Reviewerly/exHarmony huggingface dataset:
LightweightNet: Toward fast and lightweight convolutional neural networks via architecture distillation

In recent years, deep neural networks have achieved remarkable successes in many pattern recognition tasks. However, the high computational cost and large memory overhead hinder them from applications on resource-limited devices. To address this problem, many deep network acceleration and compression methods have been proposed. One group of methods adopt decomposition and pruning techniques to accelerate and compress a pre-trained model. Another group designs single compact unit to stack their own networks. These methods are subject to complicated training processes, or lack of generality and extensibility. In this paper, we propose a general framework of architecture distillation, namely LightweightNet, to accelerate and compress convolutional neural networks. Rather than compressing a pre-trained model, we directly construct the lightweight network based on a baseline network architecture. The LightweightNet, designed based on a comprehensive analysis of the network architecture, consists of network parameter compression, network structure acceleration, and non-tensor layer improvement. Specifically, we propose the strategy of low-dimensional features of fully-connected layers for substantial memory saving, and design multiple efficient compact blocks to distill convolutional layers of baseline network with accuracy-sensitive distillation rule for notable time saving. Finally, it can effectively reduce the computational cost and the model size by >4× with negligible accuracy loss. Benchmarks on MNIST, CIFAR-10, ImageNet and HCCR (handwritten Chinese character recognition) datasets demonstrate the advantages of the proposed framework in terms of speed, performance, storage and training process. In HCCR, our method even outperforms traditional handcrafted features-based classifiers in terms of speed and storage while maintaining state-of-the-art recognition performance.
Integration enhanced and noise tolerant ZNN for computing various expressions involving outer inverses

An integration-enhanced noise-tolerant zeroing neural network (IENTZNN) model for computing various expressions involving outer inverses is defined and considered. The model assumes an input matrix A, two appropriately selected matrices F, G, a regularization parameter, and will be shortly denoted by IENTZNN(A, F, G). Particularly, IENTZNN(A, F, G) is applicable in computing real-time-varying matrix outer inverse with prescribed range and null space under the presence of various kinds of noises. The model is an extension of the IEZNN model for solving the problem of real-time matrix inversion as well as an integration-enhanced and noise-tolerant extension of the ZNNATS2 model for approximating time-varying outer inverse with prescribed range and null space. Theoretical analyses show that the proposed integration-enhanced model globally and exponentially converges to the theoretical solution. Moreover, the proposed IENTZNN(A, F, G) model is proven to have a very good performance in the presence of various kinds of noise. Finally, illustrative simulation examples are presented to testify the efficacy of the proposed model.
A survey on Laplacian eigenmaps based manifold learning methods

As a well-known nonlinear dimensionality reduction method, Laplacian Eigenmaps (LE) aims to find low dimensional representations of the original high dimensional data by preserving the local geometry between them. LE has attracted great attentions because of its capability of offering useful results on a broader range of manifolds. However, when applying it to some real-world data, several limitations have been exposed such as uneven data sampling, out-of-sample problem, small sample size, discriminant feature extraction and selection, etc. In order to overcome these problems, a large number of extensions to LE have been made. So in this paper, we make a systematical survey on these extended versions of LE. Firstly, we divide these LE based dimensionality reduction approaches into several subtypes according to different motivations to address the issues existed in the original LE. Then we successively discuss them from strategies, advantages or disadvantages to performance evaluations. At last, the future works are also suggested after some conclusions are drawn.
A Bio-Inspired Self-Learning Coevolutionary Dynamic Multiobjective Optimization Algorithm for Internet of Things Services

The ultimate goal of the Internet of Things (IoT) is to provide ubiquitous services. To achieve this goal, many challenges remain to be addressed. Inspired from the cooperative mechanisms between multiple systems in the human being, this paper proposes a bio-inspired self-learning coevolutionary algorithm (BSCA) for dynamic multiobjective optimization of IoT services to reduce energy consumption and service time. BSCA consists of three layers. The first layer is composed of multiple subpopulations evolving cooperatively to obtain diverse Pareto fronts. Based on the solutions obtained by the first layer, the second layer aims to further increase the diversity of solutions. The third layer refines the solutions found in the second layer by adopting an adaptive gradient refinement search strategy and a dynamic optimization method to cope with changing concurrent multiple service requests, thereby effectively improving the accuracy of solutions. Experiments on agricultural IoT services in the presence of dynamic requests under different distributions are performed based on two service-providing strategies, i.e., single service and collaborative service. The simulation results demonstrate that BSCA performs better than four existing algorithms on IoT services, in particular for high-dimensional problems.
Human factor security: evaluating the cybersecurity capacity of the industrial workforce

