Reviewerly/exHarmony huggingface dataset:
Cross-Quality LFW: A Database for Analyzing Cross- Resolution Image Face Recognition in Unconstrained Environments

Real-world face recognition applications often deal with suboptimal image quality or resolution due to different capturing conditions such as various subject-to-camera distances, poor camera settings, or motion blur. This characteristic has an unignorable effect on performance. Recent cross-resolution face recognition approaches used simple, arbitrary, and unrealistic down- and up-scaling techniques to measure robustness against real-world edge-cases in image quality. Thus, we propose a new standardized benchmark dataset and evaluation protocol derived from the famous Labeled Faces in the Wild (LFW). In contrast to previous derivatives, which focus on pose, age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in the Wild (XQLFW) maximizes the quality difference. It contains only more realistic synthetically degraded images when necessary. Our proposed dataset is then used to further investigate the influence of image quality on several state-of-the-art approaches. With XQLFW, we show that these models perform differently in cross-quality cases, and hence, the generalizing capability is not accurately predicted by their performance on LFW. Additionally, we report baseline accuracy with recent deep learning models explicitly trained for cross-resolution applications and evaluate the susceptibility to image quality. To encourage further research in cross-resolution face recognition and incite the assessment of image quality robustness, we publish the database and code for evaluation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code, dataset and evaluation protocol available on https://martlgap.github.io/xqlfw
Technology convergence assessment: Case of blockchain within the IR 4.0 platform

Technological evolutions are leveraged by organizations to assess and formulate their strategic goals for achieving market dominance. Disruptive technologies have challenged organizations to address and transform their technological infrastructure and operational processes anew based on the market dynamics. Blockchain is considered as the disruptive technology of the decade, which has led incumbents to reassess their business models. Various applications of Blockchain have been explored by previous researchers. However, fourth industrial revolution (IR 4.0) technologies have also paved way towards the disruptive technological landscape. The purpose of this research is to identify the convergence across Blockchain and IR 4.0 technologies. This research conducts the bibliometric analysis approach, including performance analysis and network analysis to identify the research trends and themes for the convergence of select technologies. The results indicate four major thematic clusters for the convergence trends with basic, established, niche, and emerging technologies. Furthermore, the citation analysis reveals four clusters based on basic Blockchain technology domain, Blockchain and IR 4.0 technology domains, supply chain management (SCM), and healthcare applications of the convergence of select technologies. This article provides a novel approach by identifying the bibliometric trends across the concept of technological convergence of Blockchain and IR 4.0 technologies.
Zero-shot learning via a specific rank-controlled semantic autoencoder

Existing embedding zero-shot learning models usually learn a projection function from the visual feature space to the semantic embedding space, e.g. attribute space or word vector space. However, the projection learned based on seen samples may not generalize well to unseen classes, which is known as the projection domain shift problem in ZSL. To address this issue, we propose a method named Low-rank Semantic Autoencoder (LSA) to consider the low-rank structure of seen samples to maintain the sparse feature of reconstruction error, which can further improve zero-shot learning capability. Moreover, to obtain a more robust projection for unseen classes, we propose a Specific Rank-controlled Semantic Autoencoder (SRSA) to accurately control of the projection’s rank. Extensive experiments on six benchmarks demonstrate the superiority of the proposed models over most existing embedding ZSL models under the standard zero-shot setting and the more realistic generalized zero-shot setting.
Using digital image correlation to automate the measurement of crack length and fracture energy in the mode I testing of structural adhesive joints

In this study, the crack lengths in adhesively bonded double cantilever beam (DCB) test specimens have been determined using the digital image correlation technique in combination with an elastic foundation model. This method facilitates the continuous measurement of the crack length and offers significant advantages over conventional visual observation including improved accuracy and the potential for automation. This method has been applied to three joints bonded with different structural adhesives, and fracture energy Gc values calculated with the effective crack lengths determined by this method have been shown to be accurate by comparison to Jc values. Finally, a new method is proposed to correct the crack lengths that are visually measured and the Gc values determined using the standard analysis. This scheme is shown to improve the accuracy of the Gc values appreciably.
Determinants of electronic waste generation in Bitcoin network: Evidence from the machine learning approach

