Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Federated Learning via Functional Encryption, Revisited

Federated Learning (FL), emerging as a distributed machine learning, is a popular paradigm that allows multiple users to collaboratively train a intermediate model by exchanging local models without the training data leaving each user’s domain. However, FL still suffer from privacy risk such as leaking private information from users’ uploaded local models. To address the privacy concern, several approaches have been proposed to achieve privacy-preserving FL (PPFL) based on differential privacy (DP), multi-party computation (MPC), homomorphic encryption (HE) and functional encryption (FE). Compared with DP, MPC and HE, approaches based on FE is more advantageous and thus becomes the focus of this work. Moreover, all existing PPFL schemes via FE employ a multi-user extension of FE for a specific function, i.e., multi-input FE (MIFE). In this paper, we point out that existing FE-based PPFL schemes have faced with several security issues due to the misuse of MIFE. After reconsidering the security requirements of PPFL, we propose new goals of designing PPFL using FE. To achieve our goals, we propose a new FE called dual-mode decentralized multi-client FE (2DMCFE) and give a concreate construction for 2DMCFE. With 2DMCFE, we propose a new framework of PPFL where we establish a fresh 2DMCFE instance for each subset of users. Security proof shows the strong security of our framework under the semi-honest security setting. Furthermore, experiments conducted on real dataset demonstrate that our framework achieves comparable model accuracy and training efficiency to the basic FE-based scheme while providing stronger security guarantee.
Determinants and consequences of trust in AI-based customer service chatbots

According to industry reports, artificial intelligence-based chatbots could transform online customer service. Though businesses are increasingly implementing chatbots to automate customer service, the lack of consumer trust and acceptance continues to cause worry. Although trust is critical to acceptance, research on the drivers and consequences of trust in AI-based chatbots is limited. Hence a study was conducted to identify the antecedents of consumers’ trust in text-based customer service chatbots and examine the influence of trust on behavioral intentions. The data collected from 221 users was analyzed using the structural equations modeling method. Results reveal that conversational cues influence the perceived functional and social attributes of the chatbot, and these, along with personal disposition to trust technology, further influence trust formation. Finally, trust determines behavioral intentions. Incidentally, privacy risk turned out to be a non-significant predictor of trust. The study provides measures to improve trust and suggests directions for future research.
Aeriform in-action: A novel dataset for human action recognition in aerial videos

Human actions being diverse in nature cannot be generalized, thus making it quite difficult to train a machine to recognize such diversified actions. This challenge is further compounded by the lack of availability of datasets for aerial surveillance, as collecting and annotating a large dataset is a formidable task. This paper aims to solve the problem of data scarcity by introducing a new dataset, Aeriform in-action for recognizing human actions from aerial videos. The proposed dataset consists of 32 high-resolution videos containing 13 action classes with 55,477 frames (without augmentation) and almost 400,000 annotations. It includes complex and aggressive actions such as kicking and punching, as well as drone signaling actions like waving and handshaking. The dataset also includes human-object interactions like carrying and reading. In addition to the dataset, this paper also presents a two-step deep learning framework for recognizing human actions based on the integration of human detection and action recognition module. The action recognition module adopts a modified version of the ResNeXt101 architecture (M-ResNext101) to recognize human actions in aerial videos. The performance of the proposed M-ResNext101 model is compared with 13 other deep learning models, and it outperforms all of them with an accuracy of 76.44% on the test data. The proposed dataset for human action recognition in aerial videos is available on https://surbhi-31.github.io/Aeriform-in-action/.
Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment

Internet of Things (IoT) is an emerging technology and its applications are flattering amidst many users, as it makes everything easier.As a consequence of its massive growth, security and privacy are becoming crucial issues where the IoT devices are perpetually vulnerable to cyber-attacks.To overcome this issue, intrusion detection and mitigation is accomplished which enhances the security in IoT networks.In this paper, we proposed Blockchain entrenched Bi-level intrusion detection and graph based mitigation framework named as HybridChain-IDS.The proposed work embrace four sequential processes includes timebased authentication, user scheduling and access control, bi-level intrusion detection and attack graph generation.Initially, we perform time-based authentication to authenticate the legitimate users using NIK-512 hashing algorithm, password and registered time are stored in Hybridchain which is an assimilation of blockchain and Trusted Execution Environment (TEE) which enhances data privacy and security.After that, we perform user scheduling using Cheetah Optimization Algorithm (COA) which reduces the complexity and then the access control is provided to authorized users by smart contract by considering their trust and permission level.Then, we accomplish bi-level intrusion detection using ResCapsNet which extracts sufficient features and classified effectively.Finally, risk of the attack is evaluated, and then the attacks graphs are generated by employing Enhanced k-nearest neighbor (KNN) algorithm to identify the attack path.Furthermore, the countermeasures are taken based on the attack risk level and the attack graph is stored in Hybridchain for eventual attack prediction.The implementation of this proposed work is directed by network simulator of NS-3.26 and the performance of the proposed HybridChain-IDS is enumerated based on various performance metrics.
Scalable anomaly-based intrusion detection for secure Internet of Things using generative adversarial networks in fog environment

The data generated exponentially by a massive number of devices in the Internet of Things (IoT) are extremely high-dimensional, large-scale, non-labeled, which poses great challenges to timely analysis and effective decision making for anomaly detection in IoT. In this paper, we propose a novel unsupervised deep learning method to identify anomalies against IoT networks, which exploits Bidirectional Generative Adversarial Networks (BiGAN) to build model on normal IoT data. The model introduces Wasserstein distance to capture and learn the distribution of high-dimensional raw data and focuses on latent representations using an auxiliary classifier. A cycle consistency connection between data is designed to prevent information loss that helps to reduce false positive rate. The model detects outliers by utilizing reconstruction error in feature space. Another challenge facing the current anomaly detection solutions is their limited scalability, which restricts capability in handling big IoT data. This issue is resolved by deploying and jointly training the proposed method in a fog computing environment. The anomaly-based intrusion detection can be scalable by leveraging the flexibility of fog computing, which contributes to supporting efficient detection. Experimental results on two recent datasets (i.e., UNSW-NB15 and CIC-IDS2017) validate that the proposed method achieves 4% increase in accuracy and 4% reduction in false alarm rate than the state-of-the-art methods while keeping computational efficiency.
T-distributed stochastic neighbor embedding echo state network with state matrix dimensionality reduction for time series prediction

Echo state network (ESN), a novel type of recurrent neural network, possesses high nonlinear mapping capability, which is particularly appropriate for time series prediction. However, the huge reservoir may lead to ill-conditioned solutions in the output weight matrix, reducing the generalization ability and prediction performance of the network. To address this issue, a t-distributed stochastic neighbor embedding ESN (TESN) is proposed in this paper to replace the initial large-scale reservoir state matrix with a low-dimensional manifold. By maintaining the local neighbor relationship of the data in the original high-dimensional space, the ill-conditioned dilemma of the output weight matrix is successfully solved. Moreover, the proposed TESN has a strong ability to preserve the global features of the data, which effectively improves the prediction performance of the network. The superiority of the TESN model is demonstrated through two benchmark prediction tasks and a practical application.
Semi-supervised multivariate time series anomaly detection for wind turbines using generator SCADA data

The maintenance cost and unplanned downtime caused by faults are an important part of the operation cost of wind turbines. Supervisory control and data acquisition (SCADA) data is a multivariate time series (MTS) for monitoring the status of wind turbines, in which anomaly patterns may indicate potential faults. The existing anomaly detection methods can neither extract and process pattern information in MTS stably, nor make reasonable use of a small amount of valuable labeled data. In this paper, we propose an end-to-end semi-supervised anomaly detection model including reconstruction model, prediction model and auxiliary discriminator, with a joint objective function. Combining reconstruction model and prediction model, the unsupervised model can effectively extract the inter-variable correlation and temporal dependence of MTS data. Further, using the semi-supervised auxiliary discriminator based on adversarial training, the proposed model can integrate expert knowledge to incrementally upgrade performance from unsupervised to supervised level. Our evaluation experiments are conducted on a public server dataset and a real-world wind turbine SCADA dataset. The results show that the F1-score of unsupervised model can exceed the several state-of-the-art baseline methods by 3.86% and 2.89%, and the F1-score can be increased to 98.60% and 98.30% after using the auxiliary discriminator. © 2016 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd
Waste classification using vision transformer based on multilayer hybrid convolution neural network

