Reviewerly/exHarmony huggingface dataset:
Skywork-daVinci: A Novel CPSS-based Painting Support System

There are many artistic support systems available for artists, but none of them provides high-grade collaborative support for traditional artistic painting as an assistant. To collaborate with human artists, the support system should involve essential artistic procedures and be capable of imitating basic artistic techniques of human artists. In this paper, we address this issue and introduce a novel painting support system, namely Skywork-daVinci, based on a human-in-the-loop mechanism. A CPSS-based approach is proposed for collaboration between human artists and machines. Based on the approach, we develop a robotic painting system to imitate painting techniques of artists. Then, an acrylic painting case is presented to demonstrate the feasibility of our system. Furthermore, comparisons between existing robotic painting systems and ours are discussed. As experimental results show, our robotic painting system could imitate the essential painting techniques of human artists and reproduce the paintings drawn by them. This primary system acts as the basis to realize our collaborative artistic support system entirely in future work.
Investigating MMM Ponzi Scheme on Bitcoin

Cybercriminals exploit cryptocurrencies to carry out illicit activities. In this paper, we focus on Ponzi schemes that operate on Bitcoin and perform an in-depth analysis of MMM, one of the oldest and most popular Ponzi schemes. Based on 423K transactions involving 16K addresses, we show that: (1) Starting Sep 2014, the scheme goes through three phases over three years. At its peak, MMM circulated more than 150M dollars a day, after which it collapsed by the end of Jun 2016. (2) There is a high income inequality between MMM members, with the daily Gini index reaching more than 0.9. The scheme also exhibits a zero-sum investment model, in which one member's loss is another member's gain. The percentage of victims who never made any profit has grown from 0% to 41% in five months, during which the top-earning scammer has made 765K dollars in profit. (3) The scheme has a global reach with 80 different member countries but a highly-asymmetrical flow of money between them. While India and Indonesia have the largest pairwise flow in MMM, members in Indonesia have received 12x more money than they have sent to their counterparts in India.
Head Pose Estimation Based on Multivariate Label Distribution

Accurate ground-truth pose is essential to the training of most existing head pose estimation methods. However, in many cases, the "ground truth" pose is obtained in rather subjective ways, such as asking the subjects to stare at different markers on the wall. Thus it is better to use soft labels rather than explicit hard labels to indicate the pose of a face image. This paper proposes to associate a multivariate label distribution (MLD) to each image. An MLD covers a neighborhood around the original pose. Labeling the images with MLD can not only alleviate the problem of inaccurate pose labels, but also boost the training examples associated to each pose without actually increasing the total amount of training examples. Four algorithms are proposed to learn from MLD. Furthermore, an extension of MLD with the hierarchical structure is proposed to deal with fine-grained head pose estimation, which is named hierarchical multivariate label distribution (HMLD). Experimental results show that the MLD-based methods perform significantly better than the compared state-of-the-art head pose estimation algorithms. Moreover, the MLD-based methods appear much more robust against the label noise in the training set than the compared baseline methods.
A Reinforcement Learning Integrated in Heuristic search method for self-driving vehicle using blockchain in supply chain management

Blockchain is a distributed open (Public) ledger that is used to record the transaction across many computers. Blockchain technology can be applied in any domain such as banking, healthcare, real estate, travel, food, and supply chain. In supply chain management to train the self-driving vehicle in blockchain technology also integrate the Artificial Intelligence (AI) and Machine Learning (ML) Algorithms. In this paper we have proposed Reinforcement learning integrated heuristic search method (RLIH) for self-driving vehicle using blockchain in supply chain management by combining the advantage of reinforcement learning and heuristic search method. RLIH is developed using Decentralized app and result shows that proposed method outperform the existing heuristic search method in term of service time and data traffic.
An Oversampling Technique by Integrating Reverse Nearest Neighbor in SMOTE: Reverse-SMOTE

In recent years, the classification problem of an imbalanced dataset is getting a high demand in the field of machine learning. The SMOTE (Synthetic Minority Oversampling Technique) is a traditional approach to solve this issue. The main drawback of SMOTE is the issue of overfitting, as it randomly synthesized the minority data samples taking no notice of the significance of the majority class. To solve this problem, the paper proposes a new algorithm named as Reverse-Synthetic Minority Oversampling Technique (R-SMOTE), based on SMOTE and Reverse-Nearest Neighbor (R-NN). The proposed R-SMOTE extracts a significant set of data points out of the minority class and considers that set to synthesize new samples from their reverse nearest neighbors. The proposed algorithm is compared with four standard oversampling techniques. From the empirical analysis, it is observed that the proposed R-SMOTE had produced much improved results over the existing oversampling methods.
A survey exploring open source Intelligence for smarter password cracking

