Reviewerly/exHarmony huggingface dataset:
Markov Probability Fingerprints: A Method for Identifying Encrypted Video Traffic

Detecting illegal video plays an important role in preventing and countering crime in daily life. It is effective for supervisors to monitor the network by analyzing traffic from devices. In this way, illegal video can be detected when it is played on the network. Most Internet traffic is encrypted, which brings difficulties to traffic analysis. However, many researches suggest that even if the video traffic is encrypted, the segmentation prescribed by Dynamic Adaptive Streaming over HTTP (DASH) causes content-dependent fragments, which can be used to identify the encrypted video traffic without decryption. This paper presents Markov probability fingerprint for video, and then designs an algorithm for encrypted video streaming title identification. We demonstrate that an external attacker can identify the video title by analyzing the fragment sequence of encrypted video traffic. Based on the m-order Markov chain, we use the transition tensor of the fragment sequence generated by the video traffic as the video fingerprint, and prove its effectiveness. Then we explore approaches that can further improve the performance of methods in terms of discrimination accuracy. We make promising observations that the higher-order Markov chain, larger training set, and more detailed binning of fragments contribute to encrypted video traffic discrimination. We run a thorough set of experiments that illustrate that our method can achieve an outstanding accuracy rate up to 97.5%, which is superior to previous work.
Spline-RRT⁎: Coordinated Motion Planning of Dual-Arm Space Robot

This paper addresses the coordinated motion planning issue for a dual-arm free-floating space robot. Based on the sampling-based planning method, a novel coordinated RRT⁎-based path planning framework is proposed for a dual-arm manipulation. First, an asymptotically optimal sampling-based method, RRT⁎, is employed to generate the initial rough path for each end-effector in the task-space. To avoid self-collision, we present a coordinated strategy which samples from separated inertial spaces when performing RRT⁎ algorithm. Second quartic, splines are used to smooth the generated RRT⁎ path so that the robot can execute smoothly. Physical constraints including the end-effectors’ limit, joint limit and boundary conditions are all controlled within the design of the quartic splines. The effectiveness of the proposed path planning framework is illustrated and demonstrated via a kinematically redundant dual-arm space robot.
Comprehensive Analysis for Fraud Detection of Credit Card through Machine Learning

A credit card which remains a very widespread compensation method is accepted online & offline that provides cashless transactions. It's an easy, suitable then very common to make payments and other transactions. With the increase of developments credit card frauds are also growing. Financial deception is severely cumulative in the worldwide statement enhancement. Billion dollars are at loss due to these fraudulent acts. These actions are accomplished so gracefully that it is similar to genuine transactions. Therefore, simple design practices and other less composite methods will be non-operating. In directive to minimalize disorder and bring order in place having a well-organized method of fraud detection has become a need for all banks. In this paper we used Machine learning, to notice Master Card fake transactions. Also, IFA and OD approaches are applied towards enhance finest answer on behalf of scam finding problems. Approaches remain proved toward diminish untrue alarm proportions also upsurge scam discovery proportion. Dataset of card dealings stays obtained since European card owners having 284,807 communications. To detect and prevent the fraudulent, slightly of these approaches can be applied on bank credit card scam detection system, to detect and prevent the scam.
RL-PDNN: Reinforcement Learning for Privacy-Aware Distributed Neural Networks in IoT Systems

Due to their high computational and memory demand, deep learning applications are mainly restricted to high-performance units, e.g., cloud and edge servers. Particularly, in Internet of Things (IoT) systems, the data acquired by pervasive devices is sent to the computing servers for classification. However, this approach might not be always possible because of the limited bandwidth and the privacy issues. Furthermore, it presents uncertainty in terms of latency because of the unstable remote connectivity. To support resource and delay requirements of such paradigm, joint and real-time deep co-inference framework with IoT synergy was introduced. However, scheduling the distributed, dynamic and real-time Deep Neural Network (DNN) inference requests among resource-constrained devices has not been well explored in the literature. Additionally, the distribution of DNN has drawn the attention to the privacy protection of sensitive data. In this context, various threats have been presented, including white-box attacks, where malicious devices can accurately recover received inputs if the DNN model is fully exposed to participants. In this paper, we introduce a methodology aiming at distributing the DNN tasks onto the resource-constrained devices of the IoT system, while avoiding to reveal the model to participants. We formulate such an approach as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy of the data, and the limited resources of devices. Next, due to the NP-hardness of the problem, we shape our approach as a reinforcement learning design adequate for real-time applications and highly dynamic systems, namely RL-PDNN. Our system proved its ability to outperform existing static approaches and achieve close results compared to the optimal solution.
Camera pose estimation in multi-view environments: From virtual scenarios to the real world

