Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Encryption/Decryption Architectures for BFV Homomorphic Encryption Scheme

Fully homomorphic encryption (FHE) is a technique that allows computations on encrypted data without the need for decryption and it provides privacy in various applications such as privacy-preserving cloud computing. In this article, we present two hardware architectures optimized for accelerating the encryption and decryption operations of the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme with high-performance polynomial multipliers. For proof of concept, we utilize our architectures in a hardware/software codesign accelerator framework, in which encryption and decryption operations are offloaded to an FPGA device, while the rest of operations in the BFV scheme are executed in software running on an off-the-shelf desktop computer. Specifically, our accelerator framework is optimized to accelerate Simple Encrypted Arithmetic Library (SEAL), developed by the Cryptography Research Group at Microsoft Research. The hardware part of the proposed framework targets the XILINX VIRTEX7 FPGA device, which communicates with its software part via a peripheral component interconnect express (PCIe) connection. For proof of concept, we implemented our designs targeting 1024degree polynomials with 8-bit and 32-bit coefficients for plaintext and ciphertext, respectively. The proposed framework achieves almost 12× and 7× latency speedups, including I/O operations for the offloaded encryption and decryption operations, respectively, compared to their pure software implementations.
Privacy-Preserving Object Detection for Medical Images With Faster R-CNN

In this paper, we propose a lightweight privacy-preserving Faster R-CNN framework (SecRCNN) for object detection in medical images. Faster R-CNN is one of the most outstanding deep learning models for object detection. Using SecRCNN, healthcare centers can efficiently complete privacy-preserving computations of Faster R-CNN via the additive secret sharing technique and edge computing. To implement SecRCNN, we design a series of interactive protocols to perform the three stages of Faster R-CNN, namely feature map extraction, region proposal and regression and classification. To improve the efficiency of SecRCNN, we improve the existing secure computation sub-protocols involved in SecRCNN, including division, exponentiation and logarithm. The newly proposed sub-protocols can dramatically reduce the number of messages exchanged during the iterative approximation process based on the coordinate rotation digital computer algorithm. Moreover, the effectiveness, efficiency and security of SecRCNN are demonstrated through comprehensive theoretical analysis and extensive experiments. The experimental findings show that the communication overhead in computing division, logarithm and exponentiation decreases to 36.19%, 73.82% and 43.37%, respectively.
Object Detection of UAV for Anti-UAV Based on Improved YOLO v3

While unmanned aerial vehicle (UAV) technology brings convenience to modern life, it also leads to some problems. To achieve anti-UAV, the object detection technology of UAV is the key. YOLO v3, one of single-stage detectors, has the best detection performance for balancing the accuracy and speed through capturing deep and high-level features. In the basis of YOLO v3, this paper improves it to detect UAV more precisely and it's the first time to introduce YOLO v3 based algorithm to UAV object detection for anti-UAV. It adopts last four scales of feature maps instead of last three scales of feature maps to predict bounding boxes of objects, which can obtain more texture and contour information to detect small objects. At the same time, to reduce the calculation, the size of UAV in four scales feature maps is calculated according to input data, and then the number of anchor boxes is also adjusted. The experimental results demonstrate that the proposed approach achieves better detection accuracy and obtains more accurate bounding boxes of UAV with similar speed. Therefore, the proposed UAV detection technology can be applied in the field of anti-UAV.
Deep Adversarial Graph Attention Convolution Network for Text-Based Person Search

The newly emerging text-based person search task aims at retrieving the target pedestrian by a query in natural language with fine-grained description of a pedestrian. It is more applicable in reality without the requirement of image/video query of a pedestrian, as compared to image/video based person search, i.e., person re-identification. In this work, we propose a novel deep adversarial graph attention convolution network (A-GANet) for text-based person search. The A-GANet exploits both textual and visual scene graphs, consisting of object properties and relationships, from the text queries and gallery images of pedestrians, towards learning informative textual and visual representations. It learns an effective joint textual-visual latent feature space in adversarial learning manner, bridging modality gap and facilitating pedestrian matching. Specifically, the A-GANet consists of an image graph attention network, a text graph attention network and an adversarial learning module. The image and text graph attention networks are designed with a novel graph attention convolution layer, which effectively exploits graph structure in the learning of textual and visual features, leading to precise and discriminative representations. An adversarial learning module is developed with a feature transformer and a modality discriminator, to learn a joint textual-visual feature space for cross-modality matching. Extensive experimental results on two challenging benchmarks, i.e., CUHK-PEDES and Flickr30k datasets, have demonstrated the effectiveness of the proposed method.
Transferability and Hardness of Supervised Classification Tasks

