Reviewerly/exHarmony huggingface dataset:
Factors that Impact Blockchain Scalability

Blockchain systems (more precisely Distributed Ledger Technologies (DLTs)) represent a different digital ecosystem compared with traditional computer systems. One major difference are the performance and scalability factors which will be discussed and analytically investigated in this paper. In doing so, we provide guidance for defining a research agenda focusing on the investigation of the crucial role of scalability for blockchain systems. System performance -- measured in terms of (1) consensus response time (blockchain network latency or time to convergence/agreement); (2) number of transactions per second or throughput, and (3) computing (and power) resources consumed -- can be understood by considering the design dimensions of a blockchain system, namely: (i) the type of blockchain system needed from a requirements perspective which in turn determines; (ii) the complexity of the consensus protocol used; (iii) the topography of the traffic flow on the network; (iv) the performance and complexity of the domain-specific language that implements smart contracts; and (v) by the anticipated growth in size and complexity of the distributed ledger itself.
A Prototype that Fuses Virtual Reality, Robots, and Social Networks to Create a New Cyber–Physical–Social Eco-Society System for Cultural Heritage

With the rapid development of technology and the increasing use of social networks, many opportunities for the design and deployment of interconnected systems arise that could enable a paradigm shift in the ways we interact with cultural heritage. The project described in this paper aims to create a new type of conceptually led environment, a kind of Cyber–Physical–Social Eco-Society (CPSeS) system that would seamlessly blend the real with virtual worlds interactively using Virtual Reality, Robots, and Social Networking technologies, engendered by humans’ interactions and intentions. The project seeks to develop new methods of engaging the current generation of museum visitors, who are influenced by their exposure to modern technology such as social media, smart phones, Internet of Things, smart devices, and visual games, by providing a unique experience of exploring and interacting with real and virtual worlds simultaneously. The research envisions a system that connects visitors to events and/or objects separated either in time or in space, or both, providing social meeting points between them. To demonstrate the attributes of the proposed system, a Virtual Museum scenario has been chosen. The following pages will describe the RoboSHU: Virtual Museum prototype, its capabilities and features, and present a generic development framework that will also be applicable to other contexts and sociospatial domains.
Automated Complete Blood Cell Count and Malaria Pathogen Detection Using Convolution Neural Network

Complete blood cell count, which indicates the density of different blood cells in the human body is extremely important for evaluating the overall health of a person and also for detecting a wide range of disorders, including anemia, infection and leukemia. Hence, automating this task will not only increase the speed of diagnosis, but also lower the overall treatment cost. In this paper, we focus on using a convolution neural network to perform this complete blood cell count on blood smear images. The network is also trained to detect malarial pathogens in the blood, if present. Experiments show that the overall performance of the system has a mean average precision of over 0.95 when compared with the ground-truth. Furthermore, the system predicts the images containing malarial parasites as infected 100% of the time. The software is also ported to a low cost microcomputer for rapid prototyping.
LPI Radar Waveform Recognition Based on Features from Multiple Images

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is -6 dB.
Aggressive Perception-Aware Navigation Using Deep Optical Flow Dynamics and PixelMPC

Recently, vision-based control has gained traction by leveraging the power of machine learning. In this work, we couple a model predictive control (MPC) framework to a visual pipeline. We introduce deep optical flow (DOF) dynamics, which is a combination of optical flow and robot dynamics. Using the DOF dynamics, MPC explicitly incorporates the predicted movement of relevant pixels into the planned trajectory of a robot. Our implementation of DOF is memory-efficient, data-efficient, and computationally cheap so that it can be computed in real-time for use in an MPC framework. The suggested Pixel Model Predictive Control (PixelMPC) algorithm controls the robot to accomplish a high-speed racing task while maintaining visibility of the important features (gates). This improves the reliability of vision-based estimators for localization and can eventually lead to safe autonomous flight. The proposed algorithm is tested in a photorealistic simulation with a high-speed drone racing task.
Empirical Evaluation of the Impact of Class Overlap on Software Defect Prediction

