Reviewerly/exHarmony huggingface dataset:
Experimental Realization of Nonadiabatic Holonomic Single-Qubit Quantum Gates with Optimal Control in a Trapped Ion

Quantum computation with quantum gates induced by geometric phases is regarded as a promising strategy in fault-tolerant quantum computation, owing to its robustness against operational noise. However, because of the parametric restrictions in previous schemes, the main robust advantage of holonomic quantum gates is reduced. Here, we experimentally demonstrate a solution scheme, obtaining nonadiabatic holonomic single-qubit quantum gates with optimal control in a trapped ${}^{171}{\mathrm{Yb}}^{+}$ ion based on a three-level system with resonant driving, which also has the advantages of rapid evolution and convenient implementation. Compared with previous geometric gates and conventional dynamical gates, the superiority of our scheme is that it is more robust against control amplitude errors, which is confirmed by the gate infidelity as measured by both quantum-process tomography and random benchmarking methods. In addition, we outline how nontrivial two-qubit holonomic gates can also be realized using currently available experimental technology. Thus, our experiment confirms the feasibility of this robust and fast holonomic quantum-computation strategy.
Adapting Object Detectors with Conditional Domain Normalization

Real-world object detectors are often challenged by the domain gaps between different datasets. In this work, we present the Conditional Domain Normalization (CDN) to bridge the domain distribution gap. CDN is designed to encode different domain inputs into a shared latent space, where the features from different domains carry the same domain attribute. To achieve this, we first disentangle the domain-specific attribute out of the semantic features from source domain via a domain embedding module, which learns a domain-vector to characterize the domain attribute information. Then this domain-vector is used to encode the features from target domain through a conditional normalization, resulting in different domains’ features carrying the same domain attribute. We incorporate CDN into various convolution stages of an object detector to adaptively address the domain shifts of different level’s representation. In contrast to existing adaptation works that conduct domain confusion learning on semantic features to remove domain-specific factors, CDN aligns different domain distributions by modulating the semantic features of target domains conditioned on the learned domain-vector of the source domain. Extensive experiments show that CDN outperforms existing methods remarkably on both real-to-real and synthetic-to-real adaptation benchmarks, including 2D image detection and 3D point cloud detection.
Secure large‐scale E‐voting system based on blockchain contract using a hybrid consensus model combined with sharding

The evolution of blockchain-based systems has enabled researchers to develop next-generation e-voting systems. However, the classical consensus method of blockchain, that is, Proof-of-Work, as implemented in Bitcoin, has a significant impact on energy consumption and compromises the scalability, efficiency, and latency of the system. In this paper, we propose a hybrid consensus model (PSC-Bchain) composed of Proof of Credibility and Proof of Stake that work mutually to address the aforementioned problems to secure e-voting systems. Smart contracts are used to provide a trustworthy public bulletin board and a secure computing environment to ensure the accuracy of the ballot outcome. We combine a sharding mechanism with the PSC-Bchain hybrid approach to emphasize security, thus enhancing the scalability and performance of the blockchain-based e-voting system. Furthermore, we compare and discuss the execution of attacks on the classical blockchain and our proposed hybrid blockchain, and analyze the security. Our experiments yielded new observations on the overall security, performance, and scalability of blockchain-based e-voting systems.
Knowledge exploration–exploitation and information technology: crisis management of teaching–learning scenario in the COVID-19 outbreak

Education institution closure is considered as an effective way to prevent coronavirus (COVID-19) pandemic. However, there are several challenges concerning teaching and learning activities (knowledge transfer) in crisis times such a COVID-19. This study aims to provide a holistic view of knowledge transfer and information technology scenario in the education sector during COVID-19 or another uncertain environment. A systematic literature review was conducted by several search techniques such as study selection, synthesis of papers, and reporting the results. A total of 290 peer-reviewed research studies were carried out. These articles were filtered, and 51 relevant articles were selected. The study mainly contributes by illustrating the schema or scenario of effective knowledge transfer of the teaching–learning process in the context of coronavirus crisis through maximising the information technology (IT) tools and knowledge management (KM) approach. Several factors namely IT tools of remote and online learning, knowledge exploration–exploitation, education's knowledge types (tacit-explicit knowledge), and internal–external knowledge in education were identified. The study further provides the new paradigm for future study. Additionally, several concepts and theories were merged, namely SECI theory and ambidexterity view (knowledge exploration and knowledge exploitation). Future studies may extend the research area to different types of organisations and extend this model into empirical tests.
A secure and privacy preserving lightweight authentication scheme for smart-grid communication using elliptic curve cryptography

