Reviewerly/exHarmony huggingface dataset:
Improved sine cosine algorithm combined with optimal neighborhood and quadratic interpolation strategy

The sine cosine algorithm (SCA) is a new population-based stochastic optimization algorithm, utilizing the oscillating property of the sine cosine function to balance the exploration and exploitation performance of SCA. A hybrid sine cosine algorithm based on the optimal neighborhood and quadratic interpolation strategy (QISCA) was proposed to overcome the shortcoming of updating the population guided by the global optimal individual in the sine cosine algorithm. The new algorithm uses a Stochastic Optimal Neighborhood for neighborhood updates, and it adopts a Quadratic Interpolation curve for individual updates. In addition, QISCA incorporates Quasi-Opposition Learning strategies to enhance the population's global exploration capabilities, and improves the convergence speed and accuracy. The two simulation experiments of 23 benchmark functions and 30 latest CEC2017 test functions show that the new algorithm can better coordinate the exploration and exploitation capabilities and improve the global optimization ability, compared with the other improved sine cosine algorithm and the representative stochastic optimization algorithm. The three representative engineering problems validate the effectiveness of the new algorithm to solve practical problems.
An innovative learning approach for solar power forecasting using genetic algorithm and artificial neural network

Abstract Analysing the Output Power of a Solar Photo-voltaic System at the design stage and at the same time predicting the performance of solar PV System under different weather condition is a primary work i.e . to be carried out before any installation. Due to large penetration of solar Photovoltaic system into the traditional grid and increase in the construction of smart grid, now it is required to inject a very clean and economic power into the grid so that grid disturbance can be avoided. The level of solar Power that can be generated by a solar photovoltaic system depends upon the environment in which it is operated and two other important factor like the amount of solar insolation and temperature. As these two factors are intermittent in nature hence forecasting the output of solar photovoltaic system is the most difficult work. In this paper a comparative analysis of different solar photovoltaic forecasting method were presented. A MATLAB Simulink model based on Real time data which were collected from Odisha (20.9517 ∘ N, 85.0985 ∘ E), India. were used in the model for forecasting performance of solar photovoltaic system.
Training a Multi-Layer Photonic Spiking Neural Network With Modified Supervised Learning Algorithm Based on Photonic STDP

We propose a framework for hardware architecture and learning algorithm co-design of multi-layer photonic spiking neural network (SNN). The vertical-cavity surface-emitting laser with an embedded saturable absorber (VCSEL-SA) which contains two polarization-resolved modes is employed as a spiking neuron. The connection between two identical polarization modes is considered as the excitatory synapse, whereas the connection between two orthogonal polarization modes is regarded as the inhibitory synapse. The physical model of the photonic spiking neuron is derived based on the combination of spin-flip model and Yamada model. The photonic spike timing dependent plasticity (STDP) is applied to design a hardware-friendly biologically plausible supervised learning algorithm for a multi-layer photonic SNN. Thanks to the polarization mode competition effect in the VCSEL-SA, the proposed neuromorphic network is capable of solving the classical XOR problem. The effect of physical parameters of photonic neuron on the training convergence is also considered. We further extend the multi-layer photonic SNN to realize other logic learning tasks. To the best of our knowledge, such a modified supervised learning algorithm dedicated for a multi-layer photonic SNN has not yet been reported, which is interesting for spiking learning of neuromorphic photonics.
Context-Dependent Propagating-Based Video Recommendation in Multimodal Heterogeneous Information Networks

With the emergence of online social networks (OSNs), video recommendation has come to play a crucial role in mitigating the semantic gap between users and videos. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. Although these methods have achieved promising results, they fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data from OSNs. In this paper, we study the problem of video recommendation in Heterogeneous Information Networks (HINs) due to its excellence in characterizing heterogeneous and complex context information. We propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. Then, each video is represented as the composition of the multimodal content feature and global dependency structure information using an attention network. The learned video embedding with sequential based recommendation are jointly optimized for the final rating prediction. Experimental results on real-world YouTube video recommendation scenarios demonstrate the effectiveness of the proposed methods compared with strong baselines.
A Blockchain Solution based on Directed Acyclic Graph for IoT Data Security using IoTA Tangle

The growth of the Internet of Things (IoT) has recently seen an exponentially rise with the number of IoT devices been connected increasing in billions, but with such rapid demand and growth, IoT still faces few issues like data security, privacy, data integrity, authentication. The Distributed Ledger Technology like Blockchain also opens a wide range of opportunities, and it proves to be one solution for overcoming multiple issues. This paper highlights the use of Blockchain technology in the field of Internet of Things (IoT) and its practical limitations. The paper focuses on a new distributed ledger technology based on Directed Acyclic Graph(DAG) approach called IOTA, its working and features are discussed in detail. IOTA technology can overcome the practical limitations of classical Blockchain, and the implementation of IOTA for secure transferring of IoT sensor data is also discussed.
Cloud accounting risks and mitigation strategies: evidence from Australia

