Reviewerly/exHarmony huggingface dataset:
A simple big data methodology and analysis of the specific yield of all PV power plants in a power system over a long time period

In this work, we have reviewed recent literature concerning the performance of PV plants in different power systems and detected that, usually, the raw data used is not complete in terms of the number of PV plants and also main parameters of poor quality need to be removed. Then, for the first time, a study of the specific yield of all PV plants in a power system with single-PV-plant resolution is presented. Thus, we have analyzed the official 237,588 monthly energy values obtained from 2005 to 2017 for the 1523 PV plants in the Canary Islands. This dataset is obtained from PV plants ranging from 0.53 kWp up to 9 MWp, and it has been supplied by the distribution system operator and main utility (ENDESA). Then, this dataset is compared to 153,120 irradiance and temperature data from a PVGIS database. Results show that the Spanish regulation has a direct effect not only on the development of the PV capacity in the Canary Islands, but also on the specific yields. Moreover, only combining meteorological data (irradiance, temperature and wind speed) from satellites, starting year of operation, and nameplate capacity we have developed a very simple theoretical model to predict the specific yield of a PV plant at any location in the Canary Islands, avoiding the requirement of any data from the owners of the PV plants. The simulation values obtained have been validated with the real specific yields for PV plants assumed to be well managed (multi-MW power plants placed in best locations) showing errors below a 3%. This theoretical model has also been used for detecting suboptimal PV plant designs and anomalous specific yield of PV plants above the clear sky limit. Recommendations to avoid anomalous specific yields in future are included.
Estimation of electricity generation potential by solar radiation on Sardar Sarovar dam

The western region of the subcontinent of India experiences a tropical savanna climate. During summers, the temperature elevates to scorching 48ºC, which causes intensification in the evaporation rate of the surface of the water bodies of that region. One such water body is the Sardar Sarovar dam’s reservoir. This gravity dam is one of the world’s largest dams and one of the pre-eminent renewable energy sources in the country. Even though Sardar Sarovar Dam generates surplus electricity around the year, in summer, the value takes a dip. This is when the Global Horizontal Solar Irradiance (GHI) is at peak. This benefactor has been utilised by the installation of solar panels on the still water area. A method was developed to exploit this solar radiation and to depress the evaporation rate of the reservoir to increase the output by the dam. A standard model was prepared for installation of offshore solar panels at the target site. The measured value of the GHI at three nearby locations was acquired from National Institute of Wind Energy’s database. This data was interpolated at the target area to estimate monthly GHI and calculate the solar angles and the solar farm parameters. These parameters were used for sensitivity analysis to determine the variation in electricity potential by implementing different types of solar panels. The maximum value of power that can be generated by the farm was estimated to be 173.6 MW. This is 0.59% of the total power generated by the state and 2.77% of the power generated by the renewable sources in the state.
Soft Transfer Learning via Gradient Diagnosis for Visual Relationship Detection

Detecting all visual relationships is posed as the most fundamental task towards the ultimate semantic reasoning. However, due to the rich context embedded in the image and diverse language ambiguities, it is unrealistic to annotate and list all possible relationships for providing a noise-free supervised setting. All prior approaches simply adopt the traditional fully-supervised detection pipeline and ignore the effect of incomplete annotations on model convergence, resulting in the unstable optimization and unsatisfactory performance. In this work, we make the first attempt to address this critical incomplete annotations issue and reformulate this task via the Soft Transfer Learning (STL), which aims to transfer knowledge learned from the annotations in hand into the uncertain pairs in a self-supervised way. The knowledge transfer process is inferred from a principled gradient diagnosis. Extensive experiments on VRD and the large-scale VG benchmarks demonstrate the superiority of our STL method.
Local Color Mapping Combined with Color Transfer for Underwater Image Enhancement

Color correction and color transfer methods have gained a lot of attention in the past few years to circumvent color degradation that may occur due to various sources.In this paper, we propose a novel simple yet powerful strategy to profoundly enhance color distorted underwater images.The proposed approach combines both local and global information through a simple yet powerful affine transform model.Local and global information are carried through local color mapping and color covariance mapping between an input and some reference source, respectively.Several experiments on degraded underwater images demonstrate that the proposed method performs favourably to all other methods including ones that are tailored to correcting underwater images by explicit noise modelling.
Batten Down the Hatches: Securing Neighborhood Area Networks of Smart Grid in the Quantum Era

