Reviewerly/exHarmony huggingface dataset:
2D eπ-map for image encryption

A chaotic map generally employed to generate chaotic sequence is key element for an image encryption algorithm (IEA). In this study, an IEA using novel 2D chaotic map, which is based on Euler and Pi numbers so-called eπ-map, is presented. eπ-map exploits infinity diversity attribute of these numbers. Moreover, a diffusion operation referred to as “bit reversion” in which the bits of the pixels are symmetrically reverted is proposed for manipulating the pixel value. eπ-map is exhaustively examined through bifurcation and trajectory diagrams, Lyapunov exponent (LE), sample entropy (SE), permutation entropy (PE) and 0-1 test. The encryption performance of the IEA is then investigated across various cryptanalysis such as key-space, key sensitivity, histogram, information entropy, correlation coefficient, differential attack, cropping attack, noise attack and encryption execution time. Furthermore, the results are compared with the most recent literature. It is demonstrated that eπ-map has superior hyperchaotic performance in views of ergodicity, complexity and randomness. The IEA based on eπ-map and bit reversion is a secure and reliable algorithm thanks to its outperforming cryptanalysis results.
Finding Optimal Abstract Strategies in Extensive-Form Games

Extensive-form games are a powerful model for representing interactions between agents. Nash equilibrium strategies are a common solution concept for extensive-form games and, in two-player zero-sum games, there are efficient algorithms for calculating such strategies. In large games, this computation may require too much memory and time to be tractable. A standard approach in such cases is to apply a lossy state-space abstraction technique to produce a smaller abstract game that can be tractably solved, while hoping that the resulting abstract game equilibrium is close to an equilibrium strategy in the unabstracted game. Recent work has shown that this assumption is unreliable, and an arbitrary Nash equilibrium in the abstract game is unlikely to be even near the least suboptimal strategy that can be represented in that space. In this work, we present for the first time an algorithm which efficiently finds optimal abstract strategies --- strategies with minimal exploitability in the unabstracted game. We use this technique to find the least exploitable strategy ever reported for two-player limit Texas hold'em.
Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning

We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading.
Eyes are faster than hands: A soft wearable robot learns user intention from the egocentric view

We present a learning-based intention detection methodology using a first-person-view camera for wearable hand robots.
Estimating Energy Consumption of Cloud, Fog, and Edge Computing Infrastructures

In order to improve locality aspects, new Cloud-related architectures such as Edge Computing have been proposed. Despite the growing popularity of these new architectures, their energy consumption has not been well investigated yet. To move forward on such a critical question, we first introduce a taxonomy of different Cloud-related architectures. From this taxonomy, we then present an energy model to evaluate their consumption. Unlike previous proposals, our model comprises the full energy consumption of the computing facilities, including cooling systems, and the energy consumption of network devices linking end users to Cloud resources. Finally, we instantiate our model on different Cloud-related architectures, ranging from fully centralized to completely distributed ones, and compare their energy consumption. The results show that a completely distributed architecture, because of not using intra-data center network and large-size cooling systems, consumes between 14 and 25 percent less energy than fully centralized and partly distributed architectures, respectively. To the best of our knowledge, our work is the first one to propose a model that enables researchers to analyze and compare energy consumption of different Cloud-related architectures.
Superconducting optoelectronic loop neurons

