Reviewerly/exHarmony huggingface dataset:
Object Detection at 200 Frames per Second

In this paper, we propose an efficient and fast object detector which can process hundreds of frames per second. To achieve this goal we investigate three main aspects of the object detection framework: network architecture, loss function and training data (labeled and unlabeled). In order to obtain compact network architecture, we introduce various improvements, based on recent work, to develop an architecture which is computationally light-weight and achieves a reasonable performance. To further improve the performance, while keeping the complexity same, we utilize distillation loss function. Using distillation loss we transfer the knowledge of a more accurate teacher network to proposed light-weight student network. We propose various innovations to make distillation efficient for the proposed one stage detector pipeline: objectness scaled distillation loss, feature map non-maximal suppression and a single unified distillation loss function for detection. Finally, building upon the distillation loss, we explore how much can we push the performance by utilizing the unlabeled data. We train our model with unlabeled data using the soft labels of the teacher network. Our final network consists of 10x fewer parameters than the VGG based object detection network and it achieves a speed of more than 200 FPS and proposed changes improve the detection accuracy by 14 mAP over the baseline on Pascal dataset.
Spectral Alignment of Graphs

Graph alignment refers to the problem of finding a bijective mapping across vertices of two graphs such that, if two nodes are connected in the first graph, their images are connected in the second graph. This problem arises in many fields, such as computational biology, social sciences, and computer vision and is often cast as a quadratic assignment problem (QAP). Most standard graph alignment methods consider an optimization that maximizes the number of matches between the two graphs, ignoring the effect of mismatches. We propose a generalized graph alignment formulation that considers both matches and mismatches in a standard QAP formulation. This modification can have a major impact in aligning graphs with different sizes and heterogeneous edge densities. Moreover, we propose two methods for solving the generalized graph alignment problem based on spectral decomposition of matrices. We compare the performance of proposed methods with some existing graph alignment algorithms including Natalie2, GHOST, IsoRank, NetAlign, Klau's approach as well as a semidefinite programming-based method over various synthetic and real graph models. Our proposed method based on simultaneous alignment of multiple eigenvectors leads to consistently good performance in different graph models. In particular, in the alignment of regular graph structures, which is one of the most difficult graph alignment cases, our proposed method significantly outperforms other methods.
Unsupervised feature selection with multi-subspace randomization and collaboration

Unsupervised feature selection has been an important technique in high-dimensional data analysis. Despite significant success, most of the existing unsupervised feature selection methods tend to estimate the underlying structure of data in the original feature space, but lack the ability to explore various subspaces in the high-dimensional space. In this paper, we argue that the use of a large number of random subspaces can significantly benefit the unsupervised feature selection accuracy. Particularly, we propose a new unsupervised feature selection approach based on multi-subspace randomization and collaboration. A balanced subspace randomization scheme is first presented to produce multiple basic feature partitions with similar-sized random subspaces. Then, multiple K-nearest neighbor graphs are constructed in these subspaces, based on which the Laplacian scores of the features in each subspace are obtained w.r.t. their locality preserving power. Thereafter, the obtained feature score vectors of different subspaces in different basic feature partitions are integrated into a full score vector of all features, which takes into account the structure information of various subspaces and can significantly enhance the performance of unsupervised feature selection. Experiments conducted on twenty high-dimensional datasets have demonstrated the high efficiency and robustness of our approach. The MATLAB source code is available at https://www.researchgate.net/publication/334520672.
Scalable Graph Embeddings via Sparse Transpose Proximities

Graph embedding learns low-dimensional representations for nodes in a graph and effectively preserves the graph structure. Recently, a significant amount of progress has been made toward this emerging research area. However, there are several fundamental problems that remain open. First, existing methods fail to preserve the out-degree distributions on directed graphs. Second, many existing methods employ random walk based proximities and thus suffer from conflicting optimization goals on undirected graphs. Finally, existing factorization methods are unable to achieve scalability and non-linearity simultaneously. This paper presents an in-depth study on graph embedding techniques on both directed and undirected graphs. We analyze the fundamental reasons that lead to the distortion of out-degree distributions and to the conflicting optimization goals. We propose transpose proximity, a unified approach that solves both problems. Based on the concept of transpose proximity, we design STRAP, a factorization based graph embedding algorithm that achieves scalability and non-linearity simultaneously. STRAP makes use of the backward push algorithm to efficiently compute the sparse Personalized PageRank (PPR) as its transpose proximities. By imposing the sparsity constraint, we are able to apply non-linear operations to the proximity matrix and perform efficient matrix factorization to derive the embedding vectors. Finally, we present an extensive experimental study that evaluates the effectiveness of various graph embedding algorithms, and we show that \strap outperforms the state-of-the-art methods in terms of effectiveness and scalability.
Bayesian Filtering With Unknown Sensor Measurement Losses