Purpose As cyber-attacks continue to grow, organisations adopting the internet-of-things (IoT) have continued to react to security concerns that threaten their businesses within the current highly competitive environment. Many recorded industrial cyber-attacks have successfully beaten technical security solutions by exploiting human-factor vulnerabilities related to security knowledge and skills and manipulating human elements into inadvertently conveying access to critical industrial assets. Knowledge and skill capabilities contribute to human analytical proficiencies for enhanced cybersecurity readiness. Thus, a human-factored security endeavour is required to investigate the capabilities of the human constituents (workforce) to appropriately recognise and respond to cyber intrusion events within the industrial control system (ICS) environment.
Privacy aware IOTA ledger: Decentralized mixing and unlinkable IOTA transactions

IOTA is a distributed ledger technology for the Internet-of-Things (IoT) industry. The protocol distinguishes itself from existing distributed ledgers by being formed on a directed acyclic graph. To enable micro-transactions for smart devices, it uses a scalable approach for network growth and transaction confirmations. Being a public distributed ledger, the transactions on the ledger are completely transparent hence opening up the possibilities for linking and identification attacks. Different promising privacy enhancing techniques have been proposed for improving anonymity in distributed ledgers. However, many of the proposed approaches provide security guarantees only against Elliptic Curve Digital Signature (ECDSA) schemes and thus become incompatible with the IOTA ledger because IOTA uses quantum resilient hash-based signatures. While centralized solutions can still work with IOTA ledger for enhancing privacy, they are still proprietary and prone to single point of failures. We propose a novel decentralized mixing protocol for the IOTA ledger that incorporates a combination of decryption mixnets and multi-signatures. Our technique does not require any (trusted or accountable) third party and it is completely compatible with the IOTA protocol. Analysis of our results for this technique shows that the security and privacy are guaranteed even in the presence of malicious entities in the system. Our technique provides strong privacy to the IOTA ledger and the degree of anonymity it adds, protects entities against identification and linking attacks.
Deep Multitask Gaze Estimation with a Constrained Landmark-Gaze Model

As an indicator of attention, gaze is an important cue for human behavior and social interaction analysis. Recent deep learning methods for gaze estimation rely on plain regression of the gaze from images without accounting for potential mismatches in eye image cropping and normalization. This may impact the estimation of the implicit relation between visual cues and the gaze direction when dealing with low resolution images or when training with a limited amount of data. In this paper, we propose a deep multitask framework for gaze estimation, with the following contributions. (i) we proposed a multitask framework which relies on both synthetic data and real data for end-to-end training. During training, each dataset provides the label of only one task but the two tasks are combined in a constrained way. (ii) we introduce a Constrained Landmark-Gaze Model (CLGM) modeling the joint variation of eye landmark locations (including the iris center) and gaze directions. By relating explicitly visual information (landmarks) to the more abstract gaze values, we demonstrate that the estimator is more accurate and easier to learn. (iii) by decomposing our deep network into a network inferring jointly the parameters of the CLGM model and the scale and translation parameters of eye regions on one hand, and a CLGM based decoder deterministically inferring landmark positions and gaze from these parameters and head pose on the other hand, our framework decouples gaze estimation from irrelevant geometric variations in the eye image (scale, translation), resulting in a more robust model. Thorough experiments on public datasets demonstrate that our method achieves competitive results, improving over state-of-the-art results in challenging free head pose gaze estimation tasks and on eye landmark localization (iris location) ones.
A novel recurrent neural network and its finite-time solution to time-varying complex matrix inversion

A complex-valued nonlinear recurrent neural network is designed and researched for time-varying matrix inversion solving in complex field. Unlike the design methods of the conventional gradient neural network (CGNN) and the previous Zhang neural network (ZNN), the proposed complex-valued nonlinear recurrent neural network (CVNRNN) model is established on basis of a nonlinear evolution formula and possesses a better finite-time convergence Besides, the detailed theoretical analysis provides a guarantee for the finite-time convergence achievement of the CVNRNN model. In addition, the theoretical analysis is also verified by numerical simulations, which comparatively show that the proposed CVNRNN model is faster and more accurate than the ZNN model and the CGNN model in solving time-varying complex matrix inversion.
Systematic identification of threats in the cloud: A survey

