Reviewerly/exHarmony huggingface dataset:
Learning quantum dynamics with latent neural ordinary differential equations 

The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question. In the area of quantum physics, making progress towards these goals is significantly more challenging due to the curse of dimensionality as well as the counterintuitive nature of quantum mechanics. Here we present the QNODE, a latent neural ordinary differential equation (ODE) trained on expectation values of closed and open-quantum-systems dynamics. It can learn to generate such measurement data and extrapolate outside of its training region that satisfies the von Neumann and time-local Lindblad master equations for closed and open quantum systems, respectively, in an unsupervised means. Furthermore, the QNODE rediscovers quantum-mechanical laws such as the Heisenberg's uncertainty principle in a data-driven way, without any constraint or guidance. Additionally, we show that trajectories that are generated from the QNODE that are close in its latent space have similar quantum dynamics while preserving the physics of the training system.
90 nm CMOS Implementation of Multiplicative Inverse of the S-Box for AES Algorithm Using Six Transistor XOR Gate

The Substitution-BOX is the most difficult architecture and is at the heart of any Advanced Encryption Standard algorithm implementation. It is the most complicated non-linear architecture using multiplicative inverse. It consumes maximum amount of the energy and power budget of the algorithm. This paper introduces a full-custom CMOS implementation of Multiplicative Inverse module for Substitution/ Inverse Substitution transformation in composite field arithmetic using Galois field GF (28). The multiplicative inversion module utilizes large number of XOR gates in its implementation. This paper introduces implementation of a novel XOR gate using six transistors. Using this six transistor XOR gate, the multiplicative inverse module is implemented in 90 nm CMOS technology. Simulation of the proposed design is achieved using Tanner EDA v.16 software. The area of the multiplicative inverse circuit is 39.92 µm2 requiring 776 transistors. With 0.6 Volts supply voltage, the design shows a power dissipation of 2.386 µWatts making it ideal for applications such as smart cards and RFID tags.
Predicting user-item links in recommender systems based on similarity-network resource allocation

Recommender systems and link prediction techniques have been widely used in areas such as online information filtering and improving user retrieval efficiency, and their performance and principles are of significant research interest. However, existing mainstream recommendation algorithms still face many challenges, such as the contradiction between prediction accuracy and recommendation diversity, and the limited scalability of algorithms due to the need to use a large number of neighbors for prediction. To address these two issues, this paper designs a user-item link prediction algorithm based on resource allocation within the user similarity network to enhance prediction accuracy while maintaining recommendation diversity and using as few neighbors as possible to achieve better algorithm scalability. We first calculate inter-user similarity based on user history ratings and construct a similarity network among users by filtering the similarity results; subsequently, based on the centrality and community features in this network, we design a similarity measure for resource allocation that incorporates the bipartite graph model and the similarity network; finally, we use this similarity method to select the set of prediction target neighbors, synthesize and use the similarity results, centrality, and community features for the prediction of user-item links. Experimental results on two well-known datasets with three state-of-the-art algorithms show that the proposed approach can improve the prediction accuracy by 2.34% to 15.76% in a shorter time and maintain a high recommendation diversity, and the ranking accuracy of recommendation is also improved. Compared with the benchmark algorithm with the second highest performance ranking, the method designed in this paper can further reduce the number of neighbors required at optimal prediction error by 25% to 56%. The study reveals that resource allocation in similarity networks successfully mines the features embedded in the recommender system, laying the foundation for further understanding the recommender system and improving the performance of related prediction methods.
CIRNet: An improved RGBT tracking via cross-modality interaction and re-identification

RGBT tracking is receiving more and more attention because of its huge tracking potential in an all-weather environment. RGB and thermal source data contain different levels of information about the object. Utilizing the complementary advantage of different levels of information can effectively improve the tracking performance. Existing work focuses on the extraction and fusion of multi-modal features. Although these methods effectively deploy the fusion of information among multiple modalities, they ignore the potential value of multi-level shared clues in different modalities. In addition, these works cannot provide effective candidate boxes after tracking drift, resulting in limited tracker performance. In this paper, we propose a cross-modality interaction and re-identification network that performs multi-level modality-shared, modality-specific and object probability prediction learning. We designed two feature extraction sub-networks, namely, a multi-level modality-shared fusion network and modality complementary sub-network. Specifically, the two sub-networks extract and fuse multi-level modality shared information and modality specific information, respectively. To optimize tracking drift, object-aware branches that predict the object-centered state are designed. Our object-aware branching is simple, neat and efficient. Moreover, to achieve the visual tracking real-time requirement, we designed the object regression branch that does not require repeated region suggestion input. By extensive experiments and comparisons with state-of-the-art trackers on the RGBT tracking benchmark dataset, our tracker achieves leading performance and essentially real-time tracking speeds. Tracking drift caused by occlusion, fast motion and camera moving is significantly optimized.
Heralding Multiple Photonic Pulsed Bell Pairs via Frequency-Resolved Entanglement Swapping

Entanglement is a unique property of quantum systems and an essential resource for many quantum technologies. The ability to transfer or swap entanglement between systems is an important protocol in quantum information science. Entanglement swapping between photons forms the basis of distributed quantum networks. Here an experiment demonstrating entanglement swapping from two independent multimode time-frequency entangled sources is presented, resulting in multiple heralded frequency-mode Bell states. Entanglement in the heralded states is verified by measuring conditional anticorrelated joint spectra and quantum beating in two-photon interference. Our experiment heralds up to five orthogonal Bell pairs within the same setup, and this number is ultimately limited only by the entanglement of the initial sources.
Games for Change—A Comparative Systematic Review of Persuasive Strategies in Games for Behavior Change

Games for change is a growing research field and studies have shown that these games can promote positive behavior change using various persuasive strategies. This article presents a systematic review of 130 persuasive games from the literature published in the last 21 years (2001–2021) to 1) highlight the current trends in the field with respect to domains, year, country, technology platforms, and genre; 2) identify what strategies are employed in the games and their comparative analysis across domains; 3) uncover various ways the persuasive strategies are operationalized in games; 4) explore for possible relationships between persuasive games effectiveness and the number of strategies employed; and 5) highlight gaps and opportunities for future research in the area of persuasive games. Our analysis reveals the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reward</i> strategy is the most popular persuasive strategy employed in the persuasive games’ research. We also uncovered that, even though persuasive games have been strongly effective at promoting behavior change, there was a significant negative relationship between the number of persuasive strategies employed in persuasive games and their overall effectiveness. Based on these findings, we provide insights and design suggestions, operationalization, and assessment for persuasive games.
Beyond universal attack detection for continuous-variable quantum key distribution via deep learning

Removing quantum hacking attacks is a highly challenging task for quantum key distribution (QKD) since one needs to correctly detect the types of attacks. There is a big gap between the idealized theoretical model and the practical physical system, which results in various hacking strategies exploited from security loopholes. To bridge this gap, previous continuous-variable (CV) QKD systems adopted a universal statistical model to represent different dimensional characters of the physical layer. The challenge is to discriminate different attacks from the exposed key physical details. Here we present a semisupervised deep-learning method to detect the known attacks or potential threats that are hard to be captured in previous CVQKD systems. The proof-of-principle experiment result shows that it could break the present limitations that different attacks are relatively independent, and key features computed from previous threshold-based methods are not correlated with each other. We anticipate that our proposal will give insight into how quantum hacking attacks can be detected under the real system modeling constraints, and will enable the present quantum communication systems to reach a higher level of practical security without adding new assumptions.
COVID-19 Case Investigation and Contact Tracing Programs and Practice: Snapshots From the Field

Between Fall 2020 and Spring 2021, the Association of State and Territorial Health Officials conducted 2 rapid queries to collect information from the field regarding the status of COVID-19 case investigation and contact tracing (CI/CT) programs and practice. These short surveys were distributed to senior deputies in state and territorial health agencies, yielding a response rate of 45.8% (November 2020) and 40.7% (April 2021). Findings indicated that CI/CT staff roles and assigned functions varied across jurisdictions, as did staffing levels/capacity, approaches for linking individuals to social supports, and program changes that were planned or underway. Agency-reported staffing levels/capacity and programmatic challenges changed over time, highlighting the dynamic nature of CI/CT program practice and implementation. While findings from the surveys cannot be generalized to the national level, they provide critical insights from the field on CI/CT program implementation, challenges, and changes in response to the evolving COVID-19 epidemic in the United States.
Performance Modeling of Metric-Based Serverless Computing Platforms

Analytical performance models are very effective in ensuring the quality of service and cost of service deployment remain desirable under different conditions and workloads. While various analytical performance models have been proposed for previous paradigms in cloud computing, serverless computing lacks such models that can provide developers with performance guarantees. Besides, most serverless computing platforms still require developers’ input to specify the configuration for their deployment that could affect both the performance and cost of their deployment, without providing them with any direct and immediate feedback. In previous studies, we built such performance models for steady-state and transient analysis of scale-per-request serverless computing platforms (e.g., AWS Lambda, Azure Functions, Google Cloud Functions) that could give developers immediate feedback about the quality of service and cost of their deployments. In this work, we aim to develop analytical performance models for latest trend in serverless computing platforms that use concurrency value and the rate of requests per second for autoscaling decisions. Examples of such serverless computing platforms are Knative and Google Cloud Run (a managed Knative service by Google). The proposed performance model can help developers and providers predict the performance and cost of deployments with different configurations which could help them tune the configuration toward the best outcome. We validate the applicability and accuracy of the proposed performance model by extensive real-world experimentation on Knative and show that our performance model is able to accurately predict the steady-state characteristics of a given workload with minimal amount of data collection.
Prediction Based Semi-Supervised Online Personalized Federated Learning for Indoor Localization

Fingerprint-based indoor localization has drawn increasing attention with the development of deep learning. Nevertheless, it faces challenges from frequent data collection and the corresponding exposure of privacy. Federated Learning (FL) is introduced into indoor localization recently for overcoming these challenges. However, most of the current FL-based indoor localization studies only focus on the static data distributions and ignore the fact that users have their preferred localization requirements. In this paper, two typical indoor localization scenarios are considered. Clients with various localization demands and data distributions collect online unlabeled data in Scenario I. For Scenario II, only one requesting user (RU) with high mobility applies for the localization service. A Prediction based Semi-supervised Online Personalized Federated Learning (PSO-PFL) is proposed for addressing these problems in the two scenarios. Experiments are conducted based on two real-world datasets. The experiment results show that PSO-PFL achieves higher personalization accuracy than centralized training and Federated Averaging, and utilizes unlabeled data efficiently. The strategy of selecting clients via prediction achieves higher accuracy than the random selection strategy. In brief, the proposed methods protect users’ privacy via FL. And the challenges of dynamical and heterogeneous stream data in indoor localization are addressed by the proposed methods which provide better personalization localization service for users than baseline methods.
Detection Tolerant Black-Box Adversarial Attack Against Automatic Modulation Classification With Deep Learning

Advances in adversarial attack and defense technologies will enhance the reliability of deep learning (DL) systems spirally. Most existing adversarial attack methods make overly ideal assumptions, which creates the illusion that the DL system can be attacked simply and has restricted the further improvement on DL systems. To perform practical adversarial attacks, a detection tolerant black-box adversarial-attack (DTBA) method against DL-based automatic modulation classification (AMC) is presented in this article. In the DTBA method, the local DL model as a substitution of the remote target DL model is trained first. The training dataset is generated by an attacker, labeled by the target model, and augmented by Jacobian transformation. Then, the conventional gradient attack method is utilized to generate adversarial attack examples toward the local DL model. Moreover, before launching attack to the target model, the local model estimates the misclassification probability of the perturbed examples in advance and deletes those invalid adversarial examples. Compared with related attack methods of different criteria on public datasets, the DTBA method can reduce the attack cost while increasing the rate of successful attack. Adversarial attack transferability of the proposed method on the target model has increased by more than 20%. The DTBA method will be suitable for launching flexible and effective black-box adversarial attacks against DL-based AMC systems.
Time-travel Investigation: Toward Building a Scalable Attack Detection Framework on Ethereum

Ethereum has been attracting lots of attacks, hence there is a pressing need to perform timely investigation and detect more attack instances. However, existing systems suffer from the scalability issue due to the following reasons. First, the tight coupling between malicious contract detection and blockchain data importing makes them infeasible to repeatedly detect different attacks. Second, the coarse-grained archive data makes them inefficient to replay transactions. Third, the separation between malicious contract detection and runtime state recovery consumes lots of storage. In this article, we propose a scalable attack detection framework named EthScope , which overcomes the scalability issue by neatly re-organizing the Ethereum state and efficiently locating suspicious transactions. It leverages the fine-grained state to support the replay of arbitrary transactions and proposes a well-designed schema to optimize the storage consumption. The performance evaluation shows that EthScope can solve the scalability issue, i.e., efficiently performing a large-scale analysis on billions of transactions, and a speedup of around \( \text{2,300}\times \) when replaying transactions. It also has lower storage consumption compared with existing systems. Further analysis shows that EthScope can help analysts understand attack behaviors and detect more attack instances.
VideoModerator: A Risk-aware Framework for Multimodal Video Moderation in E-Commerce

Video moderation, which refers to remove deviant or explicit content from e-commerce livestreams, has become prevalent owing to social and engaging features. However, this task is tedious and time consuming due to the difficulties associated with watching and reviewing multimodal video content, including video frames and audio clips. To ensure effective video moderation, we propose VideoModerator, a risk-aware framework that seamlessly integrates human knowledge with machine insights. This framework incorporates a set of advanced machine learning models to extract the risk-aware features from multimodal video content and discover potentially deviant videos. Moreover, this framework introduces an interactive visualization interface with three views, namely, a video view, a frame view, and an audio view. In the video view, we adopt a segmented timeline and highlight high-risk periods that may contain deviant information. In the frame view, we present a novel visual summarization method that combines risk-aware features and video context to enable quick video navigation. In the audio view, we employ a storyline-based design to provide a multi-faceted overview which can be used to explore audio content. Furthermore, we report the usage of VideoModerator through a case scenario and conduct experiments and a controlled user study to validate its effectiveness.
ICU Cockpit: a platform for collecting multimodal waveform data, AI-based computational disease modeling and real-time decision support in the intensive care unit

ICU Cockpit: a secure, fast, and scalable platform for collecting multimodal waveform data, online and historical data visualization, and online validation of algorithms in the intensive care unit. We present a network of software services that continuously stream waveforms from ICU beds to databases and a web-based user interface. Machine learning algorithms process the data streams and send outputs to the user interface. The architecture and capabilities of the platform are described. Since 2016, the platform has processed over 89 billion data points (N = 979 patients) from 200 signals (0.5-500 Hz) and laboratory analyses (once a day). We present an infrastructure-based framework for deploying and validating algorithms for critical care. The ICU Cockpit is a Big Data platform for critical care medicine, especially for multimodal waveform data. Uniquely, it allows algorithms to seamlessly integrate into the live data stream to produce clinical decision support and predictions in clinical practice.
Domain-Adversarial Neural Networks for Deforestation Detection in Tropical Forests

Many deep-learning (DL)-based, domain adaptation (DA) methods for remote sensing (RS) applications rely on adversarial training strategies to align features extracted from images of different domains in a shared latent space. However, the performance of such representation matching techniques is negatively impacted when class occurrences in the target domain, for which no labeled data are available during training, are highly imbalanced. In this work, we propose a DL-based representation matching approach for DA in the context of change detection tasks. We further evaluate the approach in a deforestation mapping application, characterized by a high-class imbalance between the deforestation and no-deforestation classes. The domains represent different sites in the Amazon and Brazilian Cerrado biomes. To mitigate the class imbalance problem, we devised an unsupervised pseudolabeling scheme based on change vector analysis (CVA) that prevents the feature alignment to be biased toward the overrepresented class. The experimental results indicate that the proposed approach can improve the accuracy of cross-domain deforestation detection.
CoUX: Collaborative Visual Analysis of Think-Aloud Usability Test Videos for Digital Interfaces

Reviewing a think-aloud video is both time-consuming and demanding as it requires UX (user experience) professionals to attend to many behavioral signals of the user in the video. Moreover, challenges arise when multiple UX professionals need to collaborate to reduce bias and errors. We propose a collaborative visual analytics tool, CoUX, to facilitate UX evaluators collectively reviewing think-aloud usability test videos of digital interfaces. CoUX seamlessly supports usability problem identification, annotation, and discussion in an integrated environment. To ease the discovery of usability problems, CoUX visualizes a set of problem-indicators based on acoustic, textual, and visual features extracted from the video and audio of a think-aloud session with machine learning. CoUX further enables collaboration amongst UX evaluators for logging, commenting, and consolidating the discovered problems with a chatbox-like user interface. We designed CoUX based on a formative study with two UX experts and insights derived from the literature. We conducted a user study with six pairs of UX practitioners on collaborative think-aloud video analysis tasks. The results indicate that CoUX is useful and effective in facilitating both problem identification and collaborative teamwork. We provide insights into how different features of CoUX were used to support both independent analysis and collaboration. Furthermore, our work highlights opportunities to improve collaborative usability test video analysis.