This paper studies the state estimation problem of a stochastic nonlinear system with unknown sensor measurement losses. If the estimator knows the sensor measurement losses of a linear Gaussian system, the minimum variance estimate is easily computed by the celebrated intermittent Kalman filter (IKF). However, this will no longer be the case when the measurement losses are unknown and/or the system is nonlinear or non-Gaussian. By exploiting the binary property of the measurement loss process and the IKF, we design three suboptimal filters for the state estimation, that is, BKF-I, BKF-II, and RBPF. The BKF-I is based on the MAP estimator of the measurement loss process and the BKF-II is derived by estimating the conditional loss probability. The RBPF is a particle filter-based algorithm that marginalizes out the loss process to increase the efficiency of particles. All of the proposed filters can be easily implemented in recursive forms. Finally, a linear system, a target tracking system, and a quadrotor's path control problem are included to illustrate their effectiveness, and show the tradeoff between computational complexity and estimation accuracy of the proposed filters.
Interactive image segmentation using label propagation through complex networks

Interactive image segmentation is a topic of many studies in image processing. In a conventional approach, a user marks some pixels of the object(s) of interest and background, and an algorithm propagates these labels to the rest of the image. This paper presents a new graph-based method for interactive segmentation with two stages. In the first stage, nodes representing pixels are connected to their k-nearest neighbors to build a complex network with the small-world property to propagate the labels quickly. In the second stage, a regular network in a grid format is used to refine the segmentation on the object borders. Despite its simplicity, the proposed method can perform the task with high accuracy. Computer simulations are performed using some real-world images to show its effectiveness in both two-classes and multi-classes problems. It is also applied to all the images from the Microsoft GrabCut dataset for comparison, and the segmentation accuracy is comparable to those achieved by some state-of-the-art methods, while it is faster than them. In particular, it outperforms some recent approaches when the user input is composed only by a few “scribbles” draw over the objects. Its computational complexity is only linear on the image size at the best-case scenario and linearithmic in the worst case.
Multi-level nested pyramid network for mass segmentation in mammograms

Mass segmentation in mammograms is an important and challenging topic in breast cancer computer-aided diagnosis. In this work, we propose a novel multi-level nested pyramid network (MNPNet) for dealing with the limitations of intra-class inconsistency and inter-class indistinction that commonly existed in mass segmentation in mammograms. The MNPNet includes an encoder and a decoder. The former encodes contextual information, low-level detail information, and high-level semantic information in a multi-level multi-scale manner by multi-level nesting atrous spatial pyramid pooling (ASPP) module on the feature pyramid generated by modified ResNet34. The latter consist of a series of simple yet effective bilinear upsampling and feature fusion operations to refine the segmentation results along mass boundaries. Our proposed MNPNet is greatly demonstrated on two public mammographic mass segmentation databases including INbreast and DDSM-BCRP, respectively achieving the Dice index of 91.10% and 91.69% without any pre/post-processing.
Neural Sequential Phrase Grounding (SeqGROUND)

We propose an end-to-end approach for phrase grounding in images. Unlike prior methods that typically attempt to ground each phrase independently by building an image-text embedding, our architecture formulates grounding of multiple phrases as a sequential and contextual process. Specifically, we encode region proposals and all phrases into two stacks of LSTM cells, along with so-far grounded phrase-region pairs. These LSTM stacks collectively capture context for grounding of the next phrase. The resulting architecture, which we call SeqGROUND, supports many-to-many matching by allowing an image region to be matched to multiple phrases and vice versa. We show competitive performance on the Flickr30K benchmark dataset and, through ablation studies, validate the efficacy of sequential grounding as well as individual design choices in our model architecture.
A 0.11 pJ/Op, 0.32-128 TOPS, Scalable Multi-Chip-Module-based Deep Neural Network Accelerator with Ground-Reference Signaling in 16nm

