Reviewerly/exHarmony huggingface dataset:
Simultaneous measurement of external and internal surface shape and deformation based on photogrammetry and stereo-DIC

• A systematic solution for simultaneous measurement of external and internal surface shape and deformation is proposed, in which the traditional stereo-DIC (SDIC) units are responsible for the external surface, and the reflection-assisted stereo-DIC (RSDIC) units are responsible for the internal surface; both flexible and accurate. • The structure of the RSDIC unit is proposed. Subsequently, the reflection pinhole imaging model and image correction for camera calibration are presented. The RSDIC unit can effectively deal with the measurement difficulty due to occlusion caused by the object itself and other obstacles, expanding the application range of DIC technology. • A coordinate alignment method for multi-cameras is detailed, which employs photogrammetry to reconstruct the 3D global points to solve the camera's relative parameters through resection, both robust and general for complex engineering environments. • In addition to the application in DIC technology, the structure of the RSDIC unit in the proposed system has a good reference for shape reconstruction applications such as structured light scanning in the deep hole with complex profiles. Mechanical structures with cavities such as auto rim and aero-engine casing are widely used in engineering. It is of great significance for their dynamic performance evaluation if one can measure the shape and deformation of the internal and external surfaces together. However, due to the narrow interior space and self-occlusion of complex structures, the conventional stereo-DIC unit is not applicable to impose the simultaneous measurement in the outer and inner areas. This paper is therefore concerned with a systematic solution for the issue. A reflection-assisted multi-view DIC system is configured, which consists of the stereo-DIC (SDIC) unit and the reflection-assisted stereo-DIC (RSDIC) unit. The design idea and selection essential of the RSDIC unit are stated. Subsequently, a correction model for reflection imaging is detailed. Close-range photogrammetry is specially adopted to compute the 3D global points. The extrinsic parameters of each DIC unit are estimated via the rear resection so that the individual reference frame can be transformed into a common coordinate system. Five experiments, including two tests of calibration accuracy, shape and deformation measurement accuracy, and applications in an aero-engine casing prototype, prove the precision and effectiveness of the proposed method.
Colder Than the Warm Start and Warmer Than the Cold Start! Experience the Spawn Start in FaaS Providers

Many researchers reported considerable delay of up to a few seconds when invoking serverless functions for the first time. This phenomenon, which is known as a cold start, affects even more when users are running multiple serverless functions orchestrated in a workflow. However, in many cases users need to instantly spawn numerous serverless functions, usually as a part of parallel loops. In this paper, we introduce the spawn start and analyze the behavior of three Function-as-a-Service (FaaS) providers AWS Lambda, Google Cloud Functions, and IBM Cloud Functions when running parallel loops, both as warm and cold starts. We conducted a series of experiments and observed three insights that are beneficial for the research community. Firstly, cold start on IBM Cloud Functions, which is up to 2s delay compared to the warm start, is negligible compared to the spawn start because the latter generates additional 20s delay. Secondly, Google Cloud Functions' cold start is "warmer" than the warm start of the same serverless function. Finally, while Google Cloud Functions and IBM Cloud Functions run the same serverless function with low concurrency faster than AWS Lambda, the spawn start effect on Google Cloud Functions and IBM Cloud Functions makes AWS the preferred provider when spawning numerous serverless functions.
A Decoupled Kernel Prediction Network Guided by Soft Mask for Single Image HDR Reconstruction

Recent works on single image high dynamic range (HDR) reconstruction fail to hallucinate plausible textures, resulting in information missing and artifacts in large-scale under/over-exposed regions. In this article, a decoupled kernel prediction network is proposed to infer an HDR image from a low dynamic range (LDR) image. Specifically, we first adopt a simple module to generate a preliminary result, which can precisely estimate well-exposed HDR regions. Meanwhile, an encoder-decoder backbone network with a soft mask guidance module is presented to predict pixel-wise kernels, which is further convolved with the preliminary result to obtain the final HDR output. Instead of traditional kernels, our predicted kernels are decoupled along the spatial and channel dimensions. The advantages of our method are threefold at least. First, our model is guided by the soft mask so that it can focus on the most relevant information for under/over-exposed regions. Second, pixel-wise kernels are able to adaptively solve the different degradations for differently exposed regions. Third, decoupled kernels can avoid information redundancy across channels and reduce the solution space of our model. Thus, our method is able to hallucinate fine details in the under/over-exposed