Reviewerly/exHarmony huggingface dataset:
Effective cyber security system to secure optical data based on deep learning approach for healthcare application

In the recent days, data’s plays a significant role and its enormous quantity are extracted based on various IoT devices. Various IoT devices generates huge data and it is processed to extract the knowledge data through data analytics. To process those data, which are extracted and needs Deep Learning (DL) model as it needs large number of input and its attributes. The deep learning models helps in classifying the data and process those data generated from IoT and those useful optical data will be useful to those consumers. As huge data are circulated and it contains some private information’s related to optics to be secure from the third parties along with effective data processing. The modified deep learning approach based on Cyber Physical Systems (CPS) is propose to effectively process those IoT based data with enhanced data security. Based on deep learning model, the data access algorithm helps to process the raw data generated from IoT time to time and it helps to convert the data to knowledge data. Then the data are secured based on policy access control against various attacks like DoS and DDoS. Based on the performance analysis, the approach helps to provide effective classification of data and reliable data’s to be maintained. Then security on IoT data to be verified against DoS and DDoS attacks on cyber physical system on real time applications.
SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks

Capsule Networks (CapsNets) preserve the hierarchical spatial relationships between objects, and thereby bear the potential to surpass the performance of traditional Convolutional Neural Networks (CNNs) in performing tasks like image classification. This makes CapsNets suitable for the smart cyber–physical systems (CPS), where a large amount of training data may not be available. A large body of work has explored adversarial examples for CNNs, but their effectiveness on CapsNets has not yet been studied systematically. In our work, we perform an analysis to study the vulnerabilities in CapsNets to adversarial attacks. These perturbations, added to the test inputs, are small and imperceptible to humans, but can fool the network to mispredict. We propose a greedy algorithm to automatically generate imperceptible adversarial examples in a black-box attack scenario. We show that this kind of attacks, when applied to the German Traffic Sign Recognition Benchmark and CIFAR10 datasets, mislead CapsNets in making a correct classification, which can be catastrophic for smart CPS, like autonomous vehicles. Moreover, we apply the same kind of adversarial attacks to a 5-layer CNN (LeNet), to a 9-layer CNN (VGGNet), and to a 20-layer CNN (ResNet), and analyze the outcome, compared to the CapsNets, to study their different behaviors under the adversarial attacks.
Semi-Supervised Domain Alignment Learning for Single Image Dehazing

Convolutional neural networks (CNNs) have attracted much research attention and achieved great improvements in single-image dehazing. However, previous learning-based dehazing methods are mainly trained on synthetic data, which greatly degrades their generalization capability on natural hazy images. To address this issue, this article proposes a semi-supervised learning approach for single-image dehazing, where both synthetic and realistic images are leveraged during training. Considering the situation that it is hard to obtain the realistic pairs of hazy and haze-free images, how to utilize the realistic data is not a trivial work. In this article, a domain alignment module is introduced to narrow the distribution distance between synthetic data and realistic hazy images in a latent feature space. Meanwhile, a haze-aware attention module is designed to describe haze densities of different regions in the image, thus adaptively responds for different hazy areas. Furthermore, the dark channel prior is introduced to the framework to improve the quality of the unsupervised learning results by considering the statistical characters of haze-free images. Such a semi-supervised design can significantly address the domain shift issue between the synthetic and realistic data, and improve generalization performance in the real world. Experiments indicate that the proposed method obtains state-of-the-art performance on both public synthetic and realistic hazy images with better visual results.
Influence of deformed cavity field and atomic dipole interaction on the quantum correlations of two-qubit system

Abstract The possibility of generating of some quantum correlations of the entangled or separable initial two-qubit system interacting with a deformed cavity mode in the presence of dipole-dipole interaction is discussed. These quantum correlations are the coherence, non-separability and steering degrees which are quantifiers by using $$ l_1 $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>l</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> norm of coherence, Entanglement of Formation and Einstein–Podolsky–Rosen -steering. It is shown that maximizing or minimizing the quantum correlations depends on the initial setting states of the two-qubit system. To maximize the quantum correlations on the presence of deformation, one has to prepare the initial two-qubit system in an entangled state, setting large values of the coupling dipole and decreasing the effect of the deformation parameter.
Semi-supervised learning with pseudo-negative labels for image classification