This work presents a scalable deep neural network (DNN) accelerator consisting of 36 chips connected in a mesh network on a multi-chip-module (MCM) using ground-referenced signaling (GRS). While previous accelerators fabricated on a single monolithic die are limited to specific network sizes, the proposed architecture enables flexible scaling for efficient inference on a wide range of DNNs, from mobile to data center domains. The 16nm prototype achieves 1.29 TOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , 0.11 pJ/op energy efficiency, 4.01 TOPS peak performance for a 1chip system, and 127.8 peak TOPS and 2615 images/s ResNet50 inference for a 36-chip system.
Monetary Stabilization in Cryptocurrencies – Design Approaches and Open Questions

The price volatility of cryptocurrencies is often cited as a major hindrance to their wide-scale adoption. Consequently, during the last two years, multiple so called stablecoins have surfaced-cryptocurrencies focused on maintaining stable exchange rates. In this paper, we systematically explore and analyze the stablecoin landscape. Based on a survey of 24 specific stablecoin projects, we go beyond individual coins for extracting general concepts and approaches. We combine our findings with learnings from classical monetary policy, resulting in a comprehensive taxonomy of cryptocurrency stabilization. We use our taxonomy to highlight the current state of development from different perspectives and show blank spots. For instance, while over 91% of projects promote 1-to-1 stabilization targets to external assets, monetary policy literature suggests that the smoothing of short term volatility is often a more sustainable alternative. Our taxonomy bridges computer science and economics, fostering the transfer of expertise. For example, we find that 38% of the reviewed projects use a combination of exchange rate targeting and specific stabilization techniques that can render them vulnerable to speculative economic attacks-an avoidable design flaw.
Two-Round Oblivious Transfer from CDH or LPN

We show a new general approach for constructing maliciously-secure two-round oblivious transfer (OT). Specifically, we provide a generic sequence of transformations to upgrade a very basic notion of two-round OT, which we call elementary OT, to UC-secure OT. We then give simple constructions of elementary OT under the Computational Diffie-Hellman (CDH) assumption or the Learning Parity with Noise (LPN) assumption, yielding the first constructions of malicious (UC-secure) two-round OT under these assumptions. Since two-round OT is complete for two-round 2-party and multi-party computation in the malicious setting, we also achieve the first constructions of the latter under these assumptions.
Approximate Policy-Based Accelerated Deep Reinforcement Learning

In recent years, the deep reinforcement learning (DRL) algorithms have been developed rapidly and have achieved excellent performance in many challenging tasks. However, due to the complexity of network structure and a large amount of network parameters, the training of deep network is time-consuming, and consequently, the learning efficiency of DRL is limited. In this paper, aiming to speed up the learning process of DRL agent, we propose a novel approximate policy-based accelerated (APA) algorithm from the viewpoint of the error analysis of approximate policy iteration reinforcement learning algorithms. The proposed APA is proven to be convergent even with a more aggressive learning rate, making the DRL agent have a faster learning speed. Furthermore, to combine the accelerated algorithm with deep Q-network (DQN), Double DQN and deep deterministic policy gradient (DDPG), we proposed three novel DRL algorithms: APA-DQN, APA-Double DQN, and APA-DDPG, which demonstrates the adaptability of the accelerated algorithm with DRL algorithms. We have tested the proposed algorithms on both discrete-action and continuous-action tasks. Their superior performance demonstrates their great potential in the practical applications.
Real-Time Water Surface Object Detection Based on Improved Faster R-CNN

In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.
Automatic targetless camera–LIDAR calibration by aligning edge with Gaussian mixture model

Abstract This paper presents a calibration algorithm that does not require an artificial target object to precisely estimate a rigid‐body transformation between a camera and a light detection and ranging (LIDAR) sensor. The proposed algorithm estimates calibration parameters by minimizing a cost function that evaluates the edge alignment between two sensor measurements. In particular, the proposed cost function is constructed using a projection model‐based many‐to‐many correspondence of the edges to fully exploit measurements with different densities (dense photometry and sparse geometry). The alignment of the many‐to‐many correspondence is represented using the Gaussian mixture model (GMM) framework. Here, each component of the GMM, including weight, displacement, and standard deviation, is derived to suitably capture the intensity, location, and influential range of the edge measurements, respectively. The derived cost function is optimized by the gradient descent method with an analytical derivative. A coarse‐to‐fine scheme is also applied by gradually decreasing the standard deviation of the GMM to enhance the robustness of the algorithm. Extensive indoor and outdoor experiments validate the claim that the proposed GMM strategy improves the performance of the proposed algorithm. The experimental results also show that the proposed algorithm outperforms previous methods in terms of precision and accuracy by providing calibration parameters of standard deviations less than 0.6° and 2.1 cm with a reprojection error of 1.78 for a 2.1‐megapixel image (2,048 × 1,024) in the best case.
Intrusion Detection for IoT Devices based on RF Fingerprinting using Deep Learning

