Reviewerly/exHarmony huggingface dataset:
Multivariate Time Series Predictor With Parameter Optimization and Feature Selection Based on Modified Binary Salp Swarm Algorithm

More and more time series data appear in various fields, and the prediction of multivariate time series has been the key to solve many industrial problems. Therefore, it is necessary to establish an accurate prediction model. As an efficient recursive neural network, an echo state network (ESN) model has been widely used in time series prediction. However, it usually faces the problem of how to choose suitable reservoir parameters for different applications. In addition, selecting the input feature set is also an important issue, which will affect the accuracy and computational efficiency of the prediction model. To solve these problems, the modified binary salp swarm algorithm-based optimization ESN (MBSSA-ESN) is proposed for multivariate time series prediction, which can simultaneously realize feature subset selection and parameter optimization. In order to verify the effectiveness of the proposed model, Beijing air quality index data are used for simulation and the key index PM2.5 is used as the target variable for experiment. Compared with several related methods, the proposed model achieves the best results in all evaluation indicators, indicating that the MBSSA-ESN model is competitive in the task of multivariate time series prediction.
Inference-Reconstruction Variational Autoencoder for Light Field Image Reconstruction

Light field cameras can capture the radiance and direction of light rays by a single exposure, providing a new perspective to photography and 3D geometry perception. However, existing sub-aperture based light field cameras are limited by their sensor resolution to obtain high spatial and angular resolution images simultaneously. In this paper, we propose an inference-reconstruction variational autoencoder (IR-VAE) to reconstruct a dense light field image out of four corner reference views in a light field image. The proposed IR-VAE is comprised of one inference network and one reconstruction network, where the inference network infers novel views from existing reference views and viewpoint conditions, and the reconstruction network reconstructs novel views from a latent variable that contains the information of reference views, novel views, and viewpoints. The conditional latent variable in the inference network is regularized by the latent variable in the reconstruction network to facilitate information flow between the conditional latent variable and novel views. We also propose a statistic distance measurement dubbed the mean local maximum mean discrepancy (MLMMD) to enable the measurement of the statistic distance between two distributions with high-resolution latent variables, which can capture richer information than their low-resolution counterparts. Finally, we propose a viewpoint-dependent indirect view synthesis method to synthesize novel views more efficiently by leveraging adaptive convolution. Experimental results show that our proposed methods outperform state-of-the-art methods on different light field datasets.
Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception

In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.
A Time Series Forecasting Approach to Minimize Cold Start Time in Cloud-Serverless Platform

Serverless computing is a buzzword that is being used commonly in the world of technology and among developers and businesses. Using the Function-as-a-Service (FaaS) model of serverless, one can easily deploy their applications to the cloud and go live in a matter of days, it facilitates the developers to focus on their core business logic and the backend process such as managing the infrastructure, scaling of the application, updation of software and other dependencies is handled by the Cloud Service Provider. One of the features of serverless computing is ability to scale the containers to zero, which results in a problem called cold start. The challenging part is to reduce the cold start latency without the consumption of extra resources. In this paper, we use SARIMA (Seasonal Auto Regressive Integrated Moving Average), one of the classical time series forecasting models to predict the time at which the incoming request comes, and accordingly increase or decrease the amount of required containers to minimize the resource wastage, thus reducing the function launching time. Finally, we implement PBA (Prediction Based Autoscaler) and compare it with the default HPA (Horizontal Pod Autoscaler), which comes inbuilt with kubernetes. The results showed that PBA performs fairly better than the default HPA, while reducing the wastage of resources.
Search-based Methods for Multi-Cloud Configuration

Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in. While most cloud providers offer similar functionality, they may differ significantly in terms of performance and/or cost. A customer looking to benefit from such differences will naturally want to solve the multi-cloud configuration problem: given a workload, which cloud provider should be chosen and how should its nodes be configured in order to minimize runtime or cost? In this work, we consider possible solutions to this multi-cloud optimization problem. We develop and evaluate possible adaptations of state-of-the-art cloud configuration solutions to the multi-cloud domain. Furthermore, we identify an analogy between multi-cloud configuration and the selection-configuration problems that are commonly studied in the automated machine learning (AutoML) field. Inspired by this connection, we utilize popular optimizers from AutoML to solve multi-cloud configuration. Finally, we propose a new algorithm for solving multi-cloud configuration, CloudBandit. It treats the outer problem of cloud provider selection as a best-arm identification problem, in which each arm pull corresponds to running an arbitrary black-box optimizer on the inner problem of node configuration. Our extensive experiments indicate that (a) many state-of-the-art cloud configuration solutions can be adapted to multi-cloud, with best results obtained for adaptations which utilize the hierarchical structure of the multi-cloud configuration domain, (b) hierarchical methods from AutoML can be used for the multi-cloud configuration task and can outperform state-of-the-art cloud configuration solutions and (c) CloudBandit achieves competitive or lower regret relative to other tested algorithms, whilst also identifying configurations that have 65% lower median cost and 20% lower median runtime in production, compared to choosing a random provider and configuration.
Avengers, Assemble! Survey of WebAssembly Security Solutions

WebAssembly, abbreviated as Wasm, has emerged as a new paradigm in cloud-native developments owing to its promising properties. Native execution speed and fast startup time make Wasm an alternative for container-based cloud applications. Despite its security-by-design strategy, however, WebAssembly suffers from a variety of vulnerabilities and weaknesses, which hinder its rapid adoption in cloud computing. For instance, the native execution performance attracted cybercriminals to abuse Wasm binaries for the purpose of resource stealing such as cryptojacking. Without proper defense mechanisms, Wasm-based malware would proliferate, causing huge financial loss of cloud users. Moreover, the design principle that allows type-unsafe languages such as C/C++ inherently induces various memory bugs in an Wasm binary. Efficient and robust vulnerability analysis techniques are necessary to protect benign cloud-native Wasm applications from being exploited by attackers. Due to the young age of WebAssembly, however, there are few works in the literature that provide developers guidance to such security techniques. This makes developers to hesitate considering Wasm as their cloud-native platform. In this paper, we surveyed various techniques and methods for Wasm binary security proposed in the literature and systematically classified them according to certain criteria. As a result, we propose future research directions regarding the current lack of WebAssembly binary security research.
DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting

Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated learning typically requires the utilization of multi-party computation techniques to provide strong privacy guarantees by ensuring that an untrusted or curious aggregator cannot obtain isolated replies from parties involved in the training process, thereby preventing potential inference attacks. Until recently, it was thought that some of these secure aggregation techniques were sufficient to fully protect against inference attacks coming from a curious aggregator. However, recent research has demonstrated that a curious aggregator can successfully launch a disaggregation attack to learn information about model updates of a target party. This paper presents DeTrust-FL, an efficient privacy-preserving federated learning framework for addressing the lack of transparency that enables isolation attacks, such as disaggregation attacks, during secure aggregation by assuring that parties’ model updates are included in the aggregated model in a private and secure manner. DeTrust-FL proposes a decentralized trust consensus mechanism and incorporates a recently proposed decentralized functional encryption scheme in which all parties agree on a participation matrix before collaboratively generating decryption key fragments, thereby gaining control and trust over the secure aggregation process in a decentralized setting. Our experimental evaluation demonstrates that DeTrust-FL outperforms state-of-the-art FE-based secure multi-party aggregation solutions in terms of training time and reduces the volume of data transferred. In contrast to existing approaches, this is achieved without creating any trust dependency on external trusted entities.
A Decentralized Cross-Chain Service Protocol based on Notary Schemes and Hash-Locking

With the rapid development of blockchain technology, its applications have been extended to various fields such as supply chains, the internet of things, and finance. However, different blockchains cannot directly interconnect due to discrepancies in the blockchain architecture, resulting in data islands. Cross-chain technologies are services that provide interoperability for different blockchains, enabling them to exchange data and transfer assets. This paper summarizes and analyzes cross-chain technologies of notary schemes, hash-locking, and relays. We find that the notary may be evil in notary schemes. Furthermore, hash-locking limits application scenarios that can only be used for assets exchange, and the relay is difficult to deploy in a real scenario. To overcome the drawbacks of existing technologies, we design a secure and fully functional cross-chain service protocol, which combines notary schemes with hash-locking, and introduces rewards and punishments. This proposed protocol is easy to deploy in a real scenario and solves the problem of evil notaries, as well as prevents the sore loser attack and the wormhole attack. Finally, we evaluate the proposed protocol and experimental results illustrate that it is more efficient than the BitXHub project, and is also fault-tolerant.
Forecasting solar-to-hydrogen and solar-to-methane energy conversion efficiency using Si and IMM PV-modules: A case-study in Japan