We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models. Instead, we estimate these values using an information theoretic approach: treating training labels as random variables and exploring their statistics. When transferring from a source to a target task, we consider the conditional entropy between two such variables (i.e., label assignments of the two tasks). We show analytically and empirically that this value is related to the loss of the transferred model. We further show how to use this value to estimate task hardness. We test our claims extensively on three large scale data sets - CelebA (40 tasks), Animals with Attributes 2 (85 tasks), and Caltech-UCSD Birds 200 (312 tasks) - together representing 437 classification tasks. We provide results showing that our hardness and transferability estimates are strongly correlated with empirical hardness and transferability. As a case study, we transfer a learned face recognition model to CelebA attribute classification tasks, showing state of the art accuracy for tasks estimated to be highly transferable.
Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection

Facial landmark detection aims to localize the anatomically defined points of human faces. In this paper, we study facial landmark detection from partially labeled facial images. A typical approach is to (1) train a detector on the labeled images; (2) generate new training samples using this detector's prediction as pseudo labels of unlabeled images; (3) retrain the detector on the labeled samples and partial pseudo labeled samples. In this way, the detector can learn from both labeled and unlabeled data and become robust. In this paper, we propose an interaction mechanism between a teacher and two students to generate more reliable pseudo labels for unlabeled data, which are beneficial to semi-supervised facial landmark detection. Specifically, the two students are instantiated as dual detectors. The teacher learns to judge the quality of the pseudo labels generated by the students and filter out unqualified samples before the retraining stage. In this way, the student detectors get feedback from their teacher and are retrained by premium data generated by itself. Since the two students are trained by different samples, a combination of their predictions will be more robust as the final prediction compared to either prediction. Extensive experiments on 300-W and AFLW benchmarks show that the interactions between teacher and students contribute to better utilization of the unlabeled data and achieves state-of-the-art performance.
A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model

Water inrush disaster seriously affects the safety of karst tunnel construction. It’s essential to assess the risk level of water inrush in karst tunnels accurately, and take some effective countermeasures to reduce the damage to the project. We integrates the variable weight theory and cloud model theory to construct the VW&ICM calculation model to evaluate the risk of construction of karst tunnels. First, we select the index factors that affect the risk of water inrush from karst tunnels. Secondly, according to the theory of variable weight, we construct the zoning variable weight model, and the normalization criterion of the data from index factors is established. Thirdly, according to the attribute mathematic theory, we select the numerical characteristics of the improved cloud model, and the normal cloud generator is used to establish the point membership function. Finally, the risk index data is brought into the variable weight vector function and the point membership function to obtain the variable weight vector W and the membership degree matrix R. The risk overall evaluation vector B is further calculated, and the membership degree fluctuation is comprehensively analyzed according to the principle of maximum membership degree. The scope determines the risk of water inrush from karst tunnels. Finally, calculate the variable weight vector W and the membership matrix R, and the overall risk evaluation vector B is further integrated by W and R. According to the max-subjection principle and the fluctuation rule of membership, we get the risk level of water inrush in karst tunnel. The VM&ICM not only can quantitatively evaluate risk by considering the uncertainty of risk assessment and the influence of indicator size on weight, but also can make an analysis of the reliability to make the assessment result more convincing. The model makes improvement in weakening the influence of subjective factors on assessment results and in allocating the weight of indicators. A simple and practical software package is developed, which greatly improves the computational efficiency of VW&ICM method. The VW&ICM calculation model is applied to the risk assessment of water inrush for karst tunnels, and the results are basically consistent with the on-site construction situation.
Toward Training Recurrent Neural Networks for Lifelong Learning

Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we study these challenges in the context of sequential supervised learning with an emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step toward developing true lifelong learning systems, we unify gradient episodic memory (a catastrophic forgetting alleviation approach) and Net2Net (a capacity expansion approach). Both models are proposed in the context of feedforward networks, and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.
Stochastic Attraction-Repulsion Embedding for Large Scale Image Localization

This paper tackles the problem of large-scale image-based localization (IBL) where the spatial location of a query image is determined by finding out the most similar reference images in a large database. For solving this problem, a critical task is to learn discriminative image representation that captures informative information relevant for localization. We propose a novel representation learning method having higher location-discriminating power. It provides the following contributions: 1) we represent a place (location) as a set of exemplar images depicting the same landmarks and aim to maximize similarities among intra-place images while minimizing similarities among inter-place images; 2) we model a similarity measure as a probability distribution on L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -metric distances between intra-place and inter-place image representations; 3) we propose a new Stochastic Attraction and Repulsion Embedding (SARE) loss function minimizing the KL divergence between the learned and the actual probability distributions; 4) we give theoretical comparisons between SARE, triplet ranking and contrastive losses. It provides insights into why SARE is better by analyzing gradients. Our SARE loss is easy to implement and pluggable to any CNN. Experiments show that our proposed method improves the localization performance on standard benchmarks by a large margin. Demonstrating the broad applicability of our method, we obtained the third place out of 209 teams in the 2018 Google Landmark Retrieval Challenge. Our code and model are available at https://github.com/Liumouliu/deepIBL.
Targeting Prospective Customers: Robustness of Machine-Learning Methods to Typical Data Challenges

We investigate how firms can use the results of field experiments to optimize the targeting of promotions when prospecting for new customers. We evaluate seven widely used machine-learning methods using a series of two large-scale field experiments. The first field experiment generates a common pool of training data for each of the seven methods. We then validate the seven optimized policies provided by each method together with uniform benchmark policies in a second field experiment. The findings not only compare the performance of the targeting methods, but also demonstrate how well the methods address common data challenges. Our results reveal that when the training data are ideal, model-driven methods perform better than distance-driven methods and classification methods. However, the performance advantage vanishes in the presence of challenges that affect the quality of the training data, including the extent to which the training data captures details of the implementation setting. The challenges we study are covariate shift, concept shift, information loss through aggregation, and imbalanced data. Intuitively, the model-driven methods make better use of the information available in the training data, but the performance of these methods is more sensitive to deterioration in the quality of this information. The classification methods we tested performed relatively poorly. We explain the poor performance of the classification methods in our setting and describe how the performance of these methods could be improved. This paper was accepted by Matthew Shum, marketing.
Large-batch training for LSTM and beyond

Large-batch training approaches have enabled researchers to utilize distributed processing and greatly accelerate deep neural networks training. However, there are three problems in current large-batch research: (1) Although RNN approaches like LSTM have been widely used in many applications, current large-batch research is principally focused on CNNs. (2) Even for CNNs, there is no automated technique for extending the batch size beyond 8K. (3) To keep the variance in the gradient expectation constant, theory suggests that a Sqrt Scaling scheme should be used in large-batch training. Unfortunately, there are not many successful applications. In this paper, we propose Dynamic Adaptive-Tuning Engine (DATE) for better large-batch training. DATE achieves a 5.3x average speedup over the baselines for four LSTM-based applications on the same hardware. We finish the ImageNet training with ResNet-50 in two minutes on 1024 v3 TPUs (76.7% top-1 accuracy), which is the fastest version as of June of 2019.
Cross-lingual multi-keyword rank search with semantic extension over encrypted data

The emergence of searchable encryption technology sheds new light on the problem of secure search over encrypted data, which has been a research hotspot in the past few years. However, most existing search schemes based on searchable encryption only support queries in a certain language. The few searchable encryption schemes that have implemented multilingual search fail to achieve automated cross-lingual retrieval, which significantly impacts users search experience. Additionally, most of these schemes only support the exact matching and can-not provide semantic search. Hence, the implementation of cross-lingual and semantic search together remains an open topic for searchable encryption. To the best of our knowledge, no previous research has investigated the problem of cross-lingual ranked search over encrypted cloud data. To address this issue, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme upon the Open Multilingual Wordnet. Our CLRSE scheme can break the barrier of languages, and realizes intelligent and personalized search through flexible keyword and language preference settings. Additionally, an improved scheme is developed to speed up the sorting process. We evaluate the performance of our scheme including security, functionality, precision, and efficiency, with extensive experiments.
An effective fragile watermarking scheme for color image tampering detection and self-recovery