In last few decades, smart-grid architecture has evolved as a new power service provider that can accomplish regulations of power generation with significant capabilities to monitor power consumption behaviour of the consumers and help in stabilization of the power system. With the huge progress of the delay sensitive smart grid network, it has to encounter various challenges. One of them is cyber-threats to the transmitted messages, control information of the smart-grid, and privacy-preservation of the consumers over smart-grid environment that leave annihilate consequences in real word. One of the key solutions of these cyber-threats is mutual authentication and setting up safe communication between the consumer and the utility-provider. In this paper, we have addressed novel mutual authentication scheme between consumer and substations formed on elliptic curve cryptography with trifling operations for smart-grid environment that is robust against all possible security threats. This scheme also provides a session key negotiation phase to provide secure communication with significantly less communication and computation overheads. The mathematical security testingand formal security testing using broadly-accepted BAN-logic has established that our scheme is robust against all relevant security threats. Further, the simulation result of the proposed schemethrough well-known AVISPA confirms that our scheme is well secure.Finally, to validate the practical ability, the proposed scheme is evaluated using well-accepted network simulator tool NS-2, that shows the proposed scheme is efficient to be implemented in realistic scenario.
Facial expressions contribute more than body movements to conversational outcomes in avatar-mediated virtual environments

Abstract This study focuses on the individual and joint contributions of two nonverbal channels (i.e., face and upper body) in avatar mediated-virtual environments. 140 dyads were randomly assigned to communicate with each other via platforms that differentially activated or deactivated facial and bodily nonverbal cues. The availability of facial expressions had a positive effect on interpersonal outcomes. More specifically, dyads that were able to see their partner’s facial movements mapped onto their avatars liked each other more, formed more accurate impressions about their partners, and described their interaction experiences more positively compared to those unable to see facial movements. However, the latter was only true when their partner’s bodily gestures were also available and not when only facial movements were available. Dyads showed greater nonverbal synchrony when they could see their partner’s bodily and facial movements. This study also employed machine learning to explore whether nonverbal cues could predict interpersonal attraction. These classifiers predicted high and low interpersonal attraction at an accuracy rate of 65%. These findings highlight the relative significance of facial cues compared to bodily cues on interpersonal outcomes in virtual environments and lend insight into the potential of automatically tracked nonverbal cues to predict interpersonal attitudes.
Omni-Sourced Webly-Supervised Learning for Video Recognition

We introduce OmniSource, a novel framework for leveraging web data to train video recognition models. OmniSource overcomes the barriers between data formats, such as images, short videos, and long untrimmed videos for webly-supervised learning. First, data samples with multiple formats, curated by task-specific data collection and automatically filtered by a teacher model, are transformed into a unified form. Then a joint-training strategy is proposed to deal with the domain gaps between multiple data sources and formats in webly-supervised learning. Several good practices, including data balancing, resampling, and cross-dataset mixup are adopted in joint training. Experiments show that by utilizing data from multiple sources and formats, OmniSource is more data-efficient in training. With only 3.5M images and 800K min videos crawled from the internet without human labeling (less than $$2\%$$ of prior works), our models learned with OmniSource improve Top-1 accuracy of 2D- and 3D-ConvNet baseline models by 3.0% and 3.9%, respectively, on the Kinetics-400 benchmark. With OmniSource, we establish new records with different pretraining strategies for video recognition. Our best models achieve 80.4%, 80.5%, and 83.6% Top-1 accuracies on the Kinetics-400 benchmark respectively for training-from-scratch, ImageNet pre-training and IG-65M pre-training.
Industrial Internet-of-Things Security Enhanced With Deep Learning Approaches for Smart Cities