Semi-supervised learning frameworks usually adopt mutual learning approaches with multiple submodels to learn from different perspectives. Usually, a high threshold is used to filter out numerous low-confidence predictions for unlabeled data to avoid transferring erroneous pseudo-labels between the submodels. However, such filtering cannot fully exploit unlabeled data with low prediction confidence. In this study, we propose a mutual learning framework based on pseudo-negative labels to overcome this problem. Negative labels are those to which a corresponding data item does not belong. In each iteration, one submodel generates pseudo-negative labels for each data item, and the other submodel learns from these labels. The roles of the two submodels interchange after each iteration until convergence. The dual model can improve its prediction ability by reducing the prediction probability of the pseudo-negative labels. We also propose a mechanism for selecting a few pseudo-negative labels to be fed into the submodels. In experiments, our framework achieved state-of-the-art results on several main benchmarks. Specifically, with our framework, the error rates of the 13-layer CNN model were 9.35% and 7.94% for CIFAR-10 with 1000 and 4000 labels, respectively. In addition, our framework has an error rate of 0.81% for the non-augmented MNIST with only 20 labels, which is much smaller than that of the other approaches. Our approach also demonstrated a significant performance improvement in domain adaptation.
Data Types as a More Ergonomic Frontend for Grammar-Guided Genetic Programming

Genetic Programming (GP) is an heuristic method that can be applied to many Machine Learning, Optimization and Engineering problems. In particular, it has been widely used in Software Engineering for Test-case generation, Program Synthesis and Improvement of Software (GI).
Meta Reinforcement Learning with Hebbian Learning

Deep reinforcement learning achieves super-human performances at the cost of millions of non-optimal interactions with environments. Ideally, a well trained deep reinforcement learning algorithm should be robust to unseen cases or unseen tasks with limited data. This paper proposes a new meta reinforcement learning algorithm with the Hebbian learning algorithm. According to the neuroscientific theory, Hebbian learning mimics the learning behaviours of the human brain. Neurons are activated repeatedly, and new neural networks are built with newly formed connections. Researchers have proved that compared with deep reinforcement learning, Hebbian learning can obtain the near-optimal solutions within short training iterations. In this paper, we use a teacher-student learning architecture. In the teacher model, the Hebbian learning model is trained with an evolutionary strategy while the student model is a deep reinforcement learning model trained with modified training procedures. Experiments prove that our proposed algorithm overwhelms the performances of the Hebbian learning algorithm and the original deep reinforcement learning vastly. Besides, with strong inductive biases introduced by learned knowledge from subtasks, the data efficiency of deep reinforcement learning is significantly boosted. Moreover, we set the target task and subtasks different primarily to prove the robustness of Hebbian learning as the teacher model. Experiments prove that this proposed meta reinforcement learning algorithm could boost data efficiency largely.
Validation of Photovoltaic Spectral Effects Derived From Satellite-Based Solar Irradiance Products

The Satellite Application Facility on Climate Monitoring (CM-SAF) Spectral Resolved Irradiance (SRI) and National Renewable Energy Laboratory National Solar Radiation Database Spectral on Demand (NSRDB-S) satellite-based spectral irradiance products are tested here against benchmark data and models at seven ground stations: one in Spain for CM-SAF SRI and six in North America for NSRDB-S. Benchmarks include WISER spectroradiometers, spectra modeled from SolarSIM-G measurements, the First Solar model of spectral mismatch factor (SMM), and the SMARTS radiative code with two alternate input sources: AErosol RObotic NETwork (AERONET) and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. The satellite products are tested in terms of their ability to estimate photovoltaic (PV) spectral effects for six PV module technologies. Spectra are also compared directly. CM-SAF SRI generally outperforms First Solar and “no spectral effects” benchmarks, except for cadmium telluride modules. For NSRDB-S, predictions of long-term spectral derate factors show less skill than for instantaneous SMMs. Spectra comparisons reveal systematic differences between NSRDB-S and benchmark spectra, likely due to the NSRDB-S treatment of aerosols. Meanwhile, the mean SMARTS spectra with AERONET and MERRA-2 inputs are in good agreement, showing promise for the use of MERRA-2 as input to clear-sky models.
A homomorphic approach for security and privacy preservation of Smart Airports