In this paper, a fragile watermarking scheme for color-image authentication and self-recovery is proposed. Original image is divided into non-overlapping blocks, and for each i-th block, the watermarks used for recovery and authentication are generated, which are embedded into a different block according to an embedding sequence given by a permutation process. The designed scheme embeds the watermarks generated by each block within the 2-LSB, where a bit-adjustment phase is subsequently applied to increase the quality of the watermarked image. In order to increase the quality of the recovered image, we use in the post-processing stage the bilateral filter that efficiently suppresses noise preserving image edges. Additionally, in the tamper detection process high accuracy is achieved employing a hierarchical tamper detection algorithm. Finally, to solve tampering coincidence problem, three recovery watermarks are embedded in different positions to reconstruct a specific block, and a proposed inpainting algorithm is implemented to regenerate those regions affected by this problem. Simulation results demonstrate that the watermarked images appear to demonstrate higher quality, and the proposed novel scheme can reconstruct the alteration of extremely high rates (up to 80%), obtaining good quality for altered regions that are self-recovered with higher visual performance compared with a similar scheme from state of the-art methods.
xR-EgoPose: Egocentric 3D Human Pose From an HMD Camera

We present a new solution to egocentric 3D body pose estimation from monocular images captured from a downward looking fish-eye camera installed on the rim of a head mounted virtual reality device. This unusual viewpoint, just 2 cm away from the user's face, leads to images with unique visual appearance, characterized by severe self-occlusions and strong perspective distortions that result in a drastic difference in resolution between lower and upper body. Our contribution is two-fold. Firstly, we propose a new encoder-decoder architecture with a novel dual branch decoder designed specifically to account for the varying uncertainty in the 2D joint locations. Our quantitative evaluation, both on synthetic and real-world datasets, shows that our strategy leads to substantial improvements in accuracy over state of the art egocentric pose estimation approaches. Our second contribution is a new large-scale photorealistic synthetic dataset - xR-EgoPose - offering 383K frames of high quality renderings ofpeople with a diversity of skin tones, body shapes, clothing, in a variety of backgrounds and lighting conditions, performing a range of actions. Our experiments show that the high variability in our new synthetic training corpus leads to good generalization to real world footage and to state of the art results on real world datasets with ground truth. Moreover, an evaluation on the Human3.6M benchmark shows that the performance of our method is on par with top performing approaches on the more classic problem of 3D human pose from a third person viewpoint.
A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic

The performance of the well-known particle swarm optimization (PSO) method can be improved by minimizing the possibility of premature convergence in a local minimum. We can achieve this by modifying some of the particles with crossover and mutation operators used in genetic algorithms. However, the impact of genetic operators on the optimization process should depend on the current state of the PSO algorithm. In this article, we propose to use the neuro-fuzzy system to dynamically determine the strength with which these operators will affect the process of finding the optimal solution. Results obtained for well-known benchmark functions demonstrate the advance of the proposed method over the original PSO algorithm and its selected modifications.
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning

We present a Trojan (backdoor or trapdoor) attack that targets deep learning applications in wireless communications. A deep learning classifier is considered to classify wireless signals using raw (I/Q) samples as features and modulation types as labels. An adversary slightly manipulates training data by inserting Trojans (i.e., triggers) to only few training data samples by modifying their phases and changing the labels of these samples to a target label. This poisoned training data is used to train the deep learning classifier. In test (inference) time, an adversary transmits signals with the same phase shift that was added as a trigger during training. While the receiver can accurately classify clean (unpoisoned) signals without triggers, it cannot reliably classify signals poisoned with triggers. This stealth attack remains hidden until activated by poisoned inputs (Trojans) to bypass a signal classifier (e.g., for authentication). We show that this attack is successful over different channel conditions and cannot be mitigated by simply preprocessing the training and test data with random phase variations. To detect this attack, activation based outlier detection is considered with statistical as well as clustering techniques. We show that the latter one can detect Trojan attacks even if few samples are poisoned.