Reviewerly/exHarmony huggingface dataset:
Security and privacy of mobile wallet users in Bitcoin, Dash, Monero, and Zcash

Mobile devices play an increasingly important role in the cryptocurrency ecosystem, yet their privacy guarantees remain unstudied. To verify transactions, they either trust a server or use simple payment verification. First, we review the security and privacy of popular Android wallets for Bitcoin and the three major privacy-focused cryptocurrencies (Dash, Monero, Zcash). Then, we investigate the network-level properties of cryptocurrencies and propose a method of transaction clustering based on timing analysis. We implement and test our method on selected wallets and show that a moderately resourceful attacker can correlate transactions issued from one device with relatively high accuracy.
Real-time Arm Skeleton Tracking and Gesture Inference Tolerant to Missing Wearable Sensors

This paper presents ArmTroi, a wearable system for understanding and analyzing the detailed arm motions of people primarily by using the motion sensors from wrist-worn wearable devices. ArmTroi can achieve real-time 3D arm skeleton tracking and reliable gesture inference tolerant to missing wearable sensors for enabling numerous useful application designs. We have coped with two major challenges through ArmTroi. First, the skeleton of each arm is determined from the locations of the elbow and wrist, whereas a wearable device only senses a single point from the wrist. We find that the potential solution space is huge. This underconstrained nature fundamentally challenges the achievement of accurate and real-time arm skeleton tracking. Second, wearable sensors may not reliably provide sensory data. For example, devices are not worn by the user, yet the learning tools for gesture inference, such as deep learning, typically have static network structures, which require nontrivial network adaptation to match the input's varying availability and ensure reliable gesture inference. We propose effective techniques to address above challenges, and all computations can be conducted on the user's smartphone. ArmTroi is thus a fully lightweight and portable system. We develop a prototype and extensive evaluation shows the efficacy of the ArmTroi design.
Hierarchical Back Projection Network for Image Super-Resolution

Deep learning based single image super-resolution methods use a large number of training datasets and have recently achieved great quality progress both quantitatively and qualitatively. Most deep networks focus on nonlinear mapping from low-resolution inputs to high-resolution outputs via residual learning without exploring the feature abstraction and analysis. We propose a Hierarchical Back Projection Network (HBPN), that cascades multiple HourGlass (HG) modules to bottom-up and top-down process features across all scales to capture various spatial correlations and then consolidates the best representation for reconstruction. We adopt the back projection blocks in our proposed network to provide the error correlated up-and down-sampling process to replace simple deconvolution and pooling process for better estimation. A new Softmax based Weighted Reconstruction (WR) process is used to combine the outputs of HG modules to further improve super-resolution. Experimental results on various datasets (including the validation dataset, NTIRE2019, of the Real Image Super-resolution Challenge) show that our proposed approach can achieve and improve the performance of the state-of-the-art methods for different scaling factors.
Led3D: A Lightweight and Efficient Deep Approach to Recognizing Low-Quality 3D Faces

Due to the intrinsic invariance to pose and illumination changes, 3D Face Recognition (FR) has a promising potential in the real world. 3D FR using high-quality faces, which are of high resolutions and with smooth surfaces, have been widely studied. However, research on that with low-quality input is limited, although it involves more applications. In this paper, we focus on 3D FR using low-quality data, targeting an efficient and accurate deep learning solution. To achieve this, we work on two aspects: (1) designing a lightweight yet powerful CNN; (2) generating finer and bigger training data. For (1), we propose a Multi-Scale Feature Fusion (MSFF) module and a Spatial Attention Vectorization (SAV) module to build a compact and discriminative CNN. For (2), we propose a data processing system including point-cloud recovery, surface refinement, and data augmentation (with newly proposed shape jittering and shape scaling). We conduct extensive experiments on Lock3DFace and achieve state-of-the-art results, outperforming many heavy CNNs such as VGG-16 and ResNet-34. In addition, our model can operate at a very high speed (136 fps) on Jetson TX2, and the promising accuracy and efficiency reached show its great applicability on edge/mobile devices.
Continuous Verifiable Delay Functions