On account of the recent advances in quantum computers, it becomes pressing to consider quantum-safe authentication schemes for two-way communications in neighborhood area networks of smart grid, i.e., preventing possible attacks that the quantum computers could bring to the grid. In this paper, we take a further step toward this goal by integrating the lattice-based public-key encryption and key exchange techniques to provide mutual authentication between smart meters and the neighborhood gateway. Security analysis shows that our proposed scheme is secure under attacks from both quantum and conventional computers, including the recently introduced key reuse attacks. In addition, the proposed schemes can also achieve forward security, which guarantees that the secrets leaked in the current session will not result in the leakage of secrets in former sessions. Furthermore, the proposed scheme also gives prior consideration to the smart meters with limited computation capacity and puts as little burden on them as possible. Extensive performance analysis is also conducted, and the results demonstrate the efficiency of our proposed scheme, as well as the relatively low communication and storage costs.
Analysis Dictionary Learning: an Efficient and Discriminative Solution

Discriminative Dictionary Learning (DL) methods have been widely advocated for image classification problems. To further sharpen their discriminative capabilities, most state-of-the-art DL methods have additional constraints included in the learning stages. These various constraints, however, lead to additional computational complexity. We hence propose an efficient Discriminative Convolutional Analysis Dictionary Learning (DCADL) method, as a lower cost Discriminative DL framework, to both characterize the image structures and refine the interclass structure representations. The proposed DCADL jointly learns a convolutional analysis dictionary and a universal classifier, while greatly reducing the time complexity in both training and testing phases, and achieving a competitive accuracy, thus demonstrating great performance in many experiments with standard databases.
Searching for Old News

Modeling user interests helps to improve system support or refine recommendations in Interactive Information Retrieval. The aim of this study is to identify user interests in different parts of an online collection and investigate the related search behavior. To do this, we propose to use the metadata of selected facets and clicked documents as features for clustering sessions identified in user logs. We evaluate the session clusters by measuring their stability over a six-month period.
A layer-wise Perturbation based Privacy Preserving Deep Neural Networks

Datasets are sources of information mining where knowledge can be derived. The versatility of these dataset determines the quality of knowledge gained. However, several of these data contains personal sensitive information that can lead to infringement of privacy. Existing research tends to deliver DNN models that can preserve privacy of personal information but the accuracy of these models are rather much lower as compared to their non-privacy preserving counterparts. This is due to the degree of noise and the points where noise was added to perturb the model data. Consequently, this has led to minimal adoption of privacy preserving DNN models in the industrial world. In this paper, we present a layer-wise perturbation approach and differential privacy technique to determine points of perturbation and preserve privacy. Our approach was able to narrow down the accuracy gap between privacy-preserving and non-privacy preserving DNN model.
Extended multi-adjoint logic programming

Extended multi-adjoint logic programming arises as an extension of multi-adjoint normal logic programming where constraints and a special type of aggregator operator have been included. The use of this general aggregator operator permits to consider, for example, different negation operators in the body of the rules of a logic program. We have introduced the syntax and the semantics of this new paradigm, as well as an interesting mechanism for obtaining a multi-adjoint normal logic program from an extended multi-adjoint logic program. This mechanism will allow us to establish technical properties relating the different stable models of both logic programming frameworks. Moreover, it makes possible that the already developed and future theory associated with stable models of multi-adjoint normal logic programs can be applied to extended multi-adjoint logic programs.
Finite Non-Commutative Associative Algebras as Carriers of Hidden Discrete Logarithm Problem

The article introduces new finite algebras attractive as carriers of the discrete logarithm problem in a hidden group.In particular new 4-dimensional and 6-dimensional finite non-commutative algebras with associative multiplication operation and their properties are described.It is also proposed a general method for defining finite non-commutative associative algebras of arbitrary even dimension m ≥ 2. Some of the considered algebras contain a global unit, but the other ones include no global unit element.In the last case the elements of the algebra are invertible locally relatively local bi-side units that act in the frame of some subsets of elements of algebra.For algebras of the last type there have been derived formulas describing the sets of the (right-side, left-side, and bi-side) local units.Algebras containing a large set of the global single-side (left-side and right-side) units and no global bi-side unit are also introduced.Since the known form of defining the hidden discrete logarithm problem uses invertibility of the elements of algebra relatively global unit, there are introduced new forms of defining this computationally difficult problem.The results of the article can be applied for designing public-key cryptographic algorithms and protocols, including the post-quantum ones.For the first time it is proposed a digital signature scheme based on the hidden discrete logarithm problem.
A variable-level automated defect identification model based on machine learning