From the end of the last century to date, consumers are increasingly living their lives online. In today's world, the average person spends a significant proportion of their time connecting with people online through multiple platforms. This online activity results in people freely sharing an increasing amount of personal information – as well as having to manage how they share that information. For law enforcement, this corresponds to a slew of new sources of digital evidence valuable for digital forensic investigation. A combination of consumer level encryption becoming default on personal computing and mobile devices and the need to access information stored with third parties has resulted in a need for robust password cracking techniques to progress lawful investigation. However, current password cracking techniques are expensive, time-consuming processes that are not guaranteed to be successful in the time-frames common for investigations. In this paper, the potential for Open Source Intelligence (OSINT) being leveraged for more efficient password cracking is explored. A comprehensive survey of the literature on password strength, password cracking, and OSINT is outlined, and the law enforcement challenges surrounding these topics are discussed. Additionally, an analysis on password structure as well as demographic factors influencing password selection is presented. Finally, the potential impact of OSINT to password cracking by law enforcement is discussed.
Multimodal Aspect Extraction with Region-Aware Alignment Network

Fueled by the rise of social media, documents on these platforms (e.g., Twitter, Weibo) are increasingly multimodal in nature, with images in addition to text. To well automatically analyze the opinion information inside multimodal data, it’s crucial to perform aspect term extraction (ATE) on them. However, until now, the researches focus on multimodal ATE are rare. In this study, we take a step further than previous studies by proposing a Region-aware Alignment Network (RAN) that aligns text with object regions that show in an image for the multimodal ATE task. Experiments on the Twitter dataset showcase the effectiveness of our proposed model. Further researches prove that our model has better performance when extracting emotion polarized aspect terms.
Safety robustness of reinforcement learning policies: A view from robust control

For a reinforcement learning (RL) problem without a specified reward function, one may specify different reward functions to better guide an agent to learn. With different reward functions, the agent can learn different policies that generally have different robustness. Both the achieved reward and the success rate have been commonly used to evaluate the robustness of policies. Safety is a concern when using RL to solve problems in many safety–critical applications (e.g., robotic manipulation). However, evaluating the robustness of policies from the perspective of safety has not been discussed in the literature. The major contributions of this paper are the proposal of a novel concept of safety robustness to evaluate the robustness of policies from the perspective of safety and an algorithm to approximate the safety robustness of policies. To demonstrate how to implement the proposed algorithm, illustrative experiments are conducted and the safety robustness of three policies for controlling the manipulation of a cable-driven parallel robot is analyzed. Experiment results show that the proposed algorithm can approximate the safety robustness of policies using the ratio of the number of safe episodes to the number of total episodes and identify the best policy from multiple policies in terms of the safety of policies.
Towards Formalization of Enhanced Privacy ID (EPID)-based Remote Attestation in Intel SGX

Vulnerabilities in privileged software layers have been exploited with severe consequences. Recently, Trusted Execution Environments (TEEs) based technologies have emerged as a promising approach since they claim strong confidentiality and integrity guarantees regardless of the trustworthiness of the underlying system software. In this paper, we consider one of the most prominent TEE technologies, referred to as Intel Software Guard Extensions (SGX). Despite many formal approaches, there is still a lack of formal proof of some critical processes of Intel SGX, such as remote attestation. To fill this gap, we propose a fully automated, rigorous, and sound formal approach to specify and verify the Enhanced Privacy ID (EPID)-based remote attestation in Intel SGX under the assumption that there are no side-channel attacks and no vulnerabilities inside the enclave. The evaluation indicates that the confidentiality of attestation keys is preserved against a Dolev-Yao adversary in this technology. We also present a few of the many inconsistencies found in the existing literature on Intel SGX attestation during formal specification.
A Generic Framework and Library for Exploration of Small Multiples through Interactive Piling