The significant evolution of the Internet of Things (IoT) enabled the development of numerous devices able to improve many aspects in various fields in the industry for smart cities where machines have replaced humans. With the reduction in manual work and the adoption of automation, cities are getting more efficient and smarter. However, this evolution also made data even more sensitive, especially in the industrial segment. The latter has caught the attention of many hackers targeting Industrial IoT (IIoT) devices or networks, hence the number of malicious software, i.e., malware, has increased as well. In this article, we present the IIoT concept and applications for smart cities, besides also presenting the security challenges faced by this emerging area. We survey currently available deep learning (DL) techniques for IIoT in smart cities, mainly deep reinforcement learning, recurrent neural networks, and convolutional neural networks, and highlight the advantages and disadvantages of security-related methods. We also present insights, open issues, and future trends applying DL techniques to enhance IIoT security.
Learning Feature Embeddings for Discriminant Model Based Tracking

After observing that the features used in most online discriminatively trained trackers are not optimal, in this paper, we propose a novel and effective architecture to learn optimal feature embeddings for online discriminative tracking. Our method, called DCFST, integrates the solver of a discriminant model that is differentiable and has a closed-form solution into convolutional neural networks. Then, the resulting network can be trained in an end-to-end way, obtaining optimal feature embeddings for the discriminant model-based tracker. As an instance, we apply the popular ridge regression model in this work to demonstrate the power of DCFST. Extensive experiments on six public benchmarks, OTB2015, NFS, GOT10k, TrackingNet, VOT2018, and VOT2019, show that our approach is efficient and generalizes well to class-agnostic target objects in online tracking, thus achieves state-of-the-art accuracy, while running beyond the real-time speed. Code will be made available.
Blockchain‐based secure information sharing for supply chain management: Optimization assisted data sanitization process

Currently, the furious competitiveness in global markets and speedy improvements in information technology lead to shorter product life cycles, lesser transportation capabilities, and increased demands as well. In most business scenarios, the supply chain network is becoming one of the most vital focusing areas. In the supply chain network, blockchain technology is a promising solution for secure information sharing. However, it is a bit critical in maintaining security at each level of the blockchain and hence the public–private–key cryptography is more commonly preferred. This study intends to construct a new privacy preservation model in the field of supply chain networks based on blockchain technology by undergoing three major phases, namely, (a) data sanitization, (b) key generation, and (c) restoration. Further, the sensitive fields in the original data are selected during the data sanitization phase, and in the key generation phase; the optimal key is generated to hide the selected sensitive fields. The hidden data with the secured key is transferred from the source (manufacturer) to destination (vendor) in the supply chain network via the blockchain. The restoration process takes place in the receiver side with the help of the same key. Among all these data flow methods, the optimal key selection is the critical issue that needs to be overridden to make the data transmission secured. As a novelty, a new optimization algorithm referred to as Whale with New Crosspoint-based Update (WNU), which is the advanced version of Whale Optimization Algorithm (WOA), is developed here to select the optimal key. Finally, the proposed WNU model is analyzed in terms of Hiding Failure (HF) rate, Information Preservation (IP) Rate, and False Rule generation (FR), and Degree of Modification (DM). The proposed secured information sharing in supply chain management (SCM) with blockchain technology will be validated by comparing it over the traditional models in terms of security as well.
Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Abstract Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.
Meta-Sim2: Unsupervised Learning of Scene Structure for Synthetic Data Generation

Procedural models are being widely used to synthesize scenes for graphics, gaming, and to create (labeled) synthetic datasets for ML. In order to produce realistic and diverse scenes, a number of parameters governing the procedural models have to be carefully tuned by experts. These parameters control both the structure of scenes being generated (e.g. how many cars in the scene), as well as parameters which place objects in valid configurations. Meta-Sim aimed at automatically tuning parameters given a target collection of real images in an unsupervised way. In Meta-Sim2, we aim to learn the scene structure in addition to parameters, which is a challenging problem due to its discrete nature. Meta-Sim2 proceeds by learning to sequentially sample rule expansions from a given probabilistic scene grammar. Due to the discrete nature of the problem, we use Reinforcement Learning to train our model, and design a feature space divergence between our synthesized and target images that is key to successful training. Experiments on a real driving dataset show that, without any supervision, we can successfully learn to generate data that captures discrete structural statistics of objects, such as their frequency, in real images. We also show that this leads to downstream improvement in the performance of an object detector trained on our generated dataset as opposed to other baseline simulation methods. Project page: https://nv-tlabs.github.io/meta-sim-structure/ .
Deep Object Tracking with Shrinkage Loss