Static analysis tools, automatically detecting potential source code defects at an early phase during the software development process, are diffusely applied in safety-critical software fields. However, alarms reported by the tools need to be inspected manually by developers, which is inevitable and costly, whereas a large proportion of them are found to be false positives. Aiming at automatically classifying the reported alarms into true defects and false positives, we propose a defect identification model based on machine learning. We design a set of novel features at variable level, called variable characteristics, for building the classification model, which is more fine-grained than the existing traditional features. We select 13 base classifiers and two ensemble learning methods for model building based on our proposed approach, and the reported alarms classified as unactionable (false positives) are pruned for the purpose of mitigating the effort of manual inspection. In this paper, we firstly evaluate the approach on four open-source C projects, and the classification results show that the proposed model achieves high performance and reliability in practice. Then, we conduct a baseline experiment to evaluate the effectiveness of our proposed model in contrast to traditional features, indicating that features at variable level improve the performance significantly in defect identification. Additionally, we use machine learning techniques to rank the variable characteristics in order to identify the contribution of each feature to our proposed model.
Incorporating Source-Side Phrase Structures into Neural Machine Translation

Neural machine translation (NMT) has shown great success as a new alternative to the traditional Statistical Machine Translation model in multiple languages. Early NMT models are based on sequence-to-sequence learning that encodes a sequence of source words into a vector space and generates another sequence of target words from the vector. In those NMT models, sentences are simply treated as sequences of words without any internal structure. In this article, we focus on the role of the syntactic structure of source sentences and propose a novel end-to-end syntactic NMT model, which we call a tree-to-sequence NMT model, extending a sequence-to-sequence model with the source-side phrase structure. Our proposed model has an attention mechanism that enables the decoder to generate a translated word while softly aligning it with phrases as well as words of the source sentence. We have empirically compared the proposed model with sequence-to-sequence models in various settings on Chinese-to-Japanese and English-to-Japanese translation tasks. Our experimental results suggest that the use of syntactic structure can be beneficial when the training data set is small, but is not as effective as using a bi-directional encoder. As the size of training data set increases, the benefits of using a syntactic tree tends to diminish.
Whale–crow optimization (WCO)-based Optimal Regression model for Software Cost Estimation

Software Cost Estimation (SCE) is the emerging concern of the software companies during the development phase of the software, as it requires effort and cost factors for modelling the software. These factors are modelled using the Artificial Intelligence models, which seem to be less accurate and non-reliable by increasing the risk factor of the software projects. Thus, for estimating the software cost, meta-heuristics are employed. This paper proposes an algorithm, termed as whale–crow optimization (WCO) algorithm, which is the integration of the whale optimization algorithm (WOA) and the crow search algorithm (CSA). The main function of the WCO algorithm is to determine the Optimal Regression coefficients for the regression models, such as the Linear Regression model and the Kernel Logistic Regression model, to develop an Optimal Regression model to estimate the software cost. The experimentation is carried out using four datasets taken from the Promise software engineering repository to perform effective performance analysis. Analysis is carried out regarding the mean magnitude of relative error (MMRE) that proves that the proposed method of SCE is effective, attaining the average MMRE at a rate of 0.2442 for the proposed Linear Regression model and 0.2692 for the proposed Kernel Regression model.
Empirical Investigation of Key Factors for SaaS Architecture

Software-as-a-Service (SaaS) has received significant attention from software providers and users as a software delivery model. Most of the existing companies are transferring their business into a SaaS model. This intensely competitive environment has imposed many challenges for SaaS developers and vendors. SaaS development is a very complex process and SaaS success depends on its architecture design and development. This paper provides a better understanding of SaaS applications architecture phase during the SaaS development process. It focuses mainly on an empirical investigation of key factors of SaaS Architecture phase identified from the systematic literature review. A quantitative survey was developed and conducted to identify key architecture factors for an improved and successful SaaS application. A developed survey was used to test the proposed hypothesis presented in this study. Empirical investigation's results provide evidence that vendors and developers must consider key architecture factors for SaaS development process to stand in the current competitive environment. These key factors include customization, scalability, MTA (Multi-Tenancy Architecture), security, integration, and fault tolerance and recovery management. The main contribution of this paper is to investigate empirically the influence of identified key factors of the architecture on SaaS applications success.
Manifold Alignment via Global and Local Structures Preserving PCA Framework

