Reviewerly/exHarmony huggingface dataset:
A Novel Computing Power Allocation Algorithm for Blockchain System in Multiple Mining Pools Under Withholding Attack

To overcome the fast-changing block withholding attacks among multiple mining pools composed of miners in the blockchain system, this paper proposes a mining pool computing power allocation (MPPA) algorithm, which significantly improves the revenues of mining pools with block withholding attacks.MPPA first establishes the revenue optimization model of mining pools, which includes current adequate total computing power, the revenues of honest mining, and the revenues of block withholding attacks.Then MPPA calculates the revenue gain generated by block withholding attacks on other mining pools.To adjust the fixed computing power in each iteration, we have the mining pool computing power allocation algorithm with a fixed change of computing power (MPPA_F).To adjust the optimal recovery and attack computing power, we have the mining pool computing power allocation algorithm with an optimal change of computing power (MPPA_O).The simulation results demonstrate that MPPA_F and MPPA_O can find the optimized solutions of power computing allocation for each mining pool and outperform the state-of-arts such as WSFS, ALLC, and ALLD.
Quantum uncertainty relations of two quantum relative entropies of coherence

The uncertainty relation reveals the intrinsic difference that distinguishes the quantum world from the classical world. In this paper, the quantum uncertainty relations of the sandwiched R\'enyi relative entropy of coherence (SRREOC) and unified $(\ensuremath{\alpha},\ensuremath{\beta})$-relative entropy of coherence (UREOC) are studied. First, the uncertainty relation of the SRREOC for pure states, with extension to mixed states by convex-roof construction, is given in a $d$-dimensional quantum system. Second, the analytical formula of the UREOC is presented; it is shown that the UREOC is a good candidate for a quantum coherence measure. Furthermore, the quantum uncertainty relation of the UREOC for pure states is obtained in a $d$-dimensional quantum system, then it is generalized to mixed states. In addition, our results cover the quantum uncertainty relations of the other three coherence measures. Third, two methods are given to compare the lower bounds of the SRREOC and UREOC for single-qubit pure states. By comparison, we find that the results in this paper not only can be applied to single-qubit states but also can be extended to a $d$-dimensional quantum system.
Motion deblurring using spatiotemporal phase aperture coding

Motion-related image blur is a known issue in photography. In practice, it limits the exposure time while capturing moving objects; thus, achieving proper exposure is difficult. Extensive research has been carried out to compensate for it, to allow increased light throughput without motion artifacts. In this work, a joint optical-digital processing method for motion deblurring is proposed and demonstrated. Using dynamic phase coding in the lens aperture during the image acquisition, the motion trajectory is encoded in an intermediate optical image. This coding embeds cues for both the motion direction and extent by coloring the spatial blur of each object. These color cues serve as guidance for a digital deblurring process, implemented using a convolutional neural network (CNN) trained to utilize such coding for image restoration. Particularly, unlike previous optical coding solutions, our strategy encodes cues with no limitation on the motion direction, and without sacrificing light efficiency. We demonstrate the advantage of the proposed approach over blind deblurring methods with no optical coding, as well as over other solutions that use coded acquisition, in both simulation and real-world experiments.
On the Analysis and Design of Visual Cryptography With Error Correcting Capability

A ( k, n) visual cryptographic scheme (VCS) shares a secret image into n shadow images that are distributed over n involved participants. When k participants stack their shadow images, the secret is revealed. The secret image of VCS is a visual secret. Even though black/white dots in shadows suffer from interference by noise, the color may still retain the corresponding darkness with high probability. Therefore, VCS has noise immunity for secret recovery. Hence, it seems that there is no need to design a VCS that is robust to noise interference when transmitting or storing the files of shadow images. However, some VCSs use the permutations of subpixels in shadow images as information to realize multiple decoding options. For such schemes, we absolutely should ensure the correctness of the shadows. In this article, we investigate a VCS with t-error correcting capability (VCS- tEC). To the best of our knowledge, VCS- tEC is introduced for the first time. Three ( k, n)-VCS- tEC schemes are proposed: the separated scheme, the integrated scheme, and the nonsystematic scheme.
Automated Machine Learning—A Brief Review at the End of the Early Years

Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, preprocessing, model design, and post processing. Major contributions and achievements in AutoML have been taking place during the recent decade. We are, therefore, in perfect timing to look back and realize what we have learned. This chapter aims to summarize the main findings in the early years of AutoML. More specifically, in this chapter an introduction to AutoML for supervised learning is provided and a historical review of progress in this field is presented. Likewise, the main paradigms of AutoML are described and research opportunities are outlined.
A survey of the state-of-the-art swarm intelligence techniques and their application to an inverse design problem

This paper encompasses a detailed review of state-of-the-art swarm-based algorithms, with a focus on their applications along with a discussion on the merits and limitations of each algorithm. Further, a recently developed advanced particle swarm optimization (APSO) algorithm is compared with the different state-of-the-art algorithms through solving an electromagnetic inverse problem. Results show that the APSO algorithm outperforms the other algorithms. This research provides a scientific guideline for the comparison of different swarm-based algorithms and their utilization regarding specific applications.
Currency Identification and Forged Banknote Detection using Deep Learning

Bank note identification is most important approach based on an image processing method. Many techniques and methods are studies involved in the classification of bank notes from different countries conducted experiments on separated image data sets of each country's. Deep learning is machine learning technique that analyze & learns the features of original note. The most important aspect is to find more essential features by using neural networks. In the era of big data where for any real world application, large amount of data has to be processed, deep learning is the superior techniques. In this research, we studied bank note of various countries by extracting its features in depth and analysis it using deep learning. Our system recommended a deep learning-based algorithm to detect Forged bank note through general scanners that can be used by persons to prevent personal monetary damages caused by fake bank note.
Facial Emotion Verification by Infrared Image

Recognition and classification of face-emotion is a vital issue now a day. Emotion bears a resemblance to the people's thought process and based on that a mapping of anyone's activity is possible to establish by analyzing facial expressions. Facial emotion is recognized based on interaction or appearances of eyes, chick, forehead, lips as well as from the whole face in different forms. In this paper, facial emotion is recognized and classified them to create infrared thermal face image data-mask and tried to correlate them based on the variances and standard deviation with EPDF (Enhanced Probability Density Function) of the identified images. During the testing and recognition process, a centralized stored data has been is used to avoid redundancy of data to be stored after recognition. In this experiment, three types of emotions are taken into account and their infrared thermal facial images are recorded simultaneously. In the processing, a calibration procedure is adopted to reduce the variances produced by dissimilar image-set from the same face due to independent parts of a face analysis that are related to facial emotions. Features are taken out from pixel values of classified images. The investigational results of facial images confirmed that the proposed system attained 91.73% accuracy in identification in RGB and 92.39% in infrared images respectively. Whereas as per D. Kumar et al, it is 65% and M. A. Eid has achieved 85% accuracy on identification. The average detection is 91.73% with eight RGB images. Whereas detection from the eight infrared images the average detection rate is 92.39%. This exits the robustness of the suggested methods.
Single Triaxial Accelerometer-Gyroscope Classification for Human Activity Recognition

Evaluated activity as a detail of the human physical movement has become a leading subject for researchers. Activity recognition application is utilized in several areas, such as living, health, game, medical, rehabilitation, and other smart home system applications. For recognizing the activity, the accelerometer was popular sensors. As well as a gyroscope, in addition to dimension, low computation, and can be embedded in a smartphone. Used smartphone with an accelerometer as a popular solution for recognized daily activity. Signal was generated from the accelerometer as a time-series data is an actual approach like a human activity pattern. Traditional machine learning method in mid of the modern method worth it considering. Single position triaxial accelerometer-gyroscope Motion data have acquired in an of 30 volunteers. Basic actives (Laying, Standing, Sitting, Walking, Walking Upstairs, Walking Downstairs) were collected from volunteers. Decision Tree, Random Forest, Extra Trees Classifier, KNN, Logistic Regression, SVC, Ensemble Vote Classifier. The purposed method, logistic regression, achieves 98% accuracy. Furthermore, any feature selection and extraction method were not used.
Customer Data Sharing Platform: A Blockchain-Based Shopping Cart

