Reviewerly/exHarmony huggingface dataset:
Pauli component erasing quantum channels

Decoherence of quantum systems is described by quantum channels. However, a complete understanding of such channels, especially in the multiparticle setting, is still an ongoing difficult task. We propose the family of quantum maps that preserve or completely erase the components of a multiqubit system in the basis of Pauli strings, which we call Pauli component erasing maps. For the corresponding channels, it is shown that the preserved components can be interpreted as a finite vector subspace, from which we derive several properties and complete the characterization. Moreover, we show that the obtained family of channels forms a semigroup and derive its generators. We use this simple structure to determine physical implementations and connect the obtained family of channels with Markovian processes.
Camera and LiDAR-based point painted voxel region-based convolutional neural network for robust 3D object detection

Most of the three-dimensional (3D) object detection methods based on LiDAR point cloud data achieve relatively high performance in general cases. However, when the LiDAR points have noise or some corruptions, the detection performance can be severely affected. We propose a 3D object detection method that combines point cloud information with two-dimensional (2D) semantic segmentation information to enhance the feature representation for difficult cases, such as sparse, noisy, and partially absent data. Motivated by the Pointpainting techniques, we designed an early-stage fusion method based on a Voxel region-based convolutional neural network (R-CNN) architecture. The 2D semantic segmentation scores obtained by the Pointpainting techniques are appended to the raw point cloud data. The voxel-based features and 2D semantic information improve the performance in detecting instances when the point cloud is corrupted. In addition, we also designed a multiscale hierarchical region of interest pooling strategy that reduced the computational cost of Voxel R-CNN by at least 43%. Our method shows competitive results with the state-of-the-art methods on the standard KITTI dataset. In addition, three corrupted KITTI datasets, KITTI sparse (KITTI-S), KITTI jittering (KITTI-J), and KITTI dropout (KITTI-D), were used for robustness testing. With the noisy LiDAR points, our proposed point painted Voxel R-CNN achieved superior detection performance over that of the baseline Voxel R-CNN for the moderate case, with a notable improvement of 11.13% in average precision (AP) on the 3D object detection and 14.3% in AP on the bird’s eye view object detection.
Classifying Illegal Activities on Tor Network using Hybrid Technique

With the freedom offered by the Deep Web, people have the opportunity to express themselves freely and discretely, and sadly, this is one of the reasons why people carry out illicit activities there. In this work, a novel dataset for Dark Web active domains known as crawler-DB is presented. To build the crawler-DB, the Onion Routing Network (Tor) was sampled, and then a web crawler capable of crawling into links was built. The link addresses that are gathered by the crawler are then classified automatically into five classes. The algorithm built in this study demonstrated good performance as it achieved an accuracy of 85%. A popular text representation method was used with the proposed crawler-DB crossed by two different supervised classifiers to facilitate the categorization of the Tor concealed services. The results of the experiments conducted in this study show that using the Term Frequency-Inverse Document Frequency (TF-IDF) word representation with a linear support vector classifier achieves 91% of 5 folds cross-validation accuracy when classifying a subset of illegal activities from crawler-DB, while the accuracy of Naïve Bayes was 80.6%. The good performance of the linear SVC might support potential tools to help the authorities in the detection of these activities. Moreover, outcomes are expected to be significant in both practical and theoretical aspects, and they may pave the way for further research.
Application of New Media Big Data in Visual Performance of Axonometric Illustration

With the rapid development of Internet technology today, especially in the fields of new media, digital TV, digital movies, smartphones, smart hardware, mobile Internet and big data, new media forms bring us a shocking sensory experience. Axonometric illustration, a traditional and modern artistic expression, is becoming more and more attractive in the new media era. Today, with the continuous progress of science and technology, the continuous innovation of thinking, and the rapid improvement of public aesthetics, traditional axonometric illustrations have been unable to adapt to the gradual improvement of people’s needs for artistic aesthetics. This paper will start from the research background of “new media” and combine the characteristics of the axonometric illustration industry itself. On the basis of analyzing related theories at home and abroad, the literature research method and algorithm-driven method are used comprehensively. It can not only effectively improve the visual effect of axonometric illustration by 12.3% but also solve the problems and challenges faced by the current China’s axonometric illustration industry.
Deep scene-scale material estimation from multi-view indoor captures