The rapid advancement of deep learning technology has led to the presentation of various network architectures for classification, making it easier to implement intelligent waste classification systems. However, existing waste classification models have problems such as low accuracy and slow processing. The current system does not utilize automatic classification. The proposed method uses Vision Transformer based on Multilayer Hybrid Convolution Neural Network for automatic waste classification (VT-MLH-CNN). The proposed method enhances the accuracy of waste classification and reduces the time taken for classification. Initially, it collects the data images, then the features are extracted, and next, it is processed into data normalization. The proposed model performs better by altering the number of network modules and connections. After this study determines the proper waste picture categorization variables, the best strategy is selected as the final model. The simulation results indicated that the suggested approach has a simplified network model and greater waste categorization accuracy compared to certain current efforts. Numerous tests on the TrashNet dataset demonstrate the usefulness of the recommended method, which achieves classification accuracy of up to 95.8%, which is 5.28% and 4.6% greater than those state-of-the-art techniques.
Online job scheduling scheme for low-carbon data center operation: An information and energy nexus perspective

As the digitalization of the economy and society accelerates, the enormous and fast-growing energy consumption of data centers is becoming a global concern. With the unique power consumption flexibility introduced by computing job scheduling, data centers could play an important role in enhancing the capability to integrate renewable generation as a demand-side resource. In this paper, we propose an online job scheduling scheme for low-carbon data center operation from an information and energy nexus perspective. We formulate the job scheduling problem as a Markov decision process in which job dependencies, job heterogeneity, and quality of service are considered comprehensively. To address the challenges of large-scale heterogeneous computing jobs, we propose a deep reinforcement learning-based approach to solve the energy-aware scheduling problem and achieve an optimal online policy. The case study results based on real-world data illustrate that the proposed scheme can effectively reduce the carbon footprint and energy cost of a data center while maintaining the quality of service for cloud products.
One-Dimensional Quadratic Chaotic System and Splicing Model for Image Encryption

Digital image transmission plays a very significant role in information transmission, so it is very important to protect the security of image transmission. Based on the analysis of existing image encryption algorithms, this article proposes a new digital image encryption algorithm based on the splicing model and 1D secondary chaotic system. Step one is the algorithm of this article divides the plain image into four sub-parts by using quaternary coding, and these four sub-parts can be coded separately. Only by acquiring all the sub-parts at one time can the attacker recover the useful plain image. Therefore, the algorithm has high security. Additionally, the image encryption scheme in this article used a 1D quadratic chaotic system, which makes the key space big enough to resist exhaustive attacks. The experimental data show that the image encryption algorithm has high security and a good encryption effect.
A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties

This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.
Quantification of Uncertainty and Its Applications to Complex Domain for Autonomous Vehicles Perception System

Over the last decades,deep neural networks have been penetrated into all fields of science and the real world. As a result of the lack of quantifiable data and model uncertaint, deep learning is frequently brittle,illogical, and challenging to provide trustworthy assurance for autonomous vehicles(AV) perception.This hole is filled by the suggested approach to uncertainty quantification. Nevertheless, most of the previous studies focused on the methodology and there is still a lack of research on the application of AV. To our knowledge, this paper is the first time to review the application of uncertainty in the field of AV perception and localization. In the first place, this paper analyzes the sources of uncertainty in autonomous perception,including the uncertainty brought on by sensor internal and external factors as well as the sensor distortion caused on by complex scenes. In the second place,we propose an evaluation criterion and use the criterion to carry out a quantitative analysis of perception field of application for autonomous vehicles,and we discuss the mainstream datasets. Thirdly, we put forward a number of open issues and raise some future research directions, which is of guiding significance to readers who are beginning to enter this field.We believe that epistemic uncertainty is currently the dominant research direction and that there is still a long way to go in the study of aleatoric uncertainty.And our paper will be devoted to promoting the development of uncertainty research of AV perception.
A Bio-Inspired Virtual Machine Placement Toward Sustainable Cloud Resource Management

