Reviewerly/exHarmony huggingface dataset:
Security of the digital transformation

In the early days of computation the focus was mainly on designing, developing, maintaining, and administering infrastructures and information systems housed in data centers. To this extend, security was traditionally organized around the basic technical components (e.g. data center facilities). The point was that an associated security activity was mostly separated from a business context and in general executed by the technical staff. Security was not fully understood by other audiences because the computer terminologies were frequently used. When security elements (e.g. logical access protocols used for identification, authentication, authorization) became part of the financial statement audit, its context became clearer, and it was conducted for external auditors. However, the presented outcome of the work was not completely interpretable for these practitioners as again, it was mainly reported in Information Technology (IT) jargon, and was not linked with the financial statement either. With the emergence the Sarbanes–Oxley Act (SOX) and the fundamental role of IT in relation hereto, the context of security suddenly changed to a great extent. The audience extended as compliance, including security, became the dominating item on the agenda of many C-levels (e.g. CFOs).
Efficient Graph Encryption Scheme for Shortest Path Queries

Graph encryption schemes (introduced by [Chase and Kamara, 2010]) have been receiving growing interest across various disciplines due to their attractive tradeoff between functionality, efficiency and privacy. In this paper, we advance the state of the art on encrypted graph search by providing an efficient graph encryption scheme for shortest path queries. The preprocessing time and space and the query time are proportional to those for building and querying the search structure for the unencrypted graph. Hence, the overhead of providing structured encryption is asymptotically optimal. We implement our scheme and experimentally validate its performance on real world networks. Furthermore, we extend our scheme to support verifiability.
A large and evolving cognate database

Abstract We present CogNet , a large-scale, automatically-built database of sense-tagged cognates —words of common origin and meaning across languages. CogNet is continuously evolving: its current version contains over 8 million cognate pairs over 338 languages and 35 writing systems, with new releases already in preparation. The paper presents the algorithm and input resources used for its computation, an evaluation of the result, as well as a quantitative analysis of cognate data leading to novel insights on language diversity. Furthermore, as an example on the use of large-scale cross-lingual knowledge bases for improving the quality of multilingual applications, we present a case study on the use of CogNet for bilingual lexicon induction in the framework of cross-lingual transfer learning.
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation Using Structurally Regularized Deep Clustering

Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose distribution diverges from the target one. Mainstream UDA methods strive to learn domain-aligned features such that classifiers trained on the source features can be readily applied to the target ones. Although impressive results have been achieved, these methods have a potential risk of damaging the intrinsic data structures of target discrimination, raising an issue of generalization particularly for UDA tasks in an inductive setting. To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption. Technically, we propose a hybrid model of Structurally Regularized Deep Clustering, which integrates the regularized discriminative clustering of target data with a generative one, and we thus term our method as H-SRDC. Our hybrid model is based on a deep clustering framework that minimizes the Kullback-Leibler divergence between the distribution of network prediction and an auxiliary one, where we impose structural regularization by learning domain-shared classifier and cluster centroids. By enriching the structural similarity assumption, we are able to extend H-SRDC for a pixel-level UDA task of semantic segmentation. We conduct extensive experiments on seven UDA benchmarks of image classification and semantic segmentation. With no explicit feature alignment, our proposed H-SRDC outperforms all the existing methods under both the inductive and transductive settings. We make our implementation codes publicly available at https://github.com/huitangtang/H-SRDC.
A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.
Mesh Total Generalized Variation for Denoising

Recent studies have shown that the Total Generalized Variation (TGV) is highly effective in preserving sharp features as well as smooth transition variations for image processing tasks. However, currently there is no existing work that is suitable for applying TGV to 3D data, in particular, triangular meshes. In this article, we develop a novel framework for discretizing second-order TGV on triangular meshes. Further, we propose a TGV-based variational method for the denoising of face normal fields on triangular meshes. The TGV regularizer in our method is composed of a first-order term and a second-order term, which are automatically balanced. The first-order term allows our TGV regularizer to locate and preserve sharp features, while the second-order term allows our regularizer to recognize and recover smoothly curved regions. To solve the optimization problem, we introduce an efficient iterative algorithm based on variable-splitting and augmented Lagrangian method. Extensive results and comparisons on synthetic and real scanning data validate that the proposed method outperforms the state-of-the-art visually and numerically.
PhD Learning: Learning with Pompeiu-hausdorff Distances for Video-based Vehicle Re-Identification