The movie and video game industries have adopted photogrammetry as a way to create digital 3D assets from multiple photographs of a real-world scene. But photogrammetry algorithms typically output an RGB texture atlas of the scene that only serves as visual guidance for skilled artists to create material maps suitable for physically-based rendering. We present a learning-based approach that automatically produces digital assets ready for physically-based rendering, by estimating approximate material maps from multi-view captures of indoor scenes that are used with retopologized geometry. We base our approach on a material estimation Convolutional Neural Network (CNN) that we execute on each input image. We leverage the view-dependent visual cues provided by the multiple observations of the scene by gathering, for each pixel of a given image, the color of the corresponding point in other images. This image-space CNN provides us with an ensemble of predictions, which we merge in texture space as the last step of our approach. Our results demonstrate that the recovered assets can be directly used for physically-based rendering and editing of real indoor scenes from any viewpoint and novel lighting. Our method generates approximate material maps in a fraction of time compared to the closest previous solutions.
Unified Multimodal Model with Unlikelihood Training for Visual Dialog

The task of visual dialog requires a multimodal chatbot to answer sequential questions from humans about image content. Prior work performs the standard likelihood training for answer generation on the positive instances (involving correct answers). However, the likelihood objective often leads to frequent and dull outputs and fails to exploit the useful knowledge from negative instances (involving incorrect answers). In this paper, we propose a Unified Multimodal Model with UnLikelihood Training, named UniMM-UL, to tackle this problem. First, to improve visual dialog understanding and generation by multi-task learning, our model extends ViLBERT from only supporting answer discrimination to holding both answer discrimination and answer generation seamlessly by different attention masks. Specifically, in order to make the original discriminative model compatible with answer generation, we design novel generative attention masks to implement the autoregressive Masked Language Modeling (autoregressive MLM) task. And to attenuate the adverse effects of the likelihood objective, we exploit unlikelihood training on negative instances to make the model less likely to generate incorrect answers. Then, to utilize dense annotations, we adopt different fine-tuning methods for both generating and discriminating answers, rather than just for discriminating answers as in the prior work. Finally, on the VisDial dataset, our model achieves the best generative results (69.23 NDCG score). And our model also yields comparable discriminative results with the state-of-the-art in both single-model and ensemble settings (75.92 and 76.17 NDCG scores).
Analysis of Structured Data in Biomedicine Using Soft Computing Techniques and Computational Analysis

In the field of biomedicine, enormous data are generated in a structured and unstructured form every day. Soft computing techniques play a major role in the interpretation and classification of the data to make appropriate decisions for making policies. The field of medical science and biomedicine needs efficient soft computing-based methods which can process all kind of data such as structured data, categorical data, and unstructured data to generate meaningful outcome for decision-making. The soft-computing methods allow clustering of similar data, classification of data, predictions from big-data analysis, and decision-making on the basis of analysis of data. A novel method is proposed in the paper using soft-computing methods where clustering mechanisms and classification mechanisms are used to process the biomedicine data for productive outcomes. Fuzzy logic and C-means clustering are devised as a collaborative approach to analyze the biomedicine data by reducing the time and space complexity of the clustering solutions. This research work is considering categorical data, numeric data, and structured data for the interpretation of data to make further decisions. Timely decisions are very important especially in the field of biomedicine because human health and human lives are involved in this field and delays in decision-making may cause threats to human lives. The COVID-19 situation was a recent example where timely diagnosis and interpretations played significant roles in saving the lives of people. Therefore, this research work has attempted to use soft computing techniques for the successful clustering of similar medical data and for quicker interpretation of data to support the decision-making processes related to medical fields.
Facial Recognition System Based on Genetic Algorithm Improved ROI-KNN Convolutional Neural Network

