Reviewerly/exHarmony huggingface dataset:
Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection

Zero-shot detection, namely, localizing both seen and unseen objects, increasingly gains importance for large-scale applications, with large number of object classes, since, collecting sufficient annotated data with ground truth bounding boxes is simply not scalable. While vanilla deep neural networks deliver high performance for objects available during training, unseen object detection degrades significantly. At a fundamental level, while vanilla detectors are capable of proposing bounding boxes, which include unseen objects, they are often incapable of assigning high-confidence to unseen objects, due to the inherent precision/recall tradeoffs that requires rejecting background objects. We propose a novel detection algorithm “Don't Even Look Once (DELO),” that synthesizes visual features for unseen objects and augments existing training algorithms to incorporate unseen object detection. Our proposed scheme is evaluated on PascalVOC and MSCOCO, and we demonstrate significant improvements in test accuracy over vanilla and other state-of-art zero-shot detectors.
Squeeze U-Net: A Memory and Energy Efficient Image Segmentation Network

To facilitate implementation of deep neural networks on embedded systems keeping memory and computation requirements low is critical, particularly for real-time mobile use. In this work, we propose a SqueezeNet inspired version of U-Net for image segmentation that achieves a 12X reduction in model size to 32MB, and 3.2X reduction in Multiplication Accumulation operations (MACs) from 287 billion ops to 88 billion ops for inference on the CamVid data set while preserving accuracy. Our proposed Squeeze U-Net is efficient in both low MACs and memory use. Our performance results using Tensorflow 1.14 with Python 3.6 and CUDA 10.1.243 on an NVIDIA K40 GPU shows that Squeeze U-Net is 17% faster for inference and 52% faster for training than U-Net for the same accuracy.
Bodies at Rest: 3D Human Pose and Shape Estimation From a Pressure Image Using Synthetic Data

People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight perception is complicated by occlusion from bedding. Pressure sensing mats are a promising alternative, but training data is challenging to collect at scale. We describe a physics-based method that simulates human bodies at rest in a bed with a pressure sensing mat, and present PressurePose, a synthetic dataset with 206K pressure images with 3D human poses and shapes. We also present PressureNet, a deep learning model that estimates human pose and shape given a pressure image and gender. PressureNet incorporates a pressure map reconstruction (PMR) network that models pressure image generation to promote consistency between estimated 3D body models and pressure image input. In our evaluations, PressureNet performed well with real data from participants in diverse poses, even though it had only been trained with synthetic data. When we ablated the PMR network, performance dropped substantially.
Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics

We introduce a novel principle for self-supervised feature learning based on the discrimination of specific transformations of an image. We argue that the generalization capability of learned features depends on what image neighborhood size is sufficient to discriminate different image transformations: The larger the required neighborhood size and the more global the image statistics that the feature can describe. An accurate description of global image statistics allows to better represent the shape and configuration of objects and their context, which ultimately generalizes better to new tasks such as object classification and detection. This suggests a criterion to choose and design image transformations. Based on this criterion, we introduce a novel image transformation that we call limited context inpainting (LCI). This transformation inpaints an image patch conditioned only on a small rectangular pixel boundary (the limited context). Because of the limited boundary information, the inpainter can learn to match local pixel statistics, but is unlikely to match the global statistics of the image. We claim that the same principle can be used to justify the performance of transformations such as image rotations and warping. Indeed, we demonstrate experimentally that learning to discriminate transformations such as LCI, image warping and rotations, yields features with state of the art generalization capabilities on several datasets such as Pascal VOC, STL-10, CelebA, and ImageNet. Remarkably, our trained features achieve a performance on Places on par with features trained through supervised learning with ImageNet labels.
Part-Aware Context Network for Human Parsing

Recent works have made significant progress in human parsing by exploiting rich contexts. However, human parsing still faces a challenge of how to generate adaptive contextual features for the various sizes and shapes of human parts. In this work, we propose a Part-aware Context Network (PCNet), a novel and effective algorithm to deal with the challenge. PCNet mainly consists of three modules, including a part class module, a relational aggregation module, and a relational dispersion module. The part class module extracts the high-level representations of every human part from a categorical perspective. We design a relational aggregation module to capture the representative global context by mining associated semantics of human parts, which adaptively augments the context for human parts. We propose a relational dispersion module to generate the discriminative and effective local context and neglect disturbing one by making the affinity of human parts dispersed. The relational dispersion module ensures that features in the same class will be close to each other and away from those of different classes. By fusing the outputs of the relational aggregation module, the relational dispersion module and the backbone network, our PCNet generates adaptive contextual features for various sizes of human parts, improving the parsing accuracy. We achieve a new state-of-the-art segmentation performance on three challenging human parsing datasets, i.e., PASCAL-Person-Part, LIP, and CIHP.
One-shot signatures and applications to hybrid quantum/classical authentication