Electronic waste is generating in the Bitcoin network at an alarming rate. This study identifies the determinants of electronic waste generation in the Bitcoin network using machine learning algorithms. We model the evolutionary patterns of electronic waste and carry out a predictive analytics exercise to achieve this objective. The Maximal Information Coefficient (MIC) and Generalized Mean Information Coefficient (GMIC) help to study the association structure. A series of six state-of-the-art machine learning algorithms - Gradient Boosting (GB), Regularized Random Forest (RRF), Bagging-Multiple Adaptive Regression Splines (BM), Hybrid Neuro Fuzzy Inference Systems (HYFIS), Self-Organizing Map (SOM), and Quantile Regression Neural Network (QRNN) are used separately for predictive modeling. We compare the predictive performance of all the algorithms. Statistically, the GB is a superior model followed by RRF. The performance of SOM is the least accurate. Our findings reveal that the blockchain's size, energy consumption, and the historical number of Bitcoin are the most determinants of electronic waste generation in the Bitcoin network. The overall findings bring out exciting insights into practical relevance for effectively curbing electronic waste accumulation.
Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark

One of the main goals of Big Data research, is to find new data mining methods that are able to process large amounts of data in acceptable times. In Big Data classification, as in traditional classification, class imbalance is a common problem that must be addressed, in the case of Big Data also looking for a solution that can be applied in an acceptable execution time. In this paper we present Approx-SMOTE, a parallel implementation of the SMOTE algorithm for the Apache Spark framework. The key difference with the original SMOTE, besides parallelism, is that it uses an approximated version of k-Nearest Neighbor which makes it highly scalable. Although an implementation of SMOTE for Big Data already exists (SMOTE-BD), it uses an exact Nearest Neighbor search, which does not make it entirely scalable. Approx-SMOTE on the other hand is able to achieve up to 30 times faster run times without sacrificing the improved classification performance offered by the original SMOTE.
Cattle counting in the wild with geolocated aerial images in large pasture areas

Among the production areas with largest impact on global economy, agriculture and livestock play a prominent role. Technologies have been developed in order to automate and increase the efficiency of these fields. The use of Unmanned Aerial Vehicles (UAVs) has been extensively investigated to improve the efficiency of agricultural production and in the monitoring of animals. One of the most important and challenging tasks in animal monitoring is cattle counting. In this paper, we propose a method for detecting and counting cattle in aerial images obtained by UAVs, based on Convolutional Neural Networks (CNNs) and a graph-based optimization to remove duplicated animals detected in overlapped images. We show that maximizing the degree of matching between animals is a suitable strategy to reduce duplicate counting. We also offer a dataset of real images, obtained from large pasture areas, both for training as well as for testing/benchmarking of cattle counting techniques. Our results show that the proposed method is very competitive, outperforming the state of the art in detecting duplicated animals, while significantly reducing the computational cost of the overall counting task.
Deep and interpretable regression models for ordinal outcomes

Outcomes with a natural order commonly occur in prediction problems and often the available input data are a mixture of complex data like images and tabular predictors. Deep Learning (DL) models are state-of-the-art for image classification tasks but frequently treat ordinal outcomes as unordered and lack interpretability. In contrast, classical ordinal regression models consider the outcome’s order and yield interpretable predictor effects but are limited to tabular data. We present ordinal neural network transformation models (ontrams), which unite DL with classical ordinal regression approaches. ontrams are a special case of transformation models and trade off flexibility and interpretability by additively decomposing the transformation function into terms for image and tabular data using jointly trained neural networks. The performance of the most flexible ontram is by definition equivalent to a standard multi-class DL model trained with cross-entropy while being faster in training when facing ordinal outcomes. Lastly, we discuss how to interpret model components for both tabular and image data on two publicly available datasets.
Arabic speech recognition by end-to-end, modular systems and human

Recent advances in automatic speech recognition (ASR) have achieved accuracy levels comparable to human transcribers, which led researchers to debate if the machine has reached human performance. Previous work focused on the English language and modular hidden Markov model-deep neural network (HMM–DNN) systems. In this paper, we perform a comprehensive benchmarking for end-to-end transformer ASR, modular HMM–DNN ASR, and human speech recognition (HSR) on the Arabic language and its dialects. For the HSR, we evaluate linguist performance and lay-native speaker performance on a new dataset collected as a part of this study. For ASR the end-to-end work led to 12.5%, 27.5% , 33.8% WER; a new performance milestone for the MGB2, MGB3, and MGB5 challenges respectively. Our results suggest that human performance in the Arabic language is still considerably better than the machine with an absolute WER gap of 3.5% on average.
The algorithmic composition for music copyright protection under deep learning and blockchain