Superconducting optoelectronic hardware has been proposed for large-scale neural computing. In this work, we expand upon the circuit and network designs previously introduced. We investigate circuits using superconducting single-photon detectors and Josephson junctions to perform signal reception, synaptic weighting, and integration. Designs are presented for synapses and neurons that perform integration of rate-coded signals as well as detect coincidence events for temporal coding. A neuron with a single integration loop can receive input from thousands of synaptic connections, and many such loops can be employed for dendritic processing. We show that a synaptic weight can be modified via a superconducting flux-storage loop inductively coupled to the current bias of the synapse. Synapses with hundreds of stable states are designed. Spike-timing-dependent plasticity can be implemented using two photons to strengthen and two photons to weaken the synaptic weight via Hebbian-type learning rules. In addition to the synaptic receiver and plasticity circuits, we describe an amplifier chain that converts the current pulse generated when a neuron reaches threshold to a voltage pulse sufficient to produce light from a semiconductor diode. This light is the signal used to communicate between neurons in the network. We analyze the performance of the elements in the amplifier chain to calculate the energy consumption per photon created. The speed of the amplification sequence allows neuronal firing up to at least 20 MHz, independent of connectivity. We consider these neurons in network configurations to investigate near-term technological potential and long-term physical limitations. By modeling the physical size of superconducting optoelectronic neurons, we calculate the area of these networks. A system with 8100 neurons and 330 430 total synapses will fit on a 1×1cm2 die. Systems of millions of neurons with hundreds of millions of synapses will fit on a 300 mm wafer. For multiwafer assemblies, communication at light speed enables a neuronal pool the size of a large data center (105m2) comprised of trillions of neurons with coherent oscillations at 1 MHz.
Depth Information Guided Crowd Counting for complex crowd scenes

It is important to monitor and analyze crowd events for the sake of city safety. In an EDOF (extended depth of field) image with a crowded scene, the distribution of people is highly imbalanced. People far away from the camera look much smaller and often occlude each other heavily, while people close to the camera look larger. In such a case, it is difficult to accurately estimate the number of people by using one technique. In this paper, we propose a Depth Information Guided Crowd Counting (DigCrowd) method to deal with crowded EDOF scenes. DigCrowd first uses the depth information of an image to segment the scene into a far-view region and a near-view region. Then Digcrowd maps the far-view region to its crowd density map and uses a detection method to count the people in the near-view region. In addition, we introduce a new crowd dataset that contains 1000 images. Experimental results demonstrate the effectiveness of our DigCrowd method.
Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity

Scene flow estimation in the dynamic scene remains a challenging task. Computing scene flow by a combination of 2D optical flow and depth has shown to be considerably faster with acceptable performance. In this work, we present a unified framework for joint unsupervised learning of stereo depth and optical flow with explicit local rigidity to estimate scene flow. We estimate camera motion directly by a Perspective-n-Point method from the optical flow and depth predictions, with RANSAC outlier rejection scheme. In order to disambiguate the object motion and the camera motion in the scene, we distinguish the rigid region by the re-project error and the photometric similarity. By joint learning with the local rigidity, both depth and optical networks can be refined. This framework boosts all four tasks: depth, optical flow, camera motion estimation, and object motion segmentation. Through the evaluation on the KITTI benchmark, we show that the proposed framework achieves state-of-the-art results amongst unsupervised methods. Our models and code are available at https://github.com/lliuz/unrigidflow.
Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer

Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subject to inter-observer variability. This study aims to a) investigate whether customized, deep-learning-based auto-segmentation could overcome the limitations of manual contouring and b) compare its performance against a typical, atlas-based auto-segmentation method organ structures in liver cancer.On-contrast computer tomography image sets of 70 liver cancer patients were used, and four OARs (heart, liver, kidney, and stomach) were manually delineated by three experienced physicians as reference structures. Atlas and deep learning auto-segmentations were respectively performed with MIM Maestro 6.5 (MIM Software Inc., Cleveland, OH) and, with a deep convolution neural network (DCNN). The Hausdorff distance (HD) and, dice similarity coefficient (DSC), volume overlap error (VOE), and relative volume difference (RVD) were used to quantitatively evaluate the four different methods in the case of the reference set of the four OAR structures.The atlas-based method yielded the following average DSC and standard deviation values (SD) for the heart, liver, right kidney, left kidney, and stomach: 0.92 ± 0.04 (DSC ± SD), 0.93 ± 0.02, 0.86 ± 0.07, 0.85 ± 0.11, and 0.60 ± 0.13 respectively. The deep-learning-based method yielded corresponding values for the OARs of 0.94 ± 0.01, 0.93 ± 0.01, 0.88 ± 0.03, 0.86 ± 0.03, and 0.73 ± 0.09. The segmentation results show that the deep learning framework is superior to the atlas-based framwork except in the case of the liver. Specifically, in the case of the stomach, the DSC, VOE, and RVD showed a maximum difference of 21.67, 25.11, 28.80% respectively.In this study, we demonstrated that a deep learning framework could be used more effectively and efficiently compared to atlas-based auto-segmentation for most OARs in human liver cancer. Extended use of the deep-learning-based framework is anticipated for auto-segmentations of other body sites.
About blockchain interoperability

