Reviewerly/exHarmony huggingface dataset:
Attribute-based fine-grained access control for outscored private set intersection computation

Private set intersection (PSI) is a fundamental cryptographic protocol which has a wide range of applications. It enables two clients to compute the intersection of their private datasets without revealing non-matching elements. The advent of cloud computing drives the ambition to reduce computation and data management overhead by outsourcing such computations. However, since the cloud is not trustworthy, some cryptographic methods should be applied to maintain the confidentiality of datasets. But, in doing so, data owners may be excluded from access control on their outsourced datasets. Therefore, to control access rights and to interact with authorized users, they have to be online during the protocol. On the other hand, none of the existing cloud-based PSI schemes support fine-grained access control over outsourced datasets. This paper, for the first time, proposes an attribute-based private set intersection (AB-PSI) scheme providing fine-grained access control. AB-PSI allows a data owner to control intersection computations on its outsourced dataset by defining an access control policy. We also provide security definitions for an AB-PSI scheme and prove the security of our scheme in the standard model. We implement our scheme and report performance evaluation results.
FedGAN: Federated Generative Adversarial Networks for Distributed Data.

We propose Federated Generative Adversarial Network (FedGAN) for training a GAN across distributed sources of non-independent-and-identically-distributed data sources subject to communication and privacy constraints. Our algorithm uses local generators and discriminators which are periodically synced via an intermediary that averages and broadcasts the generator and discriminator parameters. We theoretically prove the convergence of FedGAN with both equal and two time-scale updates of generator and discriminator, under standard assumptions, using stochastic approximations and communication efficient stochastic gradient descents. We experiment FedGAN on toy examples (2D system, mixed Gaussian, and Swiss role), image datasets (MNIST, CIFAR-10, and CelebA), and time series datasets (household electricity consumption and electric vehicle charging sessions). We show FedGAN converges and has similar performance to general distributed GAN, while reduces communication complexity. We also show its robustness to reduced communications.
Aligned Cross Entropy for Non-Autoregressive Machine Translation

Non-autoregressive machine translation models significantly speed up decoding by allowing for parallel prediction of the entire target sequence. However, modeling word order is more challenging due to the lack of autoregressive factors in the model. This difficultly is compounded during training with cross entropy loss, which can highly penalize small shifts in word order. In this paper, we propose aligned cross entropy (AXE) as an alternative loss function for training of non-autoregressive models. AXE uses a differentiable dynamic program to assign loss based on the best possible monotonic alignment between target tokens and model predictions. AXE-based training of conditional masked language models (CMLMs) substantially improves performance on major WMT benchmarks, while setting a new state of the art for non-autoregressive models.
Weakly-Supervised Disentanglement Without Compromises

Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at least one of the underlying factors of variation. First, we theoretically show that only knowing how many factors have changed, but not which ones, is sufficient to learn disentangled representations. Second, we provide practical algorithms that learn disentangled representations from pairs of images without requiring annotation of groups, individual factors, or the number of factors that have changed. Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets. Finally, we evaluate our learned representations and find that they are simultaneously useful on a diverse suite of tasks, including generalization under covariate shifts, fairness, and abstract reasoning. Overall, our results demonstrate that weak supervision enables learning of useful disentangled representations in realistic scenarios.
Variational quantum eigensolvers by variance minimization

The original variational quantum eigensolver (VQE) typically minimizes energy with hybrid quantum-classical optimization that aims to find the ground state. Here, we propose a VQE based on minimizing energy variance and call it the variance-VQE, which treats the ground state and excited states on the same footing, since an arbitrary eigenstate for a Hamiltonian should have zero energy variance. We demonstrate the properties of the variance-VQE for solving a set of excited states in quantum chemistry problems. Remarkably, we show that optimization of a combination of energy and variance may be more efficient to find low-energy excited states than those of minimizing energy or variance alone. We further reveal that the optimization can be boosted with stochastic gradient descent by Hamiltonian sampling, which uses only a few terms of the Hamiltonian and thus significantly reduces the quantum resource for evaluating variance and its gradients.
A review of using object-orientation properties of C++ for designing expert system in strategic planning

Using intelligent expert systems is a necessity for improving the situation of organizations. Since the process of identifying strategy in a strategic plan is time-consuming and costly, the role of expert systems in strategic planning is considerable. Managers utilize expert system to maintain and disseminate knowledge, training, and competition in the organization. Complicated expert system with long codes, practicality is not developable because of code complexity, but structural properties of the namespace and functions in C++ with using the object, solve this complexity and all codes are accessible without knowing based codes. In this paper using object orientation properties of C++, the expert can access, develop the system in the inference engine, practically without knowing based system code. According to the presented algorithm, the expert system designs a simple system for strategic planning in two steps, first for discerning the system state, second for prioritizing the indicators.
3D Reconstruction for Super-Resolution CT Images in the Internet of Health Things Using Deep Learning