Virtual machine placement (VMP) is the process of selecting the most appropriate physical machine (PM) to place users' requested virtual machine (VM) in large cloud data centers. Several methods have been framed to deal with this problem. However, the current solutions only consider limited resource types, resulting in an unbalanced load that activates unnecessary PMs inside the data center. In this article, we suggest a flower pollination-based nondominated sorting optimization (FP-NSO) algorithm that maximizes resource usage and minimizes energy consumption and carbon emission of the data center. Multiple resource-constraint metrics are associated with our algorithm that assists in finding the most suitable PMs for deploying VMs in a cloud environment. The VMP is carried out by employing the combined concept of flower pollination optimization and nondominated sorting technique-based genetic algorithm (NSGA-II). The algorithm is evaluated using the Google cluster dataset. The performance metrics like resource utilization, power consumption, and carbon emission values are computed for static and dynamic scenarios. The obtained results are compared with existing approaches. There is a significant reduction in power consumption, carbon emission, and execution time up to 16.69%, 48.60%, and 75.87%, respectively, and an improvement in resource utilization is up to 78.18%.
Heterogeneous Ensemble Model to Optimize Software Effort Estimation Accuracy

The software industry has rapidly expanded, and software development is now essential to the success of many multinational corporations. The demand for complex software systems has also dramatically increased. Effective software development is essential, given the limitations of resources such as money, time, and labor. The cost and effort calculations affect the development process and client needs. Project failure was usually caused by errors in job estimating. Underestimating could have severe repercussions, if the project’s budget ends up being more than anticipated. Project overruns, on the other hand, can also have a detrimental impact on how successfully software projects are finished. Researchers and experts in the field of software are investigating ways to keep management and development productivity at high levels. However, a stand-alone estimation has not produced any noteworthy research results. Over the last decade, the standalone estimating models have revealed inadequacies. Literature suggests opting ensemble models, would yield better results as compared to standalone models. We have proposed a heterogeneous ensemble effort estimation (EEE) model in this research. The proposed model is comprises of standalone estimating models such as 1) Use Case Point, 2) Expert Judgment (EJ) and 3)Artificial neural network (ANN). Finally, using linear combination rules, the effort of each unique base model is combined. We applied our ensemble model on the benchmark dataset i.e. ISBSG using three different variations to avoid biasness. Furthermore, trained models were applied on industry use-cases for cross-validation. The findings of this study demonstrated that, in comparison to stand-alone estimate strategies, the ensemble technique gave better estimation results.
Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts

There is recent interest in using model hubs – a collection of pre-trained models – in computer vision tasks. To employ a model hub, we first select a source model and then adapt the model for the target to compensate for differences. There still needs to be more research on model selection and adaption for renewable power forecasts. In particular, none of the related work examines different model selection and adaptation strategies for neural network architectures. Also, none of the current studies investigates the influence of available training samples and considers seasonality in the evaluation. We close these gaps by conducting the first thorough experiment for model selection and adaptation for transfer learning in renewable power forecast, adopting recent developments from the field of computer vision on 667 wind and photovoltaic parks from six datasets. We simulate different amounts of training samples for each season to calculate informative forecast errors. We examine the marginal likelihood and forecast error for model selection for those amounts. Furthermore, we study four adaption strategies. As an extension of the current state of the art, we utilize a Bayesian linear regression for forecasting the response based on features extracted from a neural network. This approach outperforms the baseline with only seven days of training data and shows that fine-tuning is not beneficial with less than three months of data. We further show how combining multiple models through ensembles can significantly improve the model selection and adaptation approach such that we have a similar mean error with only 30 days of training data which is otherwise only possible with an entire year of training data. We achieve a mean error of 9.8 and 14 percent for the most realistic dataset for PV and wind with only seven days of training data.