We introduce the notion of a continuous verifiable delay function (cVDF): a function g which is (a) iteratively sequential—meaning that evaluating the iteration $$g^{(t)}$$ of g (on a random input) takes time roughly t times the time to evaluate g, even with many parallel processors, and (b) (iteratively) verifiable—the output of $$g^{(t)}$$ can be efficiently verified (in time that is essentially independent of t). In other words, the iterated function $$g^{(t)}$$ is a verifiable delay function (VDF) (Boneh et al., CRYPTO ’18), having the property that intermediate steps of the computation (i.e., $$g^{(t')}$$ for $$t'<t$$) are publicly and continuously verifiable. We demonstrate that cVDFs have intriguing applications: (a) they can be used to construct $$\textit{public randomness beacons}$$ that only require an initial random seed (and no further unpredictable sources of randomness), (b) enable $$\textit{outsourceable VDFs}$$ where any part of the VDF computation can be verifiably outsourced, and (c) have deep complexity-theoretic consequences: in particular, they imply the existence of depth-robust moderately-hard Nash equilibrium problem instances, i.e. instances that can be solved in polynomial time yet require a high sequential running time. Our main result is the construction of a cVDF based on the repeated squaring assumption and the soundness of the Fiat-Shamir (FS) heuristic for $$\textit{constant-round proofs}$$. We highlight that when viewed as a (plain) VDF, our construction requires a weaker FS assumption than previous ones (earlier constructions require the FS heuristic for either super-logarithmic round proofs, or for arguments).
Virtual reality check: Statistical power, reported results, and the validity of research on the psychology of virtual reality and immersive environments

Virtual reality (VR) is a popular subject of scientific study across a variety of academic fields. In the present study we evaluate methodological trends in behavioral research on VR with respect to data collection practices, statistical reporting, and data availability. In line with this goal, we conducted a meta-scientific analysis of 61 articles encompassing a total of 1122 statistical tests and highlight three emergent trends that inform our understanding of past and future studies focused on VR. Conclusions from analysis of the data include a high incidence of errors in statistical reporting, and a general lack of transparency with respect to the availability of study data. Transparency in data analysis, increased statistical power, and more careful reporting of statistical outcomes are suggested to heighten methodological rigor and improve reproducibility in the field of VR research.
Automatic clustering by multi-objective genetic algorithm with numeric and categorical features

Many clustering algorithms categorized as K-clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K-clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K-Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GAs) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm.
Suppressing Model Overfitting for Image Super-Resolution Networks

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to the limited accessible training pairs, large models exhibit undesirable behaviors such as overfitting and memorization. To suppress model overfitting and further enjoy the merits of large model capacity, we thoroughly investigate generic approaches for supplying additional training data pairs. In particular, we introduce a simple learning principle MixUp to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples. In addition, we propose a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity. This strategy proves to be successful in reducing the biases of data. By combining these components - MixUp and synthetic training data, large models can be trained without overfitting under very limited data samples and achieve satisfactory generalization performance. Our method won the second place in NTIRE2019 Real SR Challenge.
Profiling Victims of Investment Fraud: Mindsets and Risky Behaviors

Abstract Millions of Americans are targeted by investment scams, resulting in billions of dollars lost each year. Previous research indicates that investment fraud victims are more likely to be male, white, and married, and to have higher socioeconomic status compared to the general US population, but little research examines what behaviors and mindsets differentiate them from other investors. A telephone survey was administered to 214 investment fraud victims and 813 general investors recruited using random digit dialing. Based on the opportunity model of predatory victimization, the aim was to identify differences in investment behaviors and psychological mindsets that may affect exposure to investment scams and make individuals more attractive and susceptible targets. In addition to being older and male, victims were more materialistic than general investors and were more frequent stock traders, and purchased more investments sold through unsolicited calls, emails, television advertisements, or “free lunch” seminars, but were less likely to invest based on a social network member’s recommendation. As more retirees begin to take on managing their retirement assets, many may be tempted by unreasonable investment returns promised by unscrupulous brokers. Findings point to specific areas where investor education is needed to counteract poor investment decision-making and risky mindsets.
Privacy-preserving deep learning algorithm for big personal data analysis

