Reviewerly/exHarmony huggingface dataset:
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Let Fpm be a finite field of cardinality pm where p is an odd prime, n be a positive integer satisfying gcd(n,p)=1, and denote R=Fpm[u]/〈ue〉 where e≥4 be an even integer. Let δ,α∈Fpm×. Then the class of (δ+αu2)-constacyclic codes over R is a significant subclass of constacyclic codes over R of Type 2. For any integer k≥1, an explicit representation and a complete description for all distinct (δ+αu2)-constacyclic codes over R of length npk and their dual codes are given. Moreover, formulas for the number of codewords in each code and the number of all such codes are provided respectively. In particular, all distinct (δ+αu2)-constacyclic codes over Fpm[u]/〈ue〉 of length pk and their dual codes are presented precisely.
An Approach for Load Balancing in Cloud Computing Using JAYA Algorithm

Cloud computing is a jargon in the era of information technology. It acts as a metaphor for the internet. Still, it possesses several challenges related to automated resource provisioning, security, event content dissemination, server consolidation, virtual machine migration. Load balancing is one of the critical challenges in the cloud that are faced by the enterprises. Here the basic objective of load balancing is to minimize response time, data center service request time, and improve the overall performance of the system. In this article, a JAYA algorithm is used for load balancing in a cloud which uses less control parameters and provides a better optimized result. Comparisons are made with other evolutionary approaches to observe the efficiency of the proposed algorithm.
Dynamic Feature Acquisition Using Denoising Autoencoders

In real-world scenarios, different features have different acquisition costs at test time which necessitates cost-aware methods to optimize the cost and performance tradeoff. This paper introduces a novel and scalable approach for cost-aware feature acquisition at test time. The method incrementally asks for features based on the available context that are known feature values. The proposed method is based on sensitivity analysis in neural networks and density estimation using denoising autoencoders with binary representation layers. In the proposed architecture, a denoising autoencoder is used to handle unknown features (i.e., features that are yet to be acquired), and the sensitivity of predictions with respect to each unknown feature is used as a context-dependent measure of informativeness. We evaluated the proposed method on eight different real-world data sets as well as one synthesized data set and compared its performance with several other approaches in the literature. According to the results, the suggested method is capable of efficiently acquiring features at test time in a cost- and context-aware fashion.
Secure Multiparty PageRank Algorithm for Collaborative Fraud Detection

Collaboration between financial institutions helps to improve detection of fraud. However, exchange of relevant data between these institutions is often not possible due to privacy constraints and data confidentiality. An important example of relevant data for fraud detection is given by a transaction graph, where the nodes represent bank accounts and the links consist of the transactions between these accounts. Previous works show that features derived from such graphs, like PageRank, can be used to improve fraud detection. However, each institution can only see a part of the whole transaction graph, corresponding to the accounts of its own customers. In this research a new method is described, making use of secure multiparty computation (MPC) techniques, allowing multiple parties to jointly compute the PageRank values of their combined transaction graphs securely, while guaranteeing that each party only learns the PageRank values of its own accounts and nothing about the other transaction graphs. In our experiments this method is applied to graphs containing up to tens of thousands of nodes. The execution time scales linearly with the number of nodes, and the method is highly parallelizable. Secure multiparty PageRank is feasible in a realistic setting with millions of nodes per party by extrapolating the results from our experiments.
Time delay Chebyshev functional link artificial neural network

In real applications, a time delay in the parameter update of the neural network is sometimes required. In this paper, motivated by the Chebyshev functional link artificial neural network (CFLANN), a new structure based on the time delay adaptation is developed for nonlinear system identification. Particularly, we present a new CFLANN-delayed recursive least square (CFLANN-DRLS), as an online learning algorithm for parameter adaptation in CFLANN. The CFLANN-DRLS algorithm exploits the time delayed error signal with the gain vector delayed by D cycles to form the weight increment term, which provides potential implementation in the filter with pipelined structure. However, it suffers from the instability problems under imperfect network delay estimate. To overcome this problem, we further propose a modified CFLANN-DRLS (CFLANN-MDRLS) algorithm by including a compensation term to the error signal. We analyze the stability and convergence of the proposed algorithm. Simulations in nonlinear system identification contexts reveal that the newly proposed CFLANN-MDRLS algorithm can effectively compensate the time delay of system and it is even superior to the algorithm without delay in some cases.
Collaborative Optimization of Service Composition for Data-Intensive Applications in a Hybrid Cloud

