Reviewerly/exHarmony huggingface dataset:
Detecting Face Morphing Attacks by Analyzing the Directed Distances of Facial Landmarks Shifts

Face morphing attacks create face images that are verifiable to multiple identities. Associating such images to identity documents lead to building faulty identity links, causing attacks on operations like border crossing. Most of previously proposed morphing attack detection approaches directly classified features extracted from the investigated image. We discuss the operational opportunity of having a live face probe to support the morphing detection decision and propose a detection approach that take advantage of that. Our proposed solution considers the facial landmarks shifting patterns between reference and probe images. This is represented by the directed distances to avoid confusion with shifts caused by other variations. We validated our approach using a publicly available database, built on 549 identities. Our proposed detection concept is tested with three landmark detectors and proved to outperform the baseline concept based on handcrafted and transferable CNN features.
A Journey into Bitcoin Metadata

Besides recording transfers of currency, the Bitcoin blockchain is being used to save metadata — i.e. arbitrary pieces of data which do not affect transfers of bitcoins. This can be done by using different techniques, and for different purposes. For instance, a growing number of protocols embed metadata in the blockchain to certify and transfer the ownership of a variety of assets beyond cryptocurrency. A point of debate in the Bitcoin community is whether metadata negatively impact on the effectiveness of Bitcoin with respect to its primary function. This paper is a systematic analysis of the usage of Bitcoin metadata over the years. We discuss all the known techniques to embed metadata in the Bitcoin blockchain; we then extract metadata, and analyse them from different angles.
Context-Aware Sequential Recommendations withStacked Recurrent Neural Networks

Sequential history of user interactions as well as the context of interactions provide valuable information to recommender systems, for modeling user behavior. Modeling both contexts and sequential information simultaneously, in context-aware sequential recommenders, has been shown to outperform methods that model either one of the two aspects. In long sequential histories, temporal trends are also found within sequences of contexts and temporal gaps that are not modeled by previous methods. In this paper we design new context-aware sequential recommendation methods, based on Stacked Recurrent Neural Networks, that model the dynamics of contexts and temporal gaps. Experiments on two large benchmark datasets demonstrate the advantages of modeling the evolution of contexts and temporal gaps - our models significantly outperform state-of-the-art context-aware sequential recommender systems.
Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks

Abstract Histopathological whole‐slide image (WSI) analysis is still one of the most important ways to identify regions of cancer risk. For cancer in which early diagnosis is vital, pathologists are at the center of the decision‐making process. Thanks to the widespread use of digital pathology and the development of artificial intelligence methods, automatic histopathological image analysis methods help pathologists in their decision‐making process. In this process, rather than producing labels for whole‐slide image patches, semantic segmentation is very useful, which facilitates the pathologists’ interpretation. In this study, automatic semantic segmentation based on cell type is proposed for the first time in the literature using novel deep convolutional networks structure (DCNN). We presents semantic information on four classes, including white areas in the whole‐slide image, tissue without cells, tissue with normal cells and tissue with cancerous cells. This visual information presented to the pathologist is an easy‐to‐understand picture of the status of the cells and their implications for the spread of cancerous cells. A new DCNN architecture is created, inspired by the residual network and deconvolution network architecture. Our network is trained end‐to‐end manner with histopathological image patches for cell structures to be more discriminative. The proposed method not only produces more successful results than other state‐of‐art semantic segmentation algorithms with 9.2% training error and 88.89% F‐score for test, but also has the most important advantage in that it has the ability to generate automatic information about the cancer and also provides information that pathologists can quickly interpret.
Clockless Dynamic SFQ &lt;sc&gt;and&lt;/sc&gt; Gate With High Input Skew Tolerance

We have introduced a new class of dc-powered Single Flux Quantum (SFQ) logic that uses dynamic (self-resetting) internal states to achieve completely clock-free gate operation and provide high immunity to input data skew. We call it DSFQ (dynamic SFQ) logic. We have successfully designed and tested a 2-input DSFQ and gate and showed its ability to hold dynamically its internal states for over 25 ps while operating at 10 GHz clock frequency. We also demonstrated picosecond-scale dynamics of DSFQ circuit with an on-chip Josephson sampler, with only low-speed interfaces required.
Short wavelength infra-red (SWIR) characteristics of hydrothermal alteration minerals in skarn deposits: Example from the Jiguanzui Cu–Au deposit, Eastern China

