Reviewerly/exHarmony huggingface dataset:
Numerical procedures and their practical application in PV modules’ analyses. Part II: Useful fractions and APE

The article regards aspects of PV modules tested with the use of natural sunlight.The analysis of spectral structure of solar energy resources in southern Poland, carried out on the basis of meteorological data originating from SolarLab PW Wrocław and AGH Kraków, Poland [1] was used in the article.It is a continuation of the article: Analysis of solar energy resources in southern Poland for photovoltaic applications [1], describing the method to determine spectral parameters of average photon energy (APE) and useful fraction (UF) with the use of a solar radiation spectrum simulator.This article, however, includes an experimental presentation of their impact on PV conversion of modules with different absorbers.Theory and practice of the measurements were described with the use of spectral parameters such as: UF, APE.Their influence on the efficiency of modules' photovoltaic conversion with various spectral characteristics of absorbers was presented.The most recent methods described, which characterise the structure of solar energy resources such as annual distributions of APE and UF, have not been commonly used yet in Poland and other countries, even though they most precisely define adjustment of the spectral factor to the selected PV module.Practical application of UF, in detection of absorber type used in the tested PV module/cell is demonstrated in the final part of the article.
Exploring the Power of Visual Features for the Recommendation of Movies

In this paper, we explore the potential of using visual features in movie Recommender Systems. This type of content features can be extracted automatically without any human involvement and have been shown to be very effective in representing the visual content of movies. We have performed the following experiments, using a large dataset of movie trailers: (i) Experiment A: an exploratory analysis as an initial investigation on the data, and (ii) Experiment B: building a movie recommender based on the visual features and evaluating the performance. The observed results have shown promising potential of visual features in representing the movies and the excellency of recommendation based on these features.
Adversarially Regularized U-Net-based GANs for Facial Attribute Modification and Generation

Modifying and generating facial images with desired attributes are two important and highly related tasks in the field of computer vision. Some current methods can take advantage of their relationship and use a unified model to handle them simultaneously. However, producing high visual quality images on both tasks is still a challenge. To tackle this issue, we propose a novel model called adversarially regularized U-net (ARU-net)-based generative adversarial networks (ARU-GANs). The ARU-net is the major part of the ARU-GAN and is inspired by the design principle of U-net. It uses skip connections to pass different-level features from encoder to decoder, which preserves sufficient attribute-independent details for the modification task. Besides, this U-net-like architecture employs an adversarial regularization term to guide the distribution of latent representation to match the prior distribution, which guarantees to generate meaningful faces from this prior. We also propose a joint training technique for the ARU-GAN, which enables the facial attribute modification and generation tasks to learn together during training. We perform experiments on celebfaces attributes (CelebA) dataset and make visual analysis and quantitative evaluation on both tasks, which demonstrates that our model can successfully produce high visual quality facial images. Also, the results show that learning two tasks jointly can lead to performance improvement compared with learning them individually. At last, we further validate the effectiveness of our method by making an ablation study and experimenting on another dataset.
Convolution Theorems for Two-Dimensional LCT of Angularly Periodic Functions in Polar Coordinates

By focusing on a special type of functions that are angularly periodic with period 2π, a relationship between two-dimensional (2-D) linear canonical transform (LCT) and linear canonical Hankel transform (LCHT) is disclosed, implying that the mapping from the nth Fourier coefficient of the original function to that of its 2-D LCT version is an nth-order LCHT. In view of this relation, three kinds of convolution theorems for the 2-D LCT in polar coordinates are proposed. The equivalence of the multiplicative filter models derived is also illustrated.
A Framework for Knowing Who is Doing What in Aerial Surveillance Videos

Ultra-high-resolution aerial videos are used to relieve the shortage of surveillance system in sparsely populated regions. For realistic application purpose, it is important to automatically analyze “who is doing what?” in such videos. Although atomic visual action (AVA) detection has been successfully used to recognize “who is doing what?” in the movie data, it is challenging to adapt it to ultra-high-resolution aerial videos, where the target persons are relatively tiny and sparsely located. Besides, due to the lack of evaluation metrics, AVA detection has been evaluated by the single-label action; however, using multi-label actions in evaluation are more reasonable since several actions can be simultaneously performed by a person (e.g., making a phone call and walking). To tackle these issues, we propose a novel framework for multi-label AVA detection in ultra-high-resolution aerial videos and introduce novel metrics for multi-label AVA detection evaluation. The experimental results demonstrate that our framework outperforms other methods for interpreting “who is doing what?” in our target task.
Achieving Fairness in the Tangle through an Adaptive Rate Control Algorithm

