Reviewerly/exHarmony huggingface dataset:
CodeHouse: VR Code Visualization Tool

Derivative software development complicates the base software over the years of enhancement and maintenance. It reduces the source code readability, taking developers a long time to understand the software structure and its various parts. In software engineering, software is often compared to buildings. We propose to use this metaphor from a new prospective to help developers understand the existing software efficiently. In this paper, we present a tool called "CodeHouse" that visualizes the source code of the software in a VR space as if it were a house. By placing rooms, which represent modules of the software, on the inside of a virtual cylinder, the CodeHouse makes it possible for the developers to understand both the whole structure of the software and the details of individual program parts before they begin derivative development. The CodeHouse also links with a debugger and helps developers to understand the dynamic behaviors of the program.
Anonymity and Privacy in Bitcoin Escrow Trades

As a decentralized cryptocurrency, Bitcoin has been in market for around a decade. Bitcoin transactions are thought to be pseudo-anonymous, however, there were many attempts to deanonymize these transactions making use of public data. Escrow services have been introduced as a good private and secure way to handle Bitcoin payments between untrusted parties, where the escrow service acts as the arbitrator in case of disputes. In our work, we examine the privacy and anonymity level of trades done through one of the Bitcoin trading websites offering such escrow services and how using the data they provide for open access through their APIs along with some public scraped data can compromise the privacy and anonymity of trades in some cases. In this paper, we suggest some heuristics and methods to deanonymize Bitcoin escrow trades done on LocalBitcoins.com, a well-known escrow service used especially by people seeking anonymity, and link them to suspect sets of Bitcoin transactions in the blockchain and suspect sets of users. Our research spots privacy weakness points of using escrow services that affects the privacy and anonymity of their users trades and identities. It also shows how tracking down criminals activities across escrow services is possible even without any authority on the escrow service making it less attractive for criminals to use cryptocurrencies and leading it to gain more trust.
Bias Correction of Multiple MRI Images Based on an Improved Nonparametric Maximum Likelihood Method

With the wide application of nuclear magnetic resonance imaging, the multiplicative bias field in nuclear magnetic resonance images has created great difficulties for doctors in reading diagnostics and for computers in autoprocessing. Most previous methods eliminate the bias field in the image by estimating a single unknown bias field. An improved method that uses the nonparametric maximum likelihood to jointly eliminate bias from multiple magnetic resonance imaging (MRI) images is proposed in this paper. The method uses the statistics from the same location across different patient images, rather than within an image, and builds a “multiresolution” nonparametric tissue model conditioned on image location. We use a separate and nonparametric model to consider the intensity values at each pixel and utilize nonparametric maximum likelihood distance measures to simultaneously eliminate the bias of magnetic resonance (MR) images from different patients. Finally, the performance of the same was tested on a synthetic MRI dataset and a real MRI dataset and is found that the proposed algorithm provides better performance than the method of using entropy minimization across images and the most popular and widely used method, N4.
Embracing Opportunities of Livestock Big Data Integration with Privacy Constraints

Today's herd management undergoes a major transformation triggered by the penetration of cheap sensor solutions into cattle farms, and the promise of predictive analytics to detect animal health issues and product-related problems before they occur. The latter is particularly important to prevent disease spread, ensure animal health, animal welfare and product quality. Sensor businesses entering the market tend to build their solutions as end-to-end pipelines spanning sensors, proprietary algorithms, cloud services, and mobile apps. Since data privacy is an important issue in this industry, as a result, disconnected data silos, heterogeneity of APIs, and lack of common standards limit the value the sensor technologies could provide for herd management. In the last few years, researchers and communities proposed a number of data integration architectures to enable exchange between streams of sensor data. This paper surveys the existing efforts and outlines the opportunities they fail to address by treating sensor data as a black box. We discuss alternative solutions to the problem based on privacy-preserving collaborative learning, and provide a set of scenarios to show their benefits for both farmers and businesses.
Speak to your Software Visualization—Exploring Component-Based Software Architectures in Augmented Reality with a Conversational Interface

Exploring of software architectures with software visualization in Augmented Reality (AR) is possible with different interaction methods, such gesture, gaze, and speech. For interaction with speech (i.e., natural language), we present an architecture and an implementation of conversational interfaces for the Microsoft HoloLens device. We aim to remedy some peculiarities of AR devices, but also enhancing the exploration task at hand. To implement the conversational interface different natural language processing (NLP) components such as natural language generation and intent recognition are typically required. Our proposed architecture integrates conversational components with the AR-based software visualization. We describe its implementation based on different user utterances, where the system provides information about the to-be-explored component-based software architecture in the form of adjusted visualizations and speech-based results. We apply out tool to explore OSGi-based software architectures.
Issues of Motion Control of Mobile Robots Based on the Potential Guidance Method