The Jiguanzui Cu–Au deposit is an important skarn deposit in the Edong ore district of the Middle–Lower Yangtze River Valley metallogenic belt (MLYRB, Eastern China). The mineralization occurred dominantly in the Lower-Middle Triassic Jialingjiang Formation marble, either along interlayer fracture zones or along the intrusive contact with the Early Cretaceous quartz diorite. Based on the mineral assemblages and textural relationships, the Jiguanzui alteration/mineralization can be divided into five stages, namely (I) prograde skarn alteration (garnet-pyroxene), (II) retrograde alteration (hornblende-epidote-actinolite), (III) hematite mineralization, (IV) quartz-sericite-pyrite-chalcopyrite mineralization (main Cu-Au mineralization stage) and (V) calcite-sulfide mineralization (pyrite-chalcopyrite-molybdenite-galena-sphalerite). Systematic short wavelength infrared (SWIR) spectral analyses show that alteration minerals at Jiguanzui include mainly sericite group minerals (montmorillonite, illite and muscovite), kaolinite, and carbonate minerals (calcite, ankerite and dolomite), with minor chlorite, halloysite and dickite. Spectral features of Stage IV sericite group minerals indicate that the Al-OH absorption feature wavelength positions (Pos2200) have shifted toward longer wavelengths (>2209 nm) in the ore proximal area, which may be caused by the substitution of Fe and Mg in the octahedral Al site. The vertical distance of mineralization indication can reach up to ca. 450 m, but can be affected by the wall-rock lithology and the Jiguanshan fault zone. The Fe-OH absorption feature wavelength positions (Pos2250) of chlorite in the Middle Triassic Puqi Formation hornfels have also shifted toward longer wavelengths in the ore proximal area, which may be caused by the relative Mg-rich nature of the chlorite. We conclude that the high Pos2200 (>2209 nm) of sericite group minerals and the high Pos2250 of chlorite can be used as prospecting tools for the Jiguanzui Cu–Au skarn mineralization.
Who's Afraid of Adversarial Queries?

An adversarial query is an image that has been modified to disrupt content-based image retrieval (CBIR), while appearing nearly untouched to the human eye. This paper presents an analysis of adversarial queries for CBIR based on neural, local, and global features. We introduce an innovative neural image perturbation approach, called Perturbations for Image Retrieval Error (PIRE), that is capable of blocking neural-feature-based CBIR. PIRE differs significantly from existing approaches that create images adversarial with respect to CNN classifiers because it is unsupervised, i.e., it needs no labeled data from the data set to which it is applied. Our experimental analysis demonstrates the surprising effectiveness of PIRE in blocking CBIR, and also covers aspects of PIRE that must be taken into account in practical settings, including saving images, image quality and leaking adversarial queries into the background collection. Our experiments also compare PIRE (a neural approach) with existing keypoint removal and injection approaches (which modify local features). Finally, we discuss the challenges that face multimedia researchers in the future study of adversarial queries.
External clustering validity index based on chi-squared statistical test

Clustering is one of the most commonly used techniques in data mining. Its main goal is to group objects into clusters so that each group contains objects that are more similar to each other than to objects in other clusters. The evaluation of a clustering solution is a task carried out through the application of validity indices. These indices measure the quality of the solution and can be classified as either internal that calculate the quality of the solution through the data of the clusters, or as external indices that measure the quality by means of external information such as the class. Generally, indices from the literature determine their optimal result through graphical representation, whose results could be imprecisely interpreted. The aim of this paper is to present a new external validity index based on the chi-squared statistical test named Chi Index, which presents accurate results that require no further interpretation. Chi Index was analyzed using the clustering results of 3 clustering methods in 47 public datasets. Results indicate a better hit rate and a lower percentage of error against 15 external validity indices from the literature.
Digital Image Steganalysis Based on Visual Attention and Deep Reinforcement Learning

Recently, the adaptive steganography methods have been developed to embed secret information with the minimal distortion of images.As the opposite art, steganalysis methods, especially some convolutional neural network-based steganalysis methods, have been proposed to detect whether an image is embedded with secret information or not.The state-of-the-art steganography methods hide secret information in different regions of an image with different probabilities.However, most of the current steganalysis methods extract the steganalysis features from different regions without discrimination, which reduces the performance of the current deep-learning-based steganalysis methods when attacking the adaptive steganography methods.In this paper, we propose a new self-seeking steganalysis method based on visual attention and deep reinforcement learning to detect the JPEG-based adaptive steganography.First, a region is selected from the image by a visual attention method, and a continuous decision is then made to generate a summary region by reinforcement learning.Thereby, the deep learning model is guided to focus on these regions that are favorable to steganalysis and ignore those regions that are unfavorable.Finally, the quality of training set and the detection ability of steganalysis are improved by replacing the mis-classified training images with their corresponding summary regions.The experiments show that our method obtains the competitive detection accuracy, compared with the other state-of-the-art advanced detection methods.
Efficient and Anonymous Certificateless Multi-Message and Multi-Receiver Signcryption Scheme Based on ECC