To strengthen music copyright protection effectively, a new deep learning neural network music composition neural network (MCNN) is proposed. The probability distribution of LSTM generation is adjusted by constructing a reasonable reward function. Music theory rules are used to constrain the generated music style to realize the intelligent generation of specific music style. Then, the digital music copyright protection system based on blockchain is constructed from three perspectives of confirming right, using right, and protecting right. The validity of the model is further verified by relevant data. The results show that the composition algorithm based on deep learning can realize music creation, and the qualified rate reaches 95.11%. Compared with the composition algorithm in the latest study, the model achieves 62.4 percent satisfaction with subjective samples and a recognition rate of 75.6 percent for musical sentiment classification. It is proved that the music copyright protection model based on block chain can ensure that the copyright owners of works obtain corresponding economic benefits from various distribution channels, which is helpful to build a harmonious music market environment. In short, the innovation of this study is reflected in that it fills in the gap of detailed comparative study of the differences in the application of different models, realizes the framework of music copyright protection system, and provides convenient conditions for composers.
KCRec: Knowledge-aware representation Graph Convolutional Network for Recommendation

Collaborative filtering (CF) usually suffers from data sparsity and cold-start problems in real recommendation scenarios, therefore, side information like social networks and contexts have been introduced to improve its performance. In this paper, we consider the knowledge graph (KG) as a source of side information and propose a novel framework, Knowledge-aware representation Graph Convolutional Network for Recommendation (KCRec), that is an end-to-end framework that captures the inter-user and inter-item relatedness effectively. For exploring the potential long-distance interests of the user, we aggregate the item features and get the representation of the user preferences by propagating the relationships in KG between their neighborhood, and further integrates with the graph convolution network. Furthermore, we employ similarity features in different users to construct a user-adjacency graph, and utilize the user–item interaction features to establish a user-feature graph, to obtain the high-order representation of users. Extensive experiments on two real-world datasets demonstrate that our proposed method has substantially improved, which outperforms several state-of-the-art baselines.
Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementation

The emergence of quantum computing enables for researchers to apply quantum circuit on many existing studies. Utilizing quantum circuit and quantum differential programming, many research are conducted such as Quantum Machine Learning (QML). In particular, quantum reinforcement learning is a good field to test the possibility of quantum machine learning, and a lot of research is being done. This work will introduce the concept of quantum reinforcement learning using a variational quantum circuit, and confirm its possibility through implementation and experimentation. We will first present the background knowledge and working principle of quantum reinforcement learning, and then guide the implementation method using the PennyLane library. We will also discuss the power and possibility of quantum reinforcement learning from the experimental results obtained through this work.
Implementing CRYSTALS-Dilithium Signature Scheme on FPGAs

In July 2020, the lattice-based CRYSTALS-Dilithium digital signature scheme has been chosen as one of the three third-round finalists in the post-quantum cryptography standardization process by the National Institute of Standards and Technology (NIST). In this work, we present the first Very High Speed Integrated Circuit Hardware Description Language (VHDL) implementation of the CRYSTALS-Dilithium signature scheme for Field-Programmable Gate Arrays (FPGAs). Due to our parallelization-based design requiring only low numbers of cycles, running at high frequency and using reasonable amount of hardware resources on FPGA, our implementation is able to sign 15832 messages per second and verify 10524 signatures per second. In particular, the signing algorithm requires 68461 Look-Up Tables (LUTs), 86295 Flip-Flops (FFs), and the verification algorithm takes 61738 LUTs and 34963 FFs on Virtex 7 UltraScale+ FPGAs. In this article, experimental results for each Dilithium security level are provided and our VHDL-based implementation is compared with related High-Level Synthesis (HLS)-based implementations. Our solution is ca 114 times faster (in the signing algorithm) and requires less hardware resources.
The Role of Chance in the Census Bureau Database Reconstruction Experiment

The Census Bureau plans a new approach to disclosure control for the 2020 census that will add noise to every statistic the agency produces for places below the state level. The Bureau argues the new approach is needed because the confidentiality of census responses is threatened by "database reconstruction," a technique for inferring individual-level responses from tabular data. The Census Bureau constructed hypothetical individual-level census responses from public 2010 tabular data and matched them to internal census records and to outside sources. The Census Bureau did not compare these results to a null model to demonstrate that their success in matching would not be expected by chance. This is analogous to conducting a clinical trial without a control group. We implement a simple simulation to assess how many matches would be expected by chance. We demonstrate that most matches reported by the Census Bureau experiment would be expected randomly. To extend the metaphor of the clinical trial, the treatment and the placebo produced similar outcomes. The database reconstruction experiment therefore fails to demonstrate a credible threat to confidentiality.
Gaussian process machine learning and Kriging for groundwater salinity interpolation

