Reviewerly/exHarmony huggingface dataset:
BlockExplorer: Exploring Blockchain Big Data Via Parallel Processing

Today's blockchain systems store detailed runtime information in the format of transactions and blocks, which are valuable not only to understand the finance of blockchain-based ecosystems but also to audit the security of on-chain applications. However, exploring this blockchain “big data” is challenging due to data heterogeneity and the huge amount. Existing blockchain exploration techniques are either incomplete or inefficient, making them inapt in time-sensitive applications. This paper presents <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> , an efficient and flexible blockchain exploration system for Ethereum. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> builds on a master-slave architecture, where the master partitions all blocks into multiple non-overlapped sets and each slave simultaneously processes Ethereum big data based on a set of blocks. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> implements a transaction-based partitioning approach to address load balance among slaves, and a code instrumentation approach to acquire complete Ethereum big data. The evaluation shows that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> accelerates the data acquisition performance of the state-of-the-art by 4.1×, while the workload difference among slaves is up to 18%. To demonstrate the application of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> , we develop three apps upon <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math>${\sf BlockExplorer}$</tex-math></inline-formula> to detect real-life attacks against Ethereum and show that our apps can detect attacks in a large range of blocks (e.g., ten million) within a short time (e.g., multiple hours).
Shuffle Differential Private Data Aggregation for Random Population

Bridging the advantages of differential privacy in both centralized model (i.e., high accuracy) and local model (i.e., minimum trust), the shuffle privacy model has potential applications in many privacy-sensitive scenarios, such as mobile user data aggregation and federated learning. Since messages from users are anonymized by semi-trusted shufflers (e.g., anonymous channels, edge servers), every user could hide message among other users’ messages and inject only part of noises (a.k.a. privacy amplification). However, existing works assume that the participating user population is known in advance, which is unrealistic for dynamic environments (e.g., mobile computing, vehicular networks). In this work, we study the shuffle privacy model with a random participating population, and give privacy amplification bounds for population size with commonly encountered binomial, Poisson, sub-Gaussian distribution and etc. For further improving accuracy, we formulate and derive optimal dummy sizes for both non-adaptive and adaptive dummies. Finally, to break the error barrier due to the constraint of sending one single message per user, we design a multi-message shuffle private protocol supporting random population. Experiment results show that our approaches reduce more than 60% error when compared to the local model and naive approaches. We hope this work provides tailored solutions of shuffle privacy for dynamic mobile/distributed computing.
A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery

Bayesian networks (BNs) are a widely used graphical model in machine learning. As learning the structure of BNs is NP-hard, high-performance computing methods are necessary for constructing large-scale networks. In this article, we present a parallel framework to scale BN structure learning algorithms to tens of thousands of variables. Our framework is applicable to learning algorithms that rely on the discovery of Markov blankets (MBs) as an intermediate step. We demonstrate the applicability of our framework by parallelizing three different algorithms: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Grow-Shrink</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GS</i> ), <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Incremental Association MB</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IAMB</i> ), and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Interleaved IAMB</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Inter-IAMB</i> ). Our implementations are available as part of an open-source software called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ramBLe</i> , and are able to construct BNs from real data sets with tens of thousands of variables and thousands of observations in less than a minute on 1024 cores, with a speedup of up to 845X and 82.5% efficiency. Furthermore, we demonstrate using simulated data sets that our proposed parallel framework can scale to BNs of even higher dimensionality. Our implementations were selected for the reproducibility challenge component of the 2021 student cluster competition (SCC’21), which tasked undergraduate teams from around the world with reproducing the results that we obtained using the implementations. We discuss details of the challenge and the results of the experiments conducted by the top teams in the competition. The results of these experiments indicate that our key results are reproducible, despite the use of completely different data sets and experiment infrastructure, and validate the scalability of our implementations.
Bayesian Self-Attentive Speaker Embeddings for Text-Independent Speaker Verification

Learning effective and discriminative speaker embeddings is a crucial task in speaker verification. Usually, speaker embeddings are extracted from a speaker-classification DNN that averages the hidden vectors over all the spoken frames of a speaker; the hidden vectors produced from all the frames are assumed to be equally important. In our previous work, we relaxed this assumption and computed the speaker embedding as a weighted average of a speaker's frame-level hidden vectors, and their weights were automatically determined by a self-attention mechanism. The effect of multiple attention heads have also been investigated to capture different aspects of a speaker's input speech. One challenge for multi-head attention is the information redundancy problem. If there is no constraint during the training of multi-head attention, different heads may extract similar attentive features, leading to the attention redundancy problem. In this paper, we generalize the deterministic multi-head attention to a Bayesian attention framework, and provide a new understanding of multi-head attention from a Bayesian perspective. Under the Bayesian framework, we adopt the recently developed sampling method in optimization, which explicitly enforces the repulsiveness among the multiple heads. Systematic evaluation of the proposed Bayesian self-attentive speaker embeddings is performed on VoxCeleb and SITW evaluation sets. Significant and consistent improvements over other multi-head attention systems are achieved on all the evaluation datasets. The best Bayesian system with eight heads improves the EER by around 26% on VoxCeleb and 9% on SITW over the single-head baseline.
A Comparison of a Touch-Gesture- and a Keystroke-Based Password Method: Toward Shoulder-Surfing Resistant Mobile User Authentication

The pervasive use of mobile devices exposes users to an elevated risk of shoulder-surfing attacks. Despite the prior work on shoulder-surfing resistance of mobile user authentication methods, there is a lack of empirical studies on textual password authentication methods, particularly the hybrid passwords that integrate textual passwords with biometrics. To fill the literature gap, this research compares two hybrid password methods, touch-gesture- and keystroke-based passwords, with respect to their shoulder-surfing resistance performance. We select a touch-gesture-based password method that deploys multiple shoulder-surfing resistance strategies and a keystroke-based password method that leverages keystroke dynamics. To gain a holistic understanding of these password methods, we examine them under a variety of shoulder-surfing settings by varying interaction mode, observation angle, entry error, and observation effort. Going beyond effectiveness metrics, we also introduce efficiency metrics to assess shoulder-surfing resistance performance more comprehensively. We hypothesize and test the effects of shoulder-surfing settings by conducting both a longitudinal lab experiment and an online experiment with diversified participants. The results of both studies demonstrate the superior performance of the touch-gesture-based password method to the keystroke-based counterpart. The results also provide evidence for the effects of interaction mode, observation angle, and observation effort on shoulder-surfing resistance of hybrid passwords. Our findings offer suggestions for the design and strategies for strengthening the security of password authentication methods.
MRI image denoising using U-Net and Image Processing Techniques

Brain tumors are one of the leading causes of death, and hence it is critical to diagnose them early. MRI is the most effective diagnostic tool for detecting a tumor. However, thermal noise, temperature fluctuations, and other artifacts can generate noisy MRI scans, leading to inaccurate diagnoses. Deep learning algorithms combined with image processing techniques have aided in a variety of medical imaging tasks, including enhancing MRI images. Our work proposes a U-Net architecture with two encoder-decoder pairs for denoising MRI scans which were finely tuned on a dataset generated by injecting synthetic Gaussian noise. The model improved the Peak Signal to Noise Ratio (PSNR) from 11.90 to 30.96. The presented work also provides empirical evidence that the proposed denoising strategy enhances the prediction accuracy of brain tumors by nearly 23%. The developed denoising technique using U-Net would benefit radiologists and computer-aided diagnostic systems (CAD) in precisely diagnosing the disease by generating cleaner and clearer MRI scans.
GraphFly: Efficient Asynchronous Streaming Graphs Processing via Dependency-Flow

Existing streaming graph processing systems typically adopt two phases of refinement and recomputation to ensure the correctness of the incremental computation. However, severe redundant memory accesses exist due to the unnecessary synchronization among independent edge updates. In this paper, we present GraphFly, a high-performance asynchronous streaming graph processing system based on dependency-flows. GraphFly features three key designs: 1) Dependency trees (D-trees), which helps quickly identify independent graph updates with low cost; 2) Dependency-flow based processing model, which exploits the space-time dependent co-scheduling for cache efficiency; 3) Specialized graph data layout, which further reduces memory accesses. We evaluate GraphFly, and the results show that GraphFly significantly outperforms state-of-the-art systems KickStarter and GraphBolt by 5.81× and 1.78× on average, respectively. Also, GraphFly scales well with different sizes of update batch and compute resources.
SFS: Smart OS Scheduling for Serverless Functions

