Reviewerly/exHarmony huggingface dataset:
Retrieving Ideal Precision in Noisy Quantum Optical Metrology

Quantum metrology employs quantum effects to attain a measurement precision surpassing the limit achievable in classical physics. However, it was previously found that the precision returns the shot-noise limit (SNL) from the ideal Zeno limit (ZL) due to the photon loss in quantum metrology based on Mech-Zehnder interferometer. Here, we find that not only the SNL can be beaten, but also the ZL can be asymptotically recovered in long-encoding-time condition when the photon dissipation is exactly studied in its inherent non-Markovian manner. Our analysis reveals that it is due to the formation of a bound state of the photonic system and its dissipative noise. Highlighting the microscopic mechanism of the dissipative noise on the quantum optical metrology, our result supplies a guideline to realize the ultrasensitive measurement in practice by forming the bound state in the setting of reservoir engineering.
Analysis of high-dimensional genomic data employing a novel bio-inspired algorithm

Over the last decade, there has been a rapid growth in the generation and analysis of the genomics data. Though the existing data analysis methods are capable of handling a particular problem, they cannot guarantee to solve all problems with different nature. Therefore, there always lie a scope of a new algorithm to solve a problem which cannot be efficiently solved by the existing algorithms. In the present work, a novel hybrid approach is proposed based on the improved version of a recently developed bio-inspired optimization technique, namely, salp swarm algorithm (SSA) for microarray classification. Initially, the Fisher score filter is employed to pre-select a subset of relevant genes from the original high-dimensional microarray dataset. Later, a weighted-chaotic SSA (WCSSA) is proposed for the simultaneous optimal gene selection and parameter optimization of the kernel extreme learning machine (KELM) classifier. The proposed scheme is experimented on both binary-class and multi-class microarray datasets. An extensive comparison is performed against original SSA-KELM, particle swarm optimized-KELM (PSO-KELM), and genetic algorithm-KELM (GA-KELM). Lastly, the proposed method is also compared against the results of sixteen existing techniques to emphasize its capacity and competitiveness to successfully reduce the number of original genes by more than 98%. The experimental results show that the genes selected by the proposed method yield higher classification accuracy compared to the alternative techniques. The performance of the proposed scheme demonstrates its effectiveness in terms of number of selected genes (NSG), accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and F-measure. The proposed WCSSA-KELM method is validated using a ten-fold cross-validation technique.
A multi-image super-resolution algorithm applied to thermal imagery

Although thermal imaging is a widely used technique in many applications and is under continuous development, one of its limits is the relatively coarse spatial resolution. Nevertheless, in the last years, a number of super-resolution algorithms have been developed which allow to enhance the resolution of the images. They can be divided in two main different categories: single-image or multiple image-based algorithms. In this work, a multiple image-based algorithm for the super-resolution was implemented, tested and applied to terrestrial thermal imaging with the aim to overcome the limitation of the low resolution. In particular, the method relies on the use of many images acquired from slightly different positions to obtain, thanks to the redundancy of observations, a super-resolution frame having an upsampling factor of four. Several tests were performed on synthetic datasets, and the accuracy of the obtained super-resolution images was investigated. Moreover, an original algorithm capable to identify gross errors during the image registration phase, which is one of the crucial phases, is presented and its reliability assessed. Results showed the effectiveness of the proposed method on both common visible images and thermal infrared ones, since discrepancies between reconstructed and reference values are reduced by 18 and 25% respectively, when compared with a conventional bicubic algorithm. Finally, the proposed method was tested on a case study concerning the thermal survey of the façade of a historical building in Bologna (Palazzo D’Accursio). A dataset of real thermal frames was acquired and a super-resolution image of the subject was generated through the developed algorithm. Strengths and weaknesses of the method were analysed and discussed in the paper.
Game Theoretic Motion Planning for Multi-robot Racing

This paper presents a real-time game theoretic planning algorithm for a robotic vehicle (e.g. a drone or a car) to race competitively against multiple opponents on a racecourse. Our algorithm plans receding horizon trajectories to maximally advance the robot along the racecourse, while taking into account the opponents’ intentions and responses. We build on our previous work (Spica et al Theoretic approach to autonomous two-player drone racing, 2018 [1]), which only considered racing with two robots. Our algorithm uses an iterative best response scheme with a new sensitivity term to find approximate Nash equilibria in the space of the multiple robots’ trajectories. The sensitivity term seeks Nash equilibria that are advantageous to the ego robot. We demonstrate our approach through extensive multi-player racing simulations, where our planner exhibits rich behaviors such as blocking, overtaking, nudging or threatening, similar to what we observe from racing with human participants. Statistics also reveal that our game theoretic planner largely outperforms a baseline model predictive controller that does not consider the opponents’ responses. Experiments are conducted with four quadrotor aerial robots to validate our approach in real time and with physical robot hardware.
Evidence collection and forensics on social networks: Research challenges and directions