Vehicle re-identification (re-ID) is of great significance to urban operation, management, security and has gained more attention in recent years. However, two critical challenges in vehicle re-ID have primarily been underestimated, i.e., 1): how to make full use of raw data, and 2): how to learn a robust re-ID model with noisy data. In this paper, we first create a video vehicle re-ID evaluation benchmark called VVeRI-901 and verify the performance of video-based re-ID is far better than static image-based one. Then we propose a new Pompeiu-hausdorff distance (PhD) learning method for video-to-video matching. It can alleviate the data noise problem caused by the occlusion in videos and thus improve re-ID performance significantly. Extensive empirical results on video-based vehicle and person reID datasets, i.e., VVeRI-901, MARS and PRID2011, demonstrate the superiority of the proposed method. The source code of our proposed method is available at https://github.com/emdata-ailab/PhD-Learning.
Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification

Recently, video-based person re-identification (re-ID) has drawn increasing attention in compute vision community because of its practical application prospects. Due to the inaccurate person detections and pose changes, pedestrian misalignment significantly increases the difficulty of feature extraction and matching. To address this problem, in this paper, we propose a Reference-Aided Part-Aligned (RAPA) framework to disentangle robust features of different parts. Firstly, in order to obtain better references between different videos, a pose-based reference feature learning module is introduced. Secondly, an effective relation-based part feature disentangling module is explored to align frames within each video. By means of using both modules, the informative parts of pedestrian in videos are well aligned and more discriminative feature representation is generated. Comprehensive experiments on three widely-used benchmarks, i.e. iLIDS-VID, PRID-2011 and MARS datasets verify the effectiveness of the proposed framework. Our code will be made publicly available.
Removing Disparate Impact on Model Accuracy in Differentially Private Stochastic Gradient Descent

In differentially private stochastic gradient descent (DPSGD), gradient clipping and random noise addition disproportionately affect underrepresented and complex classes and subgroups. As a consequence, DPSGD has disparate impact: the accuracy of a model trained using DPSGD tends to decrease more on these classes and subgroups vs. the original, non-private model. If the original model is unfair in the sense that its accuracy is not the same across all subgroups, DPSGD exacerbates this unfairness. In this work, we study the inequality in utility loss due to differential privacy, which compares the changes in prediction accuracy w.r.t. each group between the private model and the non-private model. We analyze the cost of privacy w.r.t. each group and explain how the group sample size along with other factors is related to the privacy impact on group accuracy. Furthermore, we propose a modified DPSGD algorithm, called DPSGD-F, to achieve differential privacy, equal costs of differential privacy, and good utility. DPSGD-F adaptively adjusts the contribution of samples in a group depending on the group clipping bias such that differential privacy has no disparate impact on group accuracy. Our experimental evaluation shows the effectiveness of our removal algorithm on achieving equal costs of differential privacy with satisfactory utility.
A systematic study of the class imbalance problem: Automatically identifying empty camera trap images using convolutional neural networks

Camera traps, which are widely used in wildlife surveys, often produce massive images, and many of them are empty images not contain animals. Using the deep learning model to automatically identify the empty camera trap images can reduce the workload of manual classification significantly. However, the performance of deep learning models is easily affected by the class imbalance problem of training datasets, which is a common problem for actual wildlife survey projects. Almost all previous studies on empty image recognition used down-sampling or oversampling methods to eliminate the effect of class imbalance on the performance of deep learning classifiers. The class imbalance problem has been systematically studied in the field of traditional image recognition, yet very limited research is available in the context of identifying camera trap images taken from highly cluttered natural scenes. This study systematically studied the impact of class imbalance on model performance when using a deep learning model to identify empty camera trap images. Then we proposed the construction method of training sets of the deep learning model when the data set has different class imbalance levels. Based on results from our experiments we concluded that (i) the class imbalance showed little effect on the performance of the model when the empty image ratio (EIR) in the data set was between 10% and 70%, so the training sets can be randomly built without changing the class distribution; (ii) we recommended using oversampling to partially eliminate class imbalance to reduce omission errors when the EIR of the data set exceeded 70%; (iii) when the EIRs of the training set and the test set were close, the overall error, omission error, and commission error of the model were relatively smaller, and the model tended to achieve a better overall performance; (iv) the omission and commission errors can be adjusted by changing the percentage of empty images in the training set.
Bayesian-Based Automated Model Generation Method for Neural Network Modeling of Microwave Components

