Reviewerly/exHarmony huggingface dataset:
Multi-Group ObScure Logging (MG-OSLo) A Privacy-Preserving Protocol for Private Web Search

The Web Search Engine (WSE) is a software system used to retrieve data from the web successfully. WSE uses the user's search queries to build the user's profile and provide personalized results. Users' search queries hold identifiable information that could compromise the privacy of the respective user. This work proposes a multi-group distributed privacy-preserving protocol (MG-OSLo) and tries to investigate the state-of-the-art distributed privacy-preserving protocols for computing web search privacy. The MG-OSLo comprises multiple groups in which each group has a fixed number of users. The MG-OSLo measures the impact of the multi-group on the user's privacy. The primary objective of this work is to assess local privacy and profile privacy. It aims at evaluating the impact of group size and group count on a user's privacy. Two grouping approaches are used to group the users in MG-OSLo, i.e. a non-overlapping group design and overlapping group design. The local privacy results reveal that the probability of linking a query to the user depends on the group size and group count. The higher the group size or group count, the lower the likelihood of relating the query to the user. The profile privacy computes the profile obfuscation level using a privacy metric Profile Exposure Level (PEL). Different experiments have been performed to compute the profile privacy of the subset of an AOL query log for two situations: i) self-query submissions allowed and ii) self-query submissions not allowed. The privacy achieved by MG-OSLo is compared with the modern privacy-preserving protocol UUP(e), OSLo, and Co-utile protocols. The results show that the MG-OSLo provided better results as compared to OSLo, UUP, and Co-utile. Similarly, the multi-group has a positive impact on local privacy and user profile privacy.
TTS-by-TTS: TTS-Driven Data Augmentation for Fast and High-Quality Speech Synthesis

In this paper, we propose a text-to-speech (TTS)-driven data augmentation method for improving the quality of a non-autoregressive (AR) TTS system. Recently proposed non-AR models, such as FastSpeech 2, have successfully achieved fast speech synthesis system. However, their quality is not satisfactory, especially when the amount of training data is insufficient. To address this problem, we propose an effective data augmentation method using a well-designed AR TTS system. In this method, large-scale synthetic corpora including text-waveform pairs with phoneme duration are generated by the AR TTS system, and then used to train the target non-AR model. Perceptual listening test results showed that the proposed method significantly improved the quality of the non-AR TTS system. In particular, we augmented five hours of a training database to 179 hours of a synthetic one. Using these databases, our TTS system consisting of a FastSpeech 2 acoustic model with a Parallel WaveGAN vocoder achieved a mean opinion score of 3.74, which is 40% higher than that achieved by the conventional method.
Learning Metric Features for Writer-Independent Signature Verification using Dual Triplet Loss

Handwritten signature has long been a widely accepted biometric and applied in many verification scenarios. However, automatic signature verification remains an open research problem, which is mainly due to three reasons. 1) Skilled forgeries generated by persons who imitate the original writing pattern are very difficult to be distinguished from genuine signatures. It is especially so in the case of offline signatures, where only the signature image is captured as a feature for verification. 2) Most state-of-the-art models are writer-dependent, requiring a specific model to be trained whenever a new user is registered in verification, which is quite inconvenient. 3) Writer-independent models often have unsatisfactory performance. To this end, we propose a novel metric learning based method for offline writer-independent signature verification. Specifically, a dual triplet loss is used to train the model, where two different triplets are constructed for random and skilled forgeries, respectively. Experiments on three alphabet datasets - GPDS Synthetic, MCYT and CEDAR - show that the proposed method achieves competitive or superior performance to the state-of-the-art methods. Experiments are also conducted on a new offline Chinese signature dataset - CSIG-WHU, and the results show that the proposed method has a high feasibility on character-based signatures.
Method for large-scale structured-light system calibration

We propose a multi-stage calibration method for increasing the overall accuracy of a large-scale structured light system by leveraging the conventional stereo calibration approach using a pinhole model. We first calibrate the intrinsic parameters at a near distance and then the extrinsic parameters with a low-cost large-calibration target at the designed measurement distance. Finally, we estimate pixel-wise errors from standard stereo 3D reconstructions and determine the pixel-wise phase-to-coordinate relationships using low-order polynomials. The calibrated pixel-wise polynomial functions can be used for 3D reconstruction for a given pixel phase value. We experimentally demonstrated that our proposed method achieves high accuracy for a large volume: sub-millimeter within 1200( H ) × 800 ( V ) × 1000( D ) mm 3 .
Immersive storytelling and affective ethnography in virtual reality