The facial recognition system is an application tool that uses artificial intelligence technology and biometrics technology to analyze and recognize the facial feature information of the human face. It is widely used in various fields, such as attendance and access control management in schools and companies, identity monitoring in stations and stores, facial recognition for fugitive criminals, and facial payment on mobile terminals. However, due to the short development time of the facial recognition system, the facial recognition system has the problem of low recognition accuracy when the recognized object is not cooperative. Although some scholars have proposed the region of interest (ROI)-K nearest neighbor algorithm (KNN) convolutional neural network theory by using the ROI and KNN and applied it to face recognition, the facial recognition system based on ROI-KNN convolutional neural network did not solve the problems of insufficient facial recognition accuracy and insufficient security. Under the conditions of insufficient illumination, excessive expression change, occlusion, high similarity of different individuals, and dynamic recognition, the recognition effect of the facial recognition system based on the ROI-KNN convolutional neural network is relatively limited. Therefore, to make the recognition accuracy of the facial recognition system higher and to make the facial recognition system play a greater role in the social and economic fields, this paper used the adaptive quantum genetic algorithm, the improved marker line graph genetic algorithm, and the feature weight value genetic algorithm to study the facial recognition system of the ROI-KNN convolutional neural network. The research results showed that after improving the ROI-KNN convolutional neural network based on the genetic algorithm, the recognition accuracy of the facial recognition system was increased by 4.99%, the recognition speed was increased by 7.46%, and the recognition security was increased by 2.66%.
Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

Due to the difficulty in solving combinatorial optimization problems, it is necessary to improve the performance of the algorithms by improving techniques to deal with complex optimizations. This research addresses the metaheuristics of marriage in honey-bees optimization (MBO) based on the behavior of bees. The current study proposes a technique for solving combinatorial optimization problems within proper computation times. The purpose of this study focuses on the travelling salesperson problem and the application of chaotic methods in important sections of the MBO metaheuristic. Three experiments were conducted to measure the efficiency and quality of the solutions: (1) MBO with chaos to generate initial solutions (MBO2); (2) MBO with chaos in the workers (MBO3); and (3) MBO with chaos to generate initial solutions and the workers (MBO4). The application of chaotic functions in MBO was significantly better at solving the travelling salesperson problem.
Ûiiti: A treatment for ecological experience in mobile network culture

Ûiiti (‘the treatment’) is an Android phone app created by artist duo Greenman Muleh Mbillo, in Kenya, and Dani Ploeger, in the Netherlands. The work is a high-tech iteration of the nzevu , a ritual instrument of the Kenyan Akamba tribe. It transforms a smartphone from a networking technology with complex modes of interaction into a technology of transcendence with a minimal user interface. Through a constellation of symbolic imagery and the performance of repetitive sonic patterns, the work aims to evoke a heightened experience of the user’s immediate lifeworld.
Edge-assisted deep video denoising and super-resolution for real-time surveillance at night

Video surveillance cameras have been extensively deployed over the last few years. In case of incidents such as natural disaster rescue, it provides vital guidance in real-time. However, due to the limited camera hardware and network bandwidth, noise are observed especially at night and the resolution is low. To tackle these two issues, we design and implement EADV, an Edge-Assisted Deep Video denoising and super-resolution system for real-time surveillance at night. We demonstrate the video quality enhancement using a camera, a displayer, and an edge server. The low-quality video captured by the camera is enhanced by the server and shown on the displayer. The enhanced real-time video is smooth and the performance uplift is observable.
NestFL

In this paper, we present NestFL, a learning-efficient FL framework for edge computing, which can jointly improve the training efficiency and achieve personalization. Specifically, NestFL takes the runtime resources of the edge devices into consideration and assigns each device a sparse-structured subnetwork by progressively performing the structured pruning. During training, only the updates of these subnetworks are transmitted to the central server. Additionally, these generated subnetworks adopt a structure- and parameter-sharing mechanism, making themselves nested inside a multi-capacity global model. In doing so, the overall communication and computation costs can be significantly reduced, and each device can learn a personalized model without introducing extra parameters. Furthermore, a weighted aggregation mechanism is designed to improve the training performance and maximally preserve personalization.
Dually Enhanced Propensity Score Estimation in Sequential Recommendation

Sequential recommender systems train their models based on a large amount of implicit user feedback data and may be subject to biases when users are systematically under/over-exposed to certain items. Unbiased learning based on inverse propensity scores (IPS), which estimate the probability of observing a user-item pair given the historical information, has been proposed to address the issue. In these methods, propensity score estimation is usually limited to the view of item, that is, treating the feedback data as sequences of items that interacted with the users. However, the feedback data can also be treated from the view of user, as the sequences of users that interact with the items. Moreover, the two views can jointly enhance the propensity score estimation. Inspired by the observation, we propose to estimate the propensity scores from the views of user and item, called Dually Enhanced Propensity Score Estimation (DEPS). Specifically, given a target user-item pair and the corresponding item and user interaction sequences, DEPS first constructs a time-aware causal graph to represent the user-item observational probability. According to the graph, two complementary propensity scores are estimated from the views of item and user, respectively, based on the same set of user feedback data. Finally, two transformers are designed to make use of the two propensity scores and make the final preference prediction. Theoretical analysis showed the unbiasedness and variance of DEPS. Experimental results on three publicly available benchmarks and a proprietary industrial dataset demonstrated that DEPS can significantly outperform the state-of-the-art baselines.
Personal Health Train Architecture with Dynamic Cloud Staging