Automated model generation (AMG) has become a popular technique for systematically developing neural network models by avoiding manual trial-and-errors. However, when the initial number of hidden neurons is far from the optimal value, the existing AMG methods usually take a relatively large amount of CPU time to find the optimal structure. To deal with this problem, for the first time, Bayesian-based formulation is introduced into the AMG method. The proposed Bayesian-based AMG method can efficiently determine the minimum number of hidden neurons with maximum accuracy during the model development process. The proposed method can greatly reduce CPU time for model generation in comparison with the existing AMG technique. A microwave filter example is used to demonstrate the proposed method.
Rpattack: Refined Patch Attack on General Object Detectors

Nowadays, general object detectors like YOLO and Faster R-CNN as well as their variants are widely exploited in many applications. Many works have revealed that these detectors are extremely vulnerable to adversarial patch attacks. The perturbed regions generated by previous patch-based attack works on object detectors are very large which are not necessary for attacking and perceptible for human eyes. To generate much less but more efficient perturbation, we propose a novel patch-based method for attacking general object detectors. Firstly, we propose a patch selection and refining scheme to find the pixels which have the greatest importance for attack and remove the inconsequential perturbations gradually. Then, for a stable ensemble attack, we balance the gradients of detectors to avoid over-optimizing one of them during the training phase. Our RPAttack can achieve an amazing missed detection rate of 100% for both Yolo v4 and Faster R-CNN while only modifies 0.32% pixels on VOC 2007 test set. Our code is available at https://github.com/VDIGPKU/RPAttack.
How emotions affect restaurant digital ordering experiences: a comparison of three ordering methods

Purpose The purpose of this study is to examine the role of consumption emotion in the digital food-ordering experience by comparing the performances of the three digital ordering methods in an experimental design. Design/methodology/approach A research model was developed based on the Feelings-as-Information (FaI) theory and the expectancy-disconfirmation theory. A 3 × 2 between-subjects lab experiment was conducted to compare the three digital ordering methods (online, mobile or chatbot) in two different types of restaurants (quick-service or full-service). Findings The results indicate that the chatbot ordering method evoked more negative emotions and less positive emotions than the other two methods. The online ordering method worked the best for quick-service restaurants, whereas the mobile ordering method was most suitable for full-service restaurants. Both positive and negative emotions (comfort and annoyance) significantly mediated the relationships between the ordering method and internal responses (satisfaction and behavioral intention). Only one negative emotion (anger) significantly mediated the relationship between the ordering method and order amount. Originality/value This is the first study that attempts to explore and compare consumers’ emotional responses resulting from restaurant digital ordering experiences in the context of the three food-ordering methods. The use of the FaI theory strengthens the theoretical foundation of research on emotion in the hospitality field. This study also pioneers the application of chatbot technology in the restaurant industry.
Confidence-Guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection

Video salient object detection (VSOD) aims to locate and segment the most attractive object by exploiting both spatial cues and temporal cues hidden in video sequences. However, spatial and temporal cues are often unreliable in real-world scenarios, such as low-contrast foreground, fast motion, and multiple moving objects. To address these problems, we propose a new framework to adaptively capture available information from spatial and temporal cues, which contains Confidence-guided Adaptive Gate (CAG) modules and Dual Differential Enhancement (DDE) modules. For both RGB features and optical flow features, CAG estimates confidence scores supervised by the IoU between predictions and the ground truths to re-calibrate the information with a gate mechanism. DDE captures the differential feature representation to enrich the spatial and temporal information and generate the fused features. Experimental results on four widely used datasets demonstrate the effectiveness of the proposed method against thirteen state-of-the-art methods.
Recognition of UAV Video Signal Using RF Fingerprints in the Presence of WiFi Interference