We define the notion of one-shot signatures, which are signatures where any secret key can be used to sign only a single message, and then self-destructs. While such signatures are of course impossible classically, we construct one-shot signatures using quantum no-cloning. In particular, we show that such signatures exist relative to a classical oracle, which we can then heuristically obfuscate using known indistinguishability obfuscation schemes.
Neural Pose Transfer by Spatially Adaptive Instance Normalization

Pose transfer has been studied for decades, in which the pose of a source mesh is applied to a target mesh. Particularly in this paper, we are interested in transferring the pose of source human mesh to deform the target human mesh, while the source and target meshes may have different identity information. Traditional studies assume that the paired source and target meshes are existed with the point-wise correspondences of user annotated landmarks/mesh points, which requires heavy labelling efforts. On the other hand, the generalization ability of deep models is limited, when the source and target meshes have different identities. To break this limitation, we proposes the first neural pose transfer model that solves the pose transfer via the latest technique for image style transfer, leveraging the newly proposed component -- spatially adaptive instance normalization. Our model does not require any correspondences between the source and target meshes. Extensive experiments show that the proposed model can effectively transfer deformation from source to target meshes, and has good generalization ability to deal with unseen identities or poses of meshes. Code is available at https://github.com/jiashunwang/Neural-Pose-Transfer.
Computing the Testing Error Without a Testing Set

Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (accuracy) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trail-and-error. That is, typically, researchers will derive many DNN architectures (\ie, topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-the-testing-data is of concern. Using a sequestered testing data may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing without the need of a testing dataset. Specifically, we derive a set of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach.
Match in My Way: Fine-Grained Bilateral Access Control for Secure Cloud-Fog Computing

Cloud-fog computing is a novel paradigm to extend the functionality of cloud computing to provide a variety of on-demand data services via the edge network. Many cryptographic tools have been introduced to preserve data confidentiality against the untrustworthy network and cloud servers. However, how to efficiently identify and retrieve useful data from a large number of ciphertexts without a costly decryption mechanism remains a challenging problem. In this article, we introduce a cloud-fog-device data sharing system (CFDS) with data confidentiality and data source identification simultaneously based on a new cryptographic primitive named matchmaking attribute-based encryption (MABE) by extending matchmaking encryption in CRYPTO’19. Our solution offers a secure fine-grained bilateral access control that includes (1) fine-grained sender access control, (2) fine-grained receiver access control, (3) sender privacy, and (4) performance optimization via outsourcing data source identification to fog nodes. We give the formal definition and security models of MABE, and present a concrete construction with formal security proofs. We also offer a detailed security analysis of our proposed CFDS against real-world security threats. The extensive comparison and experimental simulation demonstrate that, by immigrating heavy workload to fog nodes, our scheme has better functionalities and performances than the most related solutions.
Fusing Wearable IMUs With Multi-View Images for Human Pose Estimation: A Geometric Approach

We propose to estimate 3D human pose from multi-view images and a few IMUs attached at person's limbs. It operates by firstly detecting 2D poses from the two signals, and then lifting them to the 3D space. We present a geometric approach to reinforce the visual features of each pair of joints based on the IMUs. This notably improves 2D pose estimation accuracy especially when one joint is occluded. We call this approach Orientation Regularized Network (ORN). Then we lift the multi-view 2D poses to the 3D space by an Orientation Regularized Pictorial Structure Model (ORPSM) which jointly minimizes the projection error between the 3D and 2D poses, along with the discrepancy between the 3D pose and IMU orientations. The simple two-step approach reduces the error of the state-of-the-art by a large margin on a public dataset. Our code will be released at https://github.com/microsoft/imu-human-pose-estimation-pytorch.
High-Resolution Daytime Translation Without Domain Labels

Modeling daytime changes in high resolution photographs, e.g., re-rendering the same scene under different illuminations typical for day, night, or dawn, is a challenging image manipulation task. We present the high-resolution daytime translation (HiDT) model for this task. HiDT combines a generative image-to-image model and a new upsampling scheme that allows to apply image translation at high resolution. The model demonstrates competitive results in terms of both commonly used GAN metrics and human evaluation. Importantly, this good performance comes as a result of training on a dataset of still landscape images with no daytime labels available.
DOPS: Learning to Detect 3D Objects and Predict Their 3D Shapes