One of the topical areas of research in modern robotics is the problem of local navigation of mobile robots (MR), which ensures the movement of the robot to the target with the bypass of obstacles in the process of movement. The navigation process includes the following steps: mapping the environment, localization of the robot and planning the route leading to the goal. Among the popular methods of local navigation of robots is the method of artificial potential fields (PF). The essence of the PF method is to implement the movement of the MR in the field of "information forces" using the forces of "attraction" to the target position and the forces of "repulsion" from obstacles. This article addresses the issues of local navigation and motion control of the MR based on the method of PF. When using traditional attracting potential forces, the structure of virtual forces near the obstacle depends on the distance of the MR from the target, and the robot movement will slow down at the end of the route, which will inevitably lead to an unjustified tightening of the total time of moving the robot to the target position. To eliminate this undesirable effect, the authors propose to use attracting potential fields of special type. The authors propose new methods of PF allowing to solve the key problems for the control of MR — "traps" (potential pits) and bypass obstacles: the method of two maps of potential fields and the method of "fairway" on the map of potential fields. The methods of "beetle" for solving the problem of bypass obstacles in the condition of the absence of a priori information about the working space of MR are discussed. A modified method of "beetle" having a number of advantages in comparison with classical methods is proposed.
Perphon

Cluster administrators are facing great pressures to improve cluster utilization through workload co-location. Guaranteeing performance of long-running applications (LRAs), however, is far from settled as unpredictable interference across applications is catastrophic to QoS [2]. Current solutions such as [1] usually employ sandboxed and offline profiling for different workload combinations and leverage them to predict incoming interference. However, the time complexity restricts the applicability to complex co-locations. Hence, this issue entails a new framework to harness runtime performance and mitigate the time cost with machine intelligence: i) It is desirable to explore a quantitative relationship between allocated resource and consequent workload performance, not relying on analyzing interference derived from different workload combinations. The majority of works, however, depend on offline profiling and training which may lead to model aging problem. Moreover, multi-resource dimensions (e.g., LLC contention) that are not completely included by existing works but have impact on performance interference need to be considered [3]. ii) Workload co-location also necessitates fine-grained isolation and access control mechanism. Once performance degradation is detected, dynamic resource adjustment will be enforced and application will be assigned an access to specific slices of each resources. Inferring a "just enough" amount of resource adjustment ensures the application performance can be secured whilst improving cluster utilization.
VR and Screen Education: An Approach to Assist Student Understanding of Narrative Emphasis, Spatiality and Structural Elements within Narrative VR

This chapter explores the challenges faced by educators in incorporating immersive, virtual reality (VR) contents into screen media education. It suggests several ‘threshold concepts’, or critical conceptual gateways, relevant to immersive media and cinematic virtual reality (CVR) concerning narrative emphasis, spatiality and structure. It shares an experimental visual and conceptual methodology that integrates these concepts and can be applied in the earliest stages of planning and conceptualising immersive VR media, with the potential for application or adaptation by other screen education practitioners. This approach has been trialled within the classroom in the context of the undergraduate subject Online Documentary at the University of Technology Sydney (UTS) (2017 and 2018), allowing students to apply the key concepts to existing projects and to implement the framework for the conceptualisation of future projects.
Robust Real-Time Object Detection Based on Deep Learning for Very High Resolution Remote Sensing Images

Recently, the development of deep learning boosts the object detection for remote sensing images. The existing deep learning methods can be divided into two types. The region-based methods represented by Faster R-CNN have progressive performance in accuracy. However, their computational cost is massive due to the deep Convolutional Neural Network (CNN) backbones, which limits the efficiency. The regression-based methods such as YOLO and Single Shot MultiBox Detector (SSD) are advantageous in speed while the accuracy is not satisfactory. To meet the increasing demand in both speed and accuracy for object detection of remote sensing images, we employ the Reception Field Block Net (RFBNet) detector. It embeds the Receptive Field Block (RFB) module into SSD to obtain better feature representation. The experimental results on NWPU VHR-10 dataset demonstrate that the mAP of RFBNet-512 reaches 91.56%, which outperforms other state-of-the-art networks. Meanwhile, the speed is also competitive.
Understanding Support Vector Machines with Polynomial Kernels