This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios.
A Novel Macro-Micro Fusion Network for User Representation Learning on Mobile Apps

The evolution of mobile apps has greatly changed the way that we live. It becomes increasingly important to understand and model the users on mobile apps. Instead of focusing on some specific app alone, it has become a popular paradigm to study the user behavior on various mobile apps in a symbiotic environment.
Sulfur and CO2 gases emitted during weathering of sulfides: Role of microbial activity and implications to exploration through cover

In mineral exploration, a thick cover is challenging and can hinder the discovery of new world class mineral deposits. Traditional surface geochemistry techniques are virtually "blind" in areas where the cover exceeds a few meters. Therefore, alternative surface geochemistry approaches such as the detection of soil gas anomalies over buried mineral deposits have been investigated and developed. This study investigates the source and potential of soil gases for mineral exploration by comparing equilibrium thermodynamic models with gases detected during weathering of "pure" sulfides and sulfide ores in the laboratory. Weathering experiments were conducted under moist settings, both for sterile and non-sterile conditions, and under water-saturated settings for non-sterile environments. Gaseous hydrocarbons were below detection limits, suggesting that any hydrocarbons produced during sulfide oxidation would only reach low abundances. Thermodynamic models undertaken in both moist and water-saturated conditions predict CO2 as the dominant gas produced. However, CS2 is the dominant sulfur gas produced in laboratory experiments, contradicting thermodynamic predictions. This suggests that experiments did not reach equilibrium. Sulfide weathering under non-sterile conditions produce greater relative abundance of gases than sterile experiments. This suggests that microbes play an important role in the weathering of sulfide to produce gases. Thus, the mechanisms involved in the production of gases from microbes-sulfide interactions must be considered in soil gas geochemical studies applied to mineral exploration.
Multi-Head Attention Graph Network for Few Shot Learning

The majority of existing graph-network-based few-shot models focus on a node-similarity update mode. The lack of adequate information intensifies the risk of overtraining. In this paper, we propose a novel Multi-head Attention Graph Network to excavate discriminative relation and fulfill effective information propagation. For edge update, the node-level attention is used to evaluate the similarities between the two nodes and the distribution-level attention extracts more in-deep global relation. The cooperation between those two parts provides a discriminative and comprehensive expression for edge feature. For node update, we embrace the label-level attention to soften the noise of irrelevant nodes and optimize the update direction. Our proposed model is verified through extensive experiments on two few-shot benchmark MiniImageNet and CIFAR-FS dataset. The results suggest that our method has a strong capability of noise immunity and quick convergence. The classification accuracy outperforms most state-of-the-art approaches.
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples

Adversarial examples are among the biggest challenges for machine learning models, especially neural network classifiers. Adversarial examples are inputs manipulated with perturbations insignificant to humans while being able to fool machine learning models. Researchers achieve great progress in utilizing adversarial training as a defense. However, the overwhelming computational cost degrades its applicability, and little has been done to overcome this issue. Single-Step adversarial training methods have been proposed as computationally viable solutions; however, they still fail to defend against iterative adversarial examples. In this work, we first experimentally analyze several different state-of-the-art (SOTA) defenses against adversarial examples. Then, based on observations from experiments, we propose a novel single-step adversarial training method that can defend against both single-step and iterative adversarial examples. Through extensive evaluations, we demonstrate that our proposed method successfully combines the advantages of both single-step (low training overhead) and iterative (high robustness) adversarial training defenses. Compared with ATDA on the CIFAR-10 dataset, for example, our proposed method achieves a 35.67% enhancement in test accuracy and a 19.14% reduction in training time. When compared with methods that use BIM or Madry examples (iterative methods) on the CIFAR-10 dataset, our proposed method saves up to 76.03% in training time, with less than 3.78% degeneration in test accuracy. Finally, our experiments with the ImageNet dataset clearly show the scalability of our approach and its performance advantages over SOTA single-step approaches.
Distributed Multirobot Path Planning in Unknown Maps Using Petri Net Models