We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make domain-specific design decisions, for example projecting points into a bird-eye view image in autonomous driving scenarios. In contrast, we propose a general-purpose method that works on both indoor and outdoor scenes. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. 3D bounding box parameters are estimated in one pass for every point, aggregated through graph convolutions, and fed into a branch of the network that predicts latent codes representing the shape of each detected object. The latent shape space and shape decoder are learned on a synthetic dataset and then used as supervision for the end-to-end training of the 3D object detection pipeline. Thus our model is able to extract shapes without access to ground-truth shape information in the target dataset. During experiments, we find that our proposed method achieves state-of-the-art results by <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">~</sub> 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Waymo Open Dataset, while reproducing the shapes of detected cars.
PharmaCrypt: Blockchain for Critical Pharmaceutical Industry to Counterfeit Drugs

This research analyzes the impact of counterfeit drugs on the health-care supply chain industry and evaluates the solutions currently in place to reduce the number of counterfeits coming to the market. Feedback information obtained from industry professionals is used to build requirements for PharmaCrypt, a new blockchain-driven tool.
Transfer pricing and corporate social responsibility: arguments, views and agenda

The central thesis of the paper is that multinational companies (MNC) should invest in the use of “soft” methods (socially responsible behavior) to mitigate costs in society accrued due to use of “hardcore” tax evasion tactics (transfer mispricing) to maximize profits from operations in developing countries and/or countries with weak or inefficient tax laws and tax collection institutions. Therefore, we articulate the argument of corporate social responsibility (CSR) as an indirect compensation for transfer mispricing. Our aim is not to present CSR as a solution to transfer mispricing. An analytical approach is based on a content analysis of the existing literature with emphasis on a case study. We first discuss the dark side of transfer pricing (TP), next we present the link between TP and poverty and finally we advance arguments for CSR as a compensation for transfer mispricing. While acknowledging that TP is a legal accounting practice, we argue that in light of its poverty and underdevelopment externalities, the practice per se should be a strong defence for CSR because it is also associated with schemes that deprive developing countries of the capital essential for investment in health, education and development programmes.
An Automated Daily Sports Activities and Gender Recognition Method Based on Novel Multikernel Local Diamond Pattern Using Sensor Signals

Sensor signals have been frequently used for activity recognition and gender classification in the literature. In this study, a new multikernel local diamond pattern (MK-LDP) is proposed as a novel local descriptor method for feature extraction. The proposed MK-LDP is aimed to generate distinctive features from a signal or image using vertical and horizontal diamond-like patterns and multikernel functions. These kernels are signum, ternary, and quaternary. MK-LDP is utilized as a feature extraction technique for human activity recognition (HAR). The proposed HAR method has four fundamental phases, namely, preprocessing, feature generation with MK-LDP, informative features selection using hybrid ReliefF and neighborhood component analysis (RFNCA), and classification using support vector machine (SVM). The proposed MK-LDP extracts 2560 features from the raw sensor signals, RFINCA selects 512 most meaningful ones from the extracted 2560 features, and then the selected 512 features are used as input of the SVM for HAR. Three cases are defined to test the proposed MK-LDP- and RFNCA-based HAR method. These cases are gender classification, activity classification, and both gender and activity classification, respectively. The achieved best accuracy rates are 99.47%, 99.71%, and 99.36% for gender, daily sports activities, and both gender and daily sports activities recognition, respectively. The proposed MK-LDP-based method is also compared to the state-of-the-art and deep learning techniques. The obtained results revealed that the proposed MK-LDP- and RFNCA-based framework is successful HAR using sensor signals.
Zero Knowledge Clustering Based Adversarial Mitigation in Heterogeneous Federated Learning

The simultaneous development of deep learning techniques and Internet of Things (IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for improving distributed computing, sensing, and data analysis. Among these technologies, federated learning has received increased attention as a privacy-preserving collaborative learning paradigm, and has shown significant potential in IoT/CPS-driven large-scale smart-world systems. At the same time, the vulnerabilities of deep neural networks, especially to adversarial attacks, cannot be overstated and should not be minimized. Moreover, the distributed nature of federated learning makes defense against such adversarial attacks a more challenging problem due to the unavailability of local data and resource heterogeneity. To tackle these challenges, in this paper, we propose ZeKoC, a Zero Knowledge Clustering approach to mitigating adversarial attacks. Particularly, we first formulate the problem of resource-constrained adversarial mitigation. Specifically, noting that a global server has no access to training samples, we reformulate the unsupervised weight clustering problem. Our proposed ZeKoC approach allows the server to automatically split and merge weight clusters for weight selection and aggregation. Theoretical analysis demonstrates that convergence is guaranteed. Further, our experimental results illustrate that, in a non-i.i.d. (i.e., independent and identically distributed) data setting, the proposed ZeKoC approach successfully mitigates general attacks while outperforming state-of-art schemes.
HIDeGan: A Hyperspectral-guided Image Dehazing GAN