When a vulnerability is discovered in a system, some key questions often asked by the security analyst are what threat(s) does it pose, what attacks may exploit it, and which parts of the system it affects. Answers to those questions provide the necessary information for the security assessment and to implement effective countermeasures. In the cloud, this problem is more challenging due to the dynamic characteristics, such as elasticity, virtualization, and migration - changing the attack surface over time. This survey explores threats to the cloud by investigating the linkages between threats, attacks and vulnerabilities, and propose a method to identify threats systematically in the cloud using the threat classifications. First, we trace vulnerabilities to threats by relating vulnerabilities-to-attacks, and then relating attacks-to-threats. We have established the traceability through an extensive literature review and synthesis that resulted in a classification of attacks in the cloud, where we use the Microsoft STRIDE threat modeling approach as a guide for relating attacks to threats. Our approach is the genesis towards a concrete method for systematically identifying potential threats to assets provisioned and managed through the cloud. We demonstrate the approach through its application using a cloud deployment case study scenario.
Embedding Bilateral Filter in Least Squares for Efficient Edge-Preserving Image Smoothing

Edge-preserving smoothing is a fundamental procedure for many computer vision and graphic applications. This can be achieved with either local methods or global methods. In most cases, global methods can yield superior performance over the local ones. However, local methods usually run much faster than the global ones. In this paper, we propose a new global method that embeds the bilateral filter (BLF) in the least squares (LS) model for efficient edge-preserving smoothing. The proposed method can show comparable performance with the state-of-the-art global method. Meanwhile, since the proposed method can take advantages of the efficiency of the BLF and the LS model, it runs much faster. In addition, we show the flexibility of our method which can be easily extended by replacing the BLF with its variants. They can be further modified to handle more applications. We validate the effectiveness and efficiency of the proposed method through comprehensive experiments in a range of applications.
The open blockchain-aided multi-agent symbiotic cyber–physical systems

Cyber–physical systems combine physical objects or systems with integrated computational facilities and data storage. From that perspective, Smart Grids are a mixture of physical actuators, sensors, physical communication networks; and cyber computational and communication protocols and algorithms. In our work, we propose to extend the applicability of decentralized blockchain consensus protocols to the broad field of Cyber–Physical systems. Our contribution includes a proposal of a new family of cyber–physical systems, an in-depth discussion of new possibilities and inherent shortcomings. The proposed coupling of the data storage mechanisms, communication and consensus protocols, allows for deployment of self-sustaining cyber–physical environments in which all the mission-critical aspects in both the cyber and physical layer are effectively incentivized, coordinated and maintained. This includes the essential data storage, data exchange, but also the production, transmission and distribution of energy. To the best of our knowledge, it is the first such a comprehensive undertaking toward symbiotic cyber–physical systems and environments. The proposed family of systems is decentralized and does not imply the necessity of reliance on trusted third parties. We propose and discuss an architecture which incorporates physical, blockchain-enabled sensors and actuators capable of monitoring and altering parameters of the underlying physical layer. We analyze a particular instance of a blockchain-aided CPS’, where we are concerned with the power grid at both the micro and macro-grid level. At the macro-grid level we explore the possibility of utilizing blockchain-enabled physical actuators. These theoretical actuators are able to introduce savings by a near real-time routing of energy and enable for tracking of power losses thanks to decentralized accountability. At the micro-grid level we propose a network of blockchain-enabled smart meters to coordinate real-time auction-based energy trade. To glue together the elements of such an open, trustless, decentralized system, we discuss an intrinsic communication protocol capable of rewarding intermediaries of a data exchange for each transmitted byte. We tackle the feasibility, security and accountability challenges which we have faced and which will need to be considered by future architects of the new, exciting family of cyber–physical systems.
Profile-based power-aware workflow scheduling framework for energy-efficient data centers

In the age of big data, software-as-a-service (SaaS) clouds provide heterogeneous and multitenant utilization of underlying virtual environments in data centers. Real-time and parallel deployment of applications with data-intensive workloads of various sizes pose challenges in optimal resource scheduling, power utilization, task completion time, network latency, and so on, causing degradation in the quality of service and affecting the user experience. In this paper, we investigate the role of application profiles in addressing the tradeoff between performance and energy efficiency of small- to medium-scale data centers. A power-aware framework for efficient placement of application workloads in the data center is proposed. The framework considers various application workflow constraints, such as CPU, memory, network I/O, and power consumption requirements to develop realistic profiles of application workloads. A system model for the efficient workflow assignment in the data center using a novel scheduler algorithm is presented. The performance of the proposed scheduler is validated through simulation studies. We compare the proposed scheduler with two scheduling algorithms: robust time cost (RTC) and heterogeneous earliest finish time (HEFT). Results show that the proposed scheduler is 19% and 38% more energy efficient than RTC and HEFT, respectively for medium–large sized workloads.
Quantum algorithm and quantum circuit for A-optimal projection: Dimensionality reduction