Small multiples are miniature representations of visual information used generically across many domains. Handling large numbers of small multiples imposes challenges on many analytic tasks like inspection, comparison, navigation, or annotation. To address these challenges, we developed a framework and implemented a library called PILlNG.JS for designing interactive piling interfaces. Based on the piling metaphor, such interfaces afford flexible organization, exploration, and comparison of large numbers of small multiples by interactively aggregating visual objects into piles. Based on a systematic analysis of previous work, we present a structured design space to guide the design of visual piling interfaces. To enable designers to efficiently build their own visual piling interfaces, PILlNG.JS provides a declarative interface to avoid having to write low-level code and implements common aspects of the design space. An accompanying GUI additionally supports the dynamic configuration of the piling interface. We demonstrate the expressiveness of PILlNG.JS with examples from machine learning, immunofluorescence microscopy, genomics, and public health.
Using Reduced Paths to Achieve Efficient Privacy-Preserving Range Query in Fog-Based IoT

The fog computing architectural model has recently seen advances with respect to bandwidth and latency issues. However, since fog devices are deployed at the network edge and are not fully trustable, there are still security and privacy challenges. In this article, aiming at improving both communication efficiency and privacy protection, we propose a new efficient and privacy-preserving range query scheme in fog-based Internet of Things (IoT). We, first, introduce a new decomposition technique to efficiently interpret a given range query [L, U], where 0 ≤ L ≤ U ≤ n - 1, as a form of inverted reduced path strings. Then, the symmetric homomorphic encryption (SHE) scheme is employed to encrypt the reduced paths and hand them over securely through a fog node to the IoT devices. This technique enables a query user to launch a privacy-preserving continuous or noncontinuous range query and receive a homomorphically aggregated encrypted response with an improved O(log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n) communication efficiency. The detailed security analysis shows that our proposed scheme is privacy preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate that our proposed scheme is by far more efficient than those previously reported schemes in terms of computational overhead and communication complexity.
FPGA-based SPHINCS<sup>+</sup>Implementations: Mind the Glitch

The digital signature scheme SPHINCS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> is a candidate in the NIST post-quantum project, whose aim is to standardize cryptographic systems that are secure against attacks originating from both quantum and classical computers. We present an efficient and, to our knowledge, first hardware implementation for SPHINCS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> . Our systematic approach of a performance-optimized FPGA architecture results in a 100x speed-up compared to the reference software-only implementation. Our investigation on a real-world implementation revealed a weakness regarding fault injection. The attack breaks the scheme completely. Collecting enough private information to forge a signature is a matter of seconds on our setup. We discuss possible countermeasures. A “sign-then-verify” operation unfortunately does not detect a faulty signature, but a full replication of the hardware might make a detection possible.
Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces

Reinforcement learning consists of finding policies that maximize an expected cumulative long-term reward in a Markov decision process with unknown transition probabilities and instantaneous rewards. In this article, we consider the problem of finding such optimal policies while assuming they are continuous functions belonging to a reproducing kernel Hilbert space (RKHS). To learn the optimal policy, we introduce a stochastic policy gradient ascent algorithm with the following three unique novel features. First, the stochastic estimates of policy gradients are unbiased. Second, the variance of stochastic gradients is reduced by drawing on ideas from numerical differentiation. Four, policy complexity is controlled using sparse RKHS representations. Novel feature, first, is instrumental in proving convergence to a stationary point of the expected cumulative reward. Novel feature, second, facilitates reasonable convergence times. Novel feature, third, is a necessity in practical implementations, which we show can be done in a way that does not eliminate convergence guarantees. Numerical examples in standard problems illustrate successful learning of policies with low complexity representations, which are close to stationary points of the expected cumulative reward.
Semi-Supervised Domain Adaptation via Asymmetric Joint Distribution Matching

An intrinsic problem in domain adaptation is the joint distribution mismatch between the source and target domains. Therefore, it is crucial to match the two joint distributions such that the source domain knowledge can be properly transferred to the target domain. Unfortunately, in semi-supervised domain adaptation (SSDA) this problem still remains unsolved. In this article, we therefore present an asymmetric joint distribution matching (AJDM) approach, which seeks a couple of asymmetric matrices to linearly match the source and target joint distributions under the relative chi-square divergence. Specifically, we introduce a least square method to estimate the divergence, which is free from estimating the two joint distributions. Furthermore, we show that our AJDM approach can be generalized to a kernel version, enabling it to handle nonlinearity in the data. From the perspective of Riemannian geometry, learning the linear and nonlinear mappings are both formulated as optimization problems defined on the product of Riemannian manifolds. Numerical experiments on synthetic and real-world data sets demonstrate the effectiveness of the proposed approach and testify its superiority over existing SSDA techniques.
Dual Temporal Memory Network for Efficient Video Object Segmentation