Virtual reality (VR) storytelling, particularly in its nonfictional modes, promises a sensory immersion among others whose lives and ways of being a privileged viewer might not otherwise experience. In this essay, by focusing on the Emmy-nominated 2018 VR film Traveling While Black, we explore how the immersive power of VR storytelling can enact ethnographic encounters premised less on the impulse to extract meaning from other people and their ways of life than on the sensory and affective force of being with others in an unfolding experience of both similitude and difference. Without wishing to overstate VR’s empathy-inducing potential, we suggest that by situating viewers at a paradoxical threshold between proximity and distance, the affective power of VR derives in part from a narrative form capable of fostering nonappropriative relations.
A Study of Impact of Cloud Computing and Artificial Intelligence on Banking Services, Profitability and Operational Benefits

Banks have seen a convergence in end-customer banking interactions over the last decade, owing to IT-enabled solutions and services such as Core Banking, Online Banking, Mobile Banking, Wallet, Wallets Kiosk Banking. The evolution of IT in the banking industry supports both banks and their clients. Banks' business models are now centered on delivering consumer joy via IT-enabled solutions and services. Cloud technology allows banks to follow a digital paradigm for providing novel channels, reducing TAT to market new offerings, fulfilling consumer expectations, and adhering to regulatory requirements at a lower cost. In constantly evolving technical paradigms, cloud-based solutions offer a higher value proposition to IT solutions and services. Device administrators can remotely assemble, upload, customize, and execute virtual tools to manage a business solution using cloud infrastructure. Furthermore, cloud IT technology can be scaled up or down at any time based on planned use and specifications without incurring high costs. To assess the impact of cloud computing and artificial intelligence in operational and service advantage Confirmatory factor analysis (CFA) technique is used. The thesis demonstrates how well the measured variables reflect the number of constructs and whether the estimation hypothesis is confirmed or rejected.
DTM-Based Morphometric Analysis of Scoria Cones of the Chaîne des Puys (France)—The Classic and a New Approach

Scoria cones are favorite targets of morphometric research. However, in-depth, DTM-based studies have appeared only recently, and new methods are being developed. This study provides a classic evaluation of the cones of Chaîne des Puys (Auvergne, France) as well as introduces a more detailed and statistics-based set of properties. Beside the classic parameters, a sectorial approach is applied to the slope distributions calculated from high resolution DTMs for 25 cones of different lithologies, in order to study the various (a)symmetries of the cones. DTM-based morphometric characteristics have been found to be different from classic descriptors, whereas the sectorial approach describes correctly the more and the less regular shapes. The distribution of interquartile ranges of the sectorial slope distributions is skewed. Sectorization discriminates various types of symmetries: there are almost circular cones, but the majority are elongated and have some asymmetry. The relationship between size parameters reflects the lithology, rather than the age of the cone. The attempt to relate morphometric parameters to age data is only partially successful: although there is a certain trend, within the same lithological group, subtle but possibly systematic trends can be detected for decreasing morphometric values (e.g., slope) with the age. The regression models indicate various outcomes. Further work is needed to understand all the diverse parameters, especially the lithology–shape relationship, and how symmetry is connected to different factors.
A New Evolutionary Computation Framework for Privacy-Preserving Optimization

Evolutionary computation (EC) is a kind of advanced computational intelligence (CI) algorithm and advanced artificial intelligence (AI) algorithm. EC algorithms have been widely studied for solving optimization and scheduling problems in various real-world applications, which act as one of the Big Three in CI and AI, together with fuzzy systems and neural networks. Even though EC has been fast developed in recent years, there is an assumption that the algorithm designer can obtain the objective function of the optimization problem so that they can calculate the fitness values of the individuals to follow the “survival of the fittest” principle in natural selection. However, in a real-world application scenario, there is a kind of problem that the objective function is privacy so that the algorithm designer can not obtain the fitness values of the individuals directly. This is the privacy-preserving optimization problem (PPOP) where the assumption of available objective function does not check out. How to solve the PPOP is a new emerging frontier with seldom study but is also a challenging research topic in the EC community. This paper proposes a rank-based cryptographic function (RCF) to protect the fitness value information. Especially, the RCF is adopted by the algorithm user to encrypt the fitness values of all the individuals as rank so that the algorithm designer does not know the exact fitness information but only the rank information. Nevertheless, the RCF can protect the privacy of the algorithm user but still can provide sufficient information to the algorithm designer to drive the EC algorithm. We have applied the RCF privacy-preserving method to two typical EC algorithms including particle swarm optimization (PSO) and differential evolution (DE). Experimental results show that the RCF-based privacy-preserving PSO and DE can solve the PPOP without performance loss.
Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios

Safe and adaptable motion planning for autonomous vehicles remains an open problem in urban environments, where the variability of situations and behaviors may become intractable using rule-based approaches. This work proposes a use-case-independent motion planning algorithm that generates a set of possible trajectories and selects the best of them according to a merit function that combines longitudinal comfort, lateral comfort, safety and utility criteria. The system was tested in urban scenarios on simulated and real environments, and the results show that different driving styles can be achieved according to the priorities set in the merit function, always meeting safety and comfort parameters imposed by design.
Recognition of 3D emotional facial expression based on handcrafted and deep feature combination

Facial emotion recognition (FER) methods have been proposed mainly using 2D images. These methods suffer from many problems caused by the difficult conditions of unconstrained environments such as light conditions and view variations. In this paper, we aim to recognize the emotional facial expressions independently of their identity using the 3D data and 2D depth images. Since the 3D FER is a very fine-grained recognition task, mapping the 3D images into 2D depth images may lack some geometric characteristics of the expressive face and decay the FER performance. Convolutional Neural Networks (CNN), however, have been successfully applied to the 2D depth images and improved handcrafted-based methods in computer vision and pattern recognition applications. For this reason, we combine in this paper two types of features; handcrafted and deep learning features and prove their complementarity for 3D FER. Favorably, covariance descriptors have proven a very good ability to combine features from different types into a compact representation. Therefore, we propose to use the covariance matrices of features (handcrafted and deep ones), instead of the features independently. Since covariance matrices belong to one of the manifold space types, formed by SPD (Symmetric Positive Definite) matrices, we mainly focus on the generalization of the RBF kernel to the manifold space for 3D FER using a supervised SVM classification. The achieved performance of the proposed method on the Bosphorus and BU-3DFE datasets outperforms similar state-of-the-arts.
Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes

We describe a Plug-and-Play controllable language generation framework, Plug-and-Blend, that allows a human user to input multiple control codes (topics).In the context of automated story generation, this allows a human user loose or fine grained control of the topics that will appear in the generated story, and can even allow for overlapping, blended topics.We show that our framework, working with different generation models, controls the generation towards given continuous-weighted control codes while keeping the generated sentences fluent, demonstrating strong blending capability.
Certified Robustness to Word Substitution Attack with Differential Privacy

The robustness and security of natural language processing (NLP) models are significantly important in real-world applications.In the context of text classification tasks, adversarial examples can be designed by substituting words with synonyms under certain semantic and syntactic constraints, such that a well-trained model will give a wrong prediction.Therefore, it is crucial to develop techniques to provide a rigorous and provable robustness guarantee against such attacks.In this paper, we propose WordDP to achieve certified robustness against word substitution attacks in text classification via differential privacy (DP).We establish the connection between DP and adversarial robustness for the first time in the text domain and propose a conceptual exponential mechanism-based algorithm to formally achieve the robustness.We further present a practical simulated exponential mechanism that has efficient inference with certified robustness.We not only provide a rigorous analytic derivation of the certified condition but also experimentally compare the utility of WordDP with existing defense algorithms.The results show that WordDP achieves higher accuracy and more than 30⇥ efficiency improvement over the state-of-theart certified robustness mechanism in typical text classification tasks.
Specific Emitter Identification Based on Software-Defined Radio and Decision Fusion

Specific emitter identification (SEI) uses the unintentional modulation information carried by the emitter waveform, i.e., radio frequency fingerprints, to realize the matching identification of the received signal and its corresponding emitter. We propose an emitter identification scheme based on deep learning (DL). The received signal is subjected to time-varying filtered empirical mode decomposition (tvf-EMD). The obtained intrinsic mode functions (IMFs) are subjected to Hilbert transformation to obtain a 3D-Hilbert spectrum, using amplitude frequency aggregation characteristics obtained by projection to express the nonstationary information of the signal and calculate its bispectral diagonal slice as the second feature. A squeeze-and-excitation block (SEB) is introduced to a convolutional neural network (CNN) to focus on the effective area in the feature map, and support vector machine (SVM) is used to fuse the decision information of the two types of features. We collect four types of steady-state signals on a software-defined radio (SDR) sensor platform based on GNU Radio and universal software radio peripherals (USRP), and study the algorithm performance. The results show that our scheme has strong generalization, high recognition accuracy, and broad application prospects compared with other SEI methods.
A Broad-Scale Method for Estimating Natural Background Levels of Dissolved Components in Groundwater Based on Lithology and Anthropogenic Pressure

