Reviewerly/exHarmony huggingface dataset:
Bridging the Gap Between Detection and Tracking: A Unified Approach

Object detection models have been a source of inspiration for many tracking-by-detection algorithms over the past decade. Recent deep trackers borrow designs or modules from the latest object detection methods, such as bounding box regression, RPN and ROI pooling, and can deliver impressive performance. In this paper, instead of redesigning a new tracking-by-detection algorithm, we aim to explore a general framework for building trackers directly upon almost any advanced object detector. To achieve this, three key gaps must be bridged: (1) Object detectors are class-specific, while trackers are class-agnostic. (2) Object detectors do not differentiate intra-class instances, while this is a critical capability of a tracker. (3) Temporal cues are important for stable long-term tracking while they are not considered in still-image detectors. To address the above issues, we first present a simple target-guidance module for guiding the detector to locate target-relevant objects. Then a meta-learner is adopted for the detector to fast learn and adapt a target-distractor classifier online. We further introduce an anchored updating strategy to alleviate the problem of overfitting. The framework is instantiated on SSD and FasterRCNN, the typical one- and two-stage detectors, respectively. Experiments on OTB, UAV123 and NfS have verified our framework and show that our trackers can benefit from deeper backbone networks, as opposed to many recent trackers.
A Text Abstraction Summary Model Based on BERT Word Embedding and Reinforcement Learning

As a core task of natural language processing and information retrieval, automatic text summarization is widely applied in many fields. There are two existing methods for text summarization task at present: abstractive and extractive. On this basis we propose a novel hybrid model of extractive-abstractive to combine BERT (Bidirectional Encoder Representations from Transformers) word embedding with reinforcement learning. Firstly, we convert the human-written abstractive summaries to the ground truth labels. Secondly, we use BERT word embedding as text representation and pre-train two sub-models respectively. Finally, the extraction network and the abstraction network are bridged by reinforcement learning. To verify the performance of the model, we compare it with the current popular automatic text summary model on the CNN/Daily Mail dataset, and use the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics as the evaluation method. Extensive experimental results show that the accuracy of the model is improved obviously.
Group-Group Loss-Based Global-Regional Feature Learning for Vehicle Re-Identification

Vehicle Re-Identification (Re-ID) is challenging because vehicles of the same model commonly show similar appearance. We tackle this challenge by proposing a Global-Regional Feature (GRF) that depicts extra local details to enhance discrimination power in addition to the global context. It is motivated by the observation that, vehicles of same color, maker, and model can be distinguished by their regional difference, e.g., the decorations on the windshields. To accelerate the GRF learning and promote its discrimination power, we propose a Group-Group Loss (GGL) to optimize the distance within and across vehicle image groups. Different from the siamese or triplet loss, GGL is directly computed on image groups rather than individual sample pairs or triplets. By avoiding traversing numerous sample combinations, GGL makes the model training easier and more efficient. Those two contributions highlight this work from previous methods on vehicle Re-ID task, which commonly learn global features with triplet loss or its variants. We evaluate our methods on two large-scale vehicle Re-ID datasets, i.e., VeRi and VehicleID. Experimental results show our methods achieve promising performance in comparison with recent works.
Distributed Bernoulli Filtering for Target Detection and Tracking Based on Arithmetic Average Fusion

We present a distributed Bernoulli filter for tracking a target that may be present or absent in the cluttered surveillance area in unknown time intervals by using a decentralized sensor network. As a key feature of the Bernoulli filter, a parameter referring to the target existence probability is online updated jointly with the target state probability density function. We propose to fuse them in parallel, both in an arithmetic average fusion manner via the standard consensus or flooding scheme. Alternatively, one may communicate and fuse merely target existence probabilities, leading to a communication-inexpensive protocol. We experimentally compare the proposed approaches, based on the Gaussian mixture implementation of the Bernoulli filter, with the cutting-edge geometric average fusion approach based on a Doppler shift sensor network. Advantages are observed in computing efficiency and in dealing with local missed detection.
Blockchain-Based Internet of Vehicles (IoV): An Efficient Secure Ad Hoc Vehicular Networking Architecture