Video Object Segmentation (VOS) is typically formulated in a semi-supervised setting. Given the ground-truth segmentation mask on the first frame, the task of VOS is to track and segment the single or multiple objects of interests in the rest frames of the video at the pixel level. One of the fundamental challenges in VOS is how to make the most use of the temporal information to boost the performance. We present an end-to-end network which stores short- and long-term video sequence information preceding the current frame as the temporal memories to address the temporal modeling in VOS. Our network consists of two temporal sub-networks including a short-term memory sub-network and a long-term memory sub-network. The short-term memory sub-network models the fine-grained spatial-temporal interactions between local regions across neighboring frames in video via a graph-based learning framework, which can well preserve the visual consistency of local regions over time. The long-term memory sub-network models the long-range evolution of object via a Simplified-Gated Recurrent Unit (S-GRU), making the segmentation be robust against occlusions and drift errors. In our experiments, we show that our proposed method achieves a favorable and competitive performance on three frequently-used VOS datasets, including DAVIS 2016, DAVIS 2017 and Youtube-VOS in terms of both speed and accuracy.
Multi-Visual Feature Saliency Detection for Sea-Surface Targets through Improved Sea-Sky-Line Detection

To visually detect sea-surface targets, the objects of interest must be effectively and rapidly isolated from the background of sea-surface images. In contrast to traditional image detection methods, which employ a single visual feature, this paper proposes a significance detection algorithm based on the fusion of multi-visual features after detecting the sea-sky-lines. The gradient edges of the sea-surface images are enhanced using a Gaussian low-pass filter to eliminate the effect of the image gradients pertaining to the clouds, wave points, and illumination. The potential region and points of the sea-sky-line are identified. The sea-sky-line is fitted through polynomial iterations to obtain a sea-surface image containing the target object. The saliency subgraphs of the high and low frequency, gradient texture, luminance, and color antagonism features are fused to obtain an integrated saliency map of the sea-surface image. The saliency target area of the sea surface is segmented. The effectiveness of the proposed method was verified. The average detection rate and time for the sea-sky-line detection were 96.3% and 1.05 fps, respectively. The proposed method outperformed the existing saliency models on the marine obstacle detection dataset and Singapore maritime dataset, with mean absolute errors of 0.075 and 0.051, respectively.
RETRACTED: Basketball action recognition based on FPGA and particle image

Fine-grained motion recognition is most important for such video retrieval, and most work nowadays focuses on coarse-grained and fine-grained actions in motion recognition, without being involved in many uses. To solve this problem, in this system, it have a dataset that challenged a basketball game by annotating detailed actions in a video. Adaptive Multi-Label Classification methods for basketball action recognition benchmark also provides data about the system. In addition, this method is proposed to integrate the FPGA into a network of two data streams in order to find the finest areas of basketball action recognition and extracts the features of the recognition system. This proposed system gives significantly a better and superior results than the existing methods. Taken individually, the surrounding first-person footage can be associated with similar situations in the past and compared with the visual semantics of the spatial and social layout of personal records. In general, first-person videos can track common interests, and can be linked to group of individuals in this system.
Attentive One-Dimensional Heatmap Regression for Facial Landmark Detection and Tracking

Although heatmap regression is considered a state-of-the-art method to locate facial landmarks, it suffers from huge spatial complexity and is prone to quantization error. To address this, we propose a novel attentive one-dimensional heatmap regression method for facial landmark localization. First, we predict two groups of 1D heatmaps to represent the marginal distributions of the x and y coordinates. These 1D heatmaps reduce spatial complexity significantly compared to current heatmap regression methods, which use 2D heatmaps to represent the joint distributions of x and y coordinates. With much lower spatial complexity, the proposed method can output high-resolution 1D heatmaps despite limited GPU memory, significantly alleviating the quantization error. Second, a co-attention mechanism is adopted to model the inherent spatial patterns existing in x and y coordinates, and therefore the joint distributions on the x and y axes are also captured. Third, based on the 1D heatmap structures, we propose a facial landmark detector capturing spatial patterns for landmark detection on an image; and a tracker further capturing temporal patterns with a temporal refinement mechanism for landmark tracking. Experimental results on four benchmark databases demonstrate the superiority of our method.
Bitrate Requirements of Non-Panoramic VR Remote Rendering

This paper shows the impact of bitrate settings on objective quality measures when streaming non-panoramic remote-rendered Virtual Reality (VR) images. Non-panoramic here refers to the images that are rendered and sent across the network, they only cover the viewport of each eye, respectively.