Software defect prediction (SDP) utilizes the learning models to detect the defective modules in project, and their performance depends on the quality of training data. The previous researches mainly focus on the quality problems of class imbalance and feature redundancy. However, training data often contains some instances that belong to different class but have similar values on features, and this leads to class overlap to affect the quality of training data. Our goal is to investigate the impact of class overlap on software defect prediction. At the same time, we propose an improved K-Means clustering cleaning approach (IKMCCA) to solve both the class overlap and class imbalance problems. Specifically, we check whether K-Means clustering cleaning approach (KMCCA) or neighborhood cleaning learning (NCL) or IKMCCA is feasible to improve defect detection performance for two cases (i) within-project defect prediction (WPDP) (ii) cross-project defect prediction (CPDP). To have an objective estimate of class overlap, we carry out our investigations on 28 open source projects, and compare the performance of state-of-the-art learning models for the above-mentioned cases by using IKMCCA or KMCCA or NCL VS. without cleaning data. The experimental results make clear that learning models obtain significantly better performance in terms of balance, Recall and AUC for both WPDP and CPDP when the overlapping instances are removed. Moreover, it is better to consider both class overlap and class imbalance.
Fansmitter: Acoustic data exfiltration from air-Gapped computers via fans noise

Abstract Computers that contain sensitive information are often maintained in air-gapped isolation. In this defensive measure, a computer is disconnected from the Internet - logically and physically - preventing accidental or intentional leakage of sensitive information outward. In recent years it has been shown that malware can leak data over an air-gap by transmitting sonic and ultrasonic signals from a computer speaker. In order to eliminate such acoustic covert channels, current best practice recommends the elimination of speakers in secured computers, thereby creating a so-called ‘audio-gapped’ system. In this paper, we present ‘Fansmitter,’ a malware that can acoustically exfiltrate data from air-gapped computers, even when audio hardware and speakers are not present. Our method utilizes the noise emitted from the CPU, GPU, and chassis fans. We show that a software can regulate the internal fans’ rotation speed in order to control their acoustic signal, known as blade pass frequency (BPF). Binary data can be modulated and transmitted over these audio signals to a remote microphone (e.g., a nearby smartphone). We present design considerations, including acoustic waveform analysis, data modulation and demodulation, and data transmission and reception. We evaluate the acoustic covert channel with various fans at different distances and present the results. We also discuss issues such as stealth, interference, and countermeasures. Using our method we successfully transmitted data from audio-less, air-gapped computers, to a mobile phone in the same room. We demonstrated an effective transmission at distances of 1–8 m, with a maximum bit rate of 60 bit/min per fan.
Remote sensing image quality evaluation based on deep support value learning networks

Aiming at the problem that the remote sensing image quality evaluation models with manually extracted features lack robustness and generality, this paper proposes a 3D CNN-based architecture and nuclear power plant for accurate remote sensing image quality assessment. The model incorporates two sub-networks. The DSVL-based sub-network is employed to extract multi-scale, multi-direction and high-level features by layer-wise training. Afterwards, the extracted feature maps are fused as flowed as input data of the second sub-network, which is designed with 3D CNN architecture and nuclear power plant for remote sensing image quality assessment. Experimental results on remote sensing image quality database from the GeoEye-1 and WorldView-2 satellites show that the proposed model can optimally discover the essential features of the image and effectively extract the high-frequency information of each level of image, and has better overall quality assessment performance than the other state-of-the-art methods.
Analysis of earnings forecast of blockchain financial products based on particle swarm optimization

The purpose of this study is to solve the problems of large number of iterations, limitations and poor fitting effect of traditional algorithms in predicting the yield rate of blockchain financial products. In this study, bitcoin yield rate is taken as the research object, and data from June 2, 2016 to December 30, 2018 are collected, totaling 943 pieces. The BP neural network, support vector regression machine algorithm and particle swarm optimization least square vector algorithm are respectively adopted to carry out model simulation and empirical analysis on the collected data, and it is concluded that particle swarm optimization least square vector algorithm has the best fitting effect. Subsequently, the Ethereum (ETH) yield rate is selected as the research object, and the model simulation and empirical analysis are carried out on it, which verifies that the optimized algorithm has better prediction and fitting on the time series. The results show that the particle swarm optimization algorithm among the three algorithms mentioned in this research has the best prediction effect. Therefore, the results of this study have a good fitting effect on the prediction of the yield rate of blockchain financial products, have a good guiding effect on the investors of blockchain financial products, and have a good guiding significance for the study of the yield rate of China's blockchain financial products.
Real-Time Optimal Trajectory Generation and Control of a Multi-Rotor With a Suspended Load for Obstacle Avoidance

