Reviewerly/exHarmony huggingface dataset:
Increasing the Raw Key Rate in Energy-Time Entanglement Based Quantum Key Distribution

A Quantum Key Distribution (QKD) protocol describes how two remote parties can establish a secret key by communicating over a quantum and a public classical channel that both can be accessed by an eavesdropper. QKD protocols using energy-time entangled photon pairs are of growing practical interest because of their potential to provide a higher secure key rate over long distances by carrying multiple bits per entangled photon pair. We consider a system where information can be extracted by measuring random times of a sequence of entangled photon arrivals. Our goal is to maximize the utility of each such pair. We propose a discrete time model for the photon arrival process, and establish a theoretical bound on the number of raw bits that can be generated under this model. We first analyse a well known simple binning encoding scheme, and show that it generates significantly lower information rate than what is theoretically possible. We then propose three adaptive schemes that increase the number of raw bits generated per photon, and compute and compare the information rates they offer. Moreover, the effect of public channel communication on the secret key rates of the proposed schemes is investigated.
Analysis of Random Local Descriptors in Face Recognition

This article describes and analyzes the new feature extraction technique, Random Local Descriptor (RLD), that is used for the Permutation Coding Neural Classifier (PCNC), and compares it with Local Binary Pattern (LBP-based) feature extraction. The paper presents a model of face feature detection using local descriptors, and describes an improvement on the PCNC for the recognition of plane rotated and small displaced face images, as applied to three databases, i.e., ORL, FRAV3D and FEI. All databases are described along with the recognition results that were obtained. We also include a comparison of our classifier with the Support Vector Machine (SVM) and Iterative Closest Point (ICP). The ORL database was selected to compare our RLDs with LBP-based algorithms. The PCNC with the RLDs demonstrated the best recognition rate, i.e., 97.49%, in comparison with 90.49% for LBPs. For the FEI image database, we obtained the best recognition rate, i.e., 93.57%, in comparison with 66.74% for LBPs. Using the RLDs and rotating the original images for FRAV3D, we improved the recognition rate by decreasing by approximately twice the number of errors. In addition, we analyzed the influence of different RLD parameters on the quality of facial recognition.
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling

In practical applications of image classification, methods based on convolutional neural networks are used as standard nowadays. However, the success of these methods is not explained sufficiently from a theoretical point of view. For this, a statistical model for image classification, namely a hierarchical max-pooling model with additional local pooling, is introduced. Here the additional local pooling enables the hierarchical model to combine parts of the image which have a variable relative distance towards each other. Various convolutional neural network image classifiers are introduced and analyzed in view of their rate of convergence of the misclassification risk of the estimates towards the optimal misclassification risk. This analysis provides a theoretical explanation why general convolutional neural network architectures that include some kind of local pooling layers are useful in some image classification situations and gives theoretical hints for choosing the right network architecture. Furthermore, the finite sample size performance of these network architectures is illustrated by applying them to simulated and real data.
Adaptive Feature Aggregation in Deep Multi-Task Convolutional Neural Networks

Multi-task learning in Convolutional Neural Networks (CNNs) has led to remarkable success in a variety of applications of computer vision. Towards effective multi-task CNN architectures, recent studies automatically learn the optimal combinations of task-specific features at single network layers. However, they generally learn an unchanged operation of feature combination after training, regardless of the characteristic changes of task-specific features across different inputs. In this paper, we propose a novel Adaptive Feature Aggregation (AFA) layer for multi-task CNNs, in which a dynamic aggregation mechanism is designed to allow each task adaptively determines the degree to which the knowledge sharing or preserving between tasks is needed based on the characteristics of inputs. We introduce two types of aggregation modules to the AFA layer, which realize the adaptive feature aggregation by capturing the feature dependencies of different tasks along the channel and spatial axes, respectively. The AFA layer is a plug-and-play component with low parameter and computation overheads, and can be trained end-to-end along with backbone networks. For both pixel-level and image-level tasks, we empirically show that our approach substantially outperforms the previous state-of-the-art methods of multi-task CNNs. The code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zhenshen-mla/AFANet</uri> .
Ambassy: A Runtime Framework to Delegate Trusted Applications in an ARM/FPGA Hybrid System