Social Media (SM) evidence is a new and rapidly emerging frontier in digital forensics. The trail of digital information on social media, if explored correctly, can offer remarkable support in criminal investigations. However, exploring social media for potential evidence and presenting these proofs in court is not a straightforward task. Social media evidence must be collected by a legally and scientifically appropriate forensic process and also coincide with the privacy rights of individuals. Following the legal process is a challenging task for legal practitioners and investigators due to the highly dynamic and heterogeneous nature of social media. Forensic investigators can conduct effective investigations and collect legally sound evidence efficiently if they are provided with sophisticated tools to manage the diversity and size of social media content. This article explains the current state of evidence acquisition, admissibility, and jurisdiction in social media forensics. It also describes the immediate challenges for the collection, analysis, presentation, and validation of social media evidence in legal proceedings. Furthermore, the research gaps in the domain and few research objectives with potential research directions are presented.
Knowledge representation learning with entity descriptions, hierarchical types, and textual relations

Knowledge representation learning methods usually only utilize triple facts, or just consider one kind of extra information. In this paper, we propose a multi-source knowledge representation learning (MKRL) model, which can combine entity descriptions, hierarchical types, and textual relations with triple facts. Specifically, for entity descriptions, a convolutional neural network is used to get representations. For hierarchical type, weighted hierarchy encoders are used to construct the projection matrixes of hierarchical types, and the projection matrix of an entity combines all hierarchical type projection matrixes of the entity with the relation-specific type constrains. For textual relations, a sentence-level attention mechanism is employed to get representations. We evaluate MKRL model on knowledge graph completion task with dataset FB15k-237, and experimental results demonstrate that our model outperforms the state-of-the-art methods, which indicates the effectiveness of multi-source information for knowledge representation.
Sarcasm Detection with Self-matching Networks and Low-rank Bilinear Pooling

Sarcasm is sophisticated linguistic expression and is commonly observed on social media and e-commerce platforms. Failure to detect sarcastic expressions in natural language processing tasks, such as opinion mining and sentiment analysis, leads to poor model performance. Traditional approaches rely heavily on discrete handcrafted features and will incur enormous human costs. It was not until recent that scholars began to employ neural networks to address these limitations and have achieved new state-of-the-art performance. In this work, we propose a novel self-matching network to capture sentence ”incongruity” information by exploring word-to-word interactions. In particular, we calculate the joint information in each word-to-word pair in the input sentence to build a self-matching attention vector, based on which we attend the sentence and build its representation vector. Such a network allows sentence to match within itself word by word and cater to the words of conflict sentiments. In addition, we incorporate a bi-directional LSTM network into our proposed network to retain compositional information. We concatenate incongruity information and compositional information through a Low-rank Bilinear Pooling method to control for potential information redundancy without losing discriminative power. Experiment results on publicly available datasets demonstrate that our model significantly outperforms extant baselines on standard evaluation metrics including precision, recall, F1 score and accuracy.
Box-level segmentation supervised deep neural networks for accurate and real-time multispectral pedestrian detection

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e.g., daytime and nighttime). In this paper, we present a novel box-level segmentation supervised learning framework for accurate and real-time multispectral pedestrian detection by incorporating features extracted in visible and infrared channels. Specifically, our method takes pairs of aligned visible and infrared images with easily obtained bounding box annotations as input and estimates accurate prediction maps to highlight the existence of pedestrians. It offers two major advantages over the existing anchor box based multispectral detection methods. Firstly, it overcomes the hyperparameter setting problem occurred during the training phase of anchor box based detectors and can obtain more accurate detection results, especially for small and occluded pedestrian instances. Secondly, it is capable of generating accurate detection results using small-size input images, leading to improvement of computational efficiency for real-time autonomous driving applications. Experimental results on KAIST multispectral dataset show that our proposed method outperforms state-of-the-art approaches in terms of both accuracy and speed.
Spear phishing in a barrel: Insights from a targeted phishing campaign

Executives in many industries have fallen prey to socially engineered attacks known as spear phishing. Using highly targeted emails, social engineers trick victims into performing unintended actions by masquerading as legitimate actors. To shed light on effective spear phishing training, we conducted a multi-round experiment. Our results indicate that training users with individual loss messaging might increase the effectiveness of the training. Additionally, we found potential evidence that organizational training can lead to increased overall spear phishing awareness, even for those not directly trained. Despite these promising results, however, individuals' susceptibility to highly targeted spear phishing attacks remains troubling for practitioners and researchers.
Autonomous Robotic Exploration by Incremental Road Map Construction