As the further extension of the multi-receiver signcryption, the multi-message and multi-receiver signcryption allows a sender to simultaneously signcrypt different messages for different receivers in only one logic operation, which makes it more flexible than the traditional multi-receiver signcryption in which only the same and unique message can be sent to all authorized receivers. The existing multi-message and multi-receiver signcryption schemes are constructed based on either the identity-based cryptography or the public key infrastructure-based cryptography, and thus, they have to suffer from the key escrow problem inherent in the identity-based cryptography or the public key certificate management burden related with the public key infrastructure-based cryptography. Certificateless public key cryptography provides an idea to solve the key escrow problem and eliminate the public key certificate management burden and has been applied to many cryptographic algorithms. In this paper, to avoid the above problems in the existing multi-message and multi-receiver signcryption schemes, the concept of the certificateless public key cryptography was introduced into the designing of the multi-message and multi-receiver signcryption, and a certificateless multi-message and multi-receiver signcryption scheme was proposed. The proposed scheme is free from the key escrow problem and the public key certificate management burden because it is constructed based on the certificateless public key cryptography. Moreover, compared with the existing schemes, it is improved in efficiency because it does not use the bilinear pairing operations but utilizes the limited number of scalar point multiplication on elliptic curve cryptography operations. At the same time, the proposed scheme achieves receiver anonymity.
Distributed Joint Probabilistic Data Association Filter With Hybrid Fusion Strategy

This paper investigates the problem of distributed multitarget tracking (MTT) over a large-scale sensor network, consisting of low-cost sensors. Each local sensor runs a joint probabilistic data association filter to obtain local estimates and communicates with its neighbors for information fusion. The conventional fusion strategies, i.e., consensus on measurement (CM) and consensus on information (CI), are extended to MTT scenarios. This means that data association uncertainty and sensor fusion problems are solved simultaneously. Motivated by the complementary characteristics of these two different fusion approaches, a novel distributed MTT algorithm using a hybrid fusion strategy, e.g., a mix of CM and CI, is proposed. A distributed counting algorithm is incorporated into the tracker to provide the knowledge of the total number of sensor nodes. The new algorithm developed shows advantages in preserving boundedness of local estimates, guaranteeing global convergence to the optimal centralized version and being implemented without requiring no global information, compared with other fusion approaches. Simulations clearly demonstrate the characteristics and tracking performance of the proposed algorithm.
Multi-Kernel Coupled Projections for Domain Adaptive Dictionary Learning

Dictionary learning has produced state-of-the-art results in various classification tasks. However, if the training data have a different distribution than the testing data, the learned sparse representation might not be optimal. Recently, several domain-adaptive dictionary learning (DADL) methods and kernels have been proposed and have achieved impressive performance. However, the performance of these single kernel-based methods heavily depends heavily on the choice of the kernel, and the question of how to combine multiple kernel learning (MKL) with the DADL framework has not been well studied. Motivated by these concerns, in this paper, we propose a multi-kernel domain-adaptive sparse representation-based classification (MK-DASRC) and then use it as a criterion to design a multi-kernel sparse representation-based domain-adaptive discriminative projection method, in which the discriminative features of the data in the two domains are simultaneously learned with the dictionary. The purpose of this method is to maximize the between-class sparse reconstruction residuals of data from both domains, and minimize the within-class sparse reconstruction residuals of data in the low-dimensional subspace. Thus, the resulting representations can satisfactorily fit MK-DASRC and simultaneously display discriminability. Extensive experimental results on a series of benchmark databases show that our method performs better than the state-of-the-art methods.
Intelligent position control for pneumatic servo system based on predictive fuzzy control

In this research, a novel method of predictive fuzzy control combined with a neural network is proposed for a pneumatic servo system. With the proposed method, the future outputs of the plant are predicted by using an imaginary plant in the predictive fuzzy scheme. The error between the desired value and the output of the imaginary plant, and the change in this error is applied as the input signals of the predictive fuzzy controller to generate a direct control input. In the proposed method, the neural network is introduced to construct the imaginary plant model of the target pneumatic servo system. To obtain teaching signals for the neural network scheme, a pre-test based on Proportional-integral-derivative (PID) control is implemented, and the results are applied as the teaching signals. With the imaginary plant model, the effectiveness of the proposed method using predictive fuzzy control is experimentally verified, and satisfactory position control using the proposed method is confirmed.
Charting the Australian quantum landscape

Australia has world-class research in quantum technology, and has been a significant presence since the earliest days of the field. There has been sustained and substantial support from all levels of government, and an industrial base that has grown from a single quantum-technology company to a spectrum of businesses covering both software and hardware. Here, we discuss the Australian approach to quantum technology, briefly outlining the research and funding, discussing the current environment, and looking forward to the future.
A New Cryptosystem of Color Image Using a Dynamic-Chaos Hill Cipher Algorithm

