Reviewerly/exHarmony huggingface dataset:
A Generative Adversarial Network Based Framework for Specific Emitter Characterization and Identification

Specific emitter identification (SEI) enables the classification of various unique emitters based on received waveforms using some external feature measurements from their transmit signals and has shown its potential for military and civil applications. However, the characterization of the received waveform is susceptible to various factors in propagation process, resulting in inaccurate representations for the individual emitters, so the discriminative performances of existing methods are usually challenging. To remedy these shortcomings, this paper presents a novel semi-supervised SEI using generative adversarial networks (GAN). We mitigated a representation deep network into Triple-GAN and construct a quadruple-structured framework. The overall feature information hidden in the original signals can be extracted by representation network to improve identification performance while the classification results of Triple-GAN can in turn help the representer learning more discriminative characteristics of individual emitters. Results from both simulations and real world data experiments are provided. Numerical performances indicate our conclusion that our proposed framework constantly outperforms other existing schemes in terms of classification accuracy.
Local vs. Global Interpretability of Machine Learning Models in Type 2 Diabetes Mellitus Screening

Machine learning based predictive models have been used in different areas of everyday life for decades. However, with the recent availability of big data, new ways emerge on how to interpret the decisions of machine learning models. In addition to global interpretation focusing on the general prediction model decisions, this paper emphasizes the importance of local interpretation of predictions. Local interpretation focuses on specifics of each individual and provides explanations that can lead to a better understanding of the feature contribution in smaller groups of individuals that are often overlooked by the global interpretation techniques. In this paper, three machine learning based prediction models were compared: Gradient Boosting Machine (GBM), Random Forest (RF) and Generalized linear model with regularization (GLM). No significant differences in prediction performance, measured by mean average error, were detected: GLM: 0.573 (0.569 − 0.577); GBM: 0.579 (0.575 − 0.583); RF: 0.579 (0.575 − 0.583). Similar to other studies that used prediction models for screening in type 2 diabetes mellitus, we found a strong contribution of features like age, gender and BMI on the global interpretation level. On the other hand, local interpretation technique discovered some features like depression, smoking status or physical activity that can be influential in specific groups of patients. This study outlines the prospects of using local interpretation techniques to improve the interpretability of prediction models in the era of personalized healthcare. At the same time, we try to warn the users and developers of prediction models that prediction performance should not be the only criteria for model selection.
An Encrypted Malicious Traffic Detection System Based on Neural Network

In recent years, with the widespread use of encrypted traffic communication technology, network traffic encryption has been gradually becoming a standard of communication. This phenomenon has a great impact on traditional traffic detection methods, especially on anomaly detection methods, which are highly dependent on the type of network protocol types and traffic data. By surveying the existing encrypted traffic classification and analyzing these methods, we found that there are two main methods for detection: payload-based detection and feature-based detection. On this basis, this paper puts forward an Encrypted Malicious Traffic Detection System which is based on multi-AEs(Autoencoder). We use malicious sandbox for traffic data collection, mark malicious flow and normal flow with labels. After that, we use multilayer networks of AEs for feature extraction and training classifier model. Our system analyzes the feature of cryptographic protocol from handshake phase to Authentication phase on the basis of existing research, and we extract the traffic feature for a better classification by further expanding flow feature vector to the higher dimensions. In addition, we compared the performance of our model with the traditional learning model under the same environment. The experimental results showed that our system had higher detection accuracy and lower loss rate. We also studied the influence of dataset imbalance on results in the detection of encrypted traffic by several experiments. According to the experimental evaluation, dataset imbalance will lead to the decrease of positive rate.
User’s Research Interests Based Paper Recommendation System: A Deep Learning Approach

Researchers and scientists read articles to improve their studies. Researchers spend a lot of time trying to find the suitable article from among a large number of publications. The purpose of the article recommendation system is to shorten the working time and to present the research articles to the researchers. Classic article recommendation systems do not consider the user's information. They show the same results in the same sort for each researcher. This chapter is based on the user's profile and aims to recommend articles that are most relevant to the user's search. In this study, an article recommendation system, that takes into consideration the researcher's previous articles and the study field, is presented. One of the most important innovations of this study is the use of TF-IDF and Cosine similarity to recommend article taking user's past articles into consideration. As a result of the study, the proposed method has achieved more successful results compared to equivalent methods according to F-Measure criterion. Our study was repeated with Doc2vec method which is one of the Deep-Learning methods. It was obtained better results than TF-IDF and Cosine methods.
From Chest X-Rays to Radiology Reports: A Multimodal Machine Learning Approach