Internet of Things (IoT) and 4G/5G wireless networks have added huge number of devices and new services, where commercial-of-the-shelf (COTS) IoT devices have been deployed extensively. To ensure secure operations of these systems with wireless transmission capabilities, Radio Frequency (RF) surveillance is important to monitor their activities in RF spectrum and detect unauthorized IoT devices. Specifically, in order to prevent an adversary from impersonating legitimate users using identical devices from the same manufacturer, unique “signatures” must be obtained for every individual device in order to uniquely identify each device. In this study, a novel intrusion detection method is proposed to detect unauthorized IoT devices using deep learning. The proposed method is based on RF fingerprinting since physical layer based features are device specific and more difficult to impersonate. RF traces are collected from six “identical” ZigBee devices via a USRP based test bed. The traces span a range of Signal-to-Noise Ratio, to ensure robustness of the model. A convolutional neural network is used to extract features from the RF traces, and dimension reduction and de-correlation are performed on the extracted features. The reduced features are then clustered to identify IoT devices. We show that the proposed method is able to identify devices that are not observed during training. The results not only highlight the benefit of deep learning based feature extraction, but also show promising prospects for being able to distinguish new devices (classes) that are not observed during training.
Virtual reality in social marketing: a process evaluation

Purpose The purpose of this paper is to present an innovative reflexive process evaluation method for a social marketing programme featuring an innovative virtual reality (VR) simulation experience for adolescents. Design/methodology/approach A process evaluation framework focusing on three key elements – context, implementation and mechanisms of impact – was followed in this study. In total, 374 participants (mean age: 15.2 years, 58 per cent female) completed outcome evaluation surveys before and after the programme delivery, and 300 participants completed qualitative feedback forms following their participation in the VR component of the programme. Findings A process evaluation delivers insights beyond those attained in outcome evaluations, enhancing the understanding of factors contributing to programme success or failure that can be used to improve future programme iterations. The VR experience demonstrated high satisfaction scores with users, and the findings demonstrate the importance of a multi-disciplinary and industry partnered programme approach to support VR implementation and delivery. Research limitations/implications This research demonstrates that additional learnings are obtained from a process evaluation. The findings are limited to one specific research programme, and the outcome effects of the VR simulation have not been assessed in isolation. Practical implications The methods outlined in this paper offer a process evaluation tool that can be used by marketers and other practitioners to reflect on programme success or failure to enhance core offerings. Originality/value The application of Moore et al. ’s (2015) process evaluation framework delivers a reflexive research tool that can be applied to critically consider three key elements: context, implementation and mechanisms of impact of developed programmes. VR’s capacity to provide a satisfying and highly valued programme resource that participants value for its realistic, novel and immersive experiential learning experience was demonstrated.
A self‐learning fuzzy approach for proactive resource provisioning in cloud environment

Summary The development of a communication infrastructure has made possible the expansion of the popular massively multiplayer online games. In these games, players all over the world can interact with one another in a virtual environment. The arrival rate of new players to the game environment causes fluctuations and players always expect services to be available and offer an acceptable service‐level agreement (SLA), especially in terms of response time and cost. Cloud computing emerged in the recent years as a scalable alternative to respond to the dynamic changes of the workload. In massively multiplayer online games applications, players are allowed to lease resources from a cloud provider in an on‐demand basis model. Proactive management of cloud resources in the face of workload fluctuations and dynamism upon the arrival of players are challenging issues. This paper presents a self‐learning fuzzy approach for proactive resource provisioning in cloud environment, where key is to predict parameters of the probability distribution of the incoming players in each period. In addition, we propose a self‐learning fuzzy autoscaling decision‐maker algorithm to compute the proper number of resources to be allocated to each tier in the massively multiplayer online games by applying the predicted workload and user SLA. We evaluate the effectiveness of the proposed approach under real and synthetic workloads. The experimental results indicate that the proposed approach is able to allocate resources more efficiently than other approaches.