This paper considers the path planning problem in multirobot systems with an unknown environment. The robots’ mission is given as a Boolean formula on the final states. We assume that the robots have partial knowledge of the environment and they are able to estimate the environment using a recursive Bayes estimator. Furthermore, they communicate between them if they are at a distance smaller than a given threshold in order to improve their own estimation. Each robot will solve an optimization problem based on the Petri net model of the environment and it will move accordingly. We provide an algorithm to be iterated by each robot and we evaluate the results by simulation.
Artificial Intelligence for marketing plan: the case for e-marketing companies

This paper summarises the arguments and counterarguments within the scientific discussion on artificial intelligence (AI) in preparing a marketing plan for e-marketing organizations. This research aims to identify the extent of the contribution of AI in preparing the marketing plan. The author noted that intended to know how e-marketing companies could use AI techniques in situation analysis, analyze competitors' strategies, strategic goals, preparing marketing strategies, preparing an estimated marketing budget, and control a marketing plan. Systematization of the scientific background and approaches on preparing a marketing plan for e-marketing organizations indicates that many companies, especially small companies, marketing their products via the Internet, cannot develop a successful marketing plan. In turn, it could be solved through the use of AI techniques. The study was conducted on a group of companies that market their products via the Internet in the Kingdom of Saudi Arabia. To gain the research goal, this study was carried out in the following logical sequence: 1) developing the stratified sample by collecting statistical information for 141 company in a variety of fields; 2) analyzing the data using SPSS; 3) predicting how AI could be used in preparing the marketing plan; 4) identifying the arrangement of the steps for preparing the marketing plan in terms of the ability of AI techniques. The methodological tools of the study were methods of the multiple regression analysis and the Friedman test. The study empirically confirms and theoretically proves that AI contributes significantly in developing marketing plans through its great contribution to environmental analysis and analysis of competitors' strategies and setting marketing goals. Besides, AI contributes to preparing the budget and appreciating the marketing plan, to its evaluation and control. The author mentioned that AI provides understanding and selecting target markets and sectors, targeting customers, and preparing appropriate marketing mix strategies for each market sector. Therefore, the study provides recommendations to online organizations to use AI in preparing their marketing plan because of its great ability to contribute to this.
Joint feature embedding learning and correlation filters for aircraft tracking with infrared imagery

Infrared object tracking is a key technology for infrared imaging guidance. Blurred imaging, strong ego-motion and frequent occlusion make it difficult to maintain robust tracking. We observe that the features trained on ImageNet are not suitable for aircraft tracking with infrared imagery. In addition, for deep feature-based tracking, the main computational burden comes from the feedforward pass through the pretrained deep network. To this end, we present an airborne infrared target tracking algorithm that employs feature embedding learning and correlation filters to obtain improved performance. We develop a shallow network and a contrastive center loss function to learn the prototypical representation of the aircraft in the embedding space. The feature embedding module is lightweight and integrated into the efficient convolution operator framework for aircraft tracking. Finally, to demonstrate the effectiveness of our tracking algorithm, we conduct extensive experiments on airborne infrared imagery and benchmark trackers.
Two iterative methods for reverse image filtering

Abstract We consider the problem of recovering an original image $${\varvec{x}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> </mml:math> from its filtered version $${\varvec{y}}={\varvec{f}}({\varvec{x}})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>y</mml:mi> </mml:mrow> <mml:mo>=</mml:mo> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , assuming that the internal structure of the filter $${\varvec{f}}(\cdot )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mo>·</mml:mo> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> is unknown to us (i.e., we can only query the filter as a black-box and, for example, cannot invert it). We present two new iterative methods to attack the problem, analyze, and evaluate them on various smoothing and edge-preserving image filters.
Research on correlation factor analysis and prediction method of overhead transmission line defect state based on association rule mining and RBF-SVM

