Reviewerly/exHarmony huggingface dataset:
Cost-optimal Operation of Latency Constrained Serverless Applications: From Theory to Practice

Serverless computing and the function as a service model are new paradigms enabling the fine granular, bottomup construction of cloud-native applications. It can significantly reduce operating costs while shifting the management tasks from developers and application providers towards the cloud operators. But these benefits are provided at the cost of less control over the underlying infrastructure and the application performance, including the end-to-end latency. However, grouping of functions into deployable serverless software artifacts remains still under our control, which has a considerable impact on performance and operation costs. In this paper, we propose fast and efficient algorithms that can partition an application’s functions into separate deployment artifacts in a cost-optimal way while meeting user-defined average end-to-end latency bounds. Moreover, our approach supports the dynamic redesign and reconfiguration of the current deployment setup in response to changes in monitored metrics. Our main contribution is threefold. First, we establish the relevant theoretical models capturing the behavior of the serverless ecosystem and we define the main problem. In addition, the concept of the integrated application management is introduced. Second, we propose novel algorithms providing optimal solutions for different variants of the core problem and the complexity of the methods are analyzed. Third, we demonstrate the applicability and the benefits of our solution by evaluating different deployment scenarios of a realistic use case in Amazon’s public cloud environment.
Research on distributed photovoltaic power prediction based on spatiotemporal information ensemble method

Distributed photovoltaic power generation can efficiently utilize idle resources and reduce carbon emissions. In order to reduce the impact of grid-connected distributed photovoltaic power fluctuations on grid operation, this paper simultaneously exploits the temporal dependence of power series and the spatial correlation of meteorological data to propose a combined prediction model with temporal characteristics and spatial relationships fused for distributed photovoltaic power plants with spatiotemporal information. First, in the study of time-dependent prediction, we propose a long and short-term memory neural network ensemble prediction model based on genetic algorithm-natural gradient boosting, which efficiently fits multiple sets of temporal characteristics of distributed photovoltaic. In the study of spatial correlation prediction, the meteorological data affecting photovoltaic power generation are selected by κ correlation coefficients, the target power plant and spatial reference power plant meteorological data are reconstructed into a two-dimensional matrix, and a two-dimensional convolutional neural network spatial feature extraction power prediction model is designed. Finally, the advantages of the two prediction models of temporal information and spatial features are fused by multiple error evaluation criteria improved information entropy, and a distributed photovoltaic power plant is constructed and implements highly accurate spatiotemporal information combination prediction model. The effect of the forecasting model in this study is validated using the photovoltaic cluster dataset in Hebei Province, China. Compared with other models, the results of this study show that the five prediction performance evaluation metrics of the proposed combined spatiotemporal information model are better.
Analysis of ERP Enterprise Management Information System based on Cloud Computing Mode

ERP management information systems are widely used in the process of enterprise operation and development. By optimizing the allocation of management resources such as enterprise customers, project operations, material procurement, supply, and production, the utility of various resources in the enterprise is maximized, with integrated, systematic, and organizational management characteristics. Based on this, this article discusses cloud computing and ERP systems, analyzes the positive effects of cloud computing ERP systems on enterprises, and studies the specific applications of ERP systems in enterprises under cloud computing mode. At present, many large and medium-sized enterprises in China have established a complete ERP enterprise management information system. Considering the expansion of enterprise scale and the multiplication of enterprise management data, the existing ERP management information system has shown some limitations. With the rapid development of computer technology, the concept of cloud computing has been gradually promoted and applied. Based on the characteristics of cloud computing, ERP enterprise management information system began to consciously combine with the concept of cloud computing. This paper mainly introduces the characteristics and advantages of ERP enterprise management information system based on cloud computing mode, and analyzes its application effect in depth.
An Innovation Development of Neuro Controller for Condition Monitoring and Smart Industrial Instrumentation

