Reviewerly/exHarmony huggingface dataset:
Randomness invalidates criminal smart contracts

A smart contract enforces specific performance on anonymous users without centralization. It facilitates payment equity in commerce by providing irreversible transactions. Smart contracts are also used for illegal activities such as money laundering and ransomware. Such contracts include criminal smart contracts (CSCs), proposed in CCS'16, that can be efficiently implemented in existing scripting languages. This aggravates concerns about the dangers of CSCs. However, PublicLeaks, a CSC for leaking private data, is conditionally implemented as it is influenced by various factors. For example, PublicLeaks does not necessarily reach a desirable terminal state for a criminal leaking private information, and other possible terminal states may invalidate the CSC. In this study, we propose a CSC based on PublicLeaks by formulating random factors such as the donation ratio. Our contract forks into five terminal states, including a unique one in PublicLeaks due to randomness. We simulated the maximal probabilities of these terminal states and found that the desirable terminal state in PublicLeaks is reachable with low probabilities (lower than 25%). The terminal state where the criminal fails to leak private information is attained with relatively high probabilities (over 65%). Therefore, our simulations show that CSCs are not always as powerful as expected, and the risk posed by them can be mitigated.
Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces

The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces.
Energy-aware cost prediction and pricing of virtual machines in cloud computing environments

With the increasing cost of electricity, Cloud providers consider energy consumption as one of the major cost factors to be maintained within their infrastructure. Consequently, various proactive and reactive management mechanisms are used to efficiently manage the cloud resources and reduce the energy consumption and cost. These mechanisms support energy-awareness at the level of Physical Machines (PM) as well as Virtual Machines (VM) to make corrective decisions. This paper introduces a novel Cloud system architecture that facilitates an energy aware and efficient cloud operation methodology and presents a cost prediction framework to estimate the total cost of VMs based on their resource usage and power consumption. The evaluation on a Cloud testbed show that the proposed energy-aware cost prediction framework is capable of predicting the workload, power consumption and estimating total cost of the VMs with good prediction accuracy for various Cloud application workload patterns. Furthermore, a set of energy-based pricing schemes are defined, intending to provide the necessary incentives to create an energy-efficient and economically sustainable ecosystem. Further evaluation results show that the adoption of energy-based pricing by cloud and application providers creates additional economic value to both under different market conditions.
Bio-inspired on-line path planner for cooperative exploration of unknown environment by a Multi-Robot System

This paper aims to present a cooperative and distributed navigation strategy, that is an on-line path planner, for an autonomous multi-robot system. The robots are intended to navigate and explore an unknown environment in order to find and reach obligatory passage points or way-points (goals), and then achieve a known final position. All robots in the team are homogeneous, independent and have limited communication skills. However they interact among them and with the environment to autonomously decide about their paths and tasks: if they should explore the environment, or avoid visiting a previously explored region, or to reach a discovered goal. Information sharing is directly carried out when the robots are into a communication area and/or indirectly by stigmergy. In this case, artificial pheromone, as a repulsive field, is used to mark regions that have already been explored by other members of the team, therefore avoiding redundant exploration and time waste. Fuzzy controllers are used for robots' motion. The proposed on line path planner performance is evaluated in different simulated environment scenarios and the main results are presented.
An Improved Dynamic Fault Tolerant Management Algorithm during VM migration in Cloud Data Center

Virtualization technology is widely accomplished in the data centers of the Cloud, for computing resources. The computation of the data for corresponding users creates via a group of connected Virtual Machines (VM) in an accessible and flexible manner. It improves the utilization of resources and thus it minimizes the usage of more hardware. The VM based infrastructure overhaul schemes are necessary to repossess affected services in an appropriate manner. Dedicated routing clarifications are also desirable for the improvement of providing reliability of Virtual DC (Domain Controller) services. In the occasion of node failure, the corresponding virtual machine face the challenges in determining which and how many VMs should be selected for migration to maintain reliability. The selection of VM plays an important part in limiting the number of VM migrations. The problem consists of selecting one or more potential VMs for migration to reduce the resource load of the considered servers. We propose a new Dynamic Fault Tolerant VM Migration (DFTM) for the enforcement of cloud data center infrastructure reliability through advanced recovery mechanism of VN (Virtual Network) demands. Integer Linear Programming model is implemented for analyzing the route traffic which considers all related numerical factors to choose optimal VM via the optimal path. Then, Alternative Switch Identification Algorithm determines the new VM migration and routing is accomplished. We used Cloudsim to implement and measure the effectiveness of the proposed DFTM algorithm. Then, we compared our results with existing methodologies in terms of overall execution time, average resource usage time, disk usage, and other performance metrics. Experimental results illustrate that the proposed fault recovery algorithm enhances the survivability of the VM based cloud data center.
Efficient Ratcheting: Almost-Optimal Guarantees for Secure Messaging

