Reviewerly/exHarmony huggingface dataset:
End-to-End Detection and Re-identification Integrated Net for Person Search

This paper proposes a pedestrian detection and re-identification (re-id) integrated net (I-Net) in an end-to-end learning framework. The I-Net is used in real-world video surveillance scenarios, where the target person needs to be searched in the whole scene videos, and the annotations of pedestrian bounding boxes are unavailable. Comparing to the successful OIM method [31] for joint detection and re-id, we have three distinct contributions. First, we implement a Siamese architecture instead of one stream for an end-to-end training strategy. Second, a novel on-line pairing loss (OLP) with a feature dictionary restricts the positive pairs. Third, hard example priority softmax loss (HEP) with little computation cast is proposed to deal with the online hard example mining. We show our results on CUHK-SYSU and PRW datasets. Our method narrows the gap between detection and re-identification, and achieves a superior performance.
The Scalability of Trustless Trust

Permission-less blockchains can realise trustless trust, albeit at the cost of limiting the complexity of computation tasks. To explain the implications for scalability, we have implemented a trust model for smart contracts, described as agents in an open multi-agent system. Agent intentions are not necessarily known and autonomous agents have to be able to make decisions under risk. The ramifications of these general conditions for scalability are analysed for Ethereum and then generalised to other current and future platforms. Finally, mechanisms from the trust model are applied to a verifiable computation algorithm and implemented in the Ethereum blockchain. We show in experiments that the algorithm needs at most six semi-honest verifiers to detect false submission.
DistanceNet: Estimating Traveled Distance From Monocular Images Using a Recurrent Convolutional Neural Network

Classical monocular vSLAM/VO methods suffer from the scale ambiguity problem. Hybrid approaches solve this problem by adding deep learning methods, for example by using depth maps which are predicted by a CNN. We suggest that it is better to base scale estimation on estimating the traveled distance for a set of subsequent images. In this paper, we propose a novel end-to-end many-to-one traveled distance estimator. By using a deep recurrent convolutional neural network (RCNN), the traveled distance between the first and last image of a set of consecutive frames is estimated by our DistanceNet. Geometric features are learned in the CNN part of our model, which are subsequently used by the RNN to learn dynamics and temporal information. Moreover, we exploit the natural order of distances by using ordinal regression to predict the distance. The evaluation on the KITTI dataset shows that our approach outperforms current state-of-the-art deep learning pose estimators and classical mono vSLAM/VO methods in terms of distance prediction. Thus, our DistanceNet can be used as a component to solve the scale problem and help improve current and future classical mono vSLAM/VO methods.
Reverse engineering of ReFS

File system forensics is an important part of Digital Forensics. Investigators of storage media have traditionally focused on the most commonly used file systems such as NTFS, FAT, ExFAT, Ext2-4, HFS+, APFS, etc. NTFS is the current file system used by Windows for the system volume, but this may change in the future. In this paper we will show the structure of the Resilient File System (ReFS), which has been available since Windows Server 2012 and Windows 8. The main purpose of ReFS is to be used on storage spaces in server systems, but it can also be used in Windows 8 or newer. Although ReFS is not the current standard file system in Windows, while users have the option to create ReFS file systems, digital forensic investigators need to investigate the file systems identified on a seized media. Further, we will focus on remnants of non-allocated metadata structures or attributes. This may allow metadata carving, which means searching for specific attributes that are not allocated. Attributes found can then be used for file recovery. ReFS uses superblocks and checkpoints in addition to a VBR, which is different from other Windows file systems. If the partition is reformatted with another file system, the backup superblocks can be used for partition recovery. Further, it is possible to search for checkpoints in order to recover both metadata and content. Another concept not seen for Windows file systems, is the sharing of blocks. When a file is copied, both the original and the new file will share the same content blocks. If the user changes the copy, new data runs will be created for the modified content, but unchanged blocks remain shared. This may impact file carving, because part of the blocks previously used by a deleted file might still be in use by another file. The large default cluster size, 64 KiB, in ReFS v1.2 is an advantage when carving for deleted files, since most deleted files are less than 64 KiB and therefore only use a single cluster. For ReFS v3.2 this advantage has decreased because the standard cluster size is 4 KiB. Preliminary support for ReFS v1.2 has been available in EnCase 7 and 8, but the implementation has not been documented or peer-reviewed. The same is true for Paragon Software, which recently added ReFS support to their forensic product. Our work documents how ReFS v1.2 and ReFS v3.2 are structured at an abstraction level that allows digital forensic investigation of this new file system. At the time of writing this paper, Paragon Software is the only digital forensic tool that supports ReFS v3.x. It is the most recent version of the ReFS file system that is most relevant for digital forensics, as Windows automatically updates the file system to the latest version on mount. This is why we have included information about ReFS v3.2. However, it is possible to change a registry value to avoid updating. The latest ReFS version observed is 3.4, but the information presented about 3.2 is still valid. In any criminal case, the investigator needs to investigate the file system version found.
Hierarchy-Dependent Cross-Platform Multi-View Feature Learning for Venue Category Prediction