The multi-valued evaluations of quality of service (QoS), the complicated constraints between cloud services (CSs) and the collaborative resource assignments add many difficulties to the problem of CS composition for data-intensive applications (DiA) in a hybrid cloud (CSCD-HC). Solving the CSCD-HC problem has become a challenging task due to the uncertain QoS, the diverse hardware configurations and the flexible pricing about CSs. This paper proposes a collaborative optimization approach for CSCD-HC. This approach models a DiA as a role-based collaboration (RBC) system and employs the environments-classes, agents, roles, groups, and objects (E-CARGO) model to formalize the CSCD-HC problem with complicated constraints. To deal with the multi-valued QoS evaluations, this paper exploits the cloud model theory to analyze the performance of CSs, and presents a new method utilizing the Mahalanobis distance to improve the similarity calculation of QoS cloud models. Based on it, the qualification of candidate CSs can be precisely measured for supporting CS composition. A solution via the IBM ILOG CPLEX optimization package is put forward to solve the CSCD-HC problem. The experimental results demonstrate that the proposed approach is effective and feasible for optimizing CSCD-HC.
Single-Road-Constrained Positioning Based on Deterministic Trajectory Geometry

We consider the single-road-constrained estimation problem for positioning a target that moves on a single, deterministic, and exactly known trajectory. Based on the geometry of the trajectory curve, we cast the constrained estimation problem as an unconstrained problem with reduced state dimension. Two approaches are devised based on a Markov transition model for unscented Kalman filtering and a continuous function of time for (weighted) least square fitting, respectively. A popular simulation model has been used for demonstrating the performance of the proposed approaches in comparison with the existing approaches.
Bio-inspired adaptive control strategy for the highly efficient speed regulation of the DC motor under parametric uncertainty

The presence of parametric uncertainties decreases the performance in controlling dynamic systems such as the DC motor. In this work, an adaptive control strategy is proposed to deal with parametric uncertainties in the speed regulation task of the DC motor. This adaptive strategy is based on a bio-inspired optimization approach, where an optimization problem is stated and solved online by using a modification of the differential evolution optimizer. This modification includes a mechanism that promotes the exploration in the early generations and takes advantage of the exploitation power of the DE/best class in the last generations of the algorithm to find suitable optimal control parameters to control the DC motor speed efficiently. Comparative statistical analysis with other bio-inspired adaptive strategies and with linear, adaptive and robust controllers shows the effectiveness of the proposed bio-inspired adaptive control approach both in simulation and experimentation.
Post-quantum EPID Signatures from Symmetric Primitives

EPID signatures are used extensively in real-world systems for hardware enclave attestation. As such, there is a strong interest in making these schemes post-quantum secure. In this paper we initiate the study of EPID signature schemes built only from symmetric primitives, such as hash functions and PRFs. We present two constructions in the random oracle model. The first is a scheme satisfying the EPID signature syntax and security definitions needed for private hardware attestation used in Intel's SGX. The second achieves significantly shorter signatures for many applications, including the use case of remote hardware attestation. While our EPID signatures for attestation are longer than standard post-quantum signatures, they are short enough for applications where the data being signed is large, such as analytics on large private data sets, or streaming media to a trusted display. We evaluate several instantiations of our schemes so that the costs and benefits of these constructions are clear. Along the way we also give improvements to the zero-knowledge Merkle inclusion proofs of Derler et al. (2017).
Everything is worth when it is close to my body: How spatial proximity and stimulus valence affect visuo-tactile integration