We propose a new free-ecommerce platform with blockchains that allows customers to connect to the seller directly, share personal data without losing control and ownership of it and apply it to the domain of shopping cart. Our new platform provides a solution to four important problems: private payment, ensuring privacy and user control, and incentives for sharing. It allows the trade to be open, transparent with immutable transactions that can be used for settling any disputes. The paper presents a case study of applying the framework for a shopping cart as one of the enterprise nodes of MultiChain which provides trading in ethers controlled by smart contracts and also collects users' profile data and allows them to receive rewards for sharing their data with other business enterprises. It tracks who shared what, with whom, when, by what means and for what purposes in a verifiable fashion. The user data from the repository is converted into an open data format and shared via stream in the blockchain so that other nodes can efficiently process and use the data. The smart contract verifies and executes the agreed terms of use of the data and transfers digital tokens as a reward to the customer. The smart contract imposes double deposit collateral to ensure that all participants act honestly.
HushRelay: A Privacy-Preserving, Efficient, and Scalable Routing Algorithm for Off-Chain Payments

Payment channel networks (PCN) are used in cryptocurrencies to enhance the performance and scalability of off-chain transactions. Except for opening and closing of a payment channel, no other transaction requests accepted by a PCN are recorded in the Blockchain. In practical scenario, it may not be possible to transfer high valued payment across a single path. For such cases, splitting up the transaction value across multiple paths is a better approach. While there exists several approaches which route transactions via several paths, such techniques are quite inefficient, as the decision on the number of splits must be taken at the initial phase of the routing algorithm [39]. Such decisions are made by not taking into account the residual capacity of each channel in the network. Other approaches leak sensitive information, and are quite computationally expensive [26]. To the best of our knowledge, our proposed scheme HushRelay is an efficient privacy preserving routing algorithm, solving the problems encountered in existing works. On comparing the performance of our algorithm with existing routing scheme SpeedyMurmur [39] on real instances, we observed that HushRelay attains a higher success ratio with less execution time.
Secure image secret sharing over distributed cloud network

A (k, n)-threshold secret image sharing (SIS) scheme protects a secret image by dividing it into n shadow images. When any k or more shadow images are collected, the secret image is reconstructed. While any (k−1) or fewer shadows give no clue about the secret. However, all previous SIS schemes do not deal with secure secret image recovery over distributed cloud network (DCN). To achieve the goal that reconstructing secret over DCN, private communication channels among all involved participants holding shadow images are required. A naive solution for this problem is to use an extra key distribution protocol that delivers a common key to all participants. In this paper, two (k, n)-SIS schemes over DCN (SISDCN) are introduced, where threshold property (recover the secret image from any k shadow images) and secure reconstruction over DCN (noninteractively share a common key among any k participants) are provided. Further, theoretical analysis, experiments and comparisons are conducted to show the effectiveness and advantages of the proposed schemes.
From perception to action using observed actions to learn gestures

Abstract Pervasive computing environments deliver a multitude of possibilities for human–computer interactions. Modern technologies, such as gesture control or speech recognition, allow different devices to be controlled without additional hardware. A drawback of these concepts is that gestures and commands need to be learned. We propose a system that is able to learn actions by observation of the user. To accomplish this, we use a camera and deep learning algorithms in a self-supervised fashion. The user can either train the system directly by showing gestures examples and perform an action, or let the system learn by itself. To evaluate the system, five experiments are carried out. In the first experiment, initial detectors are trained and used to evaluate our training procedure. The following three experiments are used to evaluate the adaption of our system and the applicability to new environments. In the last experiment, the online adaption is evaluated as well as adaption times and intervals are shown.
A probabilistic deontic argumentation framework

What does it mean that something is probably obligatory? And how does it relate to the probability that it is permitted or prohibited? In this paper, we provide a possible answer by merging deontic argumentation and probabilistic argumentation into a probabilistic deontic argumentation framework. This framework allows us to specify a semantics for the probability of deontic statuses. The deontic argumentation part builds on standard concepts from the study of computational models of argument: rule-based arguments, argumentation graphs, argument labelling semantics and statement labelling semantics. We then encapsulate this deontic composition with the approach of probabilistic labellings to probabilistic argumentation, in order to associate deontic statements with probability values. The framework is illustrated with a scenario featuring a violation and a contrary-to-duty obligation.
A new artificial bee colony algorithm employing intelligent forager forwarding strategies