In this paper, we propose a novel path planning framework for autonomous exploration in unknown environments using a mobile robot. A graph structure is incrementally constructed along with the exploration process. The structure is the road map that represents the topology of the explored environment. To construct the road map, we design a sampling strategy to get random points in the explored environment uniformly. A global path from the current location of the robot to the target area can be found on this road map efficiently. We utilize a lazy collision checking method that only checks the feasibility of the generated global path to improve the planning efficiency. The feasible global path is further optimized with our proposed trajectory optimization method considering the motion constraints of the robot. This mechanism can facilitate the path cost evaluation for the next best view selection. In order to select the next best target region, we propose a utility function that takes into account both the path cost and the information gain of a candidate target region. Moreover, we present a target reselection mechanism to evaluate the target region and reduce the extra path cost. The efficiency and effectiveness of our approach are demonstrated using a mobile robot in both simulation and real experimental studies.
Comparison of visual fatigue caused by head-mounted display for virtual reality and two-dimensional display using objective and subjective evaluation

This study aimed to evaluate objective and subjective visual fatigue experienced before and after performing a visual task while using a head-mounted display for virtual reality (VR-HMD) and two-dimensional (2D) display. Binocular fusion maintenance (BFM) was measured using a binocular open-view Shack-Hartmann wavefront aberrometer equipped with liquid crystal shutters. Twelve healthy subjects performed the BFM test and completed a questionnaire regarding subjective symptoms before and after performing a visual task that induces low visually induced motion sickness (VIMS). BFM (p = .87) and total subjective eye symptom scores (p = .38) were not significantly different between both groups, although these values were significantly lower after the visual task than before the task within both groups (p < .05). These findings suggest that visual fatigue after using a VR-HMD is not significantly different from that after using a 2D display in the presence of low-VIMS VR content. Practitioner summary: Objective and subjective evaluation of visual fatigue were not significantly different with the use of a head-mounted display for virtual reality (VR-HMD) and two-dimensional display. These results should be valuable not only to engineers developing VR content but also to researchers involved in the evaluation of visual fatigue using VR-HMD. Abbreviations: VR: virtual reality; VR-HMD: head-mounted display for virtual reality; BFM: binocular fusion maintenance; BWFA: binocular open-view Shack-Hartmann wavefront aberrometer.
A Sanitization Approach to Secure Shared Data in an IoT Environment

Internet of Things (IoT) supports high flexibility and convenience in several applications because the IoT devices continuously transfer, share, and exchange data without human intervention.During shared or exchanged progress of data, security and privacy threats result because the published or shared data mainly corresponds to a raw dataset, and an attacker can easily obtain details on the shared data in an IoT environment.In the paper, we present a sanitization approach by adopting the hierarchical-cluster method to hide confidential information while still discovering useful and meaningful information in the sanitized dataset.The multi-objective particle swarm optimization framework and an algorithm termed as HCMPSO are utilized to balance four side effects, namely, hiding failure, missing cost, artificial cost, and database dissimilarity (Dis), and thereby provide optimized solutions for data sanitization.The experiments are performed to compare the performance of the designed HCMPSO with that of the single-objective cpGA2DT and multi-objective NSGA-II-based approaches.As shown in the results, the designed HCMPSO exhibits good performance in terms of hiding failure, and thus the most confidential information is hidden after the sanitization process.The shared or published data in IoT is secured.Furthermore, the designed sanitization algorithm achieves reasonable results in terms of missing cost, artificial cost, and Dis.
Image-Based Geo-Localization Using Satellite Imagery

The problem of localization on a geo-referenced satellite map given a query ground view image is useful yet remains challenging due to the drastic change in viewpoint. To this end, in this paper we work on the extension of our earlier work on the Cross-View Matching Network (CVM-Net) (Hu et al. in IEEE conference on computer vision and pattern recognition (CVPR), 2018) for the ground-to-aerial image matching task since the traditional image descriptors fail due to the drastic viewpoint change. In particular, we show more extensive experimental results and analyses of the network architecture on our CVM-Net. Furthermore, we propose a Markov localization framework that enforces the temporal consistency between image frames to enhance the geo-localization results in the case where a video stream of ground view images is available. Experimental results show that our proposed Markov localization framework can continuously localize the vehicle within a small error on our Singapore dataset.
DELTA: A deep dual-stream network for multi-label image classification