The Internet of Health Things (IoHT) enables health devices to connect to the Internet and communicate with each other, which provides the high-accuracy and high-security diagnosis result in the medical area.As essential parts of the IoHT, computed tomography (CT) images help doctors diagnose disease.In the traditional disease diagnosing process, low-resolution medical CT images produce low-accuracy diagnosis results for microlesions.Moreover, CT images can only provide 2D information about organs, and doctors should estimate the 3D shape of a lesion based on experience.To solve these problems, we propose a 3D reconstruction method for secure super-resolution computed tomography (SRCT) images in the IoHT using deep learning.First, we use deep learning to obtain secure SRCT images from low-resolution images in the IoHT.To this end, we adopt a conditional generative adversarial network (CGAN) based on the edge detection loss function (EDLF) in the deep learning process, namely EDLF-CGAN algorithm.In this algorithm, the CGAN is employed to generate SRCT images with luminance and contrast as the input auxiliary conditions, which can improve the accuracy of super-resolution (SR) images.An EDLF is proposed to consider the edge features in the generated SRCT images, which reduces the deformation of generated image.Second, we apply the secure SR images generated from the deep learning method to perform 3D reconstruction.An advanced ray casting 3D reconstruction algorithm that can reduce the number of rays by selecting the appropriate bounding box is proposed.Compared with the traditional algorithm, the proposed ray casting 3D reconstruction algorithm can reduce the time and memory cost.The experimental results show that our EDLF-CGAN has a better SR reconstruction effect than other algorithms via the indicators of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).In addition, our advanced ray casting 3D reconstruction algorithm greatly improves the efficiency compared with the traditional ray casting algorithm.
A 3D Image Encryption Algorithm Based on the Chaotic System and the Image Segmentation

In this paper, a novel 3D image file encryption algorithm based on a new discrete chaotic system is presented. Chaotic characteristics of the novel chaotic system are analyzed by phase diagram, Lyapunov exponent, bifurcation diagram. Based on the analysis results, an encryption scheme is designed for 3D image file. The hash value which is calculated from the SHA-256 hash function is used to change the initial condition of the discrete system firstly. And then, the chaotic sequences are used to scramble and spread coordinate value for 3D image file by Arnold matrix and DNA diffusion algorithm. The security analysis shows that the proposed 3D image file encryption algorithm possesses higher security features to preserve the subject and resist conventional attack. The results of this paper give a different application scheme of the 3D image encryption algorithm.
Benchmarking parent selection for program synthesis by genetic programming

In genetic programming, the parent selection method determines which individuals in the population are selected to be parents for the next generation, and how many children they create. This process directly impacts the search performance by determining on which areas of the search space genetic programming focuses its attention and how it balances exploration and exploitation. Many parent selection methods have been proposed in the literature, with aims of improving problem-solving performance or other characteristics of the GP system. This paper aims to benchmark many recent and common parent selection methods by comparing them within a single system and set of benchmark problems. We specifically focus on the domain of general program synthesis, where solution programs must make use of multiple data types and control flow structures, and use an existing benchmark suite within the domain. We find that a few methods, all variants of lexicase selection, rise to the top and demand further study, both within the field of program synthesis and in other domains.
PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model

Photovoltaics (PV), the primary use of solar energy, is growing rapidly. However, the variable output of PV under changing weather conditions may hinder the large-scale deployment of PV. In this study, we propose a two-stage classification-prediction framework to predict contemporaneous PV power output from sky images (a so-called “nowcast”), and compare it with an end-to-end convolution neural network (CNN). The proposed framework first classifies input images into different sky conditions and then the classified images are sent to specific sub-models for PV output prediction. Two types of classifiers are developed and compared: (1) a CNN-based classifier trained on clear sky index (CSI)-labeled sky images and (2) a physics-based non-parametric classifier based on a threshold of fractional cloudiness of sky images. Different numbers of classification categories are also examined. The results suggest that the cloudiness-based classifier is more suitable than the CSI-based classifier for the framework, and the 3-class classification (i.e., sunny, cloudy, overcast) is found to be the optimal choice. We then fine-tune the cloudiness threshold for the non-parametric classifier and tailor the architecture for each sky-condition-specific sub-model. Under the best design, the proposed framework can achieve a root mean squared error (RMSE) of 2.20 kW (relative to a 30 kW rated PV array) on the test set comprising 18 complete days (9 sunny, RMSE = 0.69 kW; 9 cloudy, RMSE = 3.06 kW). Compared with the end-to-end CNN baseline model, the overall prediction performance can be improved by 6% (7% in sunny and 6% in cloudy), with 6% fewer trainable parameters needed in the architecture.
High-Performance COTS FPGA SoC for Parallel Hyperspectral Image Compression With CCSDS-123.0-B-1