Many mobile systems run on ARM-based devices today. People use these for increasingly diverse yet security-sensitive applications. ARM has adopted a security model to tackle this threat, where they manage private information in an isolated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trusted execution environment</i> (TEE) provided by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrustZone</i> . This TrustZone-based model has been proven effective, but due to security concerns, it is available solely for the vendor's applications, thereby hindering the broad use of TrustZone. Consequently, we propose a runtime framework backed by TrustZone to construct a secondary TEE. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ambassy</small> has its residence built on an on-chip field-programmable gate array (FPGA), which is a standard component in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ARM/FPGA hybrid</i> system readily available on the market today. This study, to the best of our knowledge, is the first attempt to broaden the use of TrustZone by using an FPGA to build a secondary TEE for arbitrary third-parties, which otherwise should be expelled to the Normal World. This paper describes many design challenges that we have overcome to fully implement <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ambassy</small> on an FPGA. Our experiments demonstrate the practicality of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ambassy</small> by presenting the security analysis and performance results of third-party application samples. The samples all run safely on <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ambassy</small> , with shorter execution times than regular TEE applications in TrustZone (by a factor of 5.5–52).
Geometry-Aware Graph Embedding Projection Metric Learning for Image Set Classification

By describing image sets as linear subspaces on the Grassmann manifold, image set classification has received persistent attention. Despite the success made so far, the unhelpfully intraclass diversity and interclass similarity remain two key challenges in finding an effective lower dimensional feature space for similarity measurement. To explore a feasible solution to these issues, we propose a geometry-aware graph embedding projection metric learning (GEPML) algorithm. The proposed approach first constructs the interclass and the intraclass similarity graphs on the Grassmann manifold, aiming to exploit the local structural information of the data manifold. Besides, we generalize the Euclidean collaborative representation mechanism to the Grassmann manifold to adaptively perform graph learning. Then, to learn the embedding mapping and the similarity metric jointly, we formulate the Grassmannian dimensionality reduction (GDR) problem into an elaborately designed metric learning regularization term. The proposed algorithm is appraised on five benchmarking data sets and the competitive experimental results demonstrate its feasibility and effectiveness.
Decision support systems configuration based on knowledge-driven automated service composition: requirements and conceptual model

The development of decision support systems is a complex, multi-stage process that requires considerable effort both methodically and technically. At the same time, with the proliferation of service-oriented architecture (and, more recently, the microservice approach based on it), technical development of such systems is often largely reduced to the construction of compositions of existing services implementing information processing functions, so that eventually the corresponding composition provides the necessary functionality of the decision support system. The paper proposes an approach to the construction of configurable service-oriented decision support systems based on the automated service composition, which will significantly reduce the effort required to develop such systems. Particularly, following results are presented: a) functional framework of different types of decision support systems, typical structures and patterns applicable in different types of decision support systems; b) a set of requirements for configurable service-oriented decision support systems and their main components (tools for describing services, and the purposes of the composite service, methods and algorithms for implementing the composition); c) a conceptual model of a configurable service-oriented decision support system.
A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.
A Secure and Fair Double Auction Framework for Cloud Virtual Machines

Double auction is one of the most promising solutions to allocate virtual machine (VM) resources in two-sided cloud markets, which can increase the utilization rate of VM resources. However, most cloud auction mechanisms simply assume that the auctioneer is fully trusted while ignoring bid-privacy preservation and trade fairness in the process of auction. Previous studies have indicated that some cryptographic tools can be used to resolve the above issues, but the poor performance makes those techniques difficult to practice. In this paper, we propose a Secure and Fair Double AuCtion framework (named SF-DAC) for cloud virtual machines, which performs cloud auction efficiently while guaranteeing both bid privacy and trade fairness. We design secure 3-party computation protocols that support secure comparison and secure sorting, which enable us to construct a secure double auction scheme that outperforms all prior comparable solutions. Furthermore, we propose a fair trading mechanism based on smart contracts to prevent the bidders from halting the auction without financial penalties. The extensive experiments demonstrate that SF-DAC achieves an order of magnitude reduction in computation and communication costs than prior arts.
AEML: An Acceleration Engine for Multi-GPU Load-balancing in Distributed Heterogeneous Environment

