on two critical issues including "label noise" and "domain mismatch" in the web images. Specifically, we propose an end-to-end deep denoising network (DDN) model to jointly solve these problems in the process of web images selection. To verify the effectiveness of our proposed approach, we first collect web images by using the labels in fine-grained datasets. Then we apply the proposed deep denoising network model for noise removal and domain mismatch alleviation. We leverage the selected web images as the training set for fine-grained categorization models learning. Extensive experiments and ablation studies demonstrate state-of-the-art performance gained by our proposed approach, which, at the same time, delivers a new pipeline for fine-grained visual categorization that is to be highly effective for real-world applications.
Joint Attribute Manipulation and Modality Alignment Learning for Composing Text and Image to Image Retrieval

Cross-model retrieval has attracted much attention in recent years due to its wide applications. Conventional approaches usually take one modality as query to retrieve relevant data of another modality. In this paper, we devote to an emerging task in cross-modal retrieval, Composing Text and Image to Image Retrieval (CTI-IR), which aims at retrieving images relevant to a query image with text describing desired modifications to the query image. Compared with conventional cross-modal retrieval, the new task is particularly useful for the retrieval that the query image does not perfectly match the user's expectations. Generally, the CTI-IR involves two underlying problems: how to manipulate visual features of the query image specified by the text, and how to model the modality gap between the query and target. Most previous methods focus on solving the second problem. In this paper, we aim to deal with both problems simultaneously in a unified model. Specifically, the proposed method is based on the graph attention network and adversarial learning network, which enjoys several merits. First, the query image and the modification text are constructed in a relation graph for learning text-adaptive representations. Second, semantic contents from the text are injected into the visual features through graph attention. Third, an adversarial loss is incorporated into the conventional cross-modal retrieval loss to learn more discriminative modality invariant representations for CTI-IR. Extensive experiments on three benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.
The Potential Educational Value of Mobile Augmented Reality Games: The Case of EduPARK App

New teaching methodologies are nowadays integrating mobile devices, augmented reality (AR), and game-based learning in educational contexts. The combination of these three elements is considered highly innovative, and it allows learning to move beyond traditional classroom environments to nature spaces that students can physically explore. The literature does not present many studies of this approach’s educational value. The purpose of the study is to present an illustrative case of a mobile AR game in order to analyse its educational value based on the users’ opinion, both teachers and students, and on logs of game results. Through a mixed method approach, the educational value scale was applied to 924 users after playing the EduPARK app in a Green City Park. Results revealed high educational value scores, especially among teachers and students of 2nd and 3rd Cycles of Basic Education (83.0 for both). Hence, this particular software seems to be more suitable for 10–15 years-old students who highlighted motivational features, such as treasure hunting, points gathering, the use of mobile devices in nature settings, and AR features to learn. This study empirically revealed that mobile AR games have educational value, so these specific game features might be useful for those who are interested in creating or using games supported by apps for educational purposes.
Deep visual unsupervised domain adaptation for classification tasks: a survey

Learning methods are challenged when there is not enough labelled data. It gets worse when the existing learning data have different distributions in different domains. To deal with such situations, deep unsupervised domain adaptation techniques have newly been widely used. This study surveys such domain adaptation methods that have been used for classification tasks in computer vision. The survey includes the very recent papers on this topic that have not been included in the previous surveys and introduces a taxonomy by grouping methods published on unsupervised domain adaptation into five groups of discrepancy-, adversarial-, reconstruction-, representation-, and attention-based methods.
Cooperative Bi-path Metric for Few-shot Learning

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.
Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions.The blockchain is transforming industries by enabling innovative business practices.Its revolutionary power has permeated areas such as banking, financing, trading, manufacturing, supply chain management, healthcare, and government.Blockchain and the Internet of Things (BIOT) apply the usage of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable.The integration of blockchain technology and IoT creates modern decentralized systems.The BIOT models can be applied by various industries including e-commerce to promote decentralization, scalability, and security.This research calls for innovative and advanced research on Blockchain and recommendation systems.We aim at building a secure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol.Combining the recommendation systems and blockchain technology allows online activities to be more secure and private.A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems.Learning case studies include a model to recommend movies to users.The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recommendation system with acceptable performance.
Probing nonclassicality with matrices of phase-space distributions

We devise a method to certify nonclassical features via correlations of phase-space distributions by unifying the notions of quasiprobabilities and matrices of correlation functions. Our approach complements and extends recent results that were based on Chebyshev's integral inequality \cite{BA19}. The method developed here correlates arbitrary phase-space functions at arbitrary points in phase space, including multimode scenarios and higher-order correlations. Furthermore, our approach provides necessary and sufficient nonclassicality criteria, applies to phase-space functions beyond<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>s</mml:mi></mml:math>-parametrized ones, and is accessible in experiments. To demonstrate the power of our technique, the quantum characteristics of discrete- and continuous-variable, single- and multimode, as well as pure and mixed states are certified only employing second-order correlations and Husimi functions, which always resemble a classical probability distribution. Moreover, nonlinear generalizations of our approach are studied. Therefore, a versatile and broadly applicable framework is devised to uncover quantum properties in terms of matrices of phase-space distributions.
Data-driven Meta-set Based Fine-Grained Visual Recognition