Manifold alignment is very prevalent in machine learning for extracting common latent space from multiple datasets. These algorithms generally aim to achieve higher alignment accuracies by preserving the original structure while ensuring closeness between manifolds. This paper proposes a novel semi-supervised manifold alignment method that combines, in each manifold, both global and local linear reconstructions. We preserve a local structure through multiple manifold embedding methods. Moreover, we view manifold embedding methods as special forms of principal component analysis (PCA) and, thus, present a new penalty weight PCA approach to preserving a noise-free global structure. Finally, a closed-form solution is presented in the manifold alignment. This method can concurrently match the pair-wise correspondence and preserve both the global and local structures of each dataset to obtain a latent low-dimensional space. The extensive experiments on manifold alignment prove that the proposed method achieves significantly better alignment results than the comparative methods.
A New Approach for Controlling a Trajectory Tracking Using Intelligent Methods

This study aimed to propose and compare two control methods to guide a mobile robot during trajectory tracking. The conventional proportional–integral–derivative (PID) control is widely used in several non-linear systems control area for its simplicity and robustness. But it is difficult to adjust the parameters of the PID controller to satisfy the requirements of the control systems. In this article, we present the neuro-PI + D control and fuzzy-neuro-PI + D control. We use those techniques of artificial intelligence to guarantee autonomy and intelligence for the path following behavior. The robot under consideration is a four-wheeled system and is represented by a kinematic model. Matlab/Simulink simulation results demonstrate that the fuzzy-neuro-PI + D method has a great potential for navigation issue.
Rallying Adversarial Techniques against Deep Learning for Network Security

Recent advances in artificial intelligence and the increasing need for robust defensive measures in network security have led to the adoption of deep learning approaches for network intrusion detection systems (NIDS). These methods have achieved superior performance against conventional network attacks, enabling unique and dynamic security systems in realworld applications. Adversarial machine learning, unfortunately, has recently shown that deep learning models are inherently vulnerable to adversarial modifications on their input data. In this work, we explore the potential of adversarial entities to compromise such vulnerabilities to compromise deep learning-based NIDS systems. Specifically, we show that by modifying on average as little as 1.38 of an observed packet's input features, an adversary can generate malicious inputs that effectively fool a target deep learning-based NIDS. Therefore, it is crucial to consider the performance from the conventional network security perspective and the adversarial machine learning domain when designing such systems.
Analyzing smart contract interactions and contract level state consensus

Summary Although the primary function of distributed ledgers is to store data related to users' interactions, their capabilities allow them to offer more sophisticated functionalities. Advances in blockchain technologies introduced smart contracts , software programs that define immutable rules as functions stored on the blockchain and can be executed on demand. Smart contracts can interact not only with users but also with each other via message exchange. We compare existing smart contract interactions, and develop an architecture for asynchronous state consensus, a novel type of smart contract interaction required in applications but had rarely been addressed. The proposed architecture is composed of two types of smart contracts, ie, Custodian and Client. Client smart contracts serve as network participants reaching a particular consensus collectively by forming a cluster and issuing votes towards a final state agreement. Custodian smart contracts serve as the arbiters that aggregate and calculate voting results as the finalized state consensus that is shared across the network. To test the feasibility of our proposal, we conduct experiments on the consensus reaching latency and the scalability under different network configurations with standardized Amazon Web Service instances. Lastly, we discuss the robustness our proposal concerning Byzantine Fault tolerance and list possible applications. In the gaming industry, an ERC721 smart contract does not allow contrasting structural features between individual tokens, yet only minor value‐level differences. The proposed solution can address the need for character diversity that characters can be created and attached to a gaming smart contract after deployment, which enables fine distinction between characters. The proposal can also achieve sharing states across smart contracts, such as the jackpot, which renovates the flexibility of blockchain gaming.
Proposal to Avoid Issues in the DevOps Implementation: A Systematic Literature Review

Nowadays many software companies and startups try to do software development faster and with better quality. Companies have tried to readjust their process with new techniques and practices in order to achieve these goals for example, getting continuous automation using DevOps practices or some companies tried to switch their habits getting software as faster as they can. Companies commonly use DevOps with agile methodologies such as SCRUM, XP, and lean. However most of the time they do not ensure that implementation of those practice in a correct way neither the use of models nor standards. This paper presents the results of a performed systematic review protocol focused on getting information about the real state of implementation of a DevOps approach regarding the use of methodologies, frameworks and standards. The obtained results allow us to identify challenges and barriers that companies have when they try to implement a DevOps approach.