Artificial Bee Colony (ABC) algorithm that mimics the intelligent foraging behaviors of real honey bees has been successfully applied to different types of optimization problems in recent years. Actually, the main reason lying behind the high preference of the ABC algorithm is related with its good performance on solving difficult optimization problems due to the effective search mechanisms existing in a single cycle and easily-implementable bee phases. However, with the purpose of increasing the implementation simplicity and generalizing the principal concept of the algorithm, some significant behaviors of the real foraging bees are not closely simulated and integrated into the workflow of the ABC or tried to be managed by employing simple randomized or conditional operations. In this study, in order to increase the performance of ABC algorithm, the complex behavior of the foraging employed bees related with how they decide to pass through to the dance area and how long they stay on there for informing onlookers is modeled in detail and then a new variant of ABC called intelligent forager forwarding ABC for short iff-ABC is proposed. For analyzing the possible contributions of the intelligent forager forwarding strategy on the performance of the ABC algorithm, thirteen classical benchmark problems and fifteen computationally expensive benchmark problems presented at the CEC 2015 were tested. The results obtained from the experimental studies were compared with the results of the different meta-heuristics in addition to the well-known variants of the standard ABC algorithm. From the experimental studies, it was concluded that the intelligent forager forwarding strategy significantly improves the quality of the final solutions and the convergence speed of the ABC algorithm.
HROM: Learning High-Resolution Representation and Object-Aware Masks for Visual Object Tracking

Siamese network-based trackers consider tracking as features cross-correlation between the target template and the search region. Therefore, feature representation plays an important role for constructing a high-performance tracker. However, all existing Siamese networks extract the deep but low-resolution features of the entire patch, which is not robust enough to estimate the target bounding box accurately. In this work, to address this issue, we propose a novel high-resolution Siamese network, which connects the high-to-low resolution convolution streams in parallel as well as repeatedly exchanges the information across resolutions to maintain high-resolution representations. The resulting representation is semantically richer and spatially more precise by a simple yet effective multi-scale feature fusion strategy. Moreover, we exploit attention mechanisms to learn object-aware masks for adaptive feature refinement, and use deformable convolution to handle complex geometric transformations. This makes the target more discriminative against distractors and background. Without bells and whistles, extensive experiments on popular tracking benchmarks containing OTB100, UAV123, VOT2018 and LaSOT demonstrate that the proposed tracker achieves state-of-the-art performance and runs in real time, confirming its efficiency and effectiveness.
Human Robot Interaction on Navigation platform using Robot Operating System

In the last few decades, automation has been one of the leading and emerging technologies to develop sophisticated and robust robotic solutions for various applications like military, agriculture, transport, shopping, house, hospitals, etc. One of these emerging fields is service robotics which is implemented in the shop, home, and hospital environments. As these domains deal with the interaction between Humans and Robots, the concept called Human-Robot Interaction (HRI) has come into existence. In service robotics, voice is the most important mode of communication for HRI. This research proposes the implementation and integration of such HRI on an autonomous navigation system using the Robot Operating System (ROS). This paper also discusses the system architecture that is developed for this research.
Revisiting process versus product metrics: a large scale analysis

Numerous methods can build predictive models from software data. However, what methods and conclusions should we endorse as we move from analytics in-the-small (dealing with a handful of projects) to analytics in-the-large (dealing with hundreds of projects)? To answer this question, we recheck prior small-scale results (about process versus product metrics for defect prediction and the granularity of metrics) using 722,471 commits from 700 Github projects. We find that some analytics in-the-small conclusions still hold when scaling up to analytics in-the-large. For example, like prior work, we see that process metrics are better predictors for defects than product metrics (best process/product-based learners respectively achieve recalls of 98%/44% and AUCs of 95%/54%, median values). That said, we warn that it is unwise to trust metric importance results from analytics in-the-small studies since those change dramatically when moving to analytics in-the-large. Also, when reasoning in-the-large about hundreds of projects, it is better to use predictions from multiple models (since single model predictions can become confused and exhibit a high variance).