The concept of Smart Airport is emerging in the Aviation sector because of the industry 4.0 revolution. To provide the best facility to the air travelers, most of the airports have enhanced their infrastructure using smart automation and Cloud Services (CS), etc. which play a significant role in increasing the revenue of the aviation industry. As the use of digital technology at airports is increasing, they are becoming progressively vulnerable to cyber-attacks and data breaches. The data collected by the airports is stored in specialized cloud databases, which ensures the availability of the data at any time. However, cloud servers have been compromised several times which leads to a lack of trust by the consumers. To meet the privacy and security objectives of the data collected at the airports and stored in the cloud, this research proposes a Searchable Encryption (SE) scheme based on Partial Homomorphic Encryption (PHE). The proposed scheme allows users to search on the encrypted data stored in the cloud hence enhancing the usability of the encrypted data. In this paper, a Single Keyword Searchable Encryption (SKSE) scheme is presented which is implemented using Paillier Cryptosystem. The proposed scheme comprises of two variants (a) Secure SKSE (b) Efficient SKSE. The Secure SKSE scheme exploits the probabilistic properties of the Paillier cryptosystem, whereas, the Efficient SKSE scheme is lightweight as it is based on the deterministic properties of the Paillier cryptosystem. Both variants of SKSE scheme are deployed on a public cloud platform "Contabo" and tested on the real-world aviation data to assess the performance of both approaches. The security and performance analysis illustrates that the proposed Secure SKSE maintains a high-security level by providing probabilistic encryption and trapdoors. On the other hand, the Efficient SKSE is 84 times faster in terms of performance.
Comparative study of thirteen numerical methods for evaluating Weibull parameters for solar energy generation at ten selected locations in Cameroon

This study aims to compare thirteen computational methods with the goal of pointing out the more relevant method for the determination of Weibull parameters using forty-year daily average solar irradiance data collected at the NASA website from the first of January 1980 to the thirty first of December 2020. The studied sites are the headquarters of the ten regions of Cameroon which include Maroua, Garoua, Ngaoundéré, Bamenda, Yaoundé, Douala, Buea, Bertoua, Bafoussam and Ebolowa. The methods accuracies were checked using five statistical indicators. The results revealed that the appropriate method varies from one site to another. The maximum likelihood method and the modified maximum likelihood method were found to be the suitable methods to estimate the solar irradiance distribution for the sites of Maroua, Garoua and Ngaoundéré. The empirical method of Justus was the most efficient method for the sites of Bamenda, Yaoundé, Bertoua, Bafoussam and Ebolowa while the energy pattern factor method was the method that best fits the observed solar irradiance data for the sites of Douala and Buea. The graphical method is recommended for the evaluation of the cumulative distribution of solar irradiance because it was found to be the best method of estimating the cumulative distribution of solar irradiance in all the considered sites. The highest annual energy output from a photovoltaic panel was recorded to be 604.51 kWh/year for the site of Maroua.
Optimal Thresholds for Fracton Codes and Random Spin Models with Subsystem Symmetry

Fracton models provide examples of novel gapped quantum phases of matter that host intrinsically immobile excitations and therefore lie beyond the conventional notion of topological order. Here, we calculate optimal error thresholds for quantum error correcting codes based on fracton models. By mapping the error-correction process for bit-flip and phase-flip noises into novel statistical models with Ising variables and random multibody couplings, we obtain models that exhibit an unconventional subsystem symmetry instead of a more usual global symmetry. We perform large-scale parallel tempering Monte Carlo simulations to obtain disorder-temperature phase diagrams, which are then used to predict optimal error thresholds for the corresponding fracton code. Remarkably, we found that the X-cube fracton code displays a minimum error threshold (7.5%) that is much higher than 3D topological codes such as the toric code (3.3%), or the color code (1.9%). This result, together with the predicted absence of glass order at the Nishimori line, shows great potential for fracton phases to be used as quantum memory platforms.
Adaptive Soft Contrastive Learning

Self-supervised learning has recently achieved great success in representation learning without human annotations. The dominant method -- that is contrastive learning, is generally based on instance discrimination tasks, i.e., individual samples are treated as independent categories. However, presuming all the samples are different contradicts the natural grouping of similar samples in common visual datasets, e.g., multiple views of the same dog. To bridge the gap, this paper proposes an adaptive method that introduces soft inter-sample relations, namely Adaptive Soft Contrastive Learning (ASCL). More specifically, ASCL transforms the original instance discrimination task into a multi-instance soft discrimination task, and adaptively introduces inter-sample relations. As an effective and concise plug-in module for existing self-supervised learning frameworks, ASCL achieves the best performance on several benchmarks in terms of both performance and efficiency. Code is available at https://github.com/MrChenFeng/ASCL_ICPR2022.
Human vs. Automated Sales Agents: How and Why Customer Responses Shift Across Sales Stages

Customers in sales processes increasingly encounter automated sales agents that complement or replace human sales agents. Yet, little is known about whether, how, and why customers respond to automated agents in contrast to human agents across successive decision stages of the same sales process. Even less is known about customer responses to combinations where both agents assume distinct roles and focus on complementary tasks that are traditionally performed by only one single agent. Against this backdrop, this paper explores the influence of increasingly common sales representative types on customer decisions across sales stages. Our findings demonstrate that customer responses to automated (versus human) sales agents are not stable in sales processes and instead, shift as customers move across sales stages. What is more, the paper shows that combinations of sales agents versus single sales agents do matter, yet their differential effects depend on contextual features of the sales setting. These insights are important because vendors may assume that a certain type of sales agent is always more appreciated by customers, whereas in fact, different sales agent types bring distinct attributes to the table, and customers’ appreciation of these attributes shifts across sales stages.
Blockchain adoption and firm performance: The contingent roles of intangible capital and environmental dynamism

