Reviewerly/exHarmony huggingface dataset:
Efficient Encoding of the Weighted MAX $$k$$-CUT on a Quantum Computer Using QAOA

Abstract The weighted MAX $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -CUT problem consists of finding a k -partition of a given weighted undirected graph G ( V , E ), such that the sum of the weights of the crossing edges is maximized. The problem is of particular interest as it has a multitude of practical applications. We present a formulation of the weighted MAX $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -CUT suitable for running the quantum approximate optimization algorithm (QAOA) on noisy intermediate scale quantum (NISQ) devices to get approximate solutions. The new formulation uses a binary encoding that requires only $$|V|\log _2k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mrow><mml:mo>|</mml:mo><mml:mi>V</mml:mi><mml:mo>|</mml:mo></mml:mrow><mml:msub><mml:mo>log</mml:mo><mml:mn>2</mml:mn></mml:msub><mml:mi>k</mml:mi></mml:mrow></mml:math> qubits. The contributions of this paper are as follows: (i) a novel decomposition of the phase-separation operator based on the binary encoding into basis gates is provided for the MAX $$k$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -CUT problem for $$k&gt;2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>k</mml:mi><mml:mo>&gt;</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math> . (ii) Numerical simulations on a suite of test cases comparing different encodings are performed. (iii) An analysis of the resources (number of qubits, CX gates) of the different encodings is presented. (iv) Formulations and simulations are extended to the case of weighted graphs. For small k and with further improvements when k is not a power of two, our algorithm is a possible candidate to show quantum advantage on NISQ devices.
Securing Collaborative Environment Monitoring in Smart Cities Using Blockchain Enabled Software-Defined Internet of Drones

The Internet of Drones (IoD) is a layered network control architecture, which is having a revolutionary impact on the monitoring and preservationvof the environment. Large-scale drone-assisted environmental monitoring can provide a better perspective and high-quality data by monitoring the operation of critical components of smart cities. However, with the continuous expansion of IoD scale and the increase of multi-drone collaboration tasks, the large-scale drone-assisted service in smart cities monitoring will inevitably encounter the problem of relay and transfer of drone control. Lack of a trust collaboration paradigm between drone controllers will bring huge security challenges to real-time monitoring the environment, collaboration of tasks, data, location privacy of drones, and so on. To address this important issue in IoD, this article proposes a paradigm that uses smart contracts and blockchain to ensure trusted collaboration between controllers of software defined IoD (SD-IoD). First, we propose a novel SD-IoD architecture to enhance the support for heterogeneity and flexibility of IoD for monitoring of the environment. Second, we propose a controller consortium blockchain for secure and efficient cooperation and interoperability of drone controllers, which includes a new cryptographic currency cooperation coin and a new consensus mechanism proof of security guarantee (PoSG). Third, we have designed a novel incentive mechanism to encourage controllers to maintain their security and provide safer services to other controllers. The security analysis and performance simulation results indicate the effectiveness of the proposed mechanism.
Generative Adversarial Networks: A Primer for Radiologists

Artificial intelligence techniques involving the use of artificial neural networks—that is, deep learning techniques—are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs). GANs consist of two artificial neural networks that are jointly optimized but with opposing goals. One neural network, the generator, aims to synthesize images that cannot be distinguished from real images. The second neural network, the discriminator, aims to distinguish these synthetic images from real images. These deep learning models allow, among other applications, the synthesis of new images, acceleration of image acquisitions, reduction of imaging artifacts, efficient and accurate conversion between medical images acquired with different modalities, and identification of abnormalities depicted on images. The authors provide an introduction to GANs and adversarial deep learning methods. In addition, the different ways in which GANs can be used for image synthesis and image-to-image translation tasks, as well as the principles underlying conditional GANs and cycle-consistent GANs, are described. Illustrated examples of GAN applications in radiologic image analysis for different imaging modalities and different tasks are provided. The clinical potential of GANs, future clinical GAN applications, and potential pitfalls and caveats that radiologists should be aware of also are discussed in this review. The online slide presentation from the RSNA Annual Meeting is available for this article. ©RSNA, 2021
One More Check: Making “Fake Background” Be Tracked Again