The Neuro Controller is an innovative piece of industrial instrumentation designed to monitor conditions in smart industrial settings. It is a powerful and versatile controller that can be used to monitor, control, and manage industrial processes. The Neuro Controller is equipped with advanced sensors and actuators, enabling it to accurately measure and control a variety of conditions. The Neuro Controller is able to monitor and control temperature, pressure, humidity, flow, and other parameters in an industrial setting. It is also capable of detecting changes in these parameters, allowing it to respond to changes quickly and accurately. The Neuro Controller uses advanced algorithms to analyze data and make decisions, allowing it to optimize and control processes efficiently. Additionally, the controller is able to integrate data from numerous sources, providing a comprehensive view of a process. In this paper, a smart Neuro Controller for Condition Monitoring and smart industrial instrumentation. The Neuro Controller is designed to provide enhanced safety and efficiency in industrial settings. It is capable of detecting potentially hazardous conditions and alerting personnel to take action. Additionally, the controller can be used to automate processes, reducing the risk of accidents and improving process efficiency.
The Intelligent Information Integrity Model to Ensure the Database Protection Using Blockchain in Cloud Networking

In the era of cloud networking, database protection is a problem that is becoming more and more crucial, and blockchain technology is emerging as a potent remedy. Blockchain is a digital ledger technology that uses an immutable, distributed, and decentralized record of transactions to build a secure, transparent, and auditable data protection system. The integrity of data saved in the cloud is further ensured by using blockchain technology. By using cryptographic hashes, data stored on the blockchain can be verified for accuracy and authenticity. This means that any changes or modifications to the data will be detected and addressed. Furthermore, blockchain technology provides a permanent and immutable record of transactions, making it difficult for data to be tampered with or corrupted. In addition to its security benefits, blockchain technology also offers scalability, efficiency, and cost savings. Blockchain-based databases are much more efficient than traditional databases, as they require fewer resources and are able to process a larger volume of transactions. Furthermore, since blockchain technology is decentralized, it can work across multiple devices and networks, allowing for a more efficient and cost-effective solution.
Motion Planning of Dual-Chain Manipulator Based on Artificial Bee Colony Algorithm

A dual-chain manipulator with one fixed base and two kinematic chains is presented in this paper. Aiming at the difficulty of motion planning of dual-chain manipulator, a motion planning method of dual-chain manipulator based on Artificial Bee Colony Algorithm (ABC) is proposed. Firstly, the ABC is used to optimize the initial configuration of the dual-chain manipulator. Then, the end-effector path of the dual-chain manipulator is planned by the Rapidly-Exploring Random Tree Star (RRT*). Finally, the joint trajectory is generated by Gradient Projection Method (GPM) after the initial configuration and end-effector path are determined. In the experimental part, the prototype experiment of the dual-chain manipulator is given, which proves the feasibility of the motion planning method proposed.
Growing Robot Navigation Based on Deep Reinforcement Learning

The recent progress in materials and structures has kick-started the development of soft eversion robot with the ability to grow in size. However, despite its promising capability to navigate challenging terrains, this type of robot still lacks a navigation strategy due to the robot's complexity courtesy of its increasing degrees of freedom as it grows. In this paper, we develop a growing robot navigation strategy based on deep reinforcement learning. The reinforcement learning was specifically designed to work with growing robot even as its degrees of freedom increase. The algorithm was shown to work in navigating growing robot in a planar environment towards a random target. The results show that the reinforcement learning is a promising candidate to be used for growing robot navigation.
On the Use of Deep Neural Networks for Security Vulnerabilities Detection in Smart Contracts

In this paper, we investigate the use of deep learning techniques to identify and classify smart contract code vulnerabilities. We collected a large-scale dataset of smart contracts that we used to train different Convolutional Neural Networks (CNNs) models. In particular, we used two variants of 2-dimensional CNNs working on RGB images corresponding to contract byte-code, a 1-dimensional CNN working on the bytecode directly, and a Long Short-Term Memory (LSTM) neural network. Given a set of vulnerability detectors, we employed five classes of vulnerabilities. Our results show that CNNs provide a good level of accuracy and demonstrate the viability of using deep learning techniques to identify smart contract vulnerabilities.
MichelsonLiSA: A Static Analyzer for Tezos

Smart contracts are immutable code deployed in a blockchain, whose execution modifies its global state. Code im-mutability leads to immutable bugs. To prevent such bugs, static program analysis infers information about the behavior of the code, statically, before code execution and deployment. This paper introduces MichelsonLiSA, a static analyzer based on abstract interpretation for the verification of smart contracts written in the Michelson low-level language of the Tezos blockchain. It applies MichelsonLiSA to the identification of security issues arising from cross-contract invocations.
Breast Microcalcification detection in digital mammograms using Deep Transfer learning approaches

Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women`s at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies.
Reinforcement Learning of Self-enhancing Camera Image and Signal Processing

Current camera image and signal processing pipelines (ISPs), including deep-trained versions, tend to apply a single filter that is uniformly applied to the entire image. This is despite the fact that most acquired camera images have spatially heterogeneous artifacts. This spatial heterogeneity manifests itself across the image space as varied Moire ringing, motion blur, color bleaching, or lens-based projection distortions. Moreover, combinations of these image artifacts can be present in small or large-pixel neighborhoods within an acquired image. Here, we present a deep reinforcement learning model that works in learned latent subspaces and recursively improves camera image quality through patch-based spatially adaptive artifact filtering and image enhancement. Our Recursive Self-Enhancement Reinforcement Learning(RSE-RL) model views the identification and correction of artifacts as a recursive self-learning and self-improvement exercise and consists of two major sub-modules: (i) The latent feature subspace clustering/grouping obtained through variational auto-encoders enabling rapid identification of the correspondence and discrepancy between noisy and clean image patches. (ii) The adaptive learned transformation is controlled by a soft actor-critic agent that progressively filters and enhances the noisy patches using its closest feature distance neighbors of clean patches. Artificial artifacts that may be introduced in a patch-based ISP are also removed through a reward-based deblocking recovery and image enhancement. We demonstrate the self-improvement feature of our model by recursively training and testing on images, wherein the enhanced images resulting from each epoch provide a natural data augmentation and robustness to the RSE-RL training-filtering pipeline. Our code is publicly available at https://github.com/CVC-Lab/rse_rl.git .
A General Correlation Evaluation Model on LFSR-Based Stream Ciphers

In this paper, a general model for evaluating the correlations of correlation attack distinguishers for an LFSR-based stream cipher is given by the Walsh spectrum theory of composite functions. We transform equivalently the linear approximations with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> consecutive keystream words into that of a composite function consisting of several simple functions, which enables cryptanalysts to derive linear approximations of any LFSR-based stream cipher by this model and to search for linear trails with high absolute correlations. This model suits any LFSR-based stream cipher, does not need the implicit independence assumption widely used in previous cryptanalysis, and can theoretically ensure that the correlation obtained is the accurate correlation of a correlation attack distinguisher. In addition, we prove that it is enough to consider the distinguishers where the masks of all LFSR elements are zero except for those of a maximal linearly independent system of LFSR elements involved in the update function and output function. As applications, the approximation processes for the correlation attack distinguishers of SNOW-V, SNOW2.0, ZUC, and Grain-128 are exhibited respectively by this method. Moreover, by the proposed method we can perform a full coverage search for binary linear approximations of them. For SNOW-V, we prove that the approximation given by our model is equivalent to that by Shi et al. at EUROCRYPT 2022, and is simpler and more intuitive. For SNOW2.0, we find more linear approximations with the best correlation. For ZUC, for the first time we get the accurate correlations of a series of linear approximations including the known results, and give the supremum of the absolute correlations for a larger set of linear approximations. For Grain-128, utilizing our method, we rediscover the best known correlation as well, which provides more support for the validity of our general model. Our work can give some evidence for the provable security of LFSR-based stream ciphers against correlation attack to some extent, and may provide the key clues in the analysis of complex stream ciphers.
Some Quaternary Additive Codes Outperform Linear Counterparts

The additive codes may have better parameters than linear codes. However, it is still a challenging problem to efficiently construct additive codes that outperform linear codes, especially those with greater distances than linear codes of the same lengths and dimensions. This paper focuses on constructing additive codes that outperform linear codes based on quasi-cyclic codes and combinatorial methods. Firstly, we propose a lower bound on the symplectic distance of 1-generator quasi-cyclic codes of index even. Secondly, we get many binary quasi-cyclic codes with large symplectic distances utilizing computer-supported combination and search methods, all of which correspond to good quaternary additive codes. Notably, some additive codes have greater distances than best-known quaternary linear codes in Grassl’s code table (bounds on the minimum distance of quaternary linear codes <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.codetables.de</uri> ) for the same lengths and dimensions. Moreover, employing a combinatorial approach, we partially determine the parameters of optimal quaternary additive 3.5-dimensional codes with lengths from 28 to 254. Finally, as an extension, we also construct some good additive complementary dual codes with larger distances than the best-known quaternary linear complementary dual codes in the literature.
A Decentralized Authenticated Key Agreement Scheme Based on Smart Contract for Securing Vehicular Ad-hoc Networks

