Reviewerly/exHarmony huggingface dataset:
Defending Against Neural Network Model Stealing Attacks Using Deceptive Perturbations

Machine learning architectures are readily available, but obtaining the high quality labeled data for training is costly.Pre-trained models available as cloud services can be used to generate this costly labeled data, and would allow an attacker to replicate trained models, effectively stealing them.Limiting the information provided by cloud based models by omitting class probabilities has been proposed as a means of protection but significantly impacts the utility of the models.In this work, we illustrate how cloud based models can still provide useful class probability information for users, while significantly limiting the ability of an adversary to steal the model.Our defense perturbs the model's final activation layer, slightly altering the output probabilities.This forces the adversary to discard the class probabilities, requiring significantly more queries before they can train a model with comparable performance.We evaluate our defense under diverse scenarios and defense aware attacks.Our evaluation shows our defense can degrade the accuracy of the stolen model at least 20%, or increase the number of queries required by an adversary 64 fold, all with a negligible decrease in the protected model accuracy.
A Deep Learning Approach to Detect Abusive Bengali Text

Day by day, Social media sites, online news portals and blogs commenting sections are getting saturated with abusive contents in Bangladesh. Detecting different types of abusive contents in online will not only improve these websites discussion sections but will also ensure user's safety. In this paper, several machine Learning and deep learning based algorithms e.g. Linear Support Vector Classifier (LinearSVC), Logistic Regression (Logit), Multinomial Naïve Bayes (MNB), Random Forest (RF), Artificial Neural Network (ANN), Recurrent Neural Network (RNN) with a Long Short Term Memory (LSTM) cell have been tested to detect multi-type abusive Bengali text. Besides, there has been introduced new stemming rules for Bengali language which help to achieve better performance of algorithms. Deep learning based algorithm RNN outperforms other algorithms by gaining highest accuracy 82.20%.
Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot's Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.
A Deep Learning Framework for Signal Detection and Modulation Classification

Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance.
Why do blockchains split? An actor-network perspective on Bitcoin splits

This paper investigates the focal actors in a blockchain network and their heterogeneity in splits. Disagreements in blockchain communities often lead to splits in both the blockchain and the community. We use three key elements of the actor-network theory — punctualization, translation, and actor heterogeneity—and employ case study methodology to examine Bitcoin splits. We identify several human actors, such as miners, developers, merchants, and investors, as well as non-human actors, including blockchain, exchanges, hardware manufacturers, and wallets, involved in Bitcoin splits. Our results show that the consolidation of actors in homogeneous groups plays a key role in blockchain splits. We further describe how the human and non-human actors' fluid moves into micro and macro actor positions in the network affect the development of the split. In addition, we discuss the roles of these actors and their engagement in forming micro and macro agencies in blockchain splits.
DSPNet: Deep scale purifier network for dense crowd counting

Crowd counting has produced considerable concern in recent years. However, crowd counting in highly congested scenes is a challenging problem owing to scale variation. To remedy this issue, we propose a novel deep scale purifier network (DSPNet) that can encode multiscale features and reduce the loss of contextual information for dense crowd counting. Our proposed method has two strong points. First, the DSPNet model consists of a frontend and a backend. The frontend is a conventional deep convolutional neural network, while the unified deep neural network backend adopts a “maximal ratio combining” strategy to learn complementary scale information at different levels. The scale purifier module, which improves scale representations, can effectively fuse multiscale features. Second, DSPNet performs the whole RGB image-based inference to facilitate model learning and decrease contextual information loss. Our customized network is end-to-end and has a fully convolutional architecture. We demonstrate the generalization ability of our approach by cross-scene evaluation. Extensive experiments on three publicly available crowd counting benchmarks (i.e., UCF-QNRF, ShanghaiTech, and UCF_CC_50 datasets) show that our DSPNet delivers superior performance against state-of-the-art methods.
Robust Real-Time Violence Detection in Video Using CNN And LSTM