Interpreting medical images and summarising them in the form of radiology reports is a challenging, tedious, and complex task. A radiologist provides a complete description of a medical image in the form of radiology report by describing normal or abnormal findings and providing a summary for decision making. Research shows that the radiology practice is error-prone due to the limited number of experts, increasing patient volumes, and the subjective nature of human perception. To reduce the number of diagnostic errors and to alleviate the task of radiologists, there is a need for a computer-aided report generation system that can automatically generate a radiology report for a given medical image. We propose an encoder-decoder based framework that can automatically generate radiology reports from medical images. Specifically, we use a Convolutional Neural Network as an encoder coupled with a multi-stage Stacked Long Short-Term Memory as a decoder to generate reports. We perform experiments on the Indiana University Chest X-ray collection, a publicly available dataset, to measure the effectiveness of our model. Experimental results show the effectiveness of our model in automatically generating radiology reports from medical images.
TendrilStaller: Block Delay Attack in Bitcoin

We present TendrilStaller, an eclipse attack targeting at Bitcoin's peer-to-peer network. TendrilStaller enables an adversary to delay block propagation to a victim for 10 minutes. The adversary thus impedes the victim from getting the latest blockchain state. It only takes as few as one Bitcoin full node and two light weight nodes to perform the attack. The light weight nodes perform a subset of the functions of a full Bitcoin node. The attack exploits a recent block propagation protocol introduced in April 2016. The protocol prescribes a Bitcoin node to select 3 neighbors that can send new blocks unsolicited. These neighbors are selected based on their recent performance in providing blocks quickly. The adversary induces the victim to select 3 attack nodes by having attack nodes send valid blocks to the victim more quickly than other neighbors. For this purpose, the adversary deploys a handful of light weight nodes so that the adversary itself receives new blocks faster. The adversary then performs the attack to delay blocks propagated to the victim. We implement the attack on top of current default Bitcoin protocol We deploy the attack nodes in multiple locations around the globe and randomly select victim nodes. Depending on the round-trip time between the adversary and the victim, 50%-85% of the blocks could be delayed to the victim. We further show that the adoption of light weight nodes greatly increases the attack probability by 15% in average. Finally, we propose several countermeasures to mitigate this eclipse attack.
Overcoming Bandwidth Limitations in Wireless Sensor Networks by Exploitation of Cyclic Signal Patterns: An Event-triggered Learning Approach

Wireless sensor networks are used in a wide range of applications, many of which require real-time transmission of the measurements. Bandwidth limitations result in limitations on the sampling frequency and number of sensors. This problem can be addressed by reducing the communication load via data compression and event-based communication approaches. The present paper focuses on the class of applications in which the signals exhibit unknown and potentially time-varying cyclic patterns. We review recently proposed event-triggered learning (ETL) methods that identify and exploit these cyclic patterns, we show how these methods can be applied to the nonlinear multivariable dynamics of three-dimensional orientation data, and we propose a novel approach that uses Gaussian process models. In contrast to other approaches, all three ETL methods work in real time and assure a small upper bound on the reconstruction error. The proposed methods are compared to several conventional approaches in experimental data from human subjects walking with a wearable inertial sensor network. They are found to reduce the communication load by 60-70%, which implies that two to three times more sensor nodes could be used at the same bandwidth.
Determination of local background and baseline values of elements within the soils of the Birimian Terrain of the Wassa Area of Southwest Ghana

This study establishes the local background and baseline values of elements in surface soils in Wassa area underlain by Birimian rocks using probability Q–Q plots. In this study, a total of 2868 soil samples were collected. The chemical elements in these samples were analysed by XRF technique. The results from the analysis were processed following Papastergios and Karim methods that normalized and transformed the data after which a best of fit is estimated to determine the background and baseline values. The method excludes the outliers at the extremities from the transformed data until the distribution follows a straight line. The method yielded the following background values in this study: 6.41 mg/kg for As, 52.55 mg.kg for Cr, 9.89 mg/kg for Pb, 10.21 mg/kg for Co and 17.18 mg/kg for Ni. The calculated baseline values were also 13.2 mg/kg, 80.7 mg/kg, 6.7 mg/kg, 6.4 mg/kg and 8.7 mg/kg for As, Cr, Pb, Co and Ni, respectively. The departure between the locally calculated background and baseline values of elements relative to worldwide accepted background and baseline values suggests the relevance to conduct such studies for the various geological provinces in the country as a vital step to properly and effectively manage and sustain the environment against environmental health diseases.
OpBench: A CPU Performance Benchmark for Ethereum Smart Contract Operation Code