This paper investigates the problem of unmanned aerial vehicle (UAV) recognition in the presence of WiFi interference using passive radio frequency (RF) detection. The proposed method relies on machine learning based RF recognition and considers the scenario in the bandwidth of the video signal (VS) and WiFi are identical. Our machine learning strategy involves extracting 31 features from the WiFi signal and the UAV VS, which are then input to the classifier. Among the 31 features, 30 are statistical in the time and frequency domain, while the remaining one involves the effective subcarrier feature. We evaluate four different machine learning (ML) classifier variants and demonstrate through simulation and experiments that the proposed method can accurately recognize UAV VS in the presence of WiFi interference. We also improve the feature-vector compactness and reduce the 31-feature vector to a 6-feature vector composed of the most significant features and demonstrate that the recognition performance of random forest method (RandF) classifier is not compromised. The most appealing performance for the 6-feature vector case is attained by the RandF, managing a recognition rate of 100% in the indoor experiment and 96.26% in the 2 km outdoor experiment. The recognition rate of the four ML classifiers is larger than 95.52% in the 2 km outdoor experiment, which is better than other UAV detection methods such as radar, acoustic and video.
Information-Driven Path Planning for UAV With Limited Autonomy in Large-Scale Field Monitoring

This article presents a novel information-based mission planner for a drone tasked to monitor a spatially distributed dynamical phenomenon. For the sake of simplicity, the area to be monitored is discretized. The insight behind the proposed approach is that, due to the spatiotemporal dependencies of the observed phenomenon, one does not need to collect data on the entire area, which is one of the main limiting factors in unmanned aerial vehicle (UAV) applications due to their limited autonomy. In fact, unmeasured states can be estimated using an estimator, such as a Kalman filter. In this context, the planning problem becomes the one of generating a flight plan that maximizes the quality of the state estimation while satisfying the flight constraints (e.g., flight time). The first result of this article is the formulation of this problem as a special orienteering problem where the cost function is a measure of the quality of the estimation. This results in a mixed-integer semidefinite formulation, which can be optimally solved for small instances of the problem. For larger instances, a heuristic is proposed, which provides suboptimal results. Simulations numerically demonstrate the capabilities and efficiency of the proposed path-planning strategy. We believe that this approach has the potential to increase dramatically the area that a drone can monitor, thus increasing the number of applications where monitoring with drones can become economically convenient. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article was motivated by the problem of performing large-scale field monitoring activities using unmanned aerial vehicle (UAV), which at the moment is very time-consuming and limits the definitive adoption of UAVs for this kind of activities. This problem is caused by the limited autonomy of commercial UAVs and the lack of systematic ways to plan missions so as to maximize the amount of information collected. This work starts from the observation that, in many applications, the phenomena that one wants to observe have dynamics and statistical properties. Accordingly, data that are not directly measured can be estimated with a characterizable observation error. In this article, we develop the theoretical foundations for an information-based path planning and define the problem of designing the optimal mission as an optimization problem based on the knowledge of the monitored phenomenon. The presented results have the potential to dramatically improve the effectiveness of drones for monitoring applications.
Human De-occlusion: Invisible Perception and Recovery for Humans

In this paper, we tackle the problem of human deocclusion which reasons about occluded segmentation masks and invisible appearance content of humans. In particular, a two-stage framework is proposed to estimate the invisible portions and recover the content inside. For the stage of mask completion, a stacked network structure is devised to refine inaccurate masks from a general instance segmentation model and predict integrated masks simultaneously. Additionally, the guidance from human parsing and typical pose masks are leveraged to bring prior information. For the stage of content recovery, a novel parsing guided attention module is applied to isolate body parts and capture context information across multiple scales. Besides, an Amodal Human Perception dataset (AHP) is collected to settle the task of human de-occlusion. AHP has advantages of providing annotations from real-world scenes and the number of humans is comparatively larger than other amodal perception datasets. Based on this dataset, experiments demonstrate that our method performs over the state-of-the-art techniques in both tasks of mask completion and content recovery. Our AHP dataset is available at https://sydney0zq.github.io/ahp/.