Throughput is a key property for any distributed ledger technology. However, limited resources, such as bandwidth or node computational power, can lead to network congestion when nodes try to issue more transactions than the network can handle. Consequently, priority criteria are necessary to determine whether a transaction should be accepted or not. In this paper, we present a novel adaptive rate control algorithm for the Tangle, a new-generation distributed ledger allowing large throughput. Our approach combines various concepts, such as resource tests and Proof-of-Work with dynamic difficulty. Our algorithm not only serves as an anti-spam mechanism, but also achieves fair representation. This is to be contrasted with pure Proof-of-Work blockchains, which lead to wasteful mining races.
An Architecture to Improve the Security of Cloud Computing in the Healthcare Sector

Technology plays a vast role in all aspects of our lives. In every business, technology is helpful to fulfill the needs of the customers. Cloud computing is a technology that satisfies the demand for dynamic resources and makes it easier for jobs to work on all platforms. Cloud computing platforms on the internet provide rapid access with pay-as-you-go pricing. Many business organizations have deployed their data either fully or partially on a cloud platform. A healthcare cloud is a platform where one can easily find hospitals, doctors, clinics, pharmacies, etc. While one face of this cloud is quite beneficial, the other face is challenging because of security issues. The data are stored somewhere else; hence, they can be an attractive target for cybercriminals. This chapter provides an introduction to cloud computing and the healthcare cloud. Subsequently, security issues in cloud computing, especially in the context of the healthcare cloud, are introduced. Finally, some methods to improve cloud security for healthcare are discussed along with our proposed architecture.
Evaluation of Colour Pre-processing on Patch-Based Classification of H&amp;E-Stained Images

This paper compares the effects of colour pre-processing on the classification performance of H&E-stained images. Variations in the tissue preparation procedures, acquisition systems, stain conditions and reagents are all source of artifacts that can affect negatively computer-based classification. Pre-processing methods such as colour constancy, transfer and deconvolution have been proposed to compensate the artifacts. In this paper we compare quantitatively the combined effect of six colour pre-processing procedures and 12 colour texture descriptors on patch-based classification of H&E-stained images. We found that colour pre-processing had negative effects on accuracy in most cases – particularly when used with colour descriptors. However, some pre-processing procedures proved beneficial when employed in conjunction with classic texture descriptors such as co-occurrence matrices, Gabor filters and Local Binary Patterns.
Residual learning of deep convolutional neural networks for image denoising

Image denoising is a hot topic in many research fields, such as image processing and computer vision. With the development of deep learning, deep neural networks are widely used for image denoising and have achieved good effectiveness. Inspired by the characteristics of feed-forward denoising conv olutional neural network (DnCNN) and biological neuron response, we propose a Symmetry-Rectifier Linear Unit (SyReLU) and further offer a corresponding SyReLU activation function, which has a better consistency with biological neuron characteristics in comparison with other activation functions, e.g. Rectifier Linear Unit (ReLU) and Leaky Rectifier Linear Unit(LReLU). Also, in order to denoise image, we use SyReLU activation function for residual learning of CNN (e.g. DnCNN). Specially, the experimental results indicate DnCNN with SyReLU can achieve better effectiveness than DnCNN with other activation functions (e.g.ReLU and LReLU) for image denosing on Set12 and BSD68 datasets. Briefly, the proposed method plays an important role in the development of activation function and is very useful in deep neural networks for image denosing.
Energy‐aware task scheduling with time constraint for heterogeneous cloud datacenters

Summary Energy optimization with time constraint has become a timely and significant challenge for the datacenters. In this paper, a hardware and software collaborative optimization strategy is implemented to minimize the energy cost while satisfying the time constraint of the datacenters. In the hardware aspect, a DVFS‐capable CPU/GPU/FPGA heterogeneous computing infrastructure is built. This infrastructure can adjust its hardware characteristics dynamically in terms of the software run‐time contexts so that the applications can be executed efficiently with less time and lower energy cost. In the software aspect, a deadline‐aware energy‐efficient task scheduling algorithm based on the Q‐learning approach is investigated. This algorithm can adjust its searching directions smartly in terms of the environment feedback so that it can achieve better optimization performance comparing with the traditional genetic algorithm. However, its convergence time is long due to the large amount of training work, making it inappropriate to be applied in the large‐scale datacenters. To ease this problem, we proposed another new algorithm named Rapid Local Convolution Optimization (RLCO) and combine it with the Q‐learning algorithm. By doing this, the convergence time of the Q‐learning mechanism can be decreased significantly. We conducted both the simulation and real‐world experiments to evaluate the performance of our approaches, and the results proved the proposed algorithm running on the DVFS‐capable heterogeneous hardware architecture could decrease the energy cost of the datacenter significantly even if the datacenter is in large scale.
Theoretical Scalability Analysis of Distributed Deep Convolutional Neural Networks