Much has been written about the adoption and benefits of cloud computing in different contexts, yet it is not clear how cloud computing has affected the practice of accounting. This empirical study applies transaction cost economics to explore the risks of cloud-based accounting systems and services in Australia and it identifies several risk mitigation strategies adopted by organisations. The findings based on interviews with accounting practices, amongst others, are classified according to the Technology-Organisation-Environment framework. The evidence suggests that cloud accounting not only introduces specific risks to the “accounting process” but some of the known risks associated with other cloud-based applications are more pronounced. While transaction-specific factors such as vendor selection and contractual arrangements were considered important as risk mitigation strategies, internal measures including policy development and staff training were seen as critical to cloud accounting.
Assessing blockchain selfish mining in an imperfect network: Honest and selfish miner views

Bitcoin and Ethereum, the most famous blockchain-based cryptocurrencies, both use Proof-of-Work protocol to achieve consensus, which is vulnerable to selfish mining. The existing researches on selfish mining analysis focused on Bitcoin, or ignored the blockchain details, or only investigated mining revenue without considering system performance and security. This paper aims to quantitatively evaluate the influence of selfish mining in an imperfect blockchain network from the perspective of honest miners (system performance and security) and selfish miners (selfish mining revenue ratio). We develop a novel Markov model to capture the behaviors of honest and selfish miners in both Bitcoin and Ethereum. Our model can also capture natural forks (occurring due to block propagation delay) and the varying distance between uncle and nephew blocks in Ethereum. Formulas are derived to calculate mining revenue, system performance and security metrics. The proposed model and metric formulas are validated by (1) comparing our numerical with simulation results, and (2) comparing our numerical results with the existing work results. Numerical analysis is carried out to investigate selfish mining impact over diverse parameters. These quantitative results can help detect whether there are any selfish miners in the system, help design blockchain reward mechanisms and enhance security.
Modeling continuous security: A conceptual model for automated DevSecOps using open-source software over cloud (ADOC)

Agile software development methodology and DevOps, together, have helped the business to achieve agility and velocity in delivering time-to-market applications and services. Open-source software (OSS) and cloud technologies are taking up business innovation and DevOps at new heights. However, in the quest of agility and velocity, user data security and privacy assurance often get lower priority as they are perceived as a time-consuming activity requiring specialized people, process, and technology. We see this problem being addressed by integrating security in DevOps processes. Security for DevOps has been institutionalized as DevSecOps with practical considerations for a given business context. In this work, we proposed a conceptual security model, ADOC, to facilitate adopting DevSecOps for the business processes capitalizing OSS over the cloud. This work contributes towards the following to integrate continuous security in application and service delivery: (i) A continuous security conceptual framework proposal based on the requirements elicited from the analysis of challenges in adopting DevSecOps using OSS over the cloud. (ii) An integrationist security model, ADOC, based on the proposed continuous security conceptual framework, integrating development, security, and operation activities through automation of security controls using OSS over the cloud. (iii) A set of inter-working OSS tools for automation of the proposed security controls in ADOC workflow and practices. (iv) A set of metrics for performance measurement of the ADOC model. (v) Mapping of the solutions for the analyzed challenges using the proposed security controls, followed by a use case scenario to adopt the ADOC workflow and continuous practices. The ADOC transforms security being adhoc compliance-oriented activities into continuous assurance-oriented activities by codifying security controls into an automated delivery workflow. Its practical adoption enables businesses to deliver time-to-market security ready applications and services with accelerated velocity and sustainable agility in a cost-effective way.
Training multi-layer perceptron with artificial algae algorithm

Artificial Neural Networks are commonly used to solve problems in many areas, such as classification, pattern recognition, and image processing. The most challenging and critical phase of an Artificial Neural Networks is related with its training process. The main challenge in the training process is finding optimal network parameters (i.e. weight and biase). For this purpose, numerous heuristic algorithms have been used. One of them is Artificial Algae Algorithm, which has a nature-inspired metaheuristic optimization algorithm. This algorithm is capable of successfully solving a wide variety of numerical optimization problems. In this study, Artificial Algae Algorithm is proposed for training Artificial Neural Network. Ten classification datasets with different degrees of difficulty from the UCI database repository were used to compare the proposed method performance with six well known swarm-based optimization and backpropagation algorithms. The results of the study show that Artificial Algae Algorithm is a reliable approach for training Artificial Neural Networks.
Deep clustering by maximizing mutual information in variational auto-encoder

Unsupervised clustering, which is extensively employed in deep learning and computer vision as a fundamental technique, has attracted much attention in recent years. Deep embedding clustering often uses auto-encoders to learn representations for clustering. However, auto-encoders tend to corrupt the learning representations when simultaneously learning embedded representations and performing clustering. In this paper, we propose a Deep Clustering via Variational Auto-Encoder (DC-VAE) of mutual information maximization. First, we formulate the deep clustering problem as learning soft cluster assignments within the framework of variational auto-encoder. Second, we impose mutual information maximization on the observed data and the representations to prevent soft cluster assignments from distorting learning representations. Third, we derive a new generalization evidence lower bound objects related to several previous models and introduce parameters to balance learning informative representations and clustering. It is shown that the proposed model can significantly boost the performance of clustering by learning effective and reliable representations for downstream machine learning tasks. Through experimental results on several datasets, we demonstrate that the proposed model is competitive with existing state-of-the-arts on multiple performance metrics.
Cybersecurity Risk Management in Small and Medium-Sized Enterprises: A Systematic Review of Recent Evidence