In the era of mass surveillance and information breaches, privacy of Internet communication, and messaging in particular, is a growing concern. As secure messaging protocols are executed on the not-so-secure end-user devices, and because their sessions are long-lived, they aim to guarantee strong security even if secret states and local randomness can be exposed. The most basic security properties, including forward secrecy, can be achieved using standard techniques such as authenticated encryption. Modern protocols, such as Signal, go one step further and additionally provide the so-called backward secrecy, or healing from state exposures. These additional guarantees come at the price of a moderate efficiency loss (they require public-key primitives). On the opposite side of the security spectrum are the works by Jaeger and Stepanovs and by Poettering and Rösler, which characterize the optimal security a secure-messaging scheme can achieve. However, their proof-of-concept constructions suffer from an extreme efficiency loss compared to Signal. Moreover, this caveat seems inherent. This paper explores the area in between: our starting point are the basic, efficient constructions, and then we ask how far we can go towards the optimal security without losing too much efficiency. We present a construction with guarantees much stronger than those achieved by Signal, and slightly weaker than optimal, yet its efficiency is closer to that of Signal (only standard public-key cryptography is used). On a technical level, achieving optimal guarantees inherently requires key-updating public-key primitives, where the update information is allowed to be public. We consider secret update information instead. Since a state exposure temporally breaks confidentiality, we carefully design such secretly-updatable primitives whose security degrades gracefully if the supposedly secret update information leaks.
Quantum Circuits for the CSIDH: Optimizing Quantum Evaluation of Isogenies

Choosing safe post-quantum parameters for the new CSIDH isogeny-based key-exchange system requires concrete analysis of the cost of quantum attacks. The two main contributions to attack cost are the number of queries in hidden-shift algorithms and the cost of each query. This paper analyzes algorithms for each query, introducing several new speedups while showing that some previous claims were too optimistic for the attacker. This paper includes a full computer-verified simulation of its main algorithm down to the bit-operation level.
Effective Knowledge Based Recommender System for Tailored Multiple Point of Interest Recommendation

With the massive growth of the internet, a new paradigm of recommender systems (RS's) is introduced in various real time applications. In the research for better RS's, especially in the travel domain, the evolution of location-based social networks have helped RS's to understand the changing interests of users. In this article, the authors present a new travel RS employed on the mobile device to generate personalized travel planning comprising of multiple Point of Interests (POIs). The recommended personalized list of travel locations will be predicted by generating a heat map of already visited POIs and the highly relevant POIs will be selected for recommendation as destinations. To enhance the recommendation quality, this article exploits the temporal features for increased user visits. A personalized travel plan is recommended to the user based on the user selected POIs and the proposed travel RS is experimentally evaluated with the real-time large-scale dataset. The obtained results of the developed RS are found to be proficient by means of improved diversity and accuracy of generated recommendations.
Real-time 3D surface-shape measurement using background-modulated modified Fourier transform profilometry with geometry-constraint

A new real-time 3D measurement method was developed using new background-modulated modified Fourier transform profilometry (FTP) fringe patterns and geometry constraints. The method uses only two new high-frequency fringe patterns for every 3D reconstruction from phase maps. A short camera-projector baseline allows only two corresponding-point candidates in the measurement volume for reliable corresponding-point selection, even with high-frequency fringe patterns, and a new calibration approach reduces error induced by the short baseline. The new background-modulated modified FTP method had higher measurement accuracy in dynamic measurement of a moving object than using phase-shifting-profilometry with background modulation and geometry constraints. GPU processing permits 3D reconstruction in parallel with image capture.
Image encryption using exclusive-OR with DNA complementary rules and double random phase encoding

We propose an optical image encryption scheme based on the Deoxyribonucleic Acid (DNA) theory and the double random phase encoding (DRPE) technique. The piecewise linear chaotic map (PWLCM) is used to generate key images and random phase masks, and to determine DNA encoding rules. In order to achieve ultra-fast DNA encryption, we propose using an optical exclusive-OR (XOR) gate to achieve XOR operation in DNA encryption. Different plaintexts use different initial values of PWLCM, which are generated by Message Digest Algorithm 5 (MD5). The plaintext is encrypted by two rounds of DNA and then by DRPE to form a ciphertext. Numerical simulation and the analysis of attacks on encrypted image are implemented to demonstrate the security and validity of the proposed approach.
Multiview-Based 3-D Action Recognition Using Deep Networks

In multiview learning, views may be obtained from multiple sources or extracted from a single source as different features. In this paper, effective multiple views from skeleton sequences are proposed to learn the discriminative features using multiple networks for three-dimensional human action recognition. Specifically, three views are constructed in the spatial domain and fed to a stack of long short-term memory networks to exploit temporal information and three views are constructed using the improved joint trajectory maps and fed to three convolutional neural networks to exploit spatial information. Multiply fusion is used to combine the recognition scores of all views. The proposed method has been verified and achieved the state-of-the-art results on the widely used UTD-MHAD, MSRC-12 Kinect Gesture, and NTU red, green, blue (RGB)+D datasets.
Active contours driven by global and local weighted signed pressure force for image segmentation