Scientific advances, especially in the healthcare domain, can be accelerated by making data available for analysis. However, in traditional data analysis systems, data need to be moved to a central processing unit that performs analyses, which may be undesirable, e.g. due to privacy regulations in case these data contain personal information. This paper discusses the Personal Health Train (PHT) approach in which data processing is brought to the (personal health) data rather than the other way around, allowing (private) data accessed to be controlled, and to observe ethical and legal concerns. This paper introduces the PHT architecture and discusses the data staging solution that allows processing to be delegated to components spawned in a private cloud environment in case the (health) organisation hosting the data has limited resources to execute the required processing. This paper shows the feasibility and suitability of the solution with a relatively simple, yet representative, case study of data analysis of Covid-19 infections, which is performed by components that are created on demand and run in the Amazon Web Services platform. This paper also shows that the performance of our solution is acceptable, and that our solution is scalable. This paper demonstrates that the PHT approach enables data analysis with controlled access, preserving privacy and complying with regulations such as GDPR, while the solution is deployed in a private cloud environment.
Exploring children’s exposure to voice assistants and their ontological conceptualizations of life and technology

Digital Voice Assistants (DVAs) have become a ubiquitous technology in today's home and childhood environments. Inspired by (Bernstein and Crowley, J Learn Sci 17:225-247, 2008) original study (n = 60, age 4-7 years) on how children's ontological conceptualizations of life and technology were systematically associated with their real-world exposure to robotic entities, the current study explored this association for children in their middle childhood (n = 143, age 7-11 years) and with different levels of DVA-exposure. We analyzed correlational survey data from 143 parent-child dyads who were recruited on 'Amazon Mechanical Turk' (MTurk). Children's ontological conceptualization patterns of life and technology were measured by asking them to conceptualize nine prototypical organically living and technological entities (e.g., humans, cats, smartphones, DVAs) with respect to their biology, intelligence, and psychology. Their ontological conceptualization patterns were then associated with their DVA-exposure and additional control variables (e.g., children's technological affinity, demographic/individual characteristics). Compared to biology and psychology, intelligence was a less differentiating factor for children to differentiate between organically living and technological entities. This differentiation pattern became more pronounced with technological affinity. There was some evidence that children with higher DVA-exposure differentiated more rigorously between organically living and technological entities on the basis of psychology. To the best of our knowledge, this is the first study exploring children's real-world exposure to DVAs and how it is associated with their conceptual understandings of life and technology. Findings suggest although psychological conceptualizations of technology may become more pronounced with DVA-exposure, it is far from clear such tendencies blur ontological boundaries between life and technology from children's perspective.The online version contains supplementary material available at 10.1007/s00146-022-01555-3.
HTF: Homogeneous Tree Framework for Differentially Private Release of Large Geospatial Datasets with Self-tuning Structure Height

Mobile apps that use location data are pervasive, spanning domains such as transportation, urban planning, and healthcare. Important use cases for location data rely on statistical queries, e.g., identifying hotspots where users work and travel. Such queries can be answered efficiently by building histograms. However, precise histograms can expose sensitive details about individual users. Differential privacy (DP) is a mature and widely adopted protection model, but most approaches for DP-compliant histograms work in a data-independent fashion, leading to poor accuracy. The few proposed data-dependent techniques attempt to adjust histogram partitions based on dataset characteristics, but they do not perform well due to the addition of noise required to achieve DP. In addition, they use ad hoc criteria to decide the depth of the partitioning. We identify density homogeneity as a main factor driving the accuracy of DP-compliant histograms, and we build a data structure that splits the space such that data density is homogeneous within each resulting partition. We propose a self-tuning approach to decide the depth of the partitioning structure that optimizes the use of privacy budget. Furthermore, we provide an optimization that scales the proposed split approach to large datasets while maintaining accuracy. We show through extensive experiments on large-scale real-world data that the proposed approach achieves superior accuracy compared to existing approaches.