The peripersonal space (PPS) is the space surrounding our body, represented in a multisensory fashion by integrating stimuli of different modalities. Recently, it has been demonstrated that PPS is emotionally connoted, being sensitive to the different affective valence of the stimuli located inside it. However, how visuo-tactile interactions can be spatially shaped by intrinsic or acquired valence of stimuli is not clear. To investigate this, we conducted three experiments in which participants performed a visuo-tactile interaction task, while the intrinsic valence (Exp. 1 and 2) or the learned valence (Exp. 3) of visual stimuli was manipulated. Participants were asked to respond as fast as possible to a tactile stimulus that was delivered while a visual stimulus was approaching (Exp.1 and 3) or receding (Exp.2) from the hand. Touch was synchronized with different distances of the visual stimulus from the hand. We found that both the expectancy of stimulus and the distance of the visual one from the hand impact RTs to tactile targets. Crucially, we found that spatial modulation was also influenced by stimulus valence, but only for the approaching and not the receding stimuli. At far distances, neutral stimuli yielded overall slower RTs than intrinsically positive or negative stimuli (Experiment 1), while no modulation was exerted by the level of conditioning (Experiment 3). At near distances, response to touches accompanied by looming neutral stimuli became as fast as that occurring with positive and negative ones. Stimulus valence did not interact with the expectancy of a tactile stimulus (Experiment 2). Overall, these findings support the vision that visuo-tactile interactions can be dynamically modulated by the valence of looming visual stimuli when these are located at longer distances from the body. When closer to it, all stimuli acquire saliency, regardless of their intrinsic or acquired valence, due to their proximity, and then relevance, to the body. Overall, a view of PPS as a gradient modulating visuo-tactile integration, also based on stimulus valence, is discussed.
Cross-Complementary Local Binary Pattern for Robust Texture Classification

The performance of local binary pattern (LBP) and many LBP-based variants is usually limited by rotation, illumination, scale, viewpoint, and the number of training samples. In view of this, this letter presents a robust image descriptor named crosscomplementary LBP (CCLBP) for texture classification. Based on the continuous rotation invariance and highly discriminative characteristic of principal curvatures, significant local geometrical information, which is complementary to LBP is obtained. Then, the resulting information is quantized and encoded into a binary pattern. To enhance the robustness to scale, viewpoint, and the number of training samples, a multiscale and multiresolution analysis is explored by diversifying two parameters accordantly. Subsequently, a cross-scale joint feature representation is conducted on the generated complementary binary responses, resulting in the proposed CCLBP, which captures a highly discriminative information but with low dimensionality. Experimental results on three standard texture databases demonstrate that the proposed CCLBP achieves competitive performance or outperforms state-of-the-art texture descriptors while enjoying a succinct feature representation. Impressively, under the premise of maintaining complete rotation invariance, the performance of the CCLBP approach against illumination, viewpoint, and scale changes has been improved, especially when the number of training samples is limited.
Artefacts in software engineering: a fundamental positioning

Artefacts play a vital role in software and systems development processes. Other terms like documents, deliverables, or work products are widely used in software development communities instead of the term artefact. In the following, we use the term `artefact' including all these other terms. Despite its relevance, the exact denotation of the term `artefact' is still not clear due to a variety of different understandings of the term and to a careless negligent usage. This often leads to approaches being grounded in a fuzzy, unclear understanding of the essential concepts involved. In fact, there does not exist a common terminology. Therefore, it is our goal that the term artefact be standardised so that researchers and practitioners have a common understanding for discussions and contributions. In this position paper, we provide a positioning and critical reflection upon the notion of artefact in software engineering at different levels of perception and how these relate to each other. We further contribute a meta model that provides a description of an artefact that is independent from any underlying process model. This meta model defines artefacts at three levels. Abstraction and refinement relations between these levels allow correlating artefacts to each other and defining the notion of related, refined, and equivalent artefacts. Our contribution shall foster the long overdue and too often underestimated terminological discussion on what artefacts are to provide a common ground with clearer concepts and principles for future software engineering contributions, such as the design of artefact-oriented development processes and tools.
Empirical estimates of the radiative impact of an unusually extreme dust and wildfire episode on the performance of a photovoltaic plant in Western Mediterranean