For privacy-preserving analysing of big data, a deep learning method is proposed. The method transforms the sensitive part of the personal information into non-sensitive data. To implement this process, two-stage architecture is proposed. The modified sparse denoising autoencoder and CNN models have been used in the architecture. Modified sparse denoising autoencoder performs transformation of data and CNN classifies the transformed data. In order to achieve low loss in data transformation, the sparsification parameter is added to the objective function of the autoencoder by the Kullback–Leibler divergence function. Here, the efficiency evaluation of the model is conducted by the MSE (mean squared error) loss function. In order to evaluate the accuracy of the transformation process, the features derived from the sparse denoising autoencoder algorithm fed to the input of the deep CNN algorithm and the classification of the reconstructed data is classified to the Black (0), White (1) and Gray (2) classes. Since here conducted the transformation of the Black class data to the Gray class data, in the classification stage, the CNN algorithm is classified the Black class data as the Gray class with 0.99 accuracy. The comparison of the proposed method with simple autoencoder is provided and experiments conducted on Cleveland medical dataset extracted from the Heart Disease dataset, Arrhythmia and Skoda datasets showed that the proposed method outperforms other conventional methods.
Economic LSTM Approach for Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have become a popular method for learning sequences of data. It is sometimes tough to parallelize all RNN computations on conventional hardware due to its recurrent nature. One challenge of RNN is to find its optimal structure for RNN because of computing complex hidden units that exist. This brief presents a new approach to Long Short-Term Memory (LSTM) that aims to reduce the cost of the computation unit. The proposed Economic LSTM (ELSTM) is designed using a few hardware units to perform its functionality. ELSTM has fewer units compared to the existing LSTM versions which makes it very attractive in processing speed and hardware design cost. The proposed approach is tested using three datasets and compared with other methods. The simulation results show the proposed method has comparable accuracy with other methods. At the hardware level, the proposed method is implemented on Altera FPGA.
Breaking ALASKA

This paper describes the architecture and training of detectors developed for the ALASKA steganalysis challenge. For each quality factor in the range 60-98, several multi-class tile detectors implemented as SRNets were trained on various combinations of three input channels: luminance and two chrominance channels. To accept images of arbitrary size, the detector for each quality factor was a multi-class multi-layered perceptron trained on features extracted by the tile detectors. For quality 99 and 100, a new "reverse JPEG compatibility attack" was developed and also implemented using the SRNet via the tile detector. Throughout the paper, we explain various improvements we discovered during the course of the competition and discuss the challenges we encountered and trade offs that had to be adopted in order to build a detector capable of detecting steganographic content in a stego source of great diversity.
Exemplar-Based Denoising: A Unified Low-Rank Recovery Framework

Exemplar-based image denoising algorithms have shown great potential for image restoration with a multitude of existing models. In this paper, we interpret nonlocal similar patch-based denoising as a problem of low-rank recovery. This offers a physically plausible model and unifies several existing techniques in a single low-rank recovery framework. The framework can handle complex noise models, such as zero-mean Gaussian noise, impulse noise, and any other noise that can be approximated by mixing these two kinds of noise. Moreover, we introduce a new nonconvex surrogate for the $l_{0}$ -norm and find the optimal solution of the optimization problems when the new norm is applied to low-rank recovery. The experimental results with different kinds of noise confirm the effectiveness of the proposed low-rank recovery framework and the new norm.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>π</mml:mi></mml:math> -Corrected Heisenberg Limit

We consider the precision $\Delta \varphi$ with which the parameter $\varphi$, appearing in the unitary map $U_\varphi = e^{ i \varphi \Lambda}$ acting on some type of probe system, can be estimated when there is a finite amount of prior information about $\varphi$. We show that, if $U_\varphi$ acts $n$ times in total, then, asymptotically in $n$, there is a tight lower bound $\Delta \varphi \geq \frac{\pi}{n (\lambda_+ - \lambda_-)}$, where $\lambda_+$, $\lambda_-$ are the extreme eigenvalues of the generator $\Lambda$. This is greater by a factor of $\pi$ than the conventional Heisenberg limit, derived from the properties of the quantum Fisher information. That is, the conventional bound is never saturable. Our result makes no assumptions on the measurement protocol, and is relevant not only in the noiseless case but also if noise can be eliminated using quantum error correction techniques.
Indistinguishability-enabled coherence for quantum metrology