Multi-label image classification problem is one of the most important and fundamental problems in computer vision. In an image with multiple labels, the objects usually locate at various positions with different scales and poses. Moreover, some labels are associated with the entire image instead of a small region. Therefore, both the global and local information are important for classification. To effectively extract and make full use of these information, in this paper, we present a novel deep Dual-stream nEtwork for the muLTi-lAbel image classification task, DELTA for short. As its name indicates, it is composed of two streams, i.e., the Multi-Instance network and the Global Priors network. The former is used to extract the multi-scale class-related local instances features by modeling the classification problem in a multi-instance learning framework. The latter is devised to capture the global priors from the input image as the global information. These two streams are fused by the final fusion layer. In this way, DELTA can extract and make full use of both the global and local information for classification. Extensive experiments on three benchmark datasets, i.e., PASCAL VOC 2007, PASCAL VOC 2012 and Microsoft COCO, demonstrate that DELTA significantly outperforms several state-of-the-art methods. Moreover, DELTA can automatically locate the key image patterns that trigger the labels.
Towards efficient medical lesion image super-resolution based on deep residual networks

Super-resolution reconstruction helps doctors clearly observe the details of medical lesion images and increases the likelihood of the disease being diagnosed and cured. In this paper, we propose an efficient medical lesion image super-resolution method based on deep residual networks. First, a multi-scale super-resolution reconstruction model based on a deep residual network is trained. Second, an easy-to-use interface is designed. Third, a multi-scale super-resolution reconstruction model is used to reconstruct different types of medical lesion images with different scales and calculate their peak signal-to-noise ratios and structural similarity index values. The experimental results show that the proposed super-resolution reconstruction method achieves superior performance over the other methods compared in this work.
Towards Efficient U-Nets: A Coupled and Quantized Approach

In this paper, we propose to couple stacked U-Nets for efficient visual landmark localization. The key idea is to globally reuse features of the same semantic meanings across the stacked U-Nets. The feature reuse makes each U-Net light-weighted. Specially, we propose an order-K coupling design to trim off long-distance shortcuts, together with an iterative refinement and memory sharing mechanism. To further improve the efficiency, we quantize the parameters, intermediate features, and gradients of the coupled U-Nets to low bit-width numbers. We validate our approach in two tasks: human pose estimation and facial landmark localization. The results show that our approach achieves state-of-the-art localization accuracy but using ~ 70% fewer parameters, ~ 30% less inference time, ~ 98% less model size, and saving ~ 75% training memory compared with benchmark localizers.
Privacy-Preserving Reversible Information Hiding Based on Arithmetic of Quadratic Residues

The phenomenal advances of cloud computing technology have given rise to the research area of privacy-preserving signal processing, which aims to preserve information privacy even when the signals are processed in an insecure environment. Privacy-preserving information hiding is a multidisciplinary study that has opened up a great deal of intriguing real-life applications, such as data exfiltration prevention, data origin authentication, and electronic data management. Information hiding is a practice of embedding intended messages into carrier signals through imperceptible alterations. In view of some content-sensitive scenarios, however, the ability to preserve perfect copies of signals is of crucial importance, for instance, considering the inadequate robustness of recent artificial intelligence-aided automated systems against noise perturbations. Reversibility of information hiding systems is a valuable property that permits recovery of original carrier signals if desired. In this paper, we propose a novel privacy-preserving reversible information hiding scheme inspired by the mathematical concept of quadratic residues. A quadratic residue has four (not necessarily distinct) square roots, which enables payloads to be encoded in a dynamic fashion. Furthermore, a predictive model based upon the projection theorem is devised to assist carrier signal recovery. The experimental results showed significant improvements over the state-of-the-art methods with regard to capacity, fidelity, and reversibility.
Distributed parallel deep learning of Hierarchical Extreme Learning Machine for multimode quality prediction with big process data

In this work, the distributed and parallel Extreme Learning Machine (dp-ELM) and Hierarchical Extreme Learning Machine (dp-HELM) are proposed for multimode process quality prediction with big data. The efficient ELM algorithm is transformed into the distributed and parallel modeling form according to the MapReduce framework. Since the deep learning network structure of HELM is more accurate than the single layer of ELM in feature representation, the dp-HELM is further developed through decomposing the ELM-based Auto-encoders (ELM-AE) of deep hidden layers into a loop of MapReduce jobs. Additionally, the multimode issue is solved through the “divide and rule” strategy. The distributed and parallel K-means (dp-K-means) is utilized to divide the process modes, which are further trained in a synchronous parallel way by dp-ELM and dp-HELM. Finally, the Bayesian model fusion technique is utilized to integrate the local models for online prediction. The proposed algorithms are deployed on a Hadoop MapReduce computing cluster and the feasibility and efficiency are illustrated through building a real industrial quality prediction model with big process data.