Serverless computing enables a new way of building and scaling cloud applications by allowing developers to write fine-grained serverless or cloud functions. The execution duration of a cloud function is typically short-ranging from a few milliseconds to hundreds of seconds. However, due to resource contentions caused by public clouds' deep consolidation, the function execution duration may get significantly prolonged and fail to accurately account for the function's true resource usage. We observe that the function duration can be highly unpredictable with huge amplification of more than 50× for an open-source FaaS platform (OpenLambda). Our experiments show that the OS scheduling policy of cloud functions' host server can have a crucial impact on performance. The default Linux scheduler, CFS (Completely Fair Scheduler), being oblivious to workloads, frequently context-switches short functions, causing a turnaround time that is much longer than their service time. We propose SFS (Smart Function Scheduler), which works entirely in the user space and carefully orchestrates existing Linux FIFO and CFS schedulers to approximate Shortest Remaining Time First (SRTF). SFS uses two-level scheduling that seamlessly combines a new FILTER policy with Linux CFS, to trade off increased duration of long functions for significant performance improvement for short functions. We implement SFS in the Linux user space and port it to OpenLambda. Evaluation results show that SFS significantly improves short functions' duration with a small impact on relatively longer functions, compared to CFS.
Productive Performance Engineering for Weather and Climate Modeling with Python

Earth system models are developed with a tight coupling to target hardware, often containing specialized code predicated on processor characteristics. This coupling stems from using imperative languages that hard-code computation schedules and layout. We present a detailed account of optimizing the Finite Volume Cubed-Sphere Dynamical Core (FV3), improving productivity and performance. By using a declarative Python-embedded stencil domain-specific language and data-centric optimization, we abstract hardware-specific details and define a semi-automated workflow for analyzing and optimizing weather and climate applications. The workflow utilizes both local and full-program optimization, as well as user-guided fine-tuning. To prune the infeasible global optimization space, we automatically utilize repeating code motifs via a novel transfer tuning approach. On the Piz Daint supercomputer, we scale to 2,400 GPUs, achieving speedups of up to 3.92× over the tuned production implementation at a fraction of the original code.
The Mathematical Competencies Framework and Digital Technologies

This chapter discusses the influence of the increased use of digital technologies for mathematical teaching and learning and their relevance to the Danish mathematical competencies framework (KOM) (Niss & Højgaard, 2011, 2019), and how the different types of digital technologies may influence different components of the KOM framework. Inspired by the notion of mathematical digital competency (MDC) (Geraniou & Jankvist, 2019), we address the influence and potential of digital technologies in relation to the KOM framework's three components of (1) the eight mathematical competencies (for students of mathematics); (2) the three second order competencies, known as types of overview and judgement; and (3) the framework's description of the six competencies for mathematical teaching. Using carefully chosen examples, we facilitate our discussions on how these different components of KOM may be perceived in the digital age. We conclude by discussing how mathematical competencies are "affected" by digital technologies and argue for revisiting the KOM mathematical competencies and extending the MDC framework.
An exploratory landscape analysis driven artificial bee colony algorithm with maximum entropic epistasis

Continuous real-valued optimization problems are existed in various engineering optimization scenarios. The artificial bee colony algorithm (ABC) which is inspired by the foraging behavior of bee colonies is an efficient optimization algorithm to address complex continuous real-valued optimization problems. In this paper, the maximum entropic epistasis (MEE) in the exploratory landscape analysis (ELA) is adopted in the ABC algorithm (MEEABC) to improve the performance of the ABC algorithm. The dimension interaction of the continuous functions computed by the MEE is introduced to guide the search process of the MEEABC algorithm during the employed bee phase and onlooker bee phase. The adaptive mutation methods and the strategy of dynamic population size reduction are implemented to increase the convergence speed and dynamically ameliorate the local exploitation capability. The solutions in the fitness landscape are automatically divided into different clusters to explore the local basin of the fitness landscape via the collaboration between MEE and adaptive mutation methods. The performance of MEEABC is tested on CEC 2017 benchmark test suite. From the experimental results, the MEEABC is superior to ABC variants and state-of-art algorithms regarded with the performance of effectiveness and robustness.
Local augmentation based consistency learning for semi-supervised pathology image classification