Ethereum and other blockchains rely on miners contributing computational power to execute tasks such as the proof of work consensus mechanism and the execution and validation of smart contracts. Miners receive a fee, and for the correct operation of the blockchain, rewards should be proportional to the required investment (equipment, energy use, etc.). In Ethereum, the reward obtained for executing smart contracts is set statically, associating a fee with each operation code (opcode) in the smart contract. To determine whether fees are aligned with investments made, we propose OpBench, a platform-independent benchmarking approach. OpBench measures the CPU time required to execute opcodes in the Ethereum Virtual Machine. We implemented OpBench for the PyEthApp and Go-Ethereum clients, and present results for both platforms on three different machines and operating systems. The results show that the static fees set by Ethereum are not always proportional to the invested CPU time, with up to an order of magnitude difference across opcodes. The results also show a markable difference in performance between clients, with the Go client outperforming the Python client across machines.
The Cubic Dynamic Uncertain Causality Graph: A Methodology for Temporal Process Modeling and Diagnostic Logic Inference

To meet the demand for dynamic and highly reliable real-time fault diagnosis for complex systems, we extend the dynamic uncertain causality graph (DUCG) by proposing novel temporal causality modeling and reasoning methods. A new methodology, the Cubic DUCG, is therefore developed. It exploits an efficient scheme for compactly representing and accurately reasoning about the dynamic causalities in the system fault-spreading process. The Cubic DUCG is characterized by: 1) continuous generation of a causality graph that allows for causal connections penetrating among any number of time slices and discards the restrictive assumptions (about the underlying graph structure) upon which the existing research commonly relies; 2) a modeling scheme of complex causalities that includes dynamic negative feedback loops in a natural and intuitive manner; 3) a rigorous and reliable inference algorithm based on complete causalities that reflect real-time fault situations rather than on the cumulative aggregation of static time slices; and 4) some solutions to causality simplification and reduction, graphical transformation, and logical reasoning, for the sake of reducing the reasoning complexity. A series of fault diagnosis experiments on a nuclear power plant simulator verifies the accuracy, robustness, and efficiency of the proposed methodology.
Gesture Recognition in RGB Videos Using Human Body Keypoints and Dynamic Time Warping

Gesture recognition opens up new ways for humans to intuitively interact with machines. Especially for service robots, gestures can be a valuable addition to the means of communication to, for example, draw the robot's attention to someone or something. Extracting a gesture from video data and classifying it is a challenging task and a variety of approaches have been proposed throughout the years. This paper presents a method for gesture recognition in RGB videos using OpenPose to extract the pose of a person and Dynamic Time Warping (DTW) in conjunction with One-Nearest-Neighbor (1NN) for time-series classification. The main features of this approach are the independence of any specific hardware and high flexibility, because new gestures can be added to the classifier by adding only a few examples of it. We utilize the robustness of the Deep Learning-based OpenPose framework while avoiding the data-intensive task of training a neural network ourselves. We demonstrate the classification performance of our method using a public dataset.
Air Gapped Wallet Schemes and Private Key Leakage in Permissioned Blockchain Platforms

In this paper we consider the threat surface and security of air gapped wallet schemes for permissioned blockchains as preparation for a Markov based mathematical model, and quantify the risk associated with private key leakage. We identify existing threats to the wallet scheme and existing work done to both attack and secure the scheme. We provide an overview the proposed model and outline justification for our methods. We follow with next steps in our remaining work and the overarching goals and motivation for our methods.
Convolutional neural network in the detection of lung carcinoma using transfer learning approach