We analyze the asymptotic performance of the training process of deep neural networks (NN) on clusters in order to determine the scalability. For this purpose, i) we assume a data parallel implementation of the training algorithm, which distributes the batches among the cluster nodes and replicates the model; ii) we leverage the roofline model to inspect the performance at the node level, taking into account the floating-point unit throughput and memory bandwidth; and iii) we consider distinct collective communication schemes that are optimal depending on the message size and underlying network interconnection topology. We then apply the resulting performance model to analyze the scalability of several well-known deep convolutional neural networks as a function of the batch size, node floating-point throughput, node memory bandwidth, cluster dimension, and link bandwidth.
M-HIN

To represent a complex network, paths are often employed for capturing relationships among node: random walks for (homogeneous) networks and metapaths for heterogeneous information networks (HINs). However, there is structural (and possibly semantic) information loss when using paths to represent the subgraph between two nodes, since a path is a linear structure and a subgraph often is not. Can we find a better alternative for network embeddings? We offer a novel mechanism to capture the features of HIN nodes via metagraphs, which retains more structural and semantic information than path-oriented models. Inspired by developments in knowledge graph embedding, we propose to construct HIN triplets using nodes and metagraphs between them. Metagraphs are generated by harnessing the GRAMI algorithm, which enumerates frequent subgraph patterns in a HIN. Subsequently, the Hadamard function is applied to encode relationships between nodes and metagraphs, and the probability whether a HIN triplet can be evaluated. Further, to better distinguish between symmetric and asymmetric cases of metagraphs, we introduce a complex embedding scheme that is able to precisely express fine-grained features of HIN nodes. We evaluate the proposed model, M-HIN, on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.
A hybrid SURF-DIC algorithm to estimate local displacements in structures using low-cost conventional cameras

A fast, efficient and low-cost method for the accurate determination of local displacements in structures based on computer vision techniques is presented in this study. First, displacements at pixel level are estimated using the SURF (Speeded-Up Robust Features) image matching algorithm, which detects corresponding points in the images. Then, the initial displacement estimations are refined at subpixel scale through image cross-correlation in the frequency domain. The image acquisition is simple and fast, using a conventional low-cost digital camera. The method was validated comparing the results obtained with those provided by a high performance system (ARAMIS 5 M) that integrates two cameras and image processing software. Results show that the proposed method is able to detect displacements with a precision of approximately 60% of the pixel size. The algorithm was implemented in a computer program in order to automatize the procedure.
Dynamic maintenance case base using knowledge discovery techniques for case based reasoning systems

The achievement of a Case Based Reasoning (CBR) system is strongly related to the quality of case data and the rapidity of the retrieval process that depends on the quantity of the cases. This quality can diminish especially when the number of cases gets outsized. To guarantee this quality, maintenance the case base becomes essentially. Much existing maintenance CBR approaches focus on the performance of the CBR or the study of the case base (CB) competence. Even though the two points are directly related, there is a few research on using strategies at both points at the same time. Furthermore, the proposed methods are not dynamic, they are not suitable for the frequently change in learning process. In this paper, we propose maintenance CBR method based on well-organized machine learning techniques, in the process of improving the competence and the performance of the CB and can handle incremental cases which evolve over time. We support our approach with empirical evaluation using different benchmark data sets to show the effectiveness of our method.
A Bilingual Adversarial Autoencoder for Unsupervised Bilingual Lexicon Induction

Unsupervised bilingual lexicon induction aims to generate bilingual lexicons without any cross-lingual signals. Successfully solving this problem would benefit many downstream tasks, such as unsupervised machine translation and transfer learning. In this work, we propose an unsupervised framework, named bilingual adversarial autoencoder, which automatically generates bilingual lexicon for a pair of languages from their monolingual word embeddings. In contrast to existing frameworks which learn a direct cross-lingual mapping of word embeddings from the source language to the target language, we train two autoencoders jointly to transform the source and the target monolingual word embeddings into a shared embedding space, where a word and its translation are close to each other. In this way, we capture the cross-lingual features of word embeddings from different languages and use them to induce bilingual lexicons. By conducting extensive experiments across eight language pairs, we demonstrate that the proposed method significantly outperforms the existing adversarial methods and even achieves best-published results across most language pairs.
Study on safety mode of dragon boat sports physical fitness training based on machine learning