With the transformation of connected vehicles into the Internet of Vehicles (IoV), the time is now ripe for paving the way for the next generation of connected vehicles with novel applications and innovative security measures. The connected vehicles are experiencing prenominal growth in the auto industry, but are still studded with many security and privacy vulnerabilities. Today's IoV applications are part of cyber physical communication systems that collect useful information from thousands of smart sensors associated with the connected vehicles. The technology advancement has paved the way for connected vehicles to share significant information among drivers, auto manufacturers, auto insurance companies and operational and maintenance service providers for various applications. The critical issues in engineering the IoV applications are effective to use of the available spectrum and effective allocation of good channels an opportunistic manner to establish connectivity among vehicles, and the effective utilization of the infrastructure under various traffic conditions. Security and privacy in information sharing are the main concerns in a connected vehicle communication network. Blockchain technology facilitates secured communication among users in a connected vehicles network. Originally, blockchain technology was developed and employed with the cryptocurrency. Bitcoin, to provide increased trust, reliability, and security among users based on peer-to-peer networks for transaction sharing. In this paper, we propose to integrate blockchain technology into ad hoc vehicular networking so that the vehicles can share network resources with increased trust, reliability, and security using distributed access control system and can benefit a wider scope of scalable IoV applications scenarios for decision making. The proposed architecture is the faithful environment for information sharing among connected vehicles. Blockchain technology allows multiple copies of data storage at the distribution cloud. Distributed access control system is significantly more secure than a traditional centralized system. This paper also describes how important of ad hoc vehicular networking in human life, possibilities in real-world implementation and its future trends. The ad hoc vehicular networking may become one of the most trendy networking concepts in the future that has the perspective to bring out much ease human beneficial and secured applications.
Discriminative Feature Transformation for Occluded Pedestrian Detection

Despite promising performance achieved by deep con- volutional neural networks for non-occluded pedestrian de- tection, it remains a great challenge to detect partially oc- cluded pedestrians. Compared with non-occluded pedes- trian examples, it is generally more difficult to distinguish occluded pedestrian examples from background in featue space due to the missing of occluded parts. In this paper, we propose a discriminative feature transformation which en- forces feature separability of pedestrian and non-pedestrian examples to handle occlusions for pedestrian detection. Specifically, in feature space it makes pedestrian exam- ples approach the centroid of easily classified non-occluded pedestrian examples and pushes non-pedestrian examples close to the centroid of easily classified non-pedestrian ex- amples. Such a feature transformation partially compen- sates the missing contribution of occluded parts in feature space, therefore improving the performance for occluded pedestrian detection. We implement our approach in the Fast R-CNN framework by adding one transformation net- work branch. We validate the proposed approach on two widely used pedestrian detection datasets: Caltech and CityPersons. Experimental results show that our approach achieves promising performance for both non-occluded and occluded pedestrian detection.
Automatic modulation recognition of compound signals using a deep multi-label classifier: A case study with radar jamming signals

The modern battlefield is getting more complicated due to the increasing number of different radiation sources as well as their fierce contention (interference) and confrontations (jamming) in the frequency spectrum. A radar, or a communication system usually has to struggle with multiple overlapped signals injected into its receiver to ensure desired system performance. Thus, the requirement for recognition of the modulation type of each constituent signal in a compound signal has emerged as a multiuser automatic modulation classification (mAMC) task in a signal processing field. This paper proposes a deep multi-label based mAMC framework (MLAMC) for compound signals which includes three serial steps, the time-frequency representation image (TFRI) extraction for signal preprocessing, multi-label convolutional neural network (MLCNN) construction for multi-label classification, and multi-decision thresholds optimization for output label decision. By applying the proposed MLAMC method on the compound radar jamming signals as a case study, the effectiveness and superiority of our proposed method are validated in four aspects of a smaller model size, better total performance, good extensibility for unseen signal combinations, and fine-grained analysis for recognition results.
Normalised gamma transformation‐based contrast‐limited adaptive histogram equalisation with colour correction for sand–dust image enhancement

Images captured in the sand–dust weather often suffer from serious colour cast and poor contrast, and this has serious implications for outdoor computer vision systems. To address these problems, a normalised gamma transformation-based contrast-limited adaptive histogram equalisation (CLAHE) with colour correction in Lab colour space for sand–dust image enhancement is proposed in this study. This method consists of image contrast enhancement and image colour correction. To avoid producing new colour deviation, the input sand–dust images are first transformed from red, green, and blue colour space into Lab colour space. Then, the contrast of the lightness component (L channel) of the sand–dust image is enhanced using CLAHE. To avoid unbalanced contrast, as well as to reduce the overincreased brightness caused by CLAHE, a normalised gamma correction function is introduced to CLAHE. After that, the a and b chromatic components are recovered by a grey-world-based colour correction method. Experiments on real sand–dust images demonstrate that the proposed method can obtain the highest percentage of new visible edges for all testing images. The contrast restoration exhibits good colour fidelity and proper brightness.
Explaining Models by Propagating Shapley Values of Local Components