Detection of a violence event in surveillance systems is playing a significant role in law enforcement and city safety. The effectiveness of violence event detectors measures by the speed of response and the accuracy and the generality over different kind of video sources with a different format. Several studies worked on the violence detection with focus either on speed or accuracy or both but not taking into account the generality over different kind of video sources. In this paper, we proposed a real-time violence detector based on deep-learning methods. The proposed model consists of CNN as a spatial feature extractor and LSTM as temporal relation learning method with a focus on the three-factor (overall generality - accuracy - fast response time). The suggested model achieved 98% accuracy with speed of 131 frames/sec. Comparison of the accuracy and the speed of the proposed model with previous works illustrated that the proposed model provides the highest accuracy and the fastest speed among all the previous works in the field of violence detection.
Analysis and Application of Modified ZNN Design With Robustness Against Harmonic Noise

The Zhang neural network (ZNN) has recently realized remarkable success in solving time-varying problems. Harmonic noise widely exists in industrial applications and can severely affect the solution computed by ZNN models. This article attempts to solve the aforementioned limitations by providing the first ZNN design with an inherent capability to prohibit harmonic noise. Moreover, it opens new opportunities to shift the research on ZNNs in ideal situations to that with theoretical consideration on nonideal working environments. We establish a modified ZNN design formula in a noisy environment by incorporating the dynamics of harmonic signals. Theoretical analysis shows the convergence of the proposed ZNN design. An application case study for the new ZNN model verifies its effectiveness for time-varying matrix inversion in the presence of harmonic noise. The simulation further substantiates the effectiveness, superiority, and application prospects of the proposed ZNN design.
Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation

Semi-supervised non-negative matrix factorization (NMF) exploits the strengths of NMF in effectively learning local information contained in data and is also able to achieve effective learning when only a small fraction of data is labeled. NMF is particularly useful for dimensionality reduction of high-dimensional data. However, the mapping between the low-dimensional representation, learned by semi-supervised NMF, and the original high-dimensional data contains complex hierarchical and structural information, which is hard to extract by using only single-layer clustering methods. Therefore, in this article, we propose a new deep learning method, called semi-supervised graph regularized deep NMF with bi-orthogonal constraints (SGDNMF). SGDNMF learns a representation from the hidden layers of a deep network for clustering, which contains varied and unknown attributes. Bi-orthogonal constraints on two factor matrices are introduced into our SGDNMF model, which can make the solution unique and improve clustering performance. This improves the effect of dimensionality reduction because it only requires a small fraction of data to be labeled. In addition, SGDNMF incorporates dual-hypergraph Laplacian regularization, which can reinforce high-order relationships in both data and feature spaces and fully retain the intrinsic geometric structure of the original data. This article presents the details of the SGDNMF algorithm, including the objective function and the iterative updating rules. Empirical experiments on four different data sets demonstrate state-of-the-art performance of SGDNMF in comparison with six other prominent algorithms.
Operation Mechanisms for Intelligent Logistics System: A Blockchain Perspective

Aiming at the current problems of security threats and privacy leak risks in the operation process of related data of intelligent logistics system, and the operation of system lacking of supervision and traceability, this paper proposes to use blockchain technology to resolve these problems. A scheme on applying blockchain in intelligent logistics system is proposed, including operation principle, consensus authentication mechanism, and data storage and access mechanism. By introducing and analyzing the related big data of intelligent logistics system, improving the scientific, rationality, and intelligence of the decision. The basic characteristic of blockchain is traceability. By constructing algorithm models, proposing the realization principles of consensus authentication mechanism. For different events, different “multi-authentication centers”, intelligent contracts, and blockchain systems are constructed, improving the efficiency and supervision of the operation of intelligent logistics system. By constructing the correlations between the fundamental data that corresponding to different blockchain, and the correlations between the fundamental data that corresponding to the same blockchain, making the related data easier to collect and analyze. By constructing the storage and access mechanism, ensuring the security and confidentiality of the operation data of intelligent logistics system. This paper has provided thought for the application research of intelligent logistics system based on blockchain, and has positive reference value and guiding significance to the development of blockchain application research.
Managing renewable energy and carbon footprint in multi-cloud computing environments