We have estimated the radiative impact produced by an unusually extreme dust and wildfire episode on the performance of a photovoltaic (PV) plant. The dust and wildfire events were mostly active on 26–28 and 29–30 June 2012, respectively. We took advantage of the consecutiveness of both events to separate and derive empirically the radiative effect of dust and smoke aerosols. With this purpose, we employed measurements of aerosol load, radiation and PV power output from a collocated atmospheric station and PV plant located at Burjassot (Valencia, Spain). The empirical estimates were obtained by direct comparison with a summer background day, happened right before the two consecutive case studies. The whole episode is characterized by high aerosol optical depth (AOD) at 500 nm, reaching values up to 1 and 6 for dust and smoke, respectively. Our analysis shows an average daily energy loss of −132 kWh, that represents a fraction of 20% of the energy generated by the PV plant on the reference summer background day. A significant fraction of the energy lost was due to smoke, with a daily maximum of 43% and a daily average of 34%. In case of dust, the energy reduction was moderated, with a daily average of 6%. In terms of instantaneous power reduction, we have obtained peaks of 8 and 51% for dust and smoke, respectively. The energy reduction due to aerosols is strongly dependent on AOD. Further, we have found that the power loss per unit of AOD at 870 nm is useful to separate the radiative impact of different aerosol types. Essentially, the efficiency of smoke to reduce the energy generated by the PV plant is much greater than that of dust. This effect is mainly due to the different optical properties in the near infrared that result in a daily Power Aerosol Forcing Efficiency at 870 nm of −5.7 and −19.1 kW, for dust and smoke, respectively. Our results indicate that both the AOD and aerosol type drive the radiative impact of aerosols on the PV output, confirming that extreme aerosol events may drastically reduce the power generated by operative PV plants. This reduction may be as large as that reported for particle soiling. Therefore, considering both amount and type of aerosols in the PV power analyses is mandatory in order to obtain accurate forecast scenarios and improve the grid integration, especially during extreme episodes.
Blind image quality assessment based on joint log-contrast statistics

During recent years, quality-aware features extracted from natural scene statistics (NSS) models have been used in development of blind image quality assessment (BIQA) algorithms. Generally, the univariate distributions of bandpass coefficients are used to fit a parametric probabilistic model and the model parameters serve as the quality-aware features. However, the inter-location, inter-direction and inter-scale correlations of natural images cannot be well exploited by such NSS models, as it is hard to capture such dependencies using univariate marginal distributions. In this paper, we build a novel NSS model of joint log-contrast distribution to take into account the across space and direction correlations of natural images (inter-scale correlation to be explored as the next step). Furthermore, we provide a new efficient approach to extract quality-aware features as the gradient of log-likelihood on the NSS model, instead of using model parameters directly. Finally, we develop an effective joint-NSS model based BIQA metric called BJLC (BIQA based on joint log-contrast statistics). Extensive experiments on four public large-scale image databases have validated that objective quality scores predicted by the proposed BIQA method are in higher accordance with subjective ratings generated by human observers compared with existing methods.
A trustworthy agent-based encrypted access control method for mobile cloud computing environment

To strengthen the security of access control protocols for mobile cloud environment, dynamic attributes of mobile devices are used. The weak or disconnection issue of the mobile network is a critical task to deal with. The proposed approach provides access control as well as data confidentiality using dynamic attributes encryption. The pairs of mobile agents are used to deal with the issue of network connection. The secret key is distributed using the anonymous key-issuing protocol which preserves the anonymity of the user. The approach is implemented in a real mobile cloud environment, and the performance under various parameters is evaluated.
An automated shearography system for cylindrical surface inspection

A shearography system has been developed to inspect the external heat proof coating bonded to a cylinder. The system consists of a shearography device integrated with thermal excitation, a mechanical translation and rotation device, and a central control unit. The translation and the rotation are driven with 2 servo motors. The combination of these two movements enables full inspection of the entire surface of the cylinder. The inspection sequence is automatically scheduled by inputting the geometry of the sample. Artificial intelligence (AI) has been first introduced to aid defect recognition from the resulted phase shifting fringe patterns. A recognition algorithm based on deep learning has been developed using Faster R-CNN model for recognition of bonding defects. By training the system using typical butterfly fringe patterns which are captured from bonding samples, the system can accurately identify the bonding defects on the cylindrical surface at a high success rate.
Multiple moduli prediction error expansion reversible data hiding

This manuscript considers the distinct prediction error expansion (PEE) of each pixel up to the limit of overflow/underflow in view of embedding a maximum amount of data. A moduli map is created by associating to each pixel its expansion modulus. Since the storing of the expansion moduli map requires more free space than the room provided by the PEE procedure, the quantization of the map is further investigated. The loss by PEE expansion with lower moduli values is expected to be negligible with respect to the gain due to the efficient lossless compression of the simplified map. A linear programming model is introduced in order to select the moduli that maximize the embedding bit-rate. Experimental results for two well-known predictors, the gradient-adjusted predictor (GAP) and the edge-directed predictor (EDP), are provided. The proposed multiple moduli PEE scheme outperforms the high embedding bit-rate reversible data hiding schemes proposed so far. Embedding bit-rates greater than 3 bpp can be obtained.