In healthcare, making the best possible predictions with complex models (e.g., neural networks, ensembles/stacks of different models) can impact patient welfare. In order to make these complex models explainable, we present DeepSHAP for mixed model types, a framework for layer wise propagation of Shapley values that builds upon DeepLIFT (an existing approach for explaining neural networks). We show that in addition to being able to explain neural networks, this new framework naturally enables attributions for stacks of mixed models (e.g., neural network feature extractor into a tree model) as well as attributions of the loss. Finally, we theoretically justify a method for obtaining attributions with respect to a background distribution (under a Shapley value framework).
Intent Classification for Dialogue Utterances

In this work, we investigate several machine learning methods to tackle the problem of intent classification for dialogue utterances. We start with bag-of-words in combination with Naïve Bayes. After that, we employ continuous bag-of-words coupled with support vector machines (SVM). Then, we follow long short-term memory (LSTM) networks, which are made bidirectional. The best performing model is hierarchical, such that it can take advantage of the natural taxonomy within classes. The main experiments are a comparison between these methods on an open sourced academic dataset. In the first experiment, we consider the full dataset. We also consider the given subsets of data separately, in order to compare our results with state-of-the-art vendor solutions. In general we find that the SVM models outperform the LSTM models. The former models achieve the highest macro-F1 for the full dataset, and in most of the individual datasets. We also found out that the incorporation of the hierarchical structure in the intents improves the performance.
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos

Existing video-based human pose estimation methods extensively apply large networks onto every frame in the video to localize body joints, which suffer high computational cost and hardly meet the low-latency requirement in realistic applications. To address this issue, we propose a novel Dynamic Kernel Distillation (DKD) model to facilitate small networks for estimating human poses in videos, thus significantly lifting the efficiency. In particular, DKD introduces a light-weight distillator to online distill pose kernels via leveraging temporal cues from the previous frame in a one-shot feed-forward manner. Then, DKD simplifies body joint localization into a matching procedure between the pose kernels and the current frame, which can be efficiently computed via simple convolution. In this way, DKD fast transfers pose knowledge from one frame to provide compact guidance for body joint localization in the following frame, which enables utilization of small networks in video-based pose estimation. To facilitate the training process, DKD exploits a temporally adversarial training strategy that introduces a temporal discriminator to help generate temporally coherent pose kernels and pose estimation results within a long range. Experiments on Penn Action and Sub-JHMDB benchmarks demonstrate outperforming efficiency of DKD, specifically, 10x flops reduction and 2x speedup over previous best model, and its state-of-the-art accuracy.
Secret images transfer in cloud system based on investigating quantum walks in steganography approaches

The central role of the cloud data warehouse is to ensure the security of confidential data that can be achieved via techniques of steganography and cryptography. Amidst the growth of quantum computation, modern data security tools may be cracked because of its structure based on mathematical computations. Quantum walk (QW) acts as a vital role in designing quantum algorithms, which is a universal computational model. Therefore, we allocate the advantages of QWs to design a QWs-based novel image steganography mechanism for cloud systems, which the embedding/extraction process for the secret object is based on quantum concepts. The presented technique has three algorithms of image steganography. The core idea for presenting these algorithms is to embed the sensitive object into the host media without a pre-encryption process for the secret data, which its security and embedding procedures are entirely based on quantum walks. As well opens the door for quantum technologies to be combined with information hiding techniques to achieve better security. The presented technique uses gray-scale or color images as a confidential object to be hidden into the carrier image, as well the embedded object can be any data, e.g. text or audio files and not restricted to image files. Security analysis and experimental results confirm that the suggested algorithms have good visual quality and high-security based on QWs as well high embedding capacity.
VrR-VG: Refocusing Visually-Relevant Relationships

Relationships encode the interactions among individual instances and play a critical role in deep visual scene understanding. Suffering from the high predictability with non-visual information, relationship models tend to fit the statistical bias rather than ``learning" to infer the relationships from images. To encourage further development in visual relationships, we propose a novel method to mine more valuable relationships by automatically pruning visually-irrelevant relationships. We construct a new scene graph dataset named Visually-Relevant Relationships Dataset (VrR-VG) based on Visual Genome. Compared with existing datasets, the performance gap between learnable and statistical method is more significant in VrR-VG, and frequency-based analysis does not work anymore. Moreover, we propose to learn a relationship-aware representation by jointly considering instances, attributes and relationships. By applying the representation-aware feature learned on VrR-VG, the performances of image captioning and visual question answering are systematically improved, which demonstrates the effectiveness of both our dataset and features embedding schema. Both our VrR-VG dataset and representation-aware features will be made publicly available soon.
Scaled Simplex Representation for Subspace Clustering