This letter presents real-time optimization algorithms on trajectory generation and control for a multi-rotor with a suspended load. Since the load is suspended through a cable without any actuator, movement of the load must be controlled via maneuvers of the multi-rotor, which brings about difficulties in operating this system. Additionally, the highly nonlinear dynamics of the system exacerbates the difficulties. While trajectory generation and control are essential for safety, energy efficiency, and stability, the aforementioned characteristics of the system add challenges. With this in mind, the authors propose real-time path planning and optimal control algorithms for collision-free trajectory generation and trajectory tracking. For the dynamics, simplified dynamic models of the system are proposed by considering time delay in attitude control of the multi-rotor. For collision avoidance, the vehicle, cable, and load are considered as ellipsoids with different sizes and shapes, and collision-free constraints are expressed in an efficient and nonconservative way. The augmented Lagrangian method is applied to solve the nonlinear optimization problem with the nonlinear constraints in real-time. For control of the system, model predictive control with a sequential linear quadratic solver is used. Several simulations and experiments are conducted to validate the proposed algorithm.
Brutus: Refuting the Security Claims of the Cache Timing Randomization Countermeasure Proposed in CEASER

Cache timing attacks are a serious threat to the security of computing systems. It permits sensitive information, such as cryptographic keys, to leak across virtual machines and even to remote servers. Encrypted Address Cache, proposed by CEASER - a best paper candidate at MICRO 2018 - is a promising countermeasure that stymies the timing channel by employing cryptography to randomize the cache address space. The author claims strong security guarantees by providing randomization both spatially (randomizing every address) and temporally (changing the encryption key periodically). In this letter, we point out a serious flaw in their encryption approach that undermines the proposed security guarantees. Specifically, we show that the proposed Low-Latency Block Cipher, used for encryption in CEASER, is composed of only linear functions which neutralizes the spatial and temporal randomization. Thus, we show that the complexity of a cache timing attack remains unaltered even with the presence of CEASER. Further, we compare the encryption overheads if CEASER is implemented with a stronger encryption algorithm.
Deep Learning Model for Image Classification

Starting from images captured on mobile phones, advertisements popping up on our internet browser to e-retail sites displaying flashy dresses for sale, every day we are dealing with a large quantity of visual information and sometimes, finding a particular image or visual content might become a very tedious task to deal with. By classifying the images into different logical categories, our quest to find an appropriate image becomes much easier. Image classification is generally done with the help of computer vision, eye tracking and ways as such. What we intend to implement in classifying images is the use of deep learning for classifying images into pleasant and unpleasant categories. We proposed the use of deep learning in image classification because deep learning can give us a deeper understanding as to how a subject reacts to a certain visual stimuli when exposed to it.
Hypergraph Spectral Analysis and Processing in 3D Point Cloud

Along with increasingly popular virtual reality applications, the three-dimensional (3D) point cloud has become a fundamental data structure to characterize 3D objects and surroundings. To process 3D point clouds efficiently, a suitable model for the underlying structure and outlier noises is always critical. In this work, we propose a hypergraph-based new point cloud model that is amenable to efficient analysis and processing. We introduce tensor-based methods to estimate hypergraph spectrum components and frequency coefficients of point clouds in both ideal and noisy settings. We establish an analytical connection between hypergraph frequencies and structural features. We further evaluate the efficacy of hypergraph spectrum estimation in two common point cloud applications of sampling and denoising for which also we elaborate specific hypergraph filter design and spectral properties. The empirical performance demonstrates the strength of hypergraph signal processing as a tool in 3D point clouds and the underlying properties.
SAANet: Spatial adaptive alignment network for object detection in automatic driving

Both images and point clouds are beneficial for object detection in a visual navigation module for autonomous driving. The spatial relationships between different objects at different times in a bimodal space can vary significantly. It is difficult to combine bimodal descriptions into a unified model to effectively detect objects in an efficient amount of time. In addition, conventional voxelization methods resolve point clouds into voxels at a global level, and often overlook local attributes of the voxels. To address these problems, we propose a novel fusion-based deep framework named SAANet. SAANet utilizes a spatial adaptive alignment (SAA) module to align point cloud features and image features, by automatically discovering the complementary information between point clouds and images. Specifically, we transform the point clouds into 3D voxels, and introduce local orientation encoding to represent the point clouds. Then, we use a sparse convolutional neural network to learn a point cloud feature. Simultaneously, a ResNet-like 2D convolutional neural network is used to extract an image feature. Next, the point cloud feature and image feature are fused by our SAA block to derive a comprehensive feature. Then, the labels and 3D boxes for objects are learned using a multi-task learning network. Finally, an experimental evaluation on the KITTI benchmark demonstrates the advantages of our method in terms of average precision and inference time, as compared to previous state-of-the-art results for 3D object detection.
Improved image steganography based on super-pixel and coefficient-plane-selection