The solar-to-gas (StG) conversion system is a potential pathway for accelerating zero-carbon energy sources. In our previous study, outdoor StG conversion was accomplished using triple-junction concentrator photovoltaic (CPV) modules. The system achieved the highest performance with sets of DC/DC converters and electrochemical (EC) cells. Therefore, we aimed to investigate the prospective StG conversion from PV systems if such a system is installed in Japan. A power generation prediction model considering all weather conditions, called the Miyazaki Spectrum to Energy (MS2E), has been developed using the METPV-11 meteorological database at the University of Miyazaki. Accordingly, the amount of energy generated by various PV systems could be forecasted nationwide from the global irradiance data. In this study, we applied the MS2E PV energy prediction method and approached the potential StG conversion efficiency of silicon (Si) PV and inverted metamorphic (IMM) PV systems across Japan. Based on the actual StG measurement data, we approximated the conversion of each subsystem. The simulation results show that IMM modules can provide higher StG conversion than Si modules.
GCM: Efficient video recognition with glance and combine module

In this work, we present an efficient and powerful building block for video action recognition, dubbed Glance and Combine Module (GCM). In order to obtain a broader perspective of the video features, GCM introduces an extra glancing operation with a larger receptive field over both the spatial and temporal dimensions, and combines features with different receptive fields for further processing. We show in our ablation studies that the proposed GCM is much more efficient than other forms of 3D spatio-temporal convolutional blocks. We build a series of GCM networks by stacking GCM repeatedly, and train them from scratch on the target datasets directly. On the Kinetics-400 dataset which focuses more on appearance rather than action, our GCM networks can achieve similar accuracy as others without pre-training on ImageNet. For the more action-centric recognition datasets such as Something-Something (V1 & V2) and Multi-Moments in Time, the GCM networks achieve state-of-the-art performance with less than two thirds the computational complexity of other models. With only 19.2 GFLOPs of computation, our GCMNet15 can obtain 63.9% top-1 classification accuracy on Something-Something-V2 validation set under single-crop testing. On the fine-grained action recognition dataset FineGym, we beat the previous state-of-the-art accuracy achieved with 2-stream methods by more than 6% using only RGB input.
Certificateless public auditing scheme with designated verifier and privacy-preserving property in cloud storage

In recent years, cloud storage has become an attractive service for data owners to store their personal data. Since they lose physical control of their data, it is necessary to regularly audit its integrity. The public auditing technique is very popular because it is suitable to outsource the auditing task to any third-party auditor, who has professional knowledge on auditing. However in many practical scenarios, the data owner may expect some designated verifier (instead of any) to audit its data. Thus, the public auditing scheme with designated verifier was proposed. In order to further reduce the cost of managing certificates, and to avoid the dependence on public key infrastructure , identity-based auditing scheme with designated verifier was recently proposed. Nevertheless, the proposed identity-based auditing scheme still suffers from the key escrow attack the risk of privacy leakage . In this paper, for the first time, we propose a certificateless public auditing protocol with designated verifier privacy-preserving property. More concretely, it can not only protect the privacy of the stored data against the designated verifier, but also resolve key escrow problem existed in identity-based technique. Its security is based on the Computational Diffie–Hellman and Weil Diffie–Hellman assumptions. Finally, the performance analysis experimental results of the proposed protocol show that our auditing scheme is efficient feasible to applications in real life.
SHACL and ShEx in the Wild: A Community Survey on Validating Shapes Generation and Adoption