Quantum coherence plays a fundamental and operational role in different areas of physics. A resource theory has been developed to characterize the coherence of distinguishable particles systems. Here we show that indistinguishability of identical particles is a source of coherence, even when they are independently prepared. In particular, under spatially local operations, states that are incoherent for distinguishable particles, can be coherent for indistinguishable particles under the same procedure. We present a phase discrimination protocol, in which we demonstrate the operational advantage of using two indistinguishable particles rather than distinguishable ones. The coherence due to the quantum indistinguishability significantly reduces the error probability of guessing the phase using the most general measurements.
Intentional computational level design

The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around specific mechanics in the game. We use constrained evolutionary algorithms and quality-diversity algorithms to generate small sections of Super Mario Bros levels called scenes, using three different simulation approaches: Limited Agents, Punishing Model, and Mechanics Dimensions. All three approaches are able to create scenes that give opportunity for a player to encounter or use targeted mechanics with different properties. We conclude by discussing the advantages and disadvantages of each approach and compare them to each other.
Matching Anonymized and Obfuscated Time Series to Users’ Profiles

Many popular applications use traces of user data to offer various services to their users. However, even if user data are anonymized and obfuscated, a user's privacy can be compromised through the use of statistical matching techniques that match a user trace to prior user behavior. In this paper, we derive the theoretical bounds on the privacy of users in such a scenario. We build on our recent study in the area of location privacy, in which we introduced formal notions of location privacy for anonymization-based location privacy-protection mechanisms. Here, we derive the fundamental limits of user privacy when both anonymization and obfuscation-based protection mechanisms are applied to users' time series of data. We investigate the impact of such mechanisms on the tradeoff between privacy protection and user utility. We first study achievability results for the case where the time-series of users are governed by an independent and identically distributed (i.i.d.) process. The converse results are proved both for the i.i.d. case as well as the more general Markov chain model. We demonstrate that as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect privacy; and, in the second region, no user has privacy.
Cross and Learn: Cross-Modal Self-supervision

In this paper we present a self-supervised method for representation learning utilizing two different modalities. Based on the observation that cross-modal information has a high semantic meaning we propose a method to effectively exploit this signal. For our approach we utilize video data since it is available on a large scale and provides easily accessible modalities given by RGB and optical flow. We demonstrate state-of-the-art performance on highly contested action recognition datasets in the context of self-supervised learning. We show that our feature representation also transfers to other tasks and conduct extensive ablation studies to validate our core contributions.
A hybrid deep convolutional and recurrent neural network for complex activity recognition using multimodal sensors

Complex activities refer to users' activities performed in their daily lives (e.g., having dinner, shopping, etc.). Complex activity recognition is a valuable issue in wearable and mobile computing. The time-series sensory data from multimodal sensors have sophisticated relationships to characterize the complex activities (e.g., intra-sensor relationships, inter-sensor relationships, and temporal relationships), making the traditional methods based on manually designed features ineffective. To this end, we propose HConvRNN, an end-to-end deep neural network for complex activity recognition using multimodal sensors by integrating convolutional neural network (CNN) and recurrent neural network (RNN). To be specific, it uses a hierarchical CNN to exploit the intra-sensor relationships among similar sensors and merge intra-sensor relationships of different sensor modalities into inter-sensor relationships, and uses a RNN to model the temporal relationships of signal dynamics. The experiments based on real-world datasets show that HConvRNN outperforms the existing complex activity recognition methods.
Restoration of atmospheric turbulence-distorted images via RPCA and quasiconformal maps

We address the problem of restoring a high-quality image from an observed image sequence strongly distorted by atmospheric turbulence. A novel algorithm is proposed in this paper to reduce geometric distortion as well as space-and-time-varying blur due to strong turbulence. By measuring image sharpness and a quasiconformal measure on the deformation fields, our algorithm first obtains a sharp reference image and an image subsequence containing sharp and mildly distorted image frames with respect to the reference image. The image subsequence is then stabilized by applying robust principal component analysis on the deformation fields between image frames and warping the image frames by a quasiconformal map associated with the low-rank part of the deformation matrix. After image frames are registered to the reference image, the low-rank parts of them are deblurred via a blind deconvolution, and the deblurred frames are then fused with the enhanced sparse part. Experiments have been carried out on both synthetic and real turbulence-distorted video. Results demonstrate that our method is effective in alleviating distortions and blur, restoring image details and enhancing visual quality.