This paper proposes a new global and local weighted signed pressure force (SPF) based active contour model (ACM) to segment various types of images. First, by introducing the normalized global minimum absolute differences as the coefficients of global inner and outer region fitting centers, a new global weighted SPF (GWSPF) is defined, which makes the best of the difference information of inner and outer regions and improves segmentation performance. Second, by introducing the normalized local minimum absolute differences as the coefficients of local inner and outer region fitting centers similarly, a new local weighted SPF (LWSPF) is defined and added to the above global weighted SPF. Third, the global and local within-class variances of the image are used to weight the GWSPF and the LWSPF, which can automatically adjust the effect degrees of the GWSPF and the LWSPF. Experiments on many kinds of real-world images have validated that the proposed model is superior to popular ACMs in segmentation accuracy, in addition, it is robust to the initial curve.
A survey of recent work on fine-grained image classification techniques

Image classification is a hot topic in image processing. Image classification aims to automatically classify large numbers of images. Many methods have been proposed for solving this task. Traditional methods usually leverage low-level features. Clustering is the most commonly used method of image classification. In recent years, convolutional neural networks (CNNs) is widely used in extracting deep features. Many network architectures are proposed for image classification, such as ResNeXt, Cifar10. These deep learning methods aims at fusing features of texture, color and segmentation. In this paper, we discuss the different methods and techniques of image classification, and made a detailed summary of their performance. We believe that our work plays an important role in the field of image classification.
Investigating surrogate-assisted cooperative coevolution for large-Scale global optimization

Problems involving large-scale global optimization (LSGO) are becoming more and more frequent. For this reason, the last few years have seen an increasing number of researchers interested in improving optimization metaheuristics in such a way as to cope effectively with high-dimensional search domains. Among the techniques to enhance scalability, one of the most studied is Cooperative Coevolution (CC), an effective divide-and-conquer strategy for decomposing a large-scale problem into lower-dimensional subcomponents. However, despite the progress made in the LSGO field, one of such optimizations can still require a very high number of objective function evaluations. Therefore, when the evaluation of a candidate solution requires complex calculations, LSGO can become a challenging task. Nonetheless, to date few studies have investigated the application of optimization metaheuristics to objective functions that are simultaneously high-dimensional and computationally significant. To address such a research issue, this article investigates a surrogate-assisted CC (SACC) optimizer, in which fitness surrogates are exploited within the low-dimensional subcomponents resulting from the problem decomposition. The SACC algorithm is investigated on a rich test-bed composed of 1000-dimensional problems. According to the results, SACC is able to significantly boost the convergence of the CC optimizer, leading in many cases to a relevant computational gain.
Autonomic decentralized elasticity based on a reinforcement learning controller for cloud applications

Web applications have stringent performance requirements that are sometimes violated during periods of high demand due to lack of resources. Infrastructure as a Service (IaaS) providers have made it easy to provision and terminate compute resources on demand. However, there is a need for a control mechanism that is able to provision resources and create multiple instances of a web application in response to excess load events. In this paper, we propose and implement a reinforcement learning-based controller that is able to respond to volatile and complex arrival patterns through a set of simple states and actions. The controller is implemented within a distributed architecture that is able to not only scale up quickly to meet rising demand but also scale down by shutting down excess servers to save on ongoing costs. We evaluate this decentralized control mechanism using workloads from real-world use cases and demonstrate that it reduces SLA violations while minimizing cost of provisioning infrastructure.
Shadowed Type-2 Fuzzy Systems for Dynamic Parameter Adaptation in Harmony Search and Differential Evolution Algorithms

Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization.
Path planning for autonomous bulldozers

Komatsu Ltd. is reinforcing the R&D of automation technology for our earth-movers. Based on this trend, a path planning methodology for autonomous bulldozers is proposed and developed. This methodology autonomously plans an efficient path depending on a given material profile. Conventionally, existing path planning algorithms are versatile so they can be applied to any application, typically by using a grid-based map. However, in reality, a substantial effort is still required when applying to specific industry products. In contrast to this trend, the aim of this work is to develop a path planning algorithm specifically suitable for bulldozers, from the theory development phase. The novel planning methodology was developed by incorporating industry feedback, and has successfully resolved the issues which occurred when attempting to apply the existing planning methodologies. As a result of this work, the developed methodology provides an efficient path that lets bulldozers complete given tasks with minimal operation time, without a human operator on-board, and can be applied to commercial machines immediately.
A Pipeline Neural Network for Low-Light Image Enhancement

Low-light image enhancement is an important challenge in computer vision. Most of the low-light images taken in low-light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi-scale retinex, we present a low-light image enhancement pipeline network based on an end-to-end fully convolutional networks and discrete wavelet transformation (DWT). First, we show that multiscale retinex (MSR) can be considered as a convolutional neural network with Gaussian convolution kernel, and blending the result of DWT can improve the image produced by MSR. Second, we propose our pipeline neural network, consisting of denoising net and low-light image enhancement net, which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in the synthetic dataset and public dataset. The experiments reveal that in comparison with other state-of-the-art methods, our methods achieve a better performance in the perspective of qualitative and quantitative analyses.