The conventional symmetric Hill cipher encryption algorithm, applied for data encryption, presents several disadvantages. However, a drawback of the conventional algorithm is known to be vulnerable to plain-text attack. Another setback, it does not hide all features of the image with homogeneous background and finally, the difficulty of finding the inverse of the key matrix. To overcome these problems, in this paper, we propose an improvement of the Hill cipher algorithm. The principle consists in using an affine transformation provided by a three-order invertible matrix and a dynamic translation vector. This vector is dynamically transformed at each iteration by an affine transformation composed of a chaotic matrix T, not necessarily invertible, and a pseudo random translation vector Y. This improvement overcomes the linearity problem of Hill's classic method. In addition, computational complexity can be reduced by simplifying the inverse matrix search process at the time of decryption. This inverse lies only in the search for the modular inverse of a single element of the matrix in the ring Z/256Z. The proposed scheme overcomes the disadvantages of the conventional Hill cipher, especially, the images with strong homogenous zones. The proposed algorithm guarantees a better quality of security and encryption.
Data intimacies: Building infrastructures for intensified embodied encounters with air pollution

The air is, in many urban contexts, polluted. Governments and institutions monitor particles and gas concentrations to better understand how they perform in the light of air quality guidance and legislation, and to make predictions in terms of future environmental health targets. The visibility of these data is considered crucial for citizens to manage their own health, and a proliferation of new informational forms and apps have been created to achieve this. And yet, beyond everyday decisions (when to use a mask or when to do sports outdoors), it is not clear whether current methods of engaging citizens produce behavioural change or stronger citizen engagement with air pollution. Drawing on the design, construction and ethnography of an urban infrastructure to measure, make visible and remediate particulate matter (PM2.5) through a water vapour cloud that we installed at the Seoul Biennale of Architecture and Urbanism 2017, we examined the effects and affects of producing a public space that allows for physical interaction with data. In Yellow Dust, data from PM2.5 were translated into mist, the density of which was responsive to the number of particles suspended in the air. Data were made sense/ible by the changing conditions of the air surrounding the infrastructure, which can be experienced in embodied, collective and relational ways: what we call ‘molecular intimacies’. By reflecting on how the infrastructure facilitated new modes of sensing data, we consider how ‘data intimacies’ can re-specify action by producing different forms of engagement with air pollution.
A Note on Multiple Secret Sharing Using Chinese Remainder Theorem and Exclusive-OR

This paper reviews the former existing scheme on (n,n) -multiple secret sharing (MSS) for color images along with its slight limitation. This scheme generates a set of n shared images from a set of n secret images using the Chinese remainder theorem (CRT) and Boolean exclusive-OR (XOR) operation. This scheme works well if the number of secret images n is even number. However, the former scheme has a slight problem while the number of secret images n is an odd number. This paper proposes a new technique to overcome this problem by introducing symmetric and transferred masking coefficients to generate a set of shared images. To further improve the security level of the proposed method, a set of secret images is first transformed with hyperchaotic scrambling method before generating shared images. The security of the proposed (n,n)-MSS can also be increased by merging a shared color image into 2-D matrix representation. As documented in the experimental results, the proposed method offers a promising result on (n,n) -MSS scheme regardless of the number of secret images n is odd or even number. In addition, the proposed method outperforms the former existing (n,n)-MSS schemes in terms of quantitative measurements.
Frameup: An incriminatory attack on Storj: A peer to peer blockchain enabled distributed storage system

In this work we present a primary account of frameup, an incriminatory attack made possible because of existing implementations in distributed peer to peer storage. The frameup attack shows that an adversary has the ability to store unencrypted data on the hard drives of people renting out their hard drive space. This is important to forensic examiners as it opens the door for possibly framing an innocent victim. Our work employs Storj as an example technology, due to its popularity and market size. Storj is a blockchain enabled system that allows people to rent out their hard drive space to other users around the world by employing a cryptocurrency token that is used to pay for the services rendered. It uses blockchain features like a transaction ledger, public/private key encryption, and cryptographic hash functions – but this work is not centered around blockchain. Our work discusses two frameup attacks, a preliminary and an optimized attack, both of which take advantage of Storj's implementation. Results illustrate that Storj allows a potential adversary to store incriminating unencrypted files, or parts of files that are viewable on people's systems when renting out their unused hard drive space. We offer potential solutions to mitigate our discovered attacks, a developed tool to review if a person has been a victim of a frameup attack, and a mechanism for showing that the files were stored on a hard drive without the renter's knowledge. Our hope is that this work will inspire future security and forensics research directions in the exploration of distributed peer to peer storage systems that embrace blockchain and cryptocurrency tokens.