In order to improve the safety control ability of dragon boat sports physical fitness training, this paper uses the advantages of machine learning in data analysis and feature mining in the training of dragon boat sports. A machine learning-based safety mode control model for dragon boat sports physical fitness training was proposed. Big data statistical analysis method was used to analyze the constraint parameters of the safety mode of dragon boat sports physical fitness training, and combined with the joint association rule mining method, the dragon boat sports physical fitness training safety mode training was carried out. The correlation feature quantity which constrains the safety of dragon boat physical ability training is extracted. The fuzzy clustering technique is used to classify and study the safety management data of dragon boat sport physical ability training. The method of spectral density analysis and fuzzy fusion clustering analysis is used to realize automatic mining of safety association feature data of dragon boat physical fitness training, and machine learning algorithm is combined to realize the optimization of safety pattern of dragon boat sports physical fitness training. The simulation results show that the feature extraction of the safety model of dragon boat sports physical fitness training is better and the ability of feature resolution is stronger, which improves the safety management ability of dragon boat sports physical fitness training.
Simultaneous Safe Feature and Sample Elimination for Sparse Support Vector Regression

Sparse support vector regression (SSVR) is an effective regression technique. It has been successfully applied to many practical problems. However, it remains challenging to handle the large-scale problems. A nice property of SSVR is double sparsity in the sense that most irrelevant features and samples have no effect on the regressor. Inspired by it, we propose a simultaneous safe feature and sample screening rule based on the strong convexity of objective function (FSSR1) to accelerate SSVR, including the linear and nonlinear cases. Most inactive features and samples can be simultaneously discarded before training SSVR. Only one reduced SSVR (RSSVR) needs to be solved. Moreover, to further speed up RSSVR, the screening rule based on duality gap (FSSR2) is applied to continuously discard the remaining inactive variables during the reduced model training process. Combining the rule with the grid-search method, the framework of our final method (FSSR-SSVR) is to alternatively conduct FSSR1 and FSSR2, which leads to substantial savings in both the memory usage and the computational cost. There are two appealing advantages of our FSSR-SSVR: first, it is safe, i.e., the features and samples discarded by FSSR are guaranteed to be inactive; second, it has synergy effect (in other words, the results of the previous feature screening can improve the performance of the next sample screening, and vice versa). Furthermore, the stochastic dual coordinate ascent (SDCA) method is employed as an efficient solver. Experiments on three synthetic datasets and 11 real-world datasets demonstrate the efficiency of our method.
Systemizing the Challenges of Auditing Blockchain-Based Assets

Presently, auditing firms are hesitant to accept mandates from companies that hold a significant amount of cryptoassets, primarily because the blockchain sector introduces novel, technically sophisticated and risky propositions that auditors are unequipped to handle. Abrupt recusals by auditors operating in this sector have led to several enterprises being placed on cease trade by securities regulators for failure to produce audited financial statements on time, thus impeding these companies from raising capital and bringing new investment to fund innovation in this space. Through an iterative process of interviews with senior accounting professionals, structured brainstorming among a multidisciplinary team of accountants and blockchain experts, and a focus group with experienced auditors, we critically analyze the purported roadblocks to auditing blockchain firms and map them to traditional auditing practices. We urge auditors to reconsider their resistance to the blockchain sector by demonstrating that providing an audit opinion is challenging but not insurmountable.
Is that my hand? An egocentric dataset for hand disambiguation

With the recent development of wearable cameras, the interest for research on the egocentric perspective is increasing. This opens the possibility to work on a specific object detection problem of hand detection and hand disambiguation. However, recent progress in egocentric hand disambiguation and even hand detection, especially using deep learning, has been limited by the lack of a large dataset, with suitable variations in subject, activity, and scene. In this paper, we propose a dataset that simulates daily activities, with variable illumination and people from different cultures and ethnicity to address daily life conditions. We increase the dataset size from previous works to allow robust solutions like deep neural networks that need a substantial amount of data for training. Our dataset consists of 50,000 annotated images with 10 different subjects doing 5 different daily activities (biking, eating, kitchen, office and running) in over 40 different scenes with variable illumination and changing backgrounds, and we compare with previous similar datasets. Hands in an egocentric view are challenging to detect due to a number of factors, such as shape variations, inconsistent illumination, motion blur, and occlusion. To improve hand detection and disambiguation, context information can be included to aid in the detection. In particular, we propose three neural network architectures that jointly learn the hand and context information, and we provide baseline results with current object/hand detection approaches.