Constructing fine-grained image datasets typically requires domain-specific expert knowledge, which is not always available for crowd-sourcing platform annotators. Accordingly, learning directly from web images becomes an alternative method for fine-grained visual recognition. However, label noise in the web training set can severely degrade the model performance. To this end, we propose a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition. Specifically, guided by a small amount of clean meta-set, we train a selection net in a meta-learning manner to distinguish in- and out-of-distribution noisy images. To further boost the robustness of the model, we also learn a labeling net to correct the labels of in-distribution noisy data. In this way, our proposed method can alleviate the harmful effects caused by out-of-distribution noise and properly exploit the in-distribution noisy samples for training. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is much superior to state-of-the-art noise-robust methods.
Tile Rate Allocation for 360-Degree Tiled Adaptive Video Streaming

360-degree video streaming commonly encodes and transmits the video as independently-decodable tiles to conserve bandwidth of regions out of the viewer's field of view (FoV). The bitrate of the tiles, however, can vary significantly across the tiles, complicating the choice of the representation to download for each tile in each segment to adapt to the bandwidth dynamics. In this paper, we model the tile rate allocation problem as a multiclass knapsack problem with a dynamic profit function that is a function of the FoV and the buffer occupancy. Experiments show that our approach can reduce bandwidth wastage by up to 41%, the number of stalls by up to 31%, stall durations by up to 26.5%, switches in quality by up to 20%, without sacrificing the quality of the tiles within the FoV, even when there are significant head movement and changes in FoV during streaming.
A Quantitative Analysis of Security, Anonymity and Scalability for the Lightning Network

Payment channel networks have been introduced to mitigate the scalability issues inherent to permissionless decentralized cryptocurrencies such as Bitcoin.Launched in 2018, the Lightning Network (LN) has been gaining popularity and consists today of more than 5000 nodes and 35000 payment channels that jointly hold 965 bitcoins (9.2M USD as of June 2020).This adoption has motivated research from both academia and industry.Payment channels suffer from security vulnerabilities, such as the wormhole attack [39], anonymity issues [38], and scalability limitations related to the upper bound on the number of concurrent payments per channel [28], which have been pointed out by the scientific community but never quantitatively analyzed.In this work, we first analyze the proneness of the LN to the wormhole attack and attacks against anonymity.We observe that an adversary needs to control only 2% of nodes to learn sensitive payment information (e.g., sender, receiver, and amount) or to carry out the wormhole attack.Second, we study the management of concurrent payments in the LN and quantify its negative effect on scalability.We observe that for micropayments, the forwarding capability of up to 50% of channels is restricted to a value smaller than the channel capacity.This phenomenon hinders scalability and opens the door for denial-of-service attacks: we estimate that a network-wide DoS attack costs within 1.6M USD, while isolating the biggest community costs only 238k USD.Our findings should prompt the LN community to consider the issues studied in this work when educating users about path selection algorithms, as well as to adopt multi-hop payment protocols that provide stronger security, privacy and scalability guarantees.
Quantum Error Mitigation With Artificial Neural Network

A quantum error mitigation technique based on machine learning is proposed, which learns how to adjust the probabilities estimated by measurement in the computational basis. Neural networks in two different designs are trained with random quantum circuits consisting of a set of one- and two-qubit unitary gates whose measurement statistics in the ideal (noiseless) and real (noisy) cases are known. Once the neural networks are trained, they infer the amount of probability adjustment to be made on the measurement obtained from executing an unseen quantum circuit to reduce the error. The proposed schemes are tested with two-, three-, five-, and seven-qubit quantum circuits of depth up to 20 by computer simulations with realistic error models and experiments using the IBM quantum cloud platform. In all test cases, the proposed mitigation technique reduces the error effectively. Our method can be used to improve the accuracy of noisy intermediate-scale quantum (NISQ) algorithms without relying on extensive error characterization or quantum error correction.
Globalized firefly algorithm and chaos for designing substitution box

This study adopted a metaheuristic approach based on the firefly algorithm (FA) optimization to generate an appropriate configuration for an 8 × 8 Substitution boxes (S-boxes). The FA can construct a strong S-box that satisfies the stipulated criteria by rapidly searching for the optimal or near-optimal feature subsets that minimize a given fitness function. The FA is a newly developed computation technique inspired by fireflies and their flash lighting process. However, FA may suffer from premature convergence when solving optimization problems. This study proposes a new FA modification, namely, globalized firefly algorithm (GFA), which employs random movement based on the best firefly using chaotic maps. The best firefly does not conduct any search in the standard firefly algorithm (SFA). The proposed algorithm utilizes a new design for retrieving strong S-boxes based on SFA and GFA. Bijectivity, strict avalanche criteria, nonlinearity, input/output XOR distribution, bit independence criteria, and linear probability were also analyzed. The result was compared with a few previous S-box generation methods. Overall, the experimental outcome revealed that the design of the proposed S-boxes has satisfactory cryptographic characteristics.
Design and optimization of solar tracker with U-PRU-PUS parallel mechanism

The solar tracker usually adopts the serial mechanism in application, which is greatly affected by the environment, and its load capacity and stability are limited. In order to address these issues, a new type of solar tracking device with U-PRU-PUS parallel mechanism was proposed. The U-PRU-PUS parallel mechanism was analyzed based on the screw theory, which shows that this mechanism has 2R degree of freedom and is decoupled. The direct and inverse kinematics of the parallel mechanism were derived and its singularity was analyzed based on the Jacobian algebras. Then the workspace of the parallel mechanism was obtained according to the boundary search algorithm. The adaptive genetic algorithm was used to search the optimal structure parameters of the parallel mechanism. The tracking range was improved by 4.6% and the stroke of the driving rod was shortened by 11.2% after optimization. A solar tracker prototype with the optimized parallel mechanism and its control system was built. The limit tracking position at sunrise and sunset was tested, which validated the feasibility of the designed mechanism and its tracking range. Through the solar tracking error tests, the performance of the proposed tracking mechanism can track the sun effectively.