Despite the purported benefits of blockchain technology for business applications, it is unclear how blockchain adoption affects firm performance, and large-scale empirical evidence on this relationship remains scarce. Drawing on the dynamic capabilities perspective, we examine the relationship between blockchain adoption and firm performance using a sample of 4994 firm-year observations from 2017 to 2021. Using a dynamic System GMM approach, we show that blockchain adoption significantly increases Tobin's Q (a market-based measure of expected future earnings); however, it has no relation with the current accounting performance measured using return on equity (ROE) and return on assets (ROA). We find that a firm's intangible capital and environmental dynamism positively moderate the relationship between blockchain adoption and Tobin's Q. The former finding reflects market expectations that firms with high intangible capital are better equipped to benefit from blockchain adoption. The latter finding suggests that blockchain adoption strengthens firms' dynamic capabilities, and thus it enables firms to undertake proactive and innovative initiatives in response to an uncertain business environment.
An anatomization of research paper recommender system: Overview, approaches and challenges

The purpose of this study is to present an exhaustive analysis on research paper recommender systems which have become very popular and gained a lot of research attention. Though the major focus is on developing new recommendation algorithms, other research dimensions are left untouched. Renown recommendation classes include content-based approaches, collaborative filtering, link-based algorithms, co-occurrence based approaches, global relevance and hybrid methods. These approaches mainly differ in background knowledge and modes of user behavior analysis. For instance, content-based filtering uses paper descriptions which are mostly word-based features. Collaborative filtering makes predictions based on peers’ interests. Link-based algorithms utilize academic associations that exist between different entities in academia. Co-occurrence based techniques incorporate event occurrences to locate related papers. Global relevance adopts a ‘one-for-all’ policy for recommending popular articles. Hybrid methods combine the above approaches to design efficient algorithms. We have reviewed articles implementing several versions of these classes, however minor customizations make it difficult to categorize the new methods to one of the base classes. We have defined the concept of ‘background knowledge and operating principle’ for proper classification and to make a clear distinction among the recommendation approaches. We have used a combination of systematic literature review, critical review, and conceptual review to conduct this survey which presents current advancements in the field and discusses popular recommendation approaches. This paper reveals the factors affecting users’ behavior and introduces a taxonomy of knowledge acquisition sources. Moreover, various evaluation methods and important performance criteria are explored. Finally, this paper examines the research trends and reports major loopholes in current research to foster the development of efficacious recommender systems.
A randomized deep neural network for emotion recognition with landmarks detection

In this paper, we present an innovative deep neural architecture employing parameter randomization in a complex classification model for emotion recognition. Actually, randomized deep neural networks represent an interesting alternative to exploring the efficiency-to-accuracy balance in real-life applications. Moreover, we also introduce the use of input frames composed of 468 facial landmarks coordinates and an innovative sampling procedure avoiding padding. The proposed randomized classifier is trained for emotion recognition on video sequences and the related accuracy is compared with a non-randomized version of the same model and with well-known benchmark architectures, demonstrating the robustness of the proposed approach in terms of classification accuracy and training time.
A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends

As the popularity of blockchain technology and investor confidence in Bitcoin (BTC) increased in recent years, many individuals started making BTC and other cryptocurrency investments, in expectation of high returns. However, as recent market movements have shown, the lack of regulation and oversight makes it difficult to guard against high volatility and potentially significant losses in this sector. In this study, we propose a data-driven Tree Augmented Naïve (TAN) Bayes methodology that can be used for identifying the most important factors (as well as their conditional, interdependent relationships) influencing BTC price movements. As the model is parsimonious without sacrificing accuracy, sensitivity, and specificity—as evident from the average accuracy value—the proposed methodology can be used in practice for making short-term investment decisions.
Using explainability tools to inform NILM algorithm performance

Over the years, Non-Intrusive Load Monitoring (NILM) research has focused on improving performance and more recently, generalizing over distinct datasets. However, the trustworthiness of the NILM model itself has hardly been addressed. To this end, it becomes important to provide a reasoning or explanation behind the predicted outcome for NILM models especially as machine learning models for NILM are often treated as black-box models. With this explanation, the models, not only can be improved, but also build trust for wider adoption within various applications. This paper demonstrates how some explainability tools can be used to explain the outcomes of a decision tree multi-classification approach for NILM and how model explainability informs feature selection and eventually improves performance.