Nowadays, hyperspectral imaging is recognized as a cornerstone remote sensing technology. Next generation, high-speed airborne, and space-borne imagers have increased resolution, resulting in an explosive growth in data volume and instrument data rate in the range of gigapixel per second. This competes with limited on-board resources and bandwidth, making hyperspectral image compression a mission critical on-board processing task. At the same time, the “new space” trend is emerging, where launch costs decrease, and agile approaches are exploited building smallsats using commercial-off-the-shelf (COTS) parts. In this contribution, we introduce a high-performance parallel implementation of the CCSDS-123.0-B-1 hyperspectral compression algorithm targeting SRAM field-programmable gate array (FPGA) technology. The architecture exploits image segmentation to provide the robustness to data corruption and enables scalable throughput performance by leveraging segment-level parallelism. Furthermore, we exploit the capabilities of a COTS FPGA system-on-chip (SoC) device to optimize size, weight, power, and cost (SWaP-C). The architecture partitions a hyperspectral cube stored in a DRAM framebuffer into segments, compressing them in parallel using a flexible software scheduler hosted in the SoC CPU and several compressor accelerator cores in the FPGA fabric. A 5-core implementation demonstrated on a Zynq-7045 FPGA achieves a throughput performance of 1387 Msamples/s [22.2 Gb/s at 16 bits per pixel per band (bpppb)] and outperforms previous implementations in equivalent FPGA technology, allowing seamless integration with next-generation hyperspectral sensors.
NIID-Net: Adapting Surface Normal Knowledge for Intrinsic Image Decomposition in Indoor Scenes

Intrinsic image decomposition, <i>i.e.</i>, decomposing a natural image into a reflectance image and a shading image, is used in many augmented reality applications for achieving better visual coherence between virtual contents and real scenes. The main challenge is that the decomposition is ill-posed, especially in indoor scenes where lighting conditions are complicated, while real training data is inadequate. To solve this challenge, we propose NIID-Net, a novel learning-based framework that adapts surface normal knowledge for improving the decomposition. The knowledge learned from relatively more abundant data for surface normal estimation is integrated into intrinsic image decomposition in two novel ways. First, normal feature adapters are proposed to incorporate scene geometry features when decomposing the image. Secondly, a map of integrated lighting is proposed for propagating object contour and planarity information during shading rendering. Furthermore, this map is capable of representing spatially-varying lighting conditions indoors. Experiments show that NIID-Net achieves competitive performance in reflectance estimation and outperforms all previous methods in shading estimation quantitatively and qualitatively. The source code of our implementation is released at <uri>https://github.com/zju3dv/NIID-Net</uri>.
Uncertainty-aware Self-training for Few-shot Text Classification

Recent success of large-scale pre-trained language models crucially hinge on fine-tuning them on large amounts of labeled data for the downstream task, that are typically expensive to acquire. In this work, we study self-training as one of the earliest semi-supervised learning approaches to reduce the annotation bottleneck by making use of large-scale unlabeled data for the target task. Standard self-training mechanism randomly samples instances from the unlabeled pool to pseudo-label and augment labeled data. In this work, we propose an approach to improve self-training by incorporating uncertainty estimates of the underlying neural network leveraging recent advances in Bayesian deep learning. Specifically, we propose (i) acquisition functions to select instances from the unlabeled pool leveraging Monte Carlo (MC) Dropout, and (ii) learning mechanism leveraging model confidence for self-training. As an application, we focus on text classification on five benchmark datasets. We show our uncertainty-aware few-shot self-training method leveraging only 20-30 labeled samples per class for each task can perform within 3% of fully supervised pre-trained language models like BERT fine-tuned on thousands of labeled instances with an aggregate accuracy of 91% and improving by up to 12% over baselines.
Design of Symmetric-Key Primitives for Advanced Cryptographic Protocols