The effective assessment and prediction of the defect state of transmission lines can provide important technical support for the maintenance management of transmission lines. This paper proposes a method of correlation factors analysis and prediction for transmission line defect state based on association rule mining and RBF-SVM since the single operation parameter is often used in the analysis and prediction of transmission line defect state, and ignoring the influence of internal and external factors such as the meteorological conditions, operating conditions, etc. Firstly, according to the defect state assessment of transmission lines, based on the existing data, a characteristic library of the defect state and correlation factors is constructed by considering various relevant influencing factors. Then FP-Growth algorithm is introduced into the association rules mining, which can find the internal and external factors that have a strong association with defect, and the association rules can be used as the input feature of the prediction model, so as to avoid the influence of low association factors on the accuracy of defect state prediction. Finally, RBF-SVM was used to predict the defect state, and have a better prediction accuracy compared with three commonly used methods of the linear SVM, ANN and the decision tree. The proposed approach is illustrated by predicting the defect state of an overhead transmission line in a certain area. The results verify the effectiveness of the method and provide a certain reference for the maintenance of the transmission line.
A Unified Convolutional Neural Network Classifier Aided Intelligent Channel Decoder for Coexistent Heterogeneous Networks

In coexistent heterogeneous wireless networks, receivers have to process the signaling and the data following different specifications. With the aim to automatically and intelligently identify the received signal type and then recover the data, in this paper, we propose a unified intelligent channel decoder serially concatenated by a convolutional-neural-network-based classifier and a deep learning (DL)-aided decoder. The classifier mainly consists of the convolutional layer, the batch normalization layer, and the max-pooling layer, while the DL decoder is constituted by four full connection layers. At the training stage, the unified decoder is trained to learn the distinct characteristics of encoded codewords following different specifications. Then, at the deployment stage, the classifier will extract the distinct structural features and identify the coding pattern. Thus, the DL decoder could choose an appropriate set of neural network parameters for information recovery. Simulation results demonstrate that our proposed intelligent decoder achieves better reliability performances than benchmark schemes over both additive white Gaussian noise and Rayleigh fading channels.
Perceptions of Human and Machine-Generated Articles

Automated journalism technology is transforming news production and changing how audiences perceive the news. As automated text-generation models advance, it is important to understand how readers perceive human-written and machine-generated content. This study used OpenAI’s GPT-2 text-generation model (May 2019 release) and articles from news organizations across the political spectrum to study participants’ reactions to human- and machine-generated articles. As participants read the articles, we collected their facial expression and galvanic skin response (GSR) data together with self-reported perceptions of article source and content credibility. We also asked participants to identify their political affinity and assess the articles’ political tone to gain insight into the relationship between political leaning and article perception. Our results indicate that the May 2019 release of OpenAI’s GPT-2 model generated articles that were misidentified as written by a human close to half the time, while human-written articles were identified correctly as written by a human about 70 percent of the time.
Knowledge Graph Embedding via Metagraph Learning

Knowledge graph embedding aims to represent entities and relations in a continuous feature space while preserving the structure of a knowledge graph. Most existing knowledge graph embedding methods either focus only on a flat structure of the given knowledge graph or exploit the predefined types of entities to explore an enriched structure. In this paper, we define the metagraph of a knowledge graph by proposing a new affinity metric that measures the structural similarity between entities, and then grouping close entities by hypergraph clustering. Without any prior information about entity types, a set of semantically close entities is successfully merged into one super-entity in our metagraph representation. We propose the metagraph-based pre-training model of knowledge graph embedding where we first learn representations in the metagraph and initialize the entities and relations in the original knowledge graph with the learned representations. Experimental results show that our method is effective in improving the accuracy of state-of-the-art knowledge graph embedding methods.
Liquid Level Detection in Standard Capacity Measures with Machine Vision

Capacity measures are commonly used volume standards for testing measuring systems for liquids other than water. Manual readings from the measuring scale can often be difficult due to the location of the capacity measure or to the nature of the measured liquid. This article focuses on the automation of this procedure by using a single camera machine vision system. A camera positioned perpendicular to the transparent neck captures the image of the liquid meniscus and the measuring scale. The volume reading is determined with the user-defined software in the LabVIEW programming environment, which carries out the image preprocessing, detection of the scale marks and the liquid level, correction of lens distortion and parallax effects and final unit conversions. The realized measuring system for liquid level detection in standard capacity measures is tested and validated by comparing the automated measurement results with those taken by the operators. The results confirm the appropriateness of the presented measuring system for the field of legal metrology.