Labeling pathology images is often costly and time-consuming, which is quite detrimental for supervised pathology image classification that relies heavily on sufficient labeled data during training. Exploring semi-supervised methods based on image augmentation and consistency regularization may effectively alleviate this problem. Nevertheless, traditional image-based augmentation (e.g., flip) produces only a single enhancement to an image, whereas combining multiple image sources may mix unimportant image regions resulting in poor performance. In addition, the regularization losses used in these augmentation approaches typically enforce the consistency of image level predictions, and meanwhile simply require each prediction of augmented image to be consistent bilaterally, which may force pathology image features with better predictions to be wrongly aligned towards the features with worse predictions.To tackle these problems, we propose a novel semi-supervised method called Semi-LAC for pathology image classification. Specifically, we first present local augmentation technique to randomly apply different augmentations produces to each local pathology patch, which can boost the diversity of pathology image and avoid mixing unimportant regions in other images. Moreover, we further propose the directional consistency loss to enforce restrictions on the consistency of both features and prediction results, thus improving the ability of the network to obtain robust representations and achieve accurate predictions.The proposed method is evaluated on Bioimaging2015 and BACH datasets, and the extensive experiments show the superior performance of our Semi-LAC compared with state-of-the-art methods for pathology image classification.We conclude that using the Semi-LAC method can effectively reduce the cost for annotating pathology images, and enhance the ability of classification networks to represent pathology images by using local augmentation techniques and directional consistency loss.
Introducing a general-purpose augmented reality platform for the use in engineering education

There is a lack of a universal Augmented Reality platform which can be used in manufacturing engineering and other education fields to display models, processes, animations and simulations alike. Such a platform has been developed as part of this contribution and enables instructors to manage online courses, teaching units and even entire study programs. To enhance the teaching in the classroom by using Augmented Reality visualizations, a new application has been developed, which runs on iOS as well as Android systems and displays the various objects along with additional information uploaded by the instructors. A novel storage format was devised which reduces the storage size of various models significantly wherefore performance on the phone's end is improved. Various common 3D file formats, such as STL, OFF and OBJ, can be imported and automatically converted to this new format. The same applies for results from FEM software Abaqus, MoldFlow and HyperXtrude. Results formatted to be analyzed by the popular pre-/post-processor GiD can also be uploaded at no additional expense. The users of the smartphone app can view, inspect and interact with the models and animations. The platform and app are designed for an easy-to-use setup by the educators and an intuitive use by the students.
Blockchain based smart contract for cooperative spectrum sensing in cognitive radio networks for sustainable beyond 5G wireless communication

Cognitive radio networks allow opportunistic spectrum access to the unlicensed users, which rely on effective spectrum sensing. Recently, cooperative sensing has been found to improve sensing reliability. However, sometimes the selfish users do not cooperate, and therefore, different incentive design mechanisms like contract theory have been proposed recently, where the cooperation is encouraged by offering appropriate reward to the sensing participants. However, still no secure structure exists for the reward distribution after a successful contract completion, and a user may also deny sensing even after accepting the contract. This work proposes the use of blockchain-based smart contracts, which are based upon a permissioned blockchain that helps in making the transactions private among the participants. Further, the efficiency of this approach is improved by introducing a reputation parameter for the users. This measures the trustworthiness of a particular user based upon the accuracy of their recent sensing results. Further, a money locking approach is proposed from which a penalty is deducted if the user denies or fails to submit a correct reading. Performance analysis of the proposed scheme is done to show the superiority of the scheme.
Exploiting Channel Similarity for Network Pruning

To address the limitations of existing pruning methods in practical applications, such as the necessity of training from scratch with sparsity regularization or complex data-driven optimization, we set out from a novel perspective to explore parameter redundancy and accelerate deep CNNs. Precisely, we argue that channels revealing similar feature information have functional overlap and that each such similarity group can be reduced to a few representatives with little impact on the representational power of the model. After deriving an effective metric for evaluating channel similarity via probabilistic modeling, we introduce a similarity-based pruning framework based on hierarchical clustering.In particular, the proposed algorithm can be directly applied to all kinds of pre-trained CNN models for better trade-offs between latency and accuracy. Moreover, rather than relying on a pre-defined target structure, it automatically discovers resource-efficient ones out of the original model under given budgets, which is in the same flavor as NAS. Extensive experiments on benchmark datasets well demonstrate the superior performance of our approach over prior arts. On ImageNet, our pruned ResNet-50 with 30% FLOPs reduced outperforms the original model. We further extend our algorithm to a GAN-based generative model and achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> acceleration, showing its remarkable generalization capability and flexibility.