Learning low dimensional representation is a crucial issue for many machine learning tasks such as pattern recognition and image retrieval. In this article, we present a quantum algorithm and a quantum circuit to efficiently perform A-Optimal Projection for dimensionality reduction. Compared with the best-know classical algorithms, the quantum A-Optimal Projection (QAOP) algorithm shows an exponential speedup in both the original feature space dimension $n$ and the reduced feature space dimension $k$. We show that the space and time complexity of the QAOP circuit are $O\left[ {{{\log }_2}\left( {nk} /{\epsilon} \right)} \right]$ and $O[ {\log_2(nk)} {poly}\left({{\log }_2}\epsilon^{-1} \right)]$ respectively, with fidelity at least $1-\epsilon$. Firstly, a reformation of the original QAOP algorithm is proposed to help omit the quantum-classical interactions during the QAOP algorithm. Then the quantum algorithm and quantum circuit with performance guarantees are proposed. Specifically, the quantum circuit modules for preparing the initial quantum state and implementing the controlled rotation can be also used for other quantum machine learning algorithms.
Attribute mapping and autoencoder neural network based matrix factorization initialization for recommendation systems

The recommendation algorithm is attracting increasing attention in analyzing big data. Matrix factorization (MF) is one of the recommendation methods and Singular Value Decomposition (SVD) is the most popular matrix factorization method. However, the existing SVD methods usually initialize user and item feature randomly, not fully utilize the information of the data, so require plenty of experiments to determine feature matrix dimension, with low convergence efficiency and low accuracy. This paper presents a hybrid initialization method based on attribute mapping and autoencoder neural network to solve these problems, which consists of three parts: (1) use the number of item attribute types to determine feature matrix dimension in order to avoid multiple experiments to select the optimal dimension value; (2) use items’ attributes to initialize the item feature matrix in SVD＋＋, and use an attribute mapping mechanism to get an item feature vector by fitting the rating matrix to accelerate the convergence; (3) adopt the autoencoder neural network to reduce feature dimension and obtain item latent features for initializing SVD＋＋. The experimental results show that our methods achieve better performance than SVD＋＋ random initialization and also be adopted to other matrix factorization methods.
Trusted Cloud Computing Architectures for infrastructure as a service: Survey and systematic literature review

Cloud computing is no longer the future but the present. Security and trust are critical in cloud computing, but how can cloud service tenants trust cloud service providers to store all their private data on the cloud? Trusted computing is one of the new technologies in the last decade, and the integration between cloud computing and trusted computing can create a new architecture for infrastructure as a service that motivates more cloud service tenants to trust cloud service providers. This paper provides a survey and systematic literature review on the suggested architectures for this integration.
IPSO Task Scheduling Algorithm for Large Scale Data in Cloud Computing Environment

Task scheduling is one of the essential techniques in the cloud computing environment. It is required for allocating tasks to the proper resources and optimizing the overall system performance. Particle swarm optimization (PSO) algorithm is one of the most popular scheduling algorithms, which is used to maximize resource utilization. However, the performance of the PSO scheduling algorithm decreases when the number of tasks is significant. In this paper, the improved PSO (IPSO) algorithm is proposed to provide the optimal allocation for a large number of tasks. This is achieved by splitting the submitted tasks into batches in a dynamic way. The resources utilization state is considered in each creation of batches. After getting a sub-optimal solution for each batch, the algorithm appends all the sub-optimal solutions for batches into a final allocation map. Finally, IPSO tries to balance the loads over the final allocation map. The proposed algorithm is compared with different scheduling algorithms, namely, honey bee, ant colony, and round-robin algorithms. The results of experiments show the efficiency of the proposed algorithm in terms of makespan, standard deviation of load, and degree of imbalance.
A noise correction-based approach to support a recommender system in a highly sparse rating environment

Recommender systems support consumers in decision-making for selecting desired products or services from an overloaded search space. However, this decision support system faces difficulties while dealing with sparse and noisy rating data. Therefore, this research re-classifies users and items of a system into three classes, namely strong, average and weak to identify and correct noise ratings. Later, the Bhattacharya coefficient, a well-performing similarity measure for a sparse dataset, is integrated with the proposed re-classification method to predict unrated items from the obtained noise-free sparse dataset and recommend preferred products to consumers. Furthermore, the effectiveness of the proposed model is validated on two sparse and noisy datasets and compared with various published methods in terms of the mean absolute error (MAE), root mean square error (RMSE), F1-measure, precision, and recall values. The obtained results confirm that the proposed model performs better than other published relevant methods.