For the rapid growth computation requirements in big data and artificial intelligence area, CPU-GPU heterogeneous clusters can provide more powerful computing capacity compared to CPU clusters. The number of GPUs on single computing node is scalable, which greatly improves the computing capacity of the cluster under the condition of limited cluster size. However, there is a lack of the effective load-balancing scheduling model in multi-GPU hardware environment. This paper proposes AEML, an acceleration engine for multi-GPU load-balancing in distributed heterogeneous environment. AEML can effectively integrate GPUs into distributed processing framework and achieve great load-balance among multiple heterogeneous GPUs. We propose a heterogeneous task execution model based on multiple GPUs and multiple streams (MGMS), which can effectively balance the workload of multiple GPUs. MGMS model utilizes four core techniques: a fine-grained task mapping mechanism, a device resource unified management scheme, a novel resource-aware GPU task scheduling strategy and a feedback-based streams adjustment scheme. The implementation of AEML system is based on Spark 2.4.1 and NVIDIA CUDA 10.0. We comprehensively evaluate the performance of AEML with multiple typical benchmarks. Experimental results show that AEML can fully exploit the computing power of GPUs and achieve great load-balance among multiple heterogeneous GPUs.
Bypassing Push-based Second Factor and Passwordless Authentication with Human-Indistinguishable Notifications

Second factor (2FA) or passwordless authentication based on notifications pushed to a user's personal device (e.g., a phone) that the user can simply approve (or deny) has become widely popular due to its convenience. In this paper, we show that the effortlessness of this approach gives rise to a fundamental design vulnerability. The vulnerability stems from the fact that the notification, as shown to the user, is not uniquely bound to the user's login session running through the browser, and thus if two notifications are sent around the same time (one for the user's session and one for an attacker's session), the user may not be able to distinguish between the two, likely ending up accepting the notification of the attacker's session.
User Consumption Intention Prediction in Meituan

For online life service platforms, such as Meituan, user consumption intention, as the internal driving force of consumption behaviors, plays a significant role in understanding and predicting users' demand and purchase. However, user consumption intention prediction is quite challenging. Different from consumption behaviors, consumption intention is implicit and always not reflected by behavioral data. Moreover, it is affected by both user intrinsic preference and spatio-temporal context. To overcome these challenges, in Meituan, we design a real-world system consisting of two stages, intention detection and prediction. Specifically, at the intention-detection stage, we combine the knowledge of human experts and consumption information to obtain explicit intentions and match consumption with intentions based on user review data. At the intention-prediction stage, to collectively exploit the rich heterogeneous influencing factors, we design a graph neural network-based intention prediction model GRIP, which can capture user intrinsic preference and spatio-temporal context. Extensive offline evaluations demonstrate that our prediction model outperforms the best baseline by 10.26% and 33.28% for two metrics and online A/B tests on millions of users validate the effectiveness of our system.
Design anthropological approaches in collaborative museum curation

Museums show an increasing interest in participatory activities that open their premises and processes to diverse audiences. Inspired by this turn within museums, the exhibition FOLK adopted a multi-level co-design approach to address scientific racism and its heritage in contemporary science and society. Here we focus on the processes of collaborative curation during a series of public, pre-exhibition events and use the concepts of “knowledge pieces”, “transformation” and “correspondence” to analyse how the events became curatorially consequential. We argue that the events acted as sensitising devices for the exhibition team by bringing together ethnographic and critical design methods.
Efficient Asynchronous Byzantine Agreement without Private Setups