Gaussian processes (GPs) provide statistically optimal predictions in the sense of unbiasedness and maximal precision. Although the modern implementation of GPs as a machine learning technique is more capable and flexible than Kriging, their employment in environmental science is less routine. Their flexibility and capability as a spatial data interpolation technique are demonstrated by applying them to groundwater salinity prediction in a data-sparse region in Australia. By learning from multiple data sources, including AEM and DEM data, GPs have generated groundwater salinity maps with rich local details and quantified uncertainty to support risk-based decision making. The results demonstrate the great worth of nonpoint data with regional spatial coverage to provide more realistic heterogeneity in aquifer properties that are critical for many studies such as contaminant transport. GPs should be further encouraged in groundwater science for data interpolation and prediction, especially when point measurements are sparse and multiple predictors are available.
Learning to schedule multi-NUMA virtual machines via reinforcement learning

With the rapid development of cloud computing, the importance of dynamic virtual machine scheduling is increasing. Existing works formulate the VM scheduling as a bin-packing problem and design greedy methods to solve it. However, cloud service providers widely adopt multi-NUMA architecture servers in recent years, and existing methods do not consider the architecture. This paper formulates the multi-NUMA VM scheduling into a novel structured combinatorial optimization and transforms it into a reinforcement learning problem. We propose a reinforcement learning algorithm called SchedRL with a delta reward scheme and an episodic guided sampling strategy to solve the problem efficiently. Evaluating on a public dataset of Azure under two different scenarios, our SchedRL outperforms FirstFit and BestFit on the fulfill number and allocation rate.
Toward simulating superstring/M-theory on a quantum computer

We present a novel framework for simulating matrix models on a quantum computer. Supersymmetric matrix models have natural applications to superstring/M-theory and gravitational physics, in an appropriate limit of parameters. Furthermore, for certain states in the Berenstein-Maldacena-Nastase (BMN) matrix model, several supersymmetric quantum field theories dual to superstring/M-theory can be realized on a quantum device. Our prescription consists of four steps: regularization of the Hilbert space, adiabatic state preparation, simulation of real-time dynamics, and measurements. Regularization is performed for the BMN matrix model with the introduction of energy cut-off via the truncation in the Fock space. We use the Wan-Kim algorithm for fast digital adiabatic state preparation to prepare the low-energy eigenstates of this model as well as thermofield double state. Then, we provide an explicit construction for simulating real-time dynamics utilizing techniques of block-encoding, qubitization, and quantum signal processing. Lastly, we present a set of measurements and experiments that can be carried out on a quantum computer to further our understanding of superstring/M-theory beyond analytic results.
An adaptive hybrid atom search optimization with particle swarm optimization and its application to optimal no-load PID design of hydro-turbine governor

Abstract One metaheuristic algorithm recently introduced is atom search optimization (ASO), inspired by the physical movement of atoms based on the molecular dynamics in nature. ASO displays a unique search ability by employing the interaction force from the potential energy and the constraint force. Despite some successful applications, it still suffers from a local optima stagnation and a low search efficiency. To alleviate these disadvantages, a new adaptive hybridized optimizer named AASOPSO is proposed. In this study, the individual and group cognitive components in particle swarm optimization (PSO) are integrated into ASO to accelerate the exploitation phase, and the acceleration coefficients are introduced to adaptively achieve a good balance between exploration and exploitation. Meanwhile, to improve the search performance of the algorithm, each individual atom possesses its own force constant, which is effectively and adaptively adjusted based on the feedback of the fitness of the atom in some sequential steps. The performance of AASOPSO is evaluated on two sets of benchmark functions compared to the other population-based optimizers to show its effectiveness. Additionally, AASOPSO is applied to the optimal no-load PID design of the hydro-turbine governor. The simulation results reveal that AASOPSO is more successful than its competitors in searching the global optimal PID parameters.
Experiments and Comparison of Digital Twinning of Photovoltaic Panels by Machine Learning Models and a Cyber-Physical Model in Modelica

We present two approaches for digital twinning in the context of the forecast of power production by photovoltaic panels. We employ two digital models that are complementary: the first one is a cyber-physical system, simulating the physical properties of a photovoltaic panel, built by the open- source object-oriented modeling language Modelica. The second model is data-driven, obtained by the application of machine learning techniques on the data collected in an installation of the equipment. Both approaches make use of data from the weather forecast of each day. We compare the results of the two approaches. Finally, we integrate them in more sophisticated hybrid systems that get the benefits of both.