Haze removal in images captured from a diverse set of scenarios is a very challenging problem. The existing dehazing methods either reconstruct the transmission map or directly estimate the dehazed image in RGB color space. In this paper, we make a first attempt to propose a Hyperspectral-guided Image Dehazing Generative Adversarial Network (HIDEGAN). The HIDEGAN architecture is formulated by designing an enhanced version of CYCLEGAN named R2HCYCLE and an enhanced conditional GAN named H2RGAN. The R2HCYCLE makes use of the hyperspectral-image (HSI) in combination with cycle-consistency and skeleton losses in order to improve the quality of information recovery by analyzing the entire spectrum. The H2RGAN estimates the clean RGB image from the hazy hyperspectral image generated by the R2HCYCLE. The models designed for spatial-spectral-spatial mapping generate visually better haze-free images. To facilitate HSI generation, datasets from spectral reconstruction challenge at NTIRE 2018 and NTIRE 2020 are used. A comprehensive set of experiments were conducted on the D-Hazy, and the recent RESIDE-Standard (SOTS), RESIDE-β (OTS) and RESIDE-Standard (HSTS) datasets. The proposed HIDEGAN outperforms the existing state-of-the-art in all these datasets.
Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach

In this work, global solar radiation is estimated based on sunshine duration. Solar radiation measurements have been collected from the Indian Meteorological Department (Pune, India) for the period 1986–2000. 25 model forms were selected from the literature which correlates the clearness index with sunshine duration. The coefficients of models are extracted from the data. k-fold cross-validation is then employed to improve the performance of the models. Data is split into k-groups with each group containing the same amount of data. (k-1) groups are utilized for the development of models and the rest one group is utilized for the testing of models performance. The procedure is repeated k-times and those coefficients are selected which produces the least error. Models are evaluated and compared with the help of statistical errors. Further, the statistical errors were scaled and Global Performance Indicator (GPI) was evaluated. Using GPI, models were given rank in order of suitability of estimates produced. The GPI value lies between −9.4269 and 0.4695. It has been observed that the quartic model (M-04) exhibited the best performance with MBE = 0.0259, RMSE = 1.7927, MPE = 0.1495, RRMSE = 0.0352, erMAX = 0.9072, MARE = 0.0812, MAE = 1.3843, U95 = 6.3100, t-stats = 0.2397 and R = 0.8597 resulting in the highest GPI amongst all the models which were proposed.
“Explaining” machine learning reveals policy challenges

The need to make objectives explicit may expose policy trade-offs that had previously been implicit and obscured
DTOF-ANN: An Artificial Neural Network phishing detection model based on Decision Tree and Optimal Features

Recently, phishing emerges as one of the biggest threats to human’s daily networking environments. Phishing attackers disguise illegal URLs as normal ones to steal user’s private information with the social engineering techniques, such as emails and SMS, which calls for an effective method of preventing phishing attacks to relieve the loss by them. Neural networks can be used to detect and prevent phishing attacks because of their strong active learning abilities from massive datasets and high accuracy in data classification. However, duplicate points in the public datasets and negative and useless features in the feature vectors will trap the training of the neural networks into the problem of over-fitting, which will make the trained classifier weak when detect phishing websites. This paper proposes DTOF-ANN (Decision Tree and Optimal Features based Artificial Neural Network) to tackle this shortcoming, which is a neural-network phishing detection model based on decision tree and optimal feature selection. First, the traditional K-medoids clustering algorithm is improved with an incremental selection of initial centers to remove the duplicate points from the public datasets. Then, an optimal feature selection algorithm based on the new defined feature evaluation index, decision tree and local search method is designed to prune out the negative and useless features. Finally, the optimal structure of the neural network classifier is constructed through properly adjusting parameters and trained by the selected optimal features. Experimental results have demonstrated that DTOF-ANN exhibits higher performance than many of the existing methods.