In this paper, we focus on visual venue category prediction, which can facilitate various applications for location-based service and personalization. Considering the complementarity of different media platforms, it is reasonable to leverage venue-relevant media data from different platforms to boost the prediction performance. Intuitively, recognizing one venue category involves multiple semantic cues, especially objects and scenes and, thus, they should contribute together to venue category prediction. In addition, these venues can be organized in a natural hierarchical structure, which provides prior knowledge to guide venue category estimation. Taking these aspects into account, we propose a Hierarchy-dependent Cross-platform Multi-view Feature Learning (HCM-FL) framework for venue category prediction from videos by leveraging images from other platforms. HCM-FL includes two major components, namely Cross-Platform Transfer Deep Learning (CPTDL) and Multi-View Feature Learning with the Hierarchical Venue Structure (MVFL-HVS). CPTDL is capable of reinforcing the learned deep network from videos using images from other platforms. Specifically, CPTDL first trained a deep network using videos. These images from other platforms are filtered by the learnt network and these selected images are then fed into this learnt network to enhance it. Two kinds of pre-trained networks on the ImageNet and Places dataset are employed. Therefore, we can harness both object-oriented and scene-oriented deep features through these enhanced deep networks. MVFL-HVS is then developed to enable multi-view feature fusion. It is capable of embedding the hierarchical structure ontology to support more discriminative joint feature learning. We conduct the experiment on videos from Vine and images from Foursquare. These experimental results demonstrate the advantage of our proposed framework in jointly utilizing multi-platform data, multi-view deep features, and hierarchical venue structure knowledge.
The Madrid Quantum Network: A Quantum-Classical Integrated Infrastructure

We report on the Madrid Quantum Network, designed to demonstrate that a telecommunications network can also host quantum communications in a unified, logical and physical infrastructure.Using new Quantum Key Distribution systems paired with modern networking paradigms, we demonstrate a high technology readiness level of QKD installing the network in production facilities and running relevant use cases.
Improved upper bounds on all maximal α-gapped repeats and palindromes

We show that the number of all maximal α -gapped repeats and palindromes of a word of length n is at most 3 ( π 2 / 6 + 5 / 2 ) α n and 7 ( π 2 / 6 + 1 / 2 ) α n − 3 n − 1 , respectively.
Visual Portrayals of Data and Results at ITiCSE

We present an analysis of the visual portrayals of data (including results) in the full papers and working group reports of ITiCSE from 2013 to 2018. We find that tables are the most common visual portrayal of data in these publications, but that a number of graphical forms are also widely used. We examine the quality of the data portrayals for tables, graphs, and images using visual quality indicators derived from the literature. Overall, our findings are not positive. We find that many papers present data in such a way that it cannot be readily interpreted. The most common problem is captions that do not adequately describe the table, figure, or image. In tables, the main issues affecting readability of numeric data are poor alignment of numbers within a column, unnecessary notations, and unwarranted precision. In graphs and images, the prevalent problem is text that is too small to be read. We conclude with guidelines for future authors to ITiCSE and other computing education venues, in the hope that we can contribute to an improvement in the quality of computing education publications.
Computer Vision and Deep Learning-Enabled UAVs: Proposed Use Cases for Visually Impaired People in a Smart City

Technological research and innovation have advanced at a rapid pace in recent years, and one group hoping to benefit from this, is visually impaired people (VIP). Technology may enable them to find new ways of travelling around smart cities, thus improving their quality of life (QoL). Currently, there are approximately 110 million VIP worldwide, and continuous research is crucial to find innovative solutions to their mobility problems. Recent advances such as the increase in Unmanned Aerial Vehicles (UAVs), smartphones and wearable devices, together with an ever-growing uptake of deep learning, computer vision, the Internet of Things (IoT), and virtual and augmented reality (VR)/(AR), have provided VIP with the hope of having an improved QoL. In particular, indoor and outdoor spaces could be improved with the use of such technologies to make them suitable for VIP. This paper examines use cases both indoors and outdoors and provides recommendations of how deep learning and computer vision-enabled UAVs could be employed in smart cities to improve the QoL for VIP in the coming years.
Adaptive Nonparametric Clustering