The Water Framework Directive (WFD) requires EU member states to assess the chemical status of groundwater bodies, a status defined according to threshold values for harmful elements and based on/the natural background level (NBL). The NBL is defined as the expected value of the concentration of elements naturally present in the environment. The aim of this study is to propose a methodology that will be broadly applicable to a wide range of conditions at the regional and national scale. Using a statistical approach, the methodology seeks to determine NBLs for SO4, As, Cd, Cr, Cu, Ni, Zn, and F based on the lithology of aquifers from which groundwater monitoring data were collected. The methodology was applied in six EU countries to demonstrate validity for a wide range of European regions. An average concentration was calculated for each parameter and chosen water point and linked to a lithology. Based on the dataset created, significant differences between lithologies and pressure categories (urban, agricultural, industrial, and mining) were tested using a nonparametric test. For each parameter, 90th percentiles were calculated to provide an estimation of the maximum natural concentrations possible for each lithology.
Drone-SCNet: Scaled Cascade Network for Crowd Counting on Drone Images

Crowd management is an essential task to ensure the safety and smoothness of any event. Using novel technologies, including surveillance cameras, drones, and the communication techniques between security agents, the control of the crowd has become easier. Yet, the usage of such techniques is still not effective. This article presents an approach for crowd counting from drones’ data. The proposed method exploits the dilated and scaled neural networks for feature extraction and density crowd estimation. A new dataset named ViseDrone2020 is used for training and testing of the proposed method. In order to compare the proposed method, we implemented 10 state-of-the-art methods and trained it on this dataset. The experiments show that the proposed model is more efficient for crowd counting compared with the implemented methods. Also, some of these methods give relatively accurate results in terms of estimated crowd numbers as well as the quality of estimated density maps. The proposed model was further evaluated on nondrone datasets, namely, UCF_QNRF, UCF_CC_50, and shanghaiTech_(A, B), which produced satisfying results for all the datasets. In addition, the proposed method was tested on noisy images where Gaussian noise and salt-and-pepper noise was applied to all the images of the dataset with a noise density of 0.02. The analysis showed that the quality of the density map as well as the quantity of the crowd count estimation is comparatively better than other existing methods without the presence of noise. After the acceptance, the code of the proposed method as well as 10 implemented methods will be available at: [Online]. Available: https://github.com/elharroussomar/Crowd-Conting-on-Drone-Data.
Process-extraction-based text similarity measure for emergency response plans

Measuring text similarity is an important challenge for Chinese emergency response plans, which describe complex response processes involving four level responses and collaborative interactions among multiple departments and roles. Most existing studies focus on lexical semantic features and their results are not applicable to Chinese emergency response plans. In order to reflect process semantics of Chinese emergency response plans, a novel text similarity measure is proposed. First, an emergency response process including four sub-processes is extracted from an emergency response plan, and each emergency response sub-process is represented by three types of response tasks, i.e. regular task, message sending task and message receiving task. Next, vector representations of process elements including task statements, roles and departments are generated. Then, response task vectors that are represented by a combination of process element vectors are generated and three response task set vectors are used for emergency response sub-process representation. Finally, the similarity between sub-processes is computed as the average cosine similarity of three pairs of response task set vectors and the text similarity is the average similarity of four sub-processes. A real-world data set is collected for experimental evaluation and the results illustrate that the proposed approach performs well in distinguishing topics and levels of emergency response plans.
Variable Rate ROI Image Compression Optimized for Visual Quality

With the development of compression technology, objective metrics (e.g. PSNR, MS SSIM) cannot satisfy our need, especially in extreme low bit-rate compression, thus more attention is being paid on perceptual quality. Since people have different standards for objective evaluation. For this reason, we simplify the topic with the consideration that people will strict more on interested region, so a ROI(region of interest) based image compression model is proposed with team name ‘Sub201’. For the ROI, we expect its reconstructed part to be more accurate, while the background, server distortion is tolerable, and fake texture can be generated. Firstly, a weighted mask from saliency map is used. Secondly, to balance the difference of ROI and background area, different losses are applied separately. What’s more, GAN and LPIPS are utilized to generate more texture in background. At last, variable rate method is adopted to realize rate control, and it performs well with perceptual metric. Experiment shows that our method can achieve better performance both in visual and objective quality.