Cloud computing offers attractive features for both service providers and customers. Users benefit from the pay-as-you-go model by saving expenditures and service providers are deploying their services to cloud data centers to reduce their maintenance efforts. However, due to the fast growth of cloud data centers, the energy consumed by the data centers can lead to a huge amount of carbon emission with environmental impacts, and the carbon intensity of different locations are varied among different power plants according to the sources of energy. Thus, in this paper, to address the carbon emission problem of data centers, we consider shifting the workloads among multi-cloud located in different time zones. We also formulate the energy usage and carbon emission of data centers and model the solar power corresponding to the locations. This helps to reduce the usage of brown energy and maximize the utilization of renewable energy at different locations. We propose an approach for managing carbon footprint and renewable energy for multiple data centers at California, Virginia, and Dublin, which are in different time zones. The results show that our proposed approaches that apply workload shifting can reduce around 40% carbon emission in comparison to the baseline while ensuring the average response time of user requests.
Trust Region Based Adversarial Attack on Neural Networks

Deep Neural Networks are quite vulnerable to adversarial perturbations. Current state-of-the-art adversarial attack methods typically require very time consuming hyper-parameter tuning, or require many iterations to solve an optimization based adversarial attack. To address this problem, we present a new family of trust region based adversarial attacks, with the goal of computing adversarial perturbations efficiently. We propose several attacks based on variants of the trust region optimization method. We test the proposed methods on Cifar-10 and ImageNet datasets using several different models including AlexNet, ResNet-50, VGG-16, and DenseNet-121 models. Our methods achieve comparable results with the Carlini-Wagner (CW) attack, but with significant speed up of up to 37×, for the VGG-16 model on a Titan Xp GPU. For the case of ResNet-50 on ImageNet, we can bring down its classification accuracy to less than 0.1% with at most 1.5% relative L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> (or L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) perturbation requiring only 1.02 seconds as compared to 27.04 seconds for the CW attack. We have open sourced our method which can be accessed at [1].
Generating Large Labeled Data Sets for Laparoscopic Image Processing Tasks Using Unpaired Image-to-Image Translation

In the medical domain, the lack of large training data sets and benchmarks is often a limiting factor for training deep neural networks. In contrast to expensive manual labeling, computer simulations can generate large and fully labeled data sets with a minimum of manual effort. However, models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real laparoscopic images, we exploit recent advances in unpaired image-to-image translation. We extend an image-to-image translation method to generate a diverse multitude of realistically looking synthetic images based on images from a simple laparoscopy simulation. By incorporating means to ensure that the image content is preserved during the translation process, we ensure that the labels given for the simulated images remain valid for their realistically looking translations. This lets us generate a large, fully labeled synthetic data set. We show that this data set can be used to train models for the task of liver segmentation in laparoscopic images. We achieve median dice scores of up to 0.89 in some patients without manually labeling a single laparoscopic image and show that using our synthetic data to pre-train models can greatly improve their performance. The synthetic data set is made publicly available, fully labeled with segmentation maps, depth maps, normal maps, and positions of tools and camera ( http://opencas.dkfz.de/image2image ).
Assessing heavy metal concentrations in earth-cumulic-orthic-anthrosols soils using Vis-NIR spectroscopy transform coupled with chemometrics