Efficient asynchronous Byzantine agreement (BA) protocols were mostly studied with private setups, e.g., pre-setup threshold cryptosystem. Challenges remain to reduce the large communication in the absence of such setups. Recently, Abraham et al. (PODC’21) presented the first asynchronous validated BA (VBA) with expected $\mathcal{O}$(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) messages and $\mathcal{O}$ (1) rounds, relying on only public key infrastructure (PKI) setup, but the design still costs $\mathcal{O}$ (λn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> logn) bits. Here n is the number of parties, and λ is a cryptographic security parameter.In this paper, we reduce the communication of private-setup free asynchronous BA to expected $\mathcal{O}$(λn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) bits. At the core of our design, we give a systematic treatment of common randomness protocols in the asynchronous network, and proceed as:•We give an efficient reasonably fair common coin protocol in the asynchronous setting with only PKI setup. It costs only $\mathcal{O}$ (λn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) bit and $\mathcal{O}$(1) rounds, and ensures that with at least 1/3 probability, all honest parties can output a common bit that is as if randomly flipped. This directly renders more efficient private-setup free asynchronous binary agreement (ABA) with expected $\mathcal{O}$(λn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) bits and $\mathcal{O}$(1) rounds.•Then, we lift our common coin to attain perfect agreement by using a single ABA. This gives us a reasonably fair random leader election protocol with expected $\mathcal{O}$(λn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) communication and expected constant rounds. It is pluggable in all existing VBA protocols (e.g., Cachin et al., CRYPTO’01; Abraham et al., PODC’19; Lu et al., PODC’20) to remove the needed private setup or distributed key generation (DKG). As such, the communication of private-setup free VBA is reduced to expected $\mathcal{O}$(λn <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) bits while preserving fast termination in expected $\mathcal{O}$(1) rounds. Moreover, our result paves a generic path to private-setup free asynchronous BA protocols, as it is not restricted to merely improve Abraham et al.’s specific VBA protocol (PODC’21).Our results and techniques could be found useful and interesting for a broad array of applications such as asynchronous DKG and DKG-free asynchronous random beacon that is friendly for dynamic participation and reconfiguration.
Improved Quantum data analysis

We provide more sample-efficient versions of some basic routines in quantum data analysis, along with simpler proofs. Particularly, we give a quantum ”Threshold Search” algorithm that requires only O((log2 m)/є2) samples of a d-dimensional state ρ. That is, given observables 0 ≤ A1, A2, …, Am ≤ 1 such that (ρ Ai) ≥ 1/2 for at least one i, the algorithm finds j with (ρ Aj) ≥ 1/2−є. As a consequence, we obtain a Shadow Tomography algorithm requiring only O((log2 m)(logd)/є4) samples, which simultaneously achieves the best known dependence on each parameter m, d, є. This yields the same sample complexity for quantum Hypothesis Selection among m states; we also give an alternative Hypothesis Selection method using O((log3 m)/є2) samples.
Behavior-Driven Synthesis of Human Dynamics

Generating and representing human behavior are of major importance for various computer vision applications. Commonly, human video synthesis represents behavior as sequences of postures while directly predicting their likely progressions or merely changing the appearance of the depicted persons, thus not being able to exercise control over their actual behavior during the synthesis process. In contrast, controlled behavior synthesis and transfer across individuals requires a deep understanding of body dynamics and calls for a representation of behavior that is independent of appearance and also of specific postures. In this work, we present a model for human behavior synthesis which learns a dedicated representation of human dynamics independent of postures. Using this representation, we are able to change the behavior of a person depicted in an arbitrary posture, or to even directly transfer behavior observed in a given video sequence. To this end, we propose a conditional variational framework which explicitly disentangles posture from behavior. We demonstrate the effectiveness of our approach on this novel task, evaluating capturing, transferring, and sampling fine-grained, diverse behavior, both quantitatively and qualitatively. Project page is available at https://cutt.ly/5l7rXEp
Real-time Sign Language Recognition using Computer Vision

Speech impairment is a disability that affects an individual's ability to verbal communication. To overcome this issue sign language is used which is one of the most organised languages. There is definitely a need for a method or an application that can recognize sign language gestures so that communication is possible even if someone does not understand sign language. My paper is an effort towards filling the gap between differently-abled people like deaf and dumb and the other people. Image processing combined with machine learning helped in forming a real-time system. Image processing is used for pre-processing the images and extracting different hand from the background. These images obtained after extracting background were used for forming data that contained 24 alphabets of the English language. The Convolutional Neural Network proposed here is tested on both a custom-made dataset and also with real-time hand gestures performed by people of different skin tones. The accuracy obtained by the proposed algorithm is 83%.