This paper presents a new approach to non-parametric cluster analysis called adaptive weights' clustering. The method is fully adaptive and does not require to specify the number of clusters or their structure. The clustering results are not sensitive to noise and outliers, and the procedure is able to recover different clusters with sharp edges or manifold structure. The method is also scalable and computationally feasible. Our intensive numerical study shows a state-of-the-art performance of the method in various artificial examples and applications to text data. The idea of the method is to identify the clustering structure by checking at different points and for different scales on departure from local homogeneity. The proposed procedure describes the clustering structure in terms of weights w <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ij</sub> , and each of them measures the degree of local inhomogeneity for two neighbor local clusters using statistical tests of “no gap” between them. The procedure starts from very local scale, and then, the parameter of locality grows by some factor at each step. We also provide a rigorous theoretical study of the procedure and state its optimal sensitivity to deviations from local homogeneity.
KSUFS: A Novel Unsupervised Feature Selection Method Based on Statistical Tests for Standard and Big Data Problems

The typical inaccuracy of data gathering and preparation procedures makes erroneous and unnecessary information to be a common issue in real-world applications. In this context, feature selection methods are used in order to reduce the harmful impact of such information in data analysis by removing irrelevant features from datasets. This research presents a novel feature selection method in the field of unsupervised learning, in which the complexity arises from the fact that the class labels cannot be used to select the most discriminative features as it is traditionally performed in supervised learning. The technique designed, which is called Kolmogorov-Smirnov test-based Unsupervised Feature Selection (KSUFS), is based on the computation of estimated feature distributions that are later compared to the original ones using non-parametric statistical tests to provide the most representative input variables. Two versions of the KSUFS are presented in this study: one of them is particularly designed to deal with standard data, in which the accuracy of the method prevalences over other of its aspects; the other version is designed to treat with big data problems, in which the computational complexity is improved due to the characteristics of this type of datasets. The KSUFS is successfully compared to other state-of-the-art unsupervised feature selection techniques in a thorough experimental study, which considers both standard and big data problems. The results obtained show that the method proposed is able to outperform the rest of reference unsupervised feature selection methods considered in the comparisons, selecting the first most influential features for standard datasets and particularly highlighting when big data problems are treated.
A procedural texture generation framework based on semantic descriptions

Procedural textures are normally generated from mathematical models with parameters carefully selected by experienced users. However, for naive users, the intuitive way to obtain a desired texture is to provide semantic descriptions such as “regular”, “lacelike”, and “repetitive” and then a procedural model with proper parameters will be automatically suggested to generate the corresponding textures. By contrast, it is less practical for users to learn mathematical models and tune parameters based on multiple examinations of large numbers of generated textures. In this study, we propose a novel framework that generates procedural textures according to user-defined semantic descriptions, and we establish a mapping between procedural models and semantic texture descriptions. First, based on a vocabulary of semantic attributes collected from psychophysical experiments, a multi-label learning method is employed to annotate a large number of textures with semantic attributes to form a semantic procedural texture dataset. Then, we derive a low dimensional semantic space in which the semantic descriptions can be separated from one other. Finally, given a set of semantic descriptions, the diverse properties of the samples in the semantic space can lead the framework to find an appropriate generation model that uses appropriate parameters to produce a desired texture. The experimental results show that the proposed framework is effective and that the generated textures closely correlate with the input semantic descriptions.
A machine-learning framework for supporting intelligent web-phishing detection and analysis

This paper proposes a machine-learning framework for supporting intelligent web phishing detection and analysis, and provides its experimental evaluation. In particular we make use of state-of-the-art decision tree algorithms for detecting whether a Web site is able to perform phishing activities. If this is the case, the Web site is classified as a Web-phishing site. Our experimental evaluation confirms the benefits of applying machine learning methods to the well-known web-phishing detection problem.
Quantum secrecy in thermal states

Abstract We propose to perform quantum key distribution using quantum correlations occurring within thermal states produced by low power sources such as LEDs. These correlations are exploited through the Hanbury Brown and Twiss effect. We build an optical central broadcast protocol using a superluminescent diode which allows switching between laser and thermal regimes, enabling us to provide comparable experimental key rates in both regimes. We provide a theoretical analysis and show that quantum secrecy is possible, even in high noise situations.
A Study of Malicious URL Detection Using Machine Learning and Heuristic Approaches

Malicious URL is a typical and genuine threat to cybersecurity. A Malicious URL has an assortment of spontaneous content in the form of phishing, spam in order to launch attacks. Innocent users visit such web sites move toward becoming casualties of various sorts of scams, including monetary loss, theft of private information (identity, credit-cards, etc.). It is essential to identify and follow up on such dangers in an opportune way. In this paper we had studies different techniques for detecting malicious URL and discussing each and every technique their merits and demerits.
Undermining User Privacy on Mobile Devices Using AI

Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to user privacy. This is because applications leave distinct footprints in the processor, which malware can use to infer user activities. In this work, we show that these inference attacks can greatly be enhanced with advanced AI techniques. In particular, we focus on profiling the activity in the last-level cache (LLC) of ARM processors. We employ a simple Prime+Probe based monitoring technique to obtain cache traces, which we classify with deep learning methods including convolutional neural networks. We demonstrate our approach on an off-the-shelf Android phone by launching a successful attack from an unprivileged, zero-permission app in well under a minute. The app detects running applications, opened websites, and streaming videos with up to 98% accuracy and a profiling phase of at most 6 seconds. This is possible, as deep learning compensates measurement disturbances stemming from the inherently noisy LLC monitoring and unfavorable cache characteristics. In summary, our results show that thanks to advanced AI techniques, inference attacks are becoming alarmingly easy to execute in practice. This once more calls for countermeasures that confine microarchitectural leakage and protect mobile phone applications, especially those valuing the privacy of their users.
Skin-Based Identification From Multispectral Image Data Using CNNs

User identification from hand images only is still a challenging task. In this paper, we propose a new biometric identification system based solely on a skin patch from a multispectral image. The system is utilizing a novel modified 3D CNN architecture which is taking advantage of multispectral data. We demonstrate the application of our system for the example of human identification from multispectral images of hands. To the best of our knowledge, this paper is the first to describe a pose-invariant and robust to overlapping real-time human identification system using hands. Additionally, we provide a framework to optimize the required spectral bands for the given spatial resolution limitations.
Empirical Performance Evaluation of QUIC Protocol for Tor Anonymity Network

Tor's anonymity network is one of the most widely used anonymity networks online, it consists of thousands of routers run by volunteers. Tor preserves the anonymity of its users by relaying the traffic through a number of routers (called onion routers) forming a circuit. The current design of Tor's transport layer suffers from a number of problems affecting the performance of the network. Several researches proposed changes in the transport design in order to eliminate the effect of these problems and improve the performance of Tor's network. In this paper. we propose "QuicTor", an improvement to the transport layer of Tor's network by using Google's protocol "QUIC" instead of TCP. QUIC was mainly developed to eliminate TCP's latency introduced from the handshaking delays and the head-of-line blocking problem. We provide an empirical evaluation of our proposed design and compare it to two other proposed designs, IMUX and PCTCP. We show that QuicTor significantly enhances the performance of Tor's network.
Integration of Multimodal Data for Breast Cancer Classification Using a Hybrid Deep Learning Method

Although the application of deep learning has greatly improved the performance of benign and malignant breast cancer classification algorithm, the accuracy of classification using only the pathological image has been unable to meet the requirements of clinical practice. Inspired by the real scene when the pathologist read the pathological image for diagnosis, in this paper, we propose a new hybrid deep learning method for benign and malignant breast cancer classification. From the perspective of multimodal data fusion, our method combines pathological image and structured data in the clinical electronic medical record (EMR) to further improve the accuracy of breast cancer classification. Thus, the proposed method can be useful for breast cancer diagnosis in real clinical practice. Experimental results based on our datasets show that the proposed method significantly outperforms the state-of-the-art methods in terms of overall classification accuracy.
Applying heatmaps in interpretation of geochemical baseline data on urban soils in Finland

Urban soil geochemical mapping was carried out, using the same sampling and analytical protocol, in seven Finnish towns in 2012–2017. All samples were taken from urban topsoil (0–10 cm), dried at <40 °C and sieved to <2 mm grain size. Aqua regia-extractable concentrations of 24 elements and total carbon content were assessed in this statistical study. Soil geochemical results are a typical example of compositional data. Thus, the data were isometric log-ratio (ilr) transformed before correlation analyses and heatmap production. Heatmapping involves colour coding of correlations, with different colours for positive and negative correlations. Elements were re-arranged for heatmaps by clustering the correlation matrix, using hierarchical clustering with distances based on the correlations. Heatmaps were produced for four general soil classes derived from samples taken from the urban topsoil: natural fine-grained sediments, natural coarse-grained sediments, man-made minerogenic soil material and man-made soil material rich in organic matter. The heatmaps revealed two distinct groups in all urban soil parent materials: elements related to abundance of trioctahedral mica minerals and elements bound to soil organic matter. Some man-made soil materials reflecting a single source of pollutants were also identified in the heatmaps.