Soil visible and near infrared (Vis-NIR) has become an applicable and interesting technique to predict soil properties because it is a fast, cost-effective, and non-destruction technique. This study presents an application of diffuse reflectance spectroscopy (DRS) and chemometric techniques for evaluating concentrations of heavy metals in earth-cumulic-orthic-anthrosols soils. 44 soil samples of 0-30 cm were collected from three representative agriculture areas (Fufeng, Yangling, and Wugong transects with 16, 10, and 18 samples, respectively) and analyzed for Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb by Vis-NIR spectroscopy (350-2500 nm). Average levels of Cr, Mn, Ni, Cu, Zn, As, Cd, Hg, and Pb were 17.95, 274, 12.77, 7.29, 15.81, 7.51, 0.40, 12.58, and 21.05 mg kg-1, respectively. Twenty-four preprocessing methods were extracted sensitive bands. Partial least squares regression (PLSR) used to obtain effective bands and predict soil heavy metals concentrations. The accuracy of the predictive models were assessed in terms of coefficient of determination (R2), the root mean squared error (RMSE), standard error (SE) and the ratio of performance to deviation (RPD). The results revealed that excellent predictions for Hg(Rv2 = 0.99, RPD = 8.59, RMSEP = 0.12, SEP = 0.13), Cr (Rv2 = 0.97, RPD = 5.96, RMSEP = 0.10, SEP = 0.10), Ni (Rv2 = 0.93, RPD = 3.74, RMSEP = 0.13, SEP = 0.13), Pb (Rv2 = 0.97, RPD = 5.57, RMSEP = 0.10, SEP = 0.01), and Cu (Rv2 = 0.92, RPD = 3.38, RMSEP = 0.08, SEP = 0.08). Models for As (Rv2 = 0.87, RPD = 2.58), Mn (Rv2 = 0.80, RPD = 2.09), and Cd (RPD = 2.77) had Rv2 < 0.9 and RPD<3.0, not excellent predictions. For the element of Zn, although Rv2 = 0.91, RPD = 3.13, the offset had too much deviation, and it cannot be considered an excellent model. Therefore, a combination of spectroscopic and chemometric techniques can be applied as a practical, rapid, low-cost and quantitative approach for evaluating soil physical and chemical properties in Shaanxi, China.
2D Hybrid chaos map for image security transform based on framelet and cellular automata

One of the most important data in the social networks is image, so improving the existing methods can be essential to obtain a safe environment for transforming. In this paper, we provide some safe ways to transfer images securely by using cryptography and steganography methods. In order to enhance the security of the image transmission, we introduce a new type of uniformly distributed 2D hybrid chaos map based on Logistic, Sine and Tent maps, and use the cellular automata and discrete framelet transform in the proposed algorithms and also mix the position of the image pixels by applying kinds of shifts. To show that the proposed algorithms are able to resist various attacks, different types of simulation results and security analysis are used.
A multi-criteria evaluation model based on hesitant fuzzy sets for blockchain technology in supply chain management

Nowadays, more companies are trying to implement blockchain technology (BT) that enables to increase the quality of the products/services to their supply chains in order to improve their performance. BT can be applied to different sectors according to their specific needs. Evaluation of BT with re spect to sectors needs considering several factors and it can be considered as a multi criteria decision making (MCDM) problem. In this paper, the appropriateness of BT in Supply Chain Management (SCM) according to different sectors has been evaluated by using a MCDM methodology based on hesitant fuzzy sets (HFSs). The suggested MCDM methodology consists of Delphi method, hesitant fuzzy Analytic Hierarchy Process (HF-AHP) and Hesitant Fuzzy Technique for Order Preference by Similarity to Ideal Solution (HF-TOPSIS) methods. In the first stage, the criteria and sub-criteria utilized for performance evaluation of BT in supply chain management have been determined by using Delphi method. The weights of main and sub-criteria have been obtained through HF-AHP method and finally, the alternative sectors have been ranked according to results of HF-TOPSIS method. For this aim, a hierarchical MCDM problem that consists of 5 main and 17 sub-criteria has been created and the alternative sectors have been evaluated. As a result, medicine/drug and jewelry sectors have been respectively determined as the most and the least suitable alternatives in order to implement BT by means of the proposed HFSs based methodology. Finally, a sensitivity analysis has been conducted to show the importance of the main criteria weights on ranking of alternatives.
MagicalHands

We explore the use of hand gestures for authoring animations in virtual reality (VR). We first perform a gesture elicitation study to understand user preferences for a spatiotemporal, bare-handed interaction system in VR. Specifically, we focus on creating and editing dynamic, physical phenomena (e.g., particle systems, deformations, coupling), where the mapping from gestures to animation is ambiguous and indirect. We present commonly observed mid-air gestures from the study that cover a wide range of interaction techniques, from direct manipulation to abstract demonstrations. To this end, we extend existing gesture taxonomies to the rich spatiotemporal interaction space of the target domain and distill our findings into a set of guidelines that inform the design of natural user interfaces for VR animation. Finally, based on our guidelines, we develop a proof-of-concept gesture-based VR animation system, MagicalHands. Our results, as well as feedback from user evaluation, suggest that the expressive qualities of hand gestures help users animate more effectively in VR.