While traditional symmetric algorithms like AES and SHA3 are optimized for efficient hardware and software implementations, a range of emerging applications using advanced cryptographic protocols such as multi-party computation and zero-knowledge proofs require optimization with respect to a different metric: arithmetic complexity. In this paper we study the design of secure cryptographic algorithms optimized to minimize this metric. We begin by identifying the differences in the design space between such arithmetization-oriented ciphers and traditional ones, with particular emphasis on the available tools, efficiency metrics, and relevant cryptanalysis. This discussion highlights a crucial point --- the considerations for designing arithmetization-oriented ciphers are oftentimes different from the considerations arising in the design of software- and hardware-oriented ciphers. The natural next step is to identify sound principles to securely navigate this new terrain, and to materialize these principles into concrete designs. To this end, we present the Marvellous design strategy which provides a generic way to easily instantiate secure and efficient algorithms for this emerging domain. We then show two examples for families following this approach. These families --- Vision and Rescue --- are benchmarked with respect to three use cases: the ZK-STARK proof system, proof systems based on Rank-One Constraint Satisfaction (R1CS), and Multi-Party Computation (MPC). These benchmarks show that our algorithms achieve a highly compact algebraic description, and thus benefit the advanced cryptographic protocols that employ them. Evidence is provided that this is the case also in real-world implementations.
Deep Dual-Modal Traffic Objects Instance Segmentation Method Using Camera and LIDAR Data for Autonomous Driving

Recent advancements in environmental perception for autonomous vehicles have been driven by deep learning-based approaches. However, effective traffic target detection in complex environments remains a challenging task. This paper presents a novel dual-modal instance segmentation deep neural network (DM-ISDNN) by merging camera and LIDAR data, which can be used to deal with the problem of target detection in complex environments efficiently based on multi-sensor data fusion. Due to the sparseness of the LIDAR point cloud data, we propose a weight assignment function that assigns different weight coefficients to different feature pyramid convolutional layers for the LIDAR sub-network. We compare and analyze the adaptations of early-, middle-, and late-stage fusion architectures in depth. By comprehensively considering the detection accuracy and detection speed, the middle-stage fusion architecture with a weight assignment mechanism, with the best performance, is selected. This work has great significance for exploring the best feature fusion scheme of a multi-modal neural network. In addition, we apply a mask distribution function to improve the quality of the predicted mask. A dual-modal traffic object instance segmentation dataset is established using a 7481 camera and LIDAR data pairs from the KITTI dataset, with 79,118 manually annotated instance masks. To the best of our knowledge, there is no existing instance annotation for the KITTI dataset with such quality and volume. A novel dual-modal dataset, composed of 14,652 camera and LIDAR data pairs, is collected using our own developed autonomous vehicle under different environmental conditions in real driving scenarios, for which a total of 62,579 instance masks are obtained using semi-automatic annotation method. This dataset can be used to validate the detection performance under complex environmental conditions of instance segmentation networks. Experimental results on the dual-modal KITTI Benchmark demonstrate that DM-ISDNN using middle-stage data fusion and the weight assignment mechanism has better detection performance than single- and dual-modal networks with other data fusion strategies, which validates the robustness and effectiveness of the proposed method. Meanwhile, compared to the state-of-the-art instance segmentation networks, our method shows much better detection performance, in terms of AP and F1 score, on the dual-modal dataset collected under complex environmental conditions, which further validates the superiority of our method.
Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data

The security of private information is becoming the bedrock of an increasingly digitized society. While the users are flooded with passwords and PINs, these goldstandard explicit authentications are becoming less popular and valuable. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. However, these hard-biometric-based systems require dedicated devices with powerful sensors and authentication models, which are often limited to most of the market wearables. Still, market wearables are collecting various private information of a user and are becoming an integral part of life: accessing cars, bank accounts, etc. Therefore, time demands a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from modern market wearables. In this work, we present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals. From our detailed analysis using the “leave-one-out” validation, we find that a lighter kNearest Neighbor (k-NN) model with k = 2 can obtain an average accuracy of 0.93 ± 0.06, F1 score 0.93 ± 0.03, and false positive rate (FPR) below 0.08 at 50% level of confidence, which shows the promise of this work.
A First Pilot Study to Compare Virtual Group Meetings using Video Conferences and (Immersive) Virtual Reality

Face-to-face communication has evolved as most natural means for communication. However, virtual group meetings have received considerable attention as an alternative for allowing multiple persons to communicate over distance, e. g., via video conferences or immersive virtual reality (VR) systems, but they incur numerous limitations and challenges. In particular, they often hinder spatial perception of full-body language, deictic relations, or eye-to-eye contact. The differences between video conferences and immersive VR meetings still remain poorly understood. We report about a pilot study in which we compared virtual group meetings using video conferences and VR meetings with and without head-mounted displays (HMDs). The results suggest that participants feel higher sense of presence when using an immersive VR meeting, but only if an HMD is used. Usability of video conferences as well as immersive VR is acceptable, whereas non-immersive VR without HMD was not acceptable.