The self-expressive property of data points, that is, each data point can be linearly represented by the other data points in the same subspace, has proven effective in leading subspace clustering (SC) methods. Most self-expressive methods usually construct a feasible affinity matrix from a coefficient matrix, obtained by solving an optimization problem. However, the negative entries in the coefficient matrix are forced to be positive when constructing the affinity matrix via exponentiation, absolute symmetrization, or squaring operations. This consequently damages the inherent correlations among the data. Besides, the affine constraint used in these methods is not flexible enough for practical applications. To overcome these problems, in this article, we introduce a scaled simplex representation (SSR) for the SC problem. Specifically, the non-negative constraint is used to make the coefficient matrix physically meaningful, and the coefficient vector is constrained to be summed up to a scalar s <; 1 to make it more discriminative. The proposed SSR-based SC (SSRSC) model is reformulated as a linear equality-constrained problem, which is solved efficiently under the alternating direction method of multipliers framework. Experiments on benchmark datasets demonstrate that the proposed SSRSC algorithm is very efficient and outperforms the state-of-the-art SC methods on accuracy. The code can be found at https://github.com/csjunxu/SSRSC.
Two-Qubit Spectroscopy of Spatiotemporally Correlated Quantum Noise in Superconducting Qubits

Noise that exhibits significant temporal and spatial correlations across multiple qubits can be especially harmful to both fault-tolerant quantum computation and quantum-enhanced metrology. However, a complete spectral characterization of the noise environment of even a two-qubit system has not been reported thus far. We propose and experimentally validate a protocol for two-qubit dephasing noise spectroscopy based on continuous control modulation. By combining ideas from spin-locking relaxometry with a statistically motivated robust estimation approach, our protocol allows for the simultaneous reconstruction of all the single-qubit and two-qubit cross-correlation spectra, including access to their distinctive non-classical features. Only single-qubit control manipulations and state-tomography measurements are employed, with no need for entangled-state preparation or readout of two-qubit observables. While our experimental validation uses two superconducting qubits coupled to a shared engineered noise source, our methodology is portable to a variety of dephasing-dominated qubit architectures. By pushing quantum noise spectroscopy beyond the single-qubit setting, our work paves the way to characterizing spatiotemporal correlations in both engineered and naturally occurring noise environments.
Multiuser Physical Layer Authentication in Internet of Things With Data Augmentation

Unlike most of the upper layer authentication mechanisms, the physical (PHY) layer authentication takes advantages of channel impulse response from wireless propagation to identify transmitted packages with low-resource consumption, and machine learning methods are effective ways to improve its implementation. However, the training of the machine-learning-based PHY-layer authentication requires a large number of training samples, which makes the training process time consuming and computationally resource intensive. In this article, we propose a data augmented multiuser PHY-layer authentication scheme to enhance the security of mobile-edge computing system, an emergent architecture in the Internet of Things (IoT). Three data augmentation algorithms are proposed to speed up the establishment of the authentication model and improve the authentication success rate. By combining the deep neural network with data augmentation methods, the performance of the proposed multiuser PHY-layer authentication scheme is improved and the training speed is accelerated, even with fewer training samples. Extensive simulations are conducted under the real industry IoT environment and the figures illustrate the effectiveness of our approach.
An evolving approach to data streams clustering based on typicality and eccentricity data analytics

In this paper we propose an algorithm for online clustering of data streams. This algorithm is called AutoCloud and is based on the recently introduced concept of Typicality and Eccentricity Data Analytics, mainly used for anomaly detection tasks. AutoCloud is an evolving, online and recursive technique that does not need training or prior knowledge about the data set. Thus, AutoCloud is fully online, requiring no offline processing. It allows creation and merging of clusters autonomously as new data observations become available. The clusters created by AutoCloud are called data clouds, which are structures without pre-defined shape or boundaries. AutoCloud allows each data sample to belong to multiple data clouds simultaneously using fuzzy concepts. AutoCloud is also able to handle concept drift and concept evolution, which are problems that are inherent in data streams in general. Since the algorithm is recursive and online, it is suitable for applications that require a real-time response. We validate our proposal with applications to multiple well known data sets in the literature.