In this paper, we address the issue of data imbalance in learning deep models for visual object tracking. Although it is well known that data distribution plays a crucial role in learning and inference models, considerably less attention has been paid to data imbalance in visual tracking. For the deep regression trackers that directly learn a dense mapping from input images of target objects to soft response maps, we identify their performance is limited by the extremely imbalanced pixel-to-pixel differences when computing regression loss. This prevents existing end-to-end learnable deep regression trackers from performing as well as discriminative correlation filters (DCFs) trackers. For the deep classification trackers that draw positive and negative samples to learn discriminative classifiers, there exists heavy class imbalance due to a limited number of positive samples when compared to the number of negative samples. To balance training data, we propose a novel shrinkage loss to penalize the importance of easy training data mostly coming from the background, which facilitates both deep regression and classification trackers to better distinguish target objects from the background. We extensively validate the proposed shrinkage loss function on six benchmark datasets, including the OTB-2013, OTB-2015, UAV-123, VOT-2016, VOT-2018 and LaSOT. Equipped with our shrinkage loss, the proposed one-stage deep regression tracker achieves favorable results against state-of-the-art methods, especially in comparison with DCFs trackers. Meanwhile, our shrinkage loss generalizes well to deep classification trackers. When replacing the original binary cross entropy loss with our shrinkage loss, three representative baseline trackers achieve large performance gains, even setting new state-of-the-art results.
Fifer

Datacenters are witnessing a rapid surge in the adoption of serverless functions for microservices-based applications. A vast majority of these microservices typically span less than a second, have strict SLO requirements, and are chained together as per the requirements of an application. The aforementioned characteristics introduce a new set of challenges, especially in terms of container provisioning and management, as the state-of-the-art resource management frameworks, employed in serverless platforms, tend to look at microservice-based applications similar to conventional monolithic applications. Hence, these frameworks suffer from microservice agnostic scheduling and colossal container over-provisioning, especially during workload fluctuations, thereby resulting in poor resource utilization.
A fast density peaks clustering algorithm with sparse search

Given a large unlabeled set of complex data, how to efficiently and effectively group them into clusters remains a challenging problem. Density peaks clustering (DPC) algorithm is an emerging algorithm, which identifies cluster centers based on a decision graph. Without setting the number of cluster centers, DPC can effectively recognize the clusters. However, the similarity between every two data points must be calculated to construct a decision graph, which results in high computational complexity. To overcome this issue, we propose a fast sparse search density peaks clustering (FSDPC) algorithm to enhance the DPC, which constructs a decision graph with fewer similarity calculations to identify cluster centers quickly. In FSDPC, we design a novel sparse search strategy to measure the similarity between the nearest neighbors of each data points. Therefore, FSDPC can enhance the efficiency of the DPC while maintaining satisfactory results. We also propose a novel random third-party data point method to search the nearest neighbors, which introduces no additional parameters or high computational complexity. The experimental results on synthetic datasets and real-world datasets indicate that the proposed algorithm consistently outperforms the DPC and other state-of-the-art algorithms.
Secure Multi-keyword Fuzzy Searches With Enhanced Service Quality in Cloud Computing

With the ever-increasing amount of data resided in a cloud, how to provide users with secure and practical query services has become the key to improve the quality of cloud services. Fuzzy searchable encryption (FSE) is identified as one of the most promising approaches for enabling secure query services, since it allows searching encrypted data by using keywords with spelling errors. However, existing FSE schemes are far from the practical use for the following reasons: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inflexibility.</i> It is hard for them to simultaneously support AND and OR semantics in a multi-keyword query. (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inefficiency.</i> They require sequentially scanning a whole dataset to find matched files, and thus are difficult to apply to a large-scale dataset. (3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Limited robustness.</i> It is difficult for them to resist the linear analysis attack in the known-background model. To fix the above problems, this article proposes matrix-based multi-keyword fuzzy search (M2FS) schemes, which support approximate keyword matching by exploiting the indecomposable property of primes. Specifically, we first present a basic scheme, called M2FS-B, where multiple keywords in a query or a file are constructed as prime-related matrices such that the result of matrix multiplication can be employed to determine the level of matching for different query semantics. Then, we construct an advanced scheme, named M2FS-E, which builds a searchable index as a keyword balanced binary (KBB) tree for dynamic and parallel searches, while adding random noises into a query matrix for enhanced robustness. Extensive analyses and experiments demonstrate the validity of our M2FS schemes.