The one-shot multi-object tracking, which integrates object detection and ID embedding extraction into a unified network, has achieved groundbreaking results in recent years. However, current one-shot trackers solely rely on single-frame detections to predict candidate bounding boxes, which may be unreliable when facing disastrous visual degradation, e.g., motion blur, occlusions. Once a target bounding box is mistakenly classified as background by the detector, the temporal consistency of its corresponding tracklet will be no longer maintained. In this paper, we set out to restore the bounding boxes misclassified as ``fake background'' by proposing a re-check network. The re-check network innovatively expands the role of ID embedding from data association to motion forecasting by effectively propagating previous tracklets to the current frame with a small overhead. Note that the propagation results are yielded by an independent and efficient embedding search, preventing the model from over-relying on detection results. Eventually, it helps to reload the ``fake background'' and repair the broken tracklets. Building on a strong baseline CSTrack, we construct a new one-shot tracker and achieve favorable gains by 70.7 ➡ 76.4, 70.6 ➡ 76.3 MOTA on MOT16 and MOT17, respectively. It also reaches a new state-of-the-art MOTA and IDF1 performance. Code is released at https://github.com/JudasDie/SOTS.
BCoT Sentry: A Blockchain-Based Identity Authentication Framework for IoT Devices

In Internet of Things (IoT) environments, privacy and security are among some of the significant challenges. Recently, several studies have attempted to apply blockchain technology to increase IoT network security. However, the lightweight feature of IoT devices commonly fails to meet computational intensive requirements for blockchain-based security models. In this work, we propose a mechanism to address this issue. We design an IoT blockchain architecture to store device identity information in a distributed ledger. We propose a Blockchain of Things (BCoT) Gateway to facilitate the recording of authentication transactions in a blockchain network without modifying existing device hardware or applications. Furthermore, we introduce a new device recognition model that is suitable for blockchain-based identity authentication, where we employ a novel feature selection method for device traffic flow. Finally, we develop the BCoT Sentry framework as a reference implementation of our proposed method. Experiment results verify the feasibility of our proposed framework.
Getting Ourselves Together: Data-centered participatory design research &amp; epistemic burden

Data-centered participatory design research projects—wherein researchers collaborate with community members for the purpose of gathering, generating, or communicating data about the community or their causes—can place epistemic burdens on minoritized or racialized groups, even in projects focused on social justice outcomes. Analysis of epistemic burden encourages researchers to rethink the purpose and value of data in community organizing and activism more generally. This paper describes three varieties of epistemic burden drawn from two case studies based on the authors' previous work with anti-police brutality community organizations. The authors conclude with a discussion of ways to alleviate and avoid these issues through a series of questions about participatory research design. Ultimately, we call for a reorientation of knowledge production away from putative design solutions to community problems and toward a more robust interrogation of the power dynamics of research itself.
Immutable Ubiquitous Digital Certificate Authentication Using Blockchain Protocol

A new paradigm regarding a significant technological development has been introduced, each individual gets full trust in controlling an asset and can see the level of authenticity of a particular asset. Through qualitative methods, it is possible to have structured results as the first step to the perfection of the digitalisation era, in which the current revolutionary technology called blockchain has come into play. One of the main innovations found through this research is the role of blockchain in enhancing e-certificate data security, it is proven that administration can be well distributed and reduce operational costs for every field in the world. It is clear that blockchain, especially in the world of education certification, has a big impact, because it can create a global trust network, immutable, authenticated ubiquitous, and can shape a better world for Indonesia as a superpower. With good execution using the SHA-256 hash, e-certificate blockchain technology can be of high value and have an impact on the quality of potential human resources in educational institutions.
A Modified Manta Ray Foraging Optimization for Global Optimization Problems

The Manta ray foraging optimization (MRFO) is a novel swarm-based metaheuristic optimizer. It is mainly modeled by simulating three foraging behaviors of the Manta rays, which has a good performance. However, several drawbacks of MRFO have been noticed by analyzing its mathematical model. Random selection of reference points in the early iterations weakens the exploitation capability of MRFO. Chain foraging tends to lead the algorithm into local optimum. In addition, the algorithm suffers from the deficiency of decreasing population diversity in the late iteration. To address these shortcomings, a modified MRFO using three strategies, called m-MRFO, is proposed in this paper. An elite search pool (ESP) is established in this paper to enhance exploitation capability. By using adaptive control parameter strategies (ACP), we expand the range of MRFO’s exploration in the early iterations and enhance the accuracy of exploitation in the later iterations, balancing exploiting and exploring capabilities. Furthermore, we use a distribution estimation strategy (DES) to adjust the evolutionary direction using the dominant population information to promote convergence. The m-MRFO performance was verified by selecting 23 classical test functions and CEC2017 test suite. The significance of the results was also verified by Friedman test, Wilcoxon test and Iman-Davenport test. Moreover, we have confirmed the potential of m-MRFO to solve real-world problems by solving three engineering design problems. The simulation results show that the improvement strategy proposed in this paper can effectively improve the performance of MRFO. m-MRFO is highly competitive.
The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors

We introduce a Xilinx RF System-on-Chip (RFSoC)-based qubit controller (called the Quantum Instrumentation Control Kit, or QICK for short), which supports the direct synthesis of control pulses with carrier frequencies of up to 6 GHz. The QICK can control multiple qubits or other quantum devices. The QICK consists of a digital board hosting an RFSoC field-programmable gate array, custom firmware, and software and an optional companion custom-designed analog front-end board. We characterize the analog performance of the system as well as its digital latency, important for quantum error correction and feedback protocols. We benchmark the controller by performing standard characterizations of a transmon qubit. We achieve an average gate fidelity of Favg=99.93%. All of the schematics, firmware, and software are open-source.
ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition

The self-regulated recognition of human activities from time-series smartphone sensor data is a growing research area in smart and intelligent health care. Deep learning (DL) approaches have exhibited improvements over traditional machine learning (ML) models in various domains, including human activity recognition (HAR). Several issues are involved with traditional ML approaches; these include handcrafted feature extraction, which is a tedious and complex task involving expert domain knowledge, and the use of a separate dimensionality reduction module to overcome overfitting problems and hence provide model generalization. In this article, we propose a DL-based approach for activity recognition with smartphone sensor data, i.e., accelerometer and gyroscope data. Convolutional neural networks (CNNs), autoencoders (AEs), and long short-term memory (LSTM) possess complementary modeling capabilities, as CNNs are good at automatic feature extraction, AEs are used for dimensionality reduction and LSTMs are adept at temporal modeling. In this study, we take advantage of the complementarity of CNNs, AEs, and LSTMs by combining them into a unified architecture. We explore the proposed architecture, namely, “ConvAE-LSTM”, on four different standard public datasets (WISDM, UCI, PAMAP2, and OPPORTUNITY). The experimental results indicate that our novel approach is practical and provides relative smartphone-based HAR solution performance improvements in terms of computational time, accuracy, F1-score, precision, and recall over existing state-of-the-art methods.
Toward Human-Centered Automated Driving: A Novel Spatiotemporal Vision Transformer-Enabled Head Tracker

Accurate dynamic driver head pose tracking is of great importance for driver–automotive collaboration, intelligent copilot, head-up display (HUD), and other human-centered automated driving applications. To further advance this technology, this article proposes a low-cost and markerless head-tracking system using a deep learning-based dynamic head pose estimation model. The proposed system requires only a red, green, blue (RGB) camera without other hardware or markers. To enhance the accuracy of the driver’s head pose estimation, a spatiotemporal vision transformer (ST-ViT) model, which takes an image pair as the input instead of a single frame, is proposed. Compared to a standard transformer, the ST-ViT contains a spatial–convolutional vision transformer and a temporal transformer, which can improve the model performance. To handle the error fluctuation of the head pose estimation model, this article proposes an adaptive Kalman filter (AKF). By analyzing the error distribution of the estimation model and the user experience of the head tracker, the proposed AKF includes an adaptive observation noise coefficient; this can adaptively moderate the smoothness of the curve. Comprehensive experiments show that the proposed system is feasible and effective, and it achieves a state-of-the-art performance.
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit

Recently, we made available WeNet, a production-oriented end-to-end speech recognition toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address the streaming and non-streaming decoding modes in a single model. To further improve ASR performance and facilitate various production requirements, in this paper, we present WeNet 2.0 with four important updates. (1) We propose U2++, a unified two-pass framework with bidirectional attention decoders, which includes the future contextual information by a right-to-left attention decoder to improve the representative ability of the shared encoder and the performance during the rescoring stage. (2) We introduce an n-gram based language model and a WFST-based decoder into WeNet 2.0, promoting the use of rich text data in production scenarios. (3) We design a unified contextual biasing framework, which leverages user-specific context (e.g., contact lists) to provide rapid adaptation ability for production and improves ASR accuracy in both with-LM and without-LM scenarios. (4) We design a unified IO to support large-scale data for effective model training. In summary, the brand-new WeNet 2.0 achieves up to 10\% relative recognition performance improvement over the original WeNet on various corpora and makes available several important production-oriented features.
AUC-oriented Graph Neural Network for Fraud Detection