A blockchain is designed to be a self-sufficient decentralised ledger: a peer verifying the validity of past transactions only needs to download the blockchain (the ledger) and nothing else. However, it might be of interest to make two different blockchains interoperable, i.e., to allow one to transmit information from one blockchain to another blockchain. In this paper, we give a formalisation of this problem, and we prove that blockchain interoperability is impossible according to the classical definition of a blockchain. Under a weaker definition of blockchain, we demonstrate that two blockchains are interoperable is equivalent to creating a ‘2-in-1’ blockchain containing both ledgers, thus limiting the theoretical interest of making interoperable blockchains in the first place. We also observe that all practical existing interoperable blockchain frameworks work indeed by exchanging already created tokens between two blockchains and not by offering the possibility to transfer tokens from one blockchain to another one, which implies a modification of the balance of total created tokens on both blockchains. It confirms that having interoperability is only possible by creating a ‘2-in-1’ blockchain containing both ledgers.
State Readout of a Trapped Ion Qubit Using a Trap-Integrated Superconducting Photon Detector

We report high-fidelity state readout of a trapped ion qubit using a trap-integrated photon detector. We determine the hyperfine qubit state of a single $^{9}{\mathrm{Be}}^{+}$ ion held in a surface-electrode rf ion trap by counting state-dependent ion fluorescence photons with a superconducting nanowire single-photon detector fabricated into the trap structure. The average readout fidelity is 0.9991(1), with a mean readout duration of $46\text{ }\text{ }\ensuremath{\mu}\mathrm{s}$, and is limited by the polarization impurity of the readout laser beam and by off-resonant optical pumping. Because there are no intervening optical elements between the ion and the detector, we can use the ion fluorescence as a self-calibrated photon source to determine the detector quantum efficiency and its dependence on photon incidence angle and polarization.
Pyrite trace element behavior in magmatic-hydrothermal environments: An LA-ICPMS imaging study

Trace elements in pyrite are increasingly being used in ore deposit exploration as a geochemical pathfinder to ore. This is because the trace element systematics of pyrite are sensitive to subtle changes in hydrothermal fluid composition and temperature. In this paper, we present LA-ICPMS trace element maps of pyrite from three styles of ore deposits related to magmatic-hydrothermal processes (epithermal Au-Ag, IOCG, and porphyry Cu-Au-Mo), showing the common geochemical characteristics of pyrite in each regime. Pyrite from porphyry Cu-Au-Mo and IOCG systems has high to very high amounts of Co (commonly ≥ 1000 ppm) but generally lower and variable Ni, As, and Se (<0.01 to > 1000 ppm). These four elements typically define oscillatory growth zonation patterns, reflecting fluctuations in fluid temperature and chemistry during crystal growth. The economic metals Cu, Au, and Mo are almost exclusively present as inclusions of Cu sulfides, native Au/electrum, Au-Ag tellurides, and molybdenite, respectively, which line or fill fractures in the pyrite crystals. In contrast, pyrite from epithermal Au-Ag systems is characterized by variable but generally low Co, Ni, and Se (i.e., <0.01 to 100 ppm), as well as high As (>1000 ppm). Gold and Ag are also generally high in pyrite from these systems, but unlike IOCG and porphyry-style pyrite they are predominantly deported in solid solution. Tellurium can either be low (0.01–10 ppm) or high (>100 ppm, not counting Au-Ag-Bi telluride inclusions), and other elements such as Sb can also be present in solid solution. This study highlights the importance of imaging mineral grains for elucidating growth history, defining mineral parageneses in hydrothermal systems, and providing key geological information for ore deposit genesis.
Fine-Grained Webpage Fingerprinting Using Only Packet Length Information of Encrypted Traffic