Small and medium-sized enterprises (SMEs) have been encouraged to take advantage of any possible business opportunities by utilizing and adopting new-technologies such as cloud computing services, there is a huge misunderstanding of their cyber threats from the management perspective. Underestimation of cybersecurity threats by SMEs leads to an increase in their vulnerabilities and risks, which unfortunately can become actual challenges to them and other related parties. The purpose of this paper is to provide a systematic literature review based on recently available evidence on cybersecurity risk management in SMEs in order to understand the current situation. The authors aim to reveal the role the SMEs' management is playing in addressing cybersecurityrisks in recent years, as found in the literature, and to suggest avenues for further research. The paper follows a well-known method for conducting a systematic literature review. Starting with a keyword search and an assessment of fitness for this review, 15 papers out of 50 have been analysed by NVivo software according to bibliographical information, research design and findings. The review identified 5 major perspectives that play a key role in SMEs' cybersecurity risk management, which are threats, behaviours, practices, awareness, and decision-making respectively. Importantly, empirical research on cybersecurity risk management in SMEs is needed.
A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

Context . The improvements made in the last couple of decades in the requirements engineering (RE) processes and methods have witnessed a rapid rise in effectively using diverse machine learning (ML) techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow (SO) for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing (NLP) techniques. Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. Objective . To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. Method . This paper reports a systematic literature review (SLR) collecting empirical evidence published up to May 2020. Results . This SLR study found 2,484 published papers related to RE and SO. The data extraction process of the SLR showed that (1) Latent Dirichlet Allocation (LDA) topic modeling is among the widely used ML algorithm in the selected studies and (2) precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms. Conclusion . Our SLR study revealed that while ML algorithms have phenomenal capabilities of identifying the software requirements on SO, they still are confronted with various open problems/issues that will eventually limit their practical applications and performances. Our SLR study calls for the need of close collaboration venture between the RE and ML communities/researchers to handle the open issues confronted in the development of some real world machine learning-based quality systems.
A Neural Network for Image Anomaly Detection with Deep Pyramidal Representations and Dynamic Routing

Image anomaly detection is an application-driven problem where the aim is to identify novel samples, which differ significantly from the normal ones. We here propose Pyramidal Image Anomaly DEtector (PIADE), a deep reconstruction-based pyramidal approach, in which image features are extracted at different scale levels to better catch the peculiarities that could help to discriminate between normal and anomalous data. The features are dynamically routed to a reconstruction layer and anomalies can be identified by comparing the input image with its reconstruction. Unlike similar approaches, the comparison is done by using structural similarity and perceptual loss rather than trivial pixel-by-pixel comparison. The proposed method performed at par or better than the state-of-the-art methods when tested on publicly available datasets such as CIFAR10, COIL-100 and MVTec.
Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of other human-impacted areas, such as gravel pads which are typical for gas and oil fields. A combination of results from both GBM (Sentinel-1 and -2 combined) and DL (Sentinel-2; Sentinel-1 optional) is therefore suggested for circumpolar mapping.
Guided Co-Segmentation Network for Fast Video Object Segmentation

Semi-supervised video object segmentation is a task of propagating instance masks given in the first frame to the entire video.It is a challenging task since it usually suffers from heavy occlusions, large deformation, and large variations of objects.To alleviate these problems, many existing works apply time-consuming techniques such as fine-tuning, post-processing, or extracting optical flow, which makes them intractable for online segmentation.In our work, we focus on online semisupervised video object segmentation.We propose a GCSeg (Guided Co-Segmentation) Network which is mainly composed of a Reference Module and a Co-segmentation Module, to simultaneously incorporate the short-term, middle-term, and longterm temporal inter-frame relationships.Moreover, we propose an Adaptive Search Strategy to reduce the risk of propagating inaccurate segmentation results in subsequent frames.Our GCSeg network achieves state-of-the-art performance on online semisupervised video object segmentation on Davis 2016 and Davis 2017 datasets.
Preventing Undesired Face-Touches With Wearable Devices and Haptic Feedback

The alarming morbidity of COVID-19 has drawn the attention to the social role of hygiene rules, with a particular focus on the importance of limiting face-touch occurrences. To deal with this aspect, we present No Face-Touch, a system able to estimate hand proximity to face and notify the user whenever a face-touch movement is detected. In its complete setup, the system consists of an application running on the smartwatch and a wearable accessory. Its ease of implementation allows this solution to be ready-to-use and large-scale deployable. We developed two gesture detection approaches compatible with sensors embedded in recent smartwatches, i.e. inertial and magnetic sensors. After preliminary tests to tune target gesture parameters, we tested the two approaches and compared their accuracy. The final phase of this project consisted in exploiting the most robust approach in a daily living scenario during a 6-days campaign. Experimental results revealed the effectiveness of the proposed system, demonstrating its impact in reducing the number of face-touches and their duration.