Though Graph Neural Networks (GNNs) have been successful for fraud detection tasks, they suffer from imbalanced labels due to limited fraud compared to the overall userbase. This paper attempts to resolve this label-imbalance problem for GNNs by maximizing the AUC (Area Under ROC Curve) metric since it is unbiased with label distribution. However, maximizing AUC on GNN for fraud detection tasks is intractable due to the potential polluted topological structure caused by intentional noisy edges generated by fraudsters. To alleviate this problem, we propose to decouple the AUC maximization process on GNN into a classifier parameter searching and an edge pruning policy searching, respectively. We propose a model named AO-GNN (Short for AUC-oriented GNN), to achieve AUC maximization on GNN under the aforementioned framework. In the proposed model, an AUC-oriented stochastic gradient is applied for classifier parameter searching, and an AUC-oriented reinforcement learning module supervised by a surrogate reward of AUC is devised for edge pruning policy searching. Experiments on three real-world datasets demonstrate that the proposed AO-GNN patently outperforms state-of-the-art baselines in not only AUC but also other general metrics, e.g. F1-macro, G-means.
Image De-Raining Transformer

Existing deep learning based de-raining approaches have resorted to the convolutional architectures. However, the intrinsic limitations of convolution, including local receptive fields and independence of input content, hinder the model's ability to capture long-range and complicated rainy artifacts. To overcome these limitations, we propose an effective and efficient transformer-based architecture for the image de-raining. First, we introduce general priors of vision tasks, i.e., locality and hierarchy, into the network architecture so that our model can achieve excellent de-raining performance without costly pre-training. Second, since the geometric appearance of rainy artifacts is complicated and of significant variance in space, it is essential for de-raining models to extract both local and non-local features. Therefore, we design the complementary window-based transformer and spatial transformer to enhance locality while capturing long-range dependencies. Besides, to compensate for the positional blindness of self-attention, we establish a separate representative space for modeling positional relationship, and design a new relative position enhanced multi-head self-attention. In this way, our model enjoys powerful abilities to capture dependencies from both content and position, so as to achieve better image content recovery while removing rainy artifacts. Experiments substantiate that our approach attains more appealing results than state-of-the-art methods quantitatively and qualitatively.
In situ bidirectional human-robot value alignment

A prerequisite for social coordination is bidirectional communication between teammates, each playing two roles simultaneously: as receptive listeners and expressive speakers. For robots working with humans in complex situations with multiple goals that differ in importance, failure to fulfill the expectation of either role could undermine group performance due to misalignment of values between humans and robots. Specifically, a robot needs to serve as an effective listener to infer human users’ intents from instructions and feedback and as an expressive speaker to explain its decision processes to users. Here, we investigate how to foster effective bidirectional human-robot communications in the context of value alignment—collaborative robots and users form an aligned understanding of the importance of possible task goals. We propose an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users through explanations. To learn from human feedback, our XAI system integrates a cooperative communication model for inferring human values associated with multiple desirable goals. To be interpretable to humans, the system simulates human mental dynamics and predicts optimal explanations using graphical models. We conducted psychological experiments to examine the core components of the proposed computational framework. Our results show that real-time human-robot mutual understanding in complex cooperative tasks is achievable with a learning model based on bidirectional communication. We believe that this interaction framework can shed light on bidirectional value alignment in communicative XAI systems and, more broadly, in future human-machine teaming systems.
Quantum computing

Abstract Quantum computing promises to be the next disruptive technology, with numerous possible applications and implications for organizations and markets. Quantum computers exploit principles of quantum mechanics, such as superposition and entanglement, to represent data and perform operations on them. Both of these principles enable quantum computers to solve very specific, complex problems significantly faster than standard computers. Against this backdrop, this fundamental gives a brief overview of the three layers of a quantum computer: hardware, system software, and application layer. Furthermore, we introduce potential application areas of quantum computing and possible research directions for the field of information systems.
Breaking SIDH in Polynomial Time

We show that we can break SIDH in (classical) polynomial time, even with a random starting curve $$E_0$$ .