Interpreting models learned by a support vector machine (SVM) is often difficult, if not impossible, due to working in high-dimensional spaces. In this paper, we present an investigation into polynomial kernels for the SVM. We show that the models learned by these machines are constructed from terms related to the statistical moments of the support vectors. This allows us to deepen our understanding of the internal workings of these models and, for example, gauge the importance of combinations of features. We also discuss how the SVM with a quadratic kernel is related to the likelihood-ratio test for normally distributed populations.
On Novel Object Recognition

The rich and accessible labeled data fueled the revolutionary successes of deep learning in object recognition. However, recognizing objects of novel classes with limited supervision information provided, i.e., Novel Object Recognition (NOR), remains a challenging task. We identify in this paper two key factors for the success of NOR that previous approaches fail to simultaneously guarantee. The first is producing discriminative feature representations for images of novel classes, and the second is generating a flexible classifier readily adapted to novel classes provided with limited supervision signals. To secure both key factors, we propose a framework which decouples a deep classification model into a feature extraction module and a classification module. We learn the former to ensure feature discriminability with a standard multi-class classification task by fully utilizing the competing information among all classes within a training set, and learn the latter to secure adaptability by training a meta-learner network which generates classifier weights whenever provided with minimal supervision information of target classes. Extensive experiments on common benchmark datasets in the settings of both zero-shot and few-shot learning demonstrate our method achieves state-of-the-art performance.
Retrieving Images with Generated Textual Descriptions

This paper presents a novel remote sensing (RS) image retrieval system that is defined based on generation and exploitation of textual descriptions that model the content of RS images. The proposed RS image retrieval system is composed of three main steps. The first one generates textual descriptions of the content of the RS images combining a convolutional neural network (CNN) and a recurrent neural network (RNN) to extract the features of the images and to generate the descriptions of their content, respectively. The second step encodes the semantic content of the generated descriptions using word embedding techniques able to produce semantically rich word vectors. The third step retrieves the most similar images with respect to the query image by measuring the similarity between the encoded generated textual descriptions of the query image and those of the archive. Experimental results on RS image archive composed of RS images acquired by unmanned aerial vehicles (UAVs) are reported and discussed.
DCGSA: A global self-attention network with dilated convolution for crowd density map generating

Due to non-uniform density and variations in scale and perspective, estimating crowd count in crowded scenes in different degree is an extremely challenging task. The deep learning models mostly use pooling operation so that the density map of original resolution is obtained through the last upsampling. This paper aims to solve the problem of losing local spatial information by pooling in density map estimation. Therefore, we propose a dilated convolution neural network with global self-attention, named DCGSA. Especially, we introduce a Global Self-Attention module (GSA) to provide global context as guidance of low-level features to select person location details and a Pyramid Dilated Convolution module (PDC) that extracts channel-wise and pixel-wise features more precisely. Extensive experiments on several crowd datasets show that our method achieves lower crowd counting error and better density maps compared to the recent state-of-the-art methods. In particular, our method also performs well on the sparse dataset UCSD.
Proof-of-concept of a two-stage approach for selecting suitable slopes on a highway network for solar photovoltaic systems: A case study in South Korea

The economics of photovoltaic systems are closely associated with the geographical locations of the sites. To identify appropriate land for photovoltaic systems, previous studies have mainly used geographic-information-system-based solar energy models capable of estimating spatial and temporal availability of solar energy. The models often require handling large and computationally challenging datasets to cover vast areas. In this study, we proposed and assessed the concept of a two-stage approach that sequentially searches and prioritizes suitable sites of photovoltaic panels using low- and high-resolution maps to reduce computational burdens and increase estimation accuracy. Resolutions at raster-cell sizes of 30 × 30 m and 5 × 5 m were selected to study the South Korean national highway network. Selected candidate sites in the 10th-percentile for low- and high-resolution cases produced 1,097 and 721 clusters with calculated solar energy potentials of 19,510,115 and 4,186,935 MWh, respectively. Approximately 90% of the edge-based distances between two adjacent potential sites for photovoltaic panels from the low- and high-resolution maps were found to be within 240 m. This result shows that although the low-resolution maps may not provide precise locations for prospective solar photovoltaic sites, they may help to identify potentially suitable areas for further investigation.
Automatic Registration of Optical and SAR Images VIA Improved Phase Congruency