Coverless steganographic techniques are considered as reliable solutions for avoiding steganalysis attacks. However, it demands massive shared databases and usually has low payload capacity, which makes it less appealing. An advanced DT-CWT based image steganographic approach has been presented to embed secret data over appropriate coefficient planes of the cover image. Payload capacity is boosted while reducing the embedding error using super-pixeling and intensity mapping in the preprocessing stage of the secret image. A template matching based embedding location detection is used to reduce the embedding error by making use of the similarity between secret data and DT-CWT planes. A machine learning classifier is employed for selecting the best cover-coefficient planes. Cover and stego-image difference is minimized and hence the data is retrieved from the cover image with the secret key generated during the embedding process using either the pre-shared cover image database or without it. When sharing the database, only the secret key is needed to be transmitted which is used for retrieving the concealed data from the original cover image. An automatic geometric correction stage is also proposed to defend against geometric attacks. Experimental results of the proposed approach show better performance among the state-of-the-art techniques.
Acceptance and Effectiveness of a Virtual Reality Public Speaking Training

Virtual Reality (VR) has been gaining importance due to its numerous advantages as an immersive technology for learning applications. Next to being used in fields like manufacturing, transportation, communication, retail and real estate, it has also increased in significance for human resources development. Virtual human training programs offer exposure to difficult situations without supervision in a safe and controlled environment, as they simulate nuanced interpersonal situations and therefore allow users to gain new skills and apply them to real-life situations. Furthermore, VR training engages employees in a training session by being presented with a realistic situation designed to challenge and engage them. Practising in a controlled virtual environment also allows for easy and objective measurements of user development and the identification of strengths and weaknesses. In this paper, we evaluate if there is acceptance for practising a presentation in a VR-Speech Training (VR-ST) session using six Degrees of Freedom (6DoF) and if the 44 participants of this study improved, from a subjective point of view, valuable soft skills needed to give a convincing presentation in real life. We observed a positive tendency in the acceptance and effectiveness of the VR-ST.
Smart Assistant for Blind and Visually Impaired People

Blind or visually impaired people feel miserable and helpless when people help them in their daily simple jobs. Many of them are afraid of going out unless it's necessary. Blind people usually memorize the location of objects so they can find them without struggling, but what if the location of any object has been changed for any reason? Or what if they want to go to an outside place? How they will recognize objects? Colors are also another challenge for them. Smart Assistant for blind people is a portable device. This device will make blind and visually impaired people's lives much easier, as it will help them in recognizing objects. Another aim is to identify texts on objects. An additional feature for the device is to make the device capable of recognizing different colors. This device is easy to use, and it can be used by users of different ages. There are two main parts for our project: software part and hardware part. Hardware part will contain camera, Ultrasonic sensor, Bluetooth headset, Raspberry Pi and power bank. While the software part will be OpenCV as the framework, that uses Python as the programming language. We also use a deep learning technique. This technique is a type of machine learning methods that uses neural network architectures to extract features from an image in order to recognize the object exists in the captured image.
Glyphboard: Visual Exploration of High-Dimensional Data Combining Glyphs with Dimensionality Reduction

Rigorous data science is interdisciplinary at its core. In order to make sense of high-dimensional data, data scientists need to enter into a dialogue with domain experts. We present Glyphboard, a visualization tool that aims to support this dialogue. Glyphboard is a zoomable user interface that combines well-known methods such as dimensionality reduction and glyph-based visualizations in a novel, seamless, and integrated tool. While the dimensionality reduction affords a quick overview over the data, glyph-based visualizations are able to show the most relevant dimensions in the data set at one glance. We contribute an open-source prototype of Glyphboard, a general exchange format for high-dimensional data, and a case study with nine data scientists and domain experts from four exemplary domains in order to evaluate how the different visualization and interaction features of Glyphboard are used.