Knowledge Graphs (KGs) are widely used to represent heterogeneous domain knowledge on the Web and within organizations. Various methods exist to manage KGs and ensure the quality of their data. Among these, the Shapes Constraint Language (SHACL) and the Shapes Expression Language (ShEx) are the two state-of-the-art languages to define validating shapes for KGs. Since the usage of these constraint languages has recently increased, new needs arose. One such need is to enable the efficient generation of these shapes. Yet, since these languages are relatively new, we witness a lack of understanding of how they are effectively employed for existing KGs. Therefore, in this work, we answer How validating shapes are being generated and adopted? Our contribution is threefold. First, we conducted a community survey to analyze the needs of users (both from industry and academia) generating validating shapes. Then, we cross-referenced our results with an extensive survey of the existing tools and their features. Finally, we investigated how existing automatic shape extraction approaches work in practice on real, large KGs. Our analysis shows the need for developing semi-automatic methods that can help users generate shapes from large KGs.
Training Vision Transformers in Federated Learning with Limited Edge-Device Resources

Vision transformers (ViTs) demonstrate exceptional performance in numerous computer vision tasks owing to their self-attention modules. Despite improved network performance, transformers frequently require significant computational resources. The increasing need for data privacy has encouraged the development of federated learning (FL). Traditional FL places a computing burden on edge devices. However, ViTs cannot be directly applied through FL on resource-constrained edge devices. To utilize the powerful ViT structure, we reformulated FL as a federated knowledge distillation training algorithm called FedVKD. FedVKD uses an alternating minimization strategy to train small convolutional neural networks on edge nodes and periodically transfers their knowledge to a large server-side transformer encoder via knowledge distillation. FedVKD affords the benefits of reduced edge-computing load and improved performance for vision tasks, while preserving FedGKT-like asynchronous training. We used four datasets and their non-IID variations to test the proposed FedVKD. When utilizing a larger dataset, FedVKD achieved higher accuracy than FedGKT and FedAvg.
Three‐phase service level agreements and trust management model for monitoring and managing the services by trusted cloud broker

Cloud computing is an environment where everything is provided as a service based on demand. It follows pay as per the used model in which the service consumer needs to pay for what they have consumed. Due to the increased dependence on digitalization, the number of consumers and providers tends to grow tremendously. The consumer who needs the service from the provider is not sure about the specified service outcome, and it is too hard for them to monitor and manage the service. Hence, a trusted third party called a trusted cloud broker (TCB) is introduced for managing the services. The service level agreements (SLA) management and reputation estimation framework is proposed, which includes three phases such as (i) SLA establishment between the three parties, (ii) violation detection by comparing the observed value of the TCB and (iii) the reputation and penalty estimation of the service. The novel TCB is created to monitor the deployed services, ensuring the achievement of SLA. The TCB observes the values and estimates the reputation value for each service. It is compared with the provider log-based reputation value and found that the proposed model provides a more precise reputation value for the service providers.
Real-time 3D UAV Path Planning in Dynamic Environments with Uncertainty

The integration of Unmanned Aerial Vehicles (UAVs) is being proposed in a spectrum of applications varying from military to civil. In these applications, UAVs are required to safely navigate in real-time in dynamic and uncertain environments. Uncertainty can be present in both the UAV itself and the environment. Through a literature study, this paper first identifies, quantifies and models different uncertainty sources using bounding shapes. Then, the UAV model, path planner parameters and four scenarios of different complexity are defined. To investigate the effect of uncertainty on path planning performance, uncertainty in obstacle position and orientation and UAV position is varied between 2% and 20% for each uncertainty source first separately and then concurrently. Results show a deterioration in path planning performance with the inclusion of both uncertainty types for all scenarios for both A* and the Rapidly-Exploring Random Tree (RRT) algorithms, especially for RRT. Faster and shorter paths with similar same success rates (&gt;95%) result for the RRT algorithm with respect to the A* algorithm only for simple scenarios. The A* algorithm performs better than the RRT algorithm in complex scenarios.
Collecting Individual Trajectories under Local Differential Privacy

In this paper, we tackle the problem of collecting individual trajectories under local differential privacy. The key challenge is how to achieve high utility of the collected trajectories while satisfying the strong privacy guarantee. To overcome this challenge, we present a novel approach, which is referred to as PrivTC. In PrivTC, we first design a locally differentially private grid construction method to instruct the aggregator to lay an appropriate grid on the given geospatial domain. Then we propose a locally differentially private spectral learning method to help the aggregator learn the Hidden Markov Model (HMM) from users' trajectories discretized by the constructed grid. Finally, the aggregator generates a synthetic trajectory dataset as a surrogate for the original one from the learned HMM. Extensive experiments on real datasets confirm the effectiveness of PrivTC.