Since the communication channels in vehicular ad-hoc networks (VANETs) are wireless and open, malicious adversaries can monitor or fabricate messages transmitted across them. To secure vehicular communications, an authenticated key agreement (AKA) scheme needs to be designed for VNAETs. Traditional VANETs AKA schemes require the trusted authority (TA) to authenticate the legality of message and corresponding sender. However, the TA in these schemes is vulnerable to suffer from single-point-of-failure issues. Some blockchain-based VANETs AKA schemes have been proposed recently to address the deficiency. However, these schemes rely on the consortium or private blockchain in which TAs are still required for key generation, resulting that the practicality is limited. To solve the issue, we design a smart contract-based VANETs AKA scheme, where the AKA algorithm of our proposed scheme is implemented on smart contract deployed on a public blockchain system and the TA that is responsible for key generation will not be required. The security proof and analysis show that our proposed scheme satisfies the session-key semantic security and essential security and privacy requirements, respectively. The performance analysis demonstrates that our proposed scheme outperforms existing blockchain-based VANETs AKA schemes.
Robust Information Bottleneck for Task-Oriented Communication With Digital Modulation

Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver. However, only transmitting task-relevant information without introducing any redundancy may cause robustness issues in learning due to the channel variations, and the JSCC which directly maps the source data into continuous channel input symbols poses compatibility issues on existing digital communication systems. In this paper, we address these two issues by first investigating the inherent tradeoff between the informativeness of the encoded representations and the robustness to information distortion in the received representations, and then propose a task-oriented communication scheme with digital modulation, named discrete task-oriented JSCC (DT-JSCC), where the transmitter encodes the features into a discrete representation and transmits it to the receiver with the digital modulation scheme. In the DT-JSCC scheme, we develop a robust encoding framework, named robust information bottleneck (RIB), to improve the communication robustness to the channel variations, and derive a tractable variational upper bound of the RIB objective function using the variational approximation to overcome the computational intractability of mutual information. The experimental results demonstrate that the proposed DT-JSCC achieves better inference performance than the baseline methods with low communication latency, and exhibits robustness to channel variations due to the applied RIB framework.
Artificial Intelligence f or Cybersecurity in IoT-enabled Avionics: Challenges and Solutions

Implementing Internet of Things (IoT)-based services in commercial aviation gives a painless and effortless experience to passengers and crew members, but the mitigation of cyber-attacks is challenging. Available solutions are not enough to overcome various cyber security attacks. Hence, we need more attention to overcome the challenges and cyber risks in commercial aviation. The aim of this paper is to highlight the various factors of cyber threat in avionics and discuss the major security studies conducted to date. The goal is to layout the ground for an insightful discussion about Artificial Intelligence (AI) potential, challenges, and solutions for cybersecurity in avionics. We will focus on how AI and IoT can be used to identify cyber security threats, type of attacks, and infrastructure vulnerabilities. This information will help us in developing effective preventive measures in the future.
Blockchain-Based Authentication Protocol Design from a Cloud Computing Perspective

Cloud computing is a disruptive technology that has transformed the way people access and utilize computing resources. Due to the diversity of services and complexity of environments, there is widespread interest in how to securely and efficiently authenticate users under the same domain. However, many traditional authentication methods involve untrusted third parties or overly centralized central authorities, which can compromise the security of the system. Therefore, it is crucial to establish secure authentication channels within trusted domains. In this context, we propose a secure and efficient authentication protocol, HIDA (Hyperledger Fabric Identity Authentication), for the cloud computing environment. Specifically, by introducing federated chain technology to securely isolate entities in the trust domain, and combining it with zero-knowledge proof technology, users’ data are further secured. In addition, Subsequent Access Management allows users to prove their identity by revealing only brief credentials, greatly improving the efficiency of access. To ensure the security of the protocol, we performed a formal semantic analysis and proved that it can effectively protect against various attacks. At the same time, we conducted ten simulations to prove that the protocol is efficient and reliable in practical applications. The research results in this paper can provide new ideas and technical support for identity authentication in a cloud environment and provide a useful reference for realizing the authentication problem in cloud computing application scenarios.