Abstract Background Lung carcinoma is the leading cause of 1.76 million deaths worldwide. GLOBOCAN 2018 report says lung cancer occupies the first position in terms of incidence and mortality rate. Reasons include limited access to timely diagnosis and treatment. Objective This research work presents the viability of using convolutional neural network (CNN) for lung carcinoma detection. The proposed network automatically detects the lung carcinomas tissues through transfer learning without the difficulty of specifying features for image classification. Subjects and methods The study involves lung CT images of 12 male and 4 female subjects, aging between 35 and 77 years. These images are obtained in Bitmap image format with a resolution of 512 × 512. Two cases namely healthy and carcinoma are taken for study. Carcinoma cases include poorly differentiated carcinoma, moderately differentiated carcinoma. The training data consists of 800 lung CT slices, validation set consists of 100 CT images, and test data consist of 100 images, respectively. Results Experimental results demonstrate the VGG16 and VGG 19 network implementation with the area under the receiver operating characteristics of 0.930 and 0.90, respectively. VGG19 compete well with a sensitivity and specificity of 80% and 100% compared to VGG16 with 98% and 88%, respectively. Conclusion Hence, the implementation of VGG16 and VGG19 with good accuracy proves that CNN used for lung carcinoma detection leading to timely diagnosis and treatment may decrease the mortality rate.
Multispectral Information Fusion With Reinforcement Learning for Object Tracking in IoT Edge Devices

With recent advances in sensor technology, multispectral systems are becoming increasingly attractive for intelligence, surveillance, and reconnaissance applications. Fusing information from multiple imaging modalities is a major task for such systems. Combining feature maps obtained from multiple deep neural network pipelines demonstrates promising performance for object detection and tracking. However, feature fusion using multiple deep networks is computationally intensive and therefore not suitable for resource-constrained IoT edge devices. In this paper, we propose a novel method to fuse the input space to enable processing of multispectral data via a single deep network. We use task-driven feedback as a reward signal for our reinforcement learning-based multispectral input fusion. Proposed approach not only improves tracking accuracy but also maximizes modality-specific information as intended by the user.
A bi-phased multi-objective genetic algorithm based classifier

This paper presents a novel Bi-Phased Multi-Objective Genetic Algorithm (BPMOGA) based classification method. It is a Learning Classifier System (LCS) designed for supervised learning tasks. Here we have used Genetic Algorithms (GAs) to discover optimal classifiers from data sets. The objective of the work is to find out a classifier or Complete Rule (CR) which comprises of several Class Specific Rules (CSRs). Phase-I of BPMOGA extracts optimized CSRs in IF−THEN form by following Michigan approach, without considering interaction among the rules. Phase-II of BPMOGA builds optimized CRs from CSRs by following Pittsburgh way. It combines the advantages of both approaches. Extracted CRs help to build CSRs for the next run of phase-I. Hence, phase-I and phase-II are cyclically related, which is one of the uniqueness of BPMOGA. With the help of twenty one benchmark data sets from the University of California at Irvine (UCI) machine learning repository we have compared performance of BPMOGA based classifier with fourteen GA and non-GA based classifiers. Statistical test shows that the performance of the proposed classifier is either superior or comparable to other classifiers.
Towards Service Composition Aware Virtual Machine Migration Approach in the Cloud

There is a growing trend for service providers to migrate their services from local clusters to the cloud data center. When there is no single service can satisfy the functionality requirement of the end user, existing services are combined together to fulfill the requirements. The data communication between component service hosting servers imposes a heavy burden on the data center network. In this article, we seek to reduce the data center network resource consumption by designing a novel service composition aware virtual machine migration approach. First, we formulate the problem as a multi-object integer non-linear(INLP) programming problem. The problem, which can be reduced into a well-known multi-object quadratic assignment problem, is proved to be NP-hard. Second, we simplify the multiple-objects INLP formulation into an equivalent, but much simplified single object ILP formulation. Then, we prove that the simplified formulation can also lead to the optimal solutions. Finally, optimization problem solvers, such as LPSolver, are employed to solve the problem. Experimental results in a large scale cloud data center demonstrate that our method significantly reduce the network resource consumption than other approaches.
Attention-Based Video Hashing for Large-Scale Video Retrieval

Large-scale video retrieval is a challenging problem because of the exponential growth of video collections on the Internet. To address this challenge, we propose an attention-based video hashing (AVH) method for large-scale video retrieval. Unlike most of the existing video hashing methods, which consider different frames within a video separately for hash learning, we use a convolutional neural network and long short-term memory (LSTM) network as the backbone to learn compact and discriminative hash codes by exploiting the structural information among different frames. To better capture informative clues in the video, an attention mechanism is added into the backbone, which can assign different weights to different LSTM time steps. Experiments were conducted to evaluate the proposed AVH method in comparison with existing methods. The experimental results on two widely used data sets show that our method outperforms existing state-of-the-art methods.