Owing to the property of being constant to image contrast and the identification of various types of features, phase congruency (PC) model has been widely used in remote sensing applications. However, when the PC is directly applied to optical and synthetic aperture radar (SAR) image registration, it fails to handle large radiometric and geometric differences. In this paper, we propose an automatic algorithm to solve this problem. First, evenly-distributed keypoints are extracted from the optical images via the block harris method. Complementary grid points are selected in image regions with poor structure and texture information. Then a robust similarity metric based on the improved PC model is proposed. Since the two images show diverse properties, we utilize two different PC models, the traditional PC and the SAR-PC. The PC values of several directions are aggregated to construct the feature descriptors on the basis of which, as a result, a similarity metric using the normalized correlation coefficient (NCC) is obtained. We compare the proposed metric with two baselines (mutual information and NCC) and a state-of-the-art method (histogram of the oriented phase congruency, HOPC) in the case of various scenarios, the results show that our method outperforms the baselines and show comparable performance with HOPC in regions with abundant structure information and better performance in untextured regions.
Multilevel Neural Network for Reducing Expected Inference Time

It is widely known that deep neural networks (DNNs) can perform well in many applications, and can sometimes exceed human ability.However, their cost limits their impact in a variety of realworld applications, such as IoT and mobile computing.Recently, many DNN compression and acceleration methods have been employed to overcome this problem.Most methods succeed in reducing the number of parameters and FLOPs, but only a few can speed up expected inference times because of either the overhead generated from using such methods or DNN framework deficiencies.Edge-cloud computing has recently emerged and presents an opportunity for new model acceleration and compression techniques.To address the aforementioned problem, we propose a novel technique to speed up expected inference times by using several networks that perform the exact same task with different strengths.Although our method is based on edge-cloud computing, it is suitable for any other hierarchical computing paradigm.Using a simple yet strong enough estimator, the system predicts whether the data should be passed to a larger network or not.Extensive experimental results demonstrate that the proposed technique can speed up expected inference times and beat almost all state-of-the-art compression techniques, including pruning, low-rank approximation, knowledge distillation, and branchy-type networks, on both CPUs and GPUs.
Distilled Camera-Aware Self Training for Semi-Supervised Person Re-Identification

Person re-identification (Re-ID), which is for matching pedestrians across disjoint camera views in surveillance, has made great progress in supervised learning. However, requirement of a large number of labelled identities leads to high cost for large-scale Re-ID systems. Consequently, it is significant to study learning Re-ID with unlabelled data and limited labelled data, that is, semi-supervised person re-identification. When labelled data is limited, the learned model tends to overfit the data and cannot generalize well. Moreover, the scene variations between cameras lead to domain shift in the feature space, which makes mining auxiliary supervision information from unlabelled data more difficult. To address these problems, we propose a Distilled Camera-Aware Self Training framework for semi-supervised person re-identification. To alleviate the overfitting problem for learning from limited labelled data, we propose a Multi-Teacher Selective Similarity Distillation Loss to selectively aggregate the knowledge of multiple weak teacher models trained with different subsets and distill a stronger student model. Then, we exploit the unlabelled data by learning pseudo labels by clustering based on the student model for self training. To alleviate the effect of scene variations between cameras, we propose a Camera-Aware Hierarchical Clustering (CAHC) algorithm to perform intra-camera clustering and cross-camera clustering hierarchically. Experiments show that our method outperformed the state-of-the-art semi-supervised person re-identification methods.
Table Localization and Segmentation using GAN and CNN

Table localization and segmentation is an important but critical step in document image analysis. Table segmentation is much harder than table localization particularly in the invoice document because sometimes there are nested rows or nested columns or even nested table in an invoice. Moreover, rows or columns are very close to each other and sometimes columns overlaps with each other. Most of the existing techniques fail to generalize because they rely on hand engineered features which are not robust to layout variations. Recently, deep learning approaches are applied in table localization and segmentation which achieved promising result. However, these techniques are mostly applied to contemporary document images like UNLV or Marmot datasets. Additionally, there is still some limitation to generalize them on different layout variations and preprocessing. In this paper, we have applied conditional Generative Adversarial Networks (cGAN) based architecture for table area localization and SegNet based encoder-decoder with skip connections architecture for table structure segmentation. We applied ICDAR 2013 table competition dataset for evaluating the table localization performance. Result shows that our approaches are outperforming with existing models. On the other hand, we used private complex invoice dataset for table area segmentation.