Encrypted web traffic can reveal sensitive information of users, such as their browsing behaviors. Existing studies on encrypted traffic analysis focus on website fingerprinting. We claim that fine-grained webpage fingerprinting, which speculates specific webpages on a same website visited by a victim, allows exploiting more user private information, e.g., shopping interests in an online shopping mall. Since webpages from the same website usually have very similar traffic traces that make them indistinguishable, existing solutions may end up with low accuracy. In this paper, we propose FineWP, a novel fine-grained webpage fingerprinting method. We make an observation that the length information of packets in bidirectional client-server interactions can be distinctive features for webpage fingerprinting. The extracted features are then fed into traditional machine learning models to train classifiers, which achieve both high accuracy and low training overhead. We collect two real-world traffic datasets and construct closed- and open-world evaluations to verify the effectiveness of FineWP. The experimental results demonstrate that FineWP is superior to the state-of-the-art methods in terms of accuracy, time complexity and stability.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection

The objective of this paper is to learn context- and depth- aware feature representation to solve the problem of monocular 3D object detection. We make following contributions: (i) rather than appealing to the complicated pseudo-LiDAR based approach, we propose a depth-conditioned dynamic message propagation (DDMP) network to effectively integrate the multi-scale depth information with the image context; (ii) this is achieved by first adaptively sampling context-aware nodes in the image context and then dynamically predicting hybrid depth-dependent filter weights and affinity matrices for propagating information; (Hi) by augmenting a center-aware depth encoding (CDE) task, our method successfully alleviates the inaccurate depth prior; (iv) we thoroughly demonstrate the effectiveness of our proposed approach and show state-of-the-art results among the monocular-based approaches on the KITTI benchmark dataset. Particularly, we rank 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> in the highly competitive KITTI monocular 3D object detection track on the submission day (November 16th, 2020). Code and models are released at https: //github.com/fudan-zvg/DDMP
MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?

Deep learning-based methods for video pedestrian detection and tracking require large volumes of training data to achieve good performance. However, data acquisition in crowded public environments raises data privacy concerns – we are not allowed to simply record and store data without the explicit consent of all participants. Furthermore, the annotation of such data for computer vision applications usually requires a substantial amount of manual effort, especially in the video domain. Labeling instances of pedestrians in highly crowded scenarios can be challenging even for human annotators and may introduce errors in the training data. In this paper, we study how we can advance different aspects of multi-person tracking using solely synthetic data. To this end, we generate MOTSynth, a large, highly diverse synthetic dataset for object detection and tracking using a rendering game engine. Our experiments show that MOTSynth can be used as a replacement for real data on tasks such as pedestrian detection, re-identification, segmentation, and tracking.
Feedback Graph Convolutional Network for Skeleton-Based Action Recognition

Skeleton-based action recognition has attracted considerable attention since the skeleton data is more robust to the dynamic circumstances and complicated backgrounds than other modalities. Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization. However, conventional GCNs are feedforward networks for which it is impossible for the shallower layers to access semantic information in the high-level layers. In this paper, we propose a novel network, named Feedback Graph Convolutional Network (FGCN). This is the first work that introduces a feedback mechanism into GCNs for action recognition. Compared with conventional GCNs, FGCN has the following advantages: (1) A multi-stage temporal sampling strategy is designed to extract spatial-temporal features for action recognition in a coarse to fine process; (2) A Feedback Graph Convolutional Block (FGCB) is proposed to introduce dense feedback connections into the GCNs. It transmits the high-level semantic features to the shallower layers and conveys temporal information stage by stage to model video level spatial-temporal features for action recognition; (3) The FGCN model provides predictions on-the-fly. In the early stages, its predictions are relatively coarse. These coarse predictions are treated as priors to guide the feature learning in later stages, to obtain more accurate predictions. Extensive experiments on three datasets, NTU-RGB+D, NTU-RGB+D120 and Northwestern-UCLA, demonstrate that the proposed FGCN is effective for action recognition. It achieves the state-of-the-art performance on all three datasets.
A Novel Machine Learning Approach for Sentiment Analysis on Twitter Incorporating the Universal Language Model Fine-Tuning and SVM

Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset.