Reviewerly/exHarmony huggingface dataset:
Agile-AES: Implementation of configurable AES primitive with agile design approach

In the data-centric era, interconnected devices must be able to communicate efficiently and securely with their hosts even over untrusted networks. This led to the adoption of several end-to-end security protocols that employ various efficient and elegant encryption algorithms, such as Advanced Encryption Standard (AES), often implemented with hardware modules since there is usually no good solution to install security software on the device itself. State-of-the-art AES hardware implementations typically focus on optimizing a single metric, e.g. throughput or area, and are tedious to adapt to a wider set of design constraints. Applying an agile approach to hardware development is increasingly important, and this is especially critical for hardware security primitives that need to be consumed in various systems. In this work, we develop Agile-AES, an open-source, flexible, parameterizable, hardware implementation of AES, combining many best-known practices. The agile and feature-rich implementation was based on the Chisel framework, through which flexibility is instrumented to support various key length, topology, mode of operation, local memory type, S-box fabric, side-channel attack defense techniques, and interfaces. Despite covering a larger design space, our proposed implementation has 50% fewer lines of code compared to existing Verilog versions — a crucial advantage in terms of maintainability and development productivity. To evaluate the QoR out of this implementation approach, we pick representative configurations and evaluate utilization, power and throughput on both entry level and server-grade FPGAs and compare against the Verilog and HLS counterpart implementations. It shows very comparable results to the Verilog implementation and better QoR compared to the HLS-based implementation.
FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity

Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices subject to limited communication bandwidths, heterogeneity in data distributions and computational resources, as well as privacy considerations. In this paper, we introduce a new FL protocol termed FedADMM based on primal-dual optimization. The proposed method leverages dual variables to tackle sta-tistical heterogeneity, and accommodates system heterogeneity by tolerating variable amount of work performed by clients. FedADMM maintains identical communication costs per round as FedAvg/Prox, and generalizes them via the augmented Lagrangian. A convergence proof is established for nonconvex objectives, under no restrictions in terms of data dissimilarity or number of participants per round of the algorithm. We demon-strate the merits through extensive experiments on real datasets, under both IID and non-IID data distributions across clients. FedADMM consistently outperforms all baseline methods in terms of communication efficiency, with the number of rounds needed to reach a prescribed accuracy reduced by up to 87%. The algorithm effectively adapts to heterogeneous data distributions through the use of dual variables, without the need for hyperparameter tuning, and its advantages are more pronounced in large-scale systems.
RobIn: A robust interpretable deep network for schizophrenia diagnosis

Schizophrenia is a severe mental health condition that requires a long and complicated diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning has recently become a popular way to analyse and interpret medical data. Past attempts to use deep learning for schizophrenia diagnosis from brain-imaging data have shown promise but suffer from a large training-application gap — it is difficult to apply lab research to the real world. We propose to reduce this training-application gap by focusing on readily accessible data. We collect a data set of psychiatric observations of patients based on DSM-5 criteria. Because similar data is already recorded in all mental health clinics that diagnose schizophrenia using DSM-5, our method could be easily integrated into current processes as a tool to assist clinicians, whilst abiding by formal diagnostic criteria. To facilitate real-world usage of our system, we show that it is interpretable and robust. Understanding how a machine learning tool reaches its diagnosis is essential to allow clinicians to trust that diagnosis. To interpret the framework, we fuse two complementary attention mechanisms, 'squeeze and excitation' and 'self-attention', to determine global attribute importance and attribute interactivity, respectively. The model uses these importance scores to make decisions. This allows clinicians to understand how a diagnosis was reached, improving trust in the model. Because machine learning models often struggle to generalise to data from different sources, we perform experiments with augmented test data to evaluate the model's applicability to the real world. We find that our model is more robust to perturbations, and should therefore perform better in a clinical setting. It achieves 98% accuracy with 10-fold cross-validation.
Experimentally Finding Dense Subgraphs Using a Time-Bin Encoded Gaussian Boson Sampling Device

Gaussian boson sampling is a quantum computing concept based on drawing samples from a multimode nonclassical Gaussian state using photon-number resolving detectors. It was initially posed as a near-term approach to achieve quantum advantage, and several applications have been proposed since, including the calculation of graph features. For the first time, we use a time-bin encoded interferometer to implement Gaussian boson sampling experimentally and extract samples to enhance the search for dense subgraphs in a graph. Our results indicate an improvement over classical methods for subgraphs of sizes three and four in a graph containing ten nodes. In addition, we numerically explore the role of imperfections in the optical circuit and on the performance of the algorithm.2 MoreReceived 21 June 2022Revised 26 August 2022Accepted 29 August 2022DOI:https://doi.org/10.1103/PhysRevX.12.031045Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasBoson samplingOptical quantum information processingQuantum information processing with continuous variablesQuantum simulationQuantum Information
A Survey on Classifying Big Data with Label Noise

Class label noise is a critical component of data quality that directly inhibits the predictive performance of machine learning algorithms. While many data-level and algorithm-level methods exist for treating label noise, the challenges associated with big data call for new and improved methods. This survey addresses these concerns by providing an extensive literature review on treating label noise within big data. We begin with an introduction to the class label noise problem and traditional methods for treating label noise. Next, we present 30 methods for treating class label noise in a range of big data contexts, i.e., high-volume, high-variety, and high-velocity problems. The surveyed works include distributed solutions capable of operating on datasets of arbitrary sizes, deep learning techniques for large-scale datasets with limited clean labels, and streaming techniques for detecting class noise in the presence of concept drift. Common trends and best practices are identified in each of these areas, implementation details are reviewed, empirical results are compared across studies when applicable, and references to 17 open-source projects and programming packages are provided. An emphasis on label noise challenges, solutions, and empirical results as they relate to big data distinguishes this work as a unique contribution that will inspire future research and guide machine learning practitioners.
3D Shape Analysis Through a Quantum Lens: the Average Mixing Kernel Signature

Abstract The Average Mixing Kernel Signature is a novel spectral signature for points on non-rigid three-dimensional shapes. It is based on a quantum exploration process of the shape surface, where the average transition probabilities between the points of the shape are summarised in the finite-time average mixing kernel. A band-filtered spectral analysis of this kernel then yields the AMKS. Crucially, we show that opting for a finite time-evolution allows the signature to account for a mixing of the Laplacian eigenspaces, similar to what is observed in the presence of noise, explaining the increased noise robustness of this signature when compared to alternative signatures. We perform an extensive experimental analysis of the AMKS under a wide range of problem scenarios, evaluating the performance of our descriptor under different sources of noise (vertex jitter and topological), shape representations (mesh and point clouds), as well as when only a partial view of the shape is available. Our experiments show that the AMKS consistently outperforms two of the most widely used spectral signatures, the Heat Kernel Signature and the Wave Kernel Signature, and suggest that the AMKS should be the signature of choice for various compute vision problems, including as input of deep convolutional architectures for shape analysis.
An approach for learning resource recommendation using deep matrix factorization

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
An empirical evaluation of two natural hand interaction systems in augmented reality

Abstract Human-computer interaction based on hand gesture tracking is not uncommon in Augmented Reality. In fact, the most recent optical Augmented Reality devices include this type of natural interaction. However, due to hardware and system limitations, these devices, more often than not, settle for semi-natural interaction techniques, which may not always be appropriate for some of the tasks needed in Augmented Reality applications. For this reason, we compare two different optical Augmented Reality setups equipped with hand tracking. The first one is based on a Microsoft HoloLens (released in 2016) and the other one is based on a Magic Leap One (released more than two years later). Both devices offer similar solutions for the visualization and registration problems but differ in the hand tracking approach, since the former uses a metaphoric hand-gesture tracking and the latter relies on an isomorphic approach. We raise seven research questions regarding these two setups, which we answer after performing two task-based experiments using virtual elements, of different sizes, that are moved using natural hand interaction. The questions deal with the accuracy and performance achieved with these setups and also with user preference, recommendation and perceived usefulness. For this purpose, we collect both subjective and objective data about the completion of these tasks. Our initial hypothesis was that there would be differences, in favor of the isomorphic and newer setup, in the use of hand interaction. However, the results surprisingly show that there are very small objective differences between these setups, and the isomorphic approach is not significantly better in terms of accuracy and mistakes, although it allows a faster completion of one of the tasks. In addition, no remarkable statistically significant differences can be found between the two setups in the subjective datasets gathered through a specific questionnaire. We also analyze the opinions of the participants in terms of usefulness, preference and recommendation. The results show that, although the Magic Leap-based system gets more support, the differences are not statistically significant.
A systematized review on data librarianship literature: Current services, challenges, skills, and motivational factors

Data librarianship is becoming more common as a means of developing and integrating data-driven library services. Consequently, the academic and research libraries’ traditional role in providing information support and training has been expanded to include support in all aspects of the research lifecycle. Hence, this study systematically reviews the data librarianship literature focusing on current data librarianship services, challenges, skills, and motivational factors. A systematic review was conducted following the PRISMA guidelines. A comprehensive search strategy was formulated to extract maximum relevant results. The bibliographic data were retrieved from the Scopus, Web of Science, Library, Information Science and Technology Abstracts (LISTA), and Library and Information Science Abstracts (LISA). Finally, 27 studies that fulfill the criteria were included in this study. The findings revealed that two main factors that contribute to the success or failure in this emerging data librarianship roles are skills, knowledge, and expertise; and limited support and advocacy from library leadership and higher authorities. One is on the part of library professionals who can develop the required skills, knowledge, and expertise and the other is on the part of library leadership. The library professionals are hesitant to embrace this new role due to non-additional benefits, no relevant job description, and lacking leadership support. Overall, the findings revealed that the data librarianship scope is dynamic and has been expanded, albeit the progress is slow. The theoretical, practical, policy, and social implications described that the data librarianship services tend to be improved, and the relevant skills, knowledge, and expertise should be developed. The policy initiatives need to be taken, improved, and expanded to advance technical services related to data librarianship.
Privacy Preservation for Trajectory Publication Based on Differential Privacy

With the proliferation of location-aware devices, trajectory data have been used widely in real-life applications. However, trajectory data are often associated with sensitive labels, such as users’ purchase transactions and planned activities. As such, inappropriate sharing or publishing of these data could threaten users’ privacy, especially when an adversary has sufficient background knowledge about a trajectory through other data sources, such as social media (check-in tags). Though differential privacy has been used to address the privacy of trajectory data, no existing method can protect the privacy of both trajectory data and sensitive labels. In this article, we propose a comprehensive trajectory publishing algorithm with three effective procedures. First, we apply density-based clustering to determine hotspots and outliers and then blur their locations by generalization. Second, we propose a graph-based model to efficiently capture the relationship among sensitive labels and trajectory points in all records and leverage Laplace noise to achieve differential privacy. Finally, we generate and publish trajectories by traversing and updating this graph until we travel all vertexes. Our experiments on synthetic and real-life datasets demonstrate that our algorithm effectively protects the privacy of both sensitive labels and location data in trajectory publication. Compared with existing works on trajectory publishing, our algorithm can also achieve higher data utility.
Tackling Nonconvex Collision Avoidance Constraints for Optimal Trajectory Planning Using Saturation Functions

The problem of optimal trajectory planning with nonconvex collision avoidance constraints is discussed in this paper. The trajectory planning algorithm aims to generate a feasible trajectory with specified performance indexes to reduce collisions of aerospace vehicles in the presence of second-order smooth obstacles. The algorithm design that manages such mission scenarios is challenging because of the stringent avoidance constraints of various nonconvex types, especially considering the requirements of efficient computation. This problem is addressed within the sequential convex programming (SCP) framework by utilizing the characteristics of saturation functions. The introduction of the saturation function is found to significantly improve the convergence of the SCP algorithm by overcoming the drawbacks of potential interruptions from the iteration process and controlling the search directions for successive solutions. Theoretical analyses are performed to guarantee that the original constraints are satisfied and to ensure the iterative solution searches in the directions of the true optimal solution. Under multiple randomly generated test environments, the applications of the proposed approach in highly constrained aircraft path planning problems and spacecraft rendezvous operations are numerically simulated by comparing two representative conventional SCP-based methods to demonstrate the effectiveness, efficiency, and convergence of the proposed methodology.
Pay to view answers: determinants of listeners' payment decisions on social Q&amp;A platforms

Purpose Paying to view others' answers is a new mode for question and answer (Q&amp;A) platforms. The purpose is to build a model to explore the determinants of the number of listeners and further explore certain meaningful characteristics of the model in the context of different types of questions and answerers. Design/methodology/approach The authors develop an empirical model and use real panel data to test the hypothesis. Specifically, cues from the answerer and from the question elicit the listener's trust in the answerer (including direct and indirect trust) and perceived value in the question (including intrinsic and extrinsic attributes), respectively. Findings The authors find that cues from answerers (experience for paid Q&amp;As and popularity for free Q&amp;As) and questions (length, sentence structure, value and number of likes) all have positive effects on the number of listeners. The impact of answerer authentication is more significant than the popularity of free Q&amp;As. Moreover, the length of the question matters only for subjective questions, while sentence structure matters only for objective questions. In addition, the answerer's own attributes and the behavior and feedback of others have greater impacts when the answerer is below average in popularity. Originality/value The authors summarize the unique features of the mode of paying to view others' answers in contrast with the traditional mode of paid Q&amp;As. In addition, the authors focus on the characteristics of the question (including the subjectivity and the sentence structure of the question), a topic which has not been studied previously. Our research provides a reference for exploring user behavior patterns. The practical implications for knowledge platforms are also concretely described.
Federated Unsupervised Domain Adaptation for Face Recognition

Given labeled data in a source domain, unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, whose data distributions are different. However, existing works are inapplicable to face recognition under privacy constraints because they re-quire sharing of sensitive face images between domains. To address this problem, we propose federated unsupervised do-main adaptation for face recognition, FedFR. FedFR jointly optimizes clustering-based domain adaptation and federated learning to elevate performance on the target domain. Specif-ically, for unlabeled data in the target domain, we enhance a clustering algorithm with distance constrain to improve the quality of predicted pseudo labels. Besides, we propose a new domain constraint loss (DCL) to regularize source do-main training in federated learning. Extensive experiments on a newly constructed benchmark demonstrate that FedFR outperforms the baseline and classic methods on the target domain by 3% to 14% on different evaluation metrics.
A Survey of Game-Theoretic Approach for Resource Management in Cloud Computing

Cloud computing is a groundbreaking technique that provides a whole lot of facilities such as storage, memory, and CPU as well as facilities such as servers and web service. It allows businesses and individuals to subcontract their computing needs as well as trust a network provider with its data warehousing and processing. The fact remains that cloud computing is a resource-finite domain where cloud users contend for available resources to carry out desired tasks. Resource management (RM) is a process that deals with the procurement and release of resources. The management of cloud resources is desirable for improved usage and service delivery. In this paper, we reviewed various resource management techniques embraced in literature. We concentrated majorly on investigating game-theoretic submission for the management of required resources, as a potential solution in modeling the resource allocation, scheduling, provisioning, and load balancing problems in cloud computing. This paper presents a survey of several game-theoretic techniques implemented in cloud computing resource management. Based on this survey, we presented a guideline to aid the adoption and utilization of game-theoretic resource management strategy.
An FA-SegNet Image Segmentation Model Based on Fuzzy Attention and Its Application in Cardiac MRI Segmentation

Abstract Aiming at the medical images segmentation with low-recognition and high background noise, a deep convolution neural network image segmentation model based on fuzzy attention mechanism is proposed, which is called FA-SegNet. It takes SegNet as the basic framework. In the down-sampling module for image feature extraction, a fuzzy channel-attention module is added to strengthen the discrimination of different target regions. In the up-sampling module for image size restoration and multi-scale feature fusion, a fuzzy spatial-attention module is added to reduce the loss of image details and expand the receptive field. In this paper, fuzzy cognition is introduced into the feature fusion of CNNs. Based on the attention mechanism, fuzzy membership is used to re-calibrate the importance of the pixel value in local regions. It can strengthen the distinguishing ability of image features, and the fusion ability of the contextual information, which improves the segmentation accuracy of the target regions. Taking MRI segmentation as an experimental example, multiple targets such as the left ventricles, right ventricles, and left ventricular myocardium are selected as the segmentation targets. The pixels accuracy is 92.47%, the mean intersection to union is 86.18%, and the Dice coefficient is 92.44%, which are improved compared with other methods. It verifies the accuracy and applicability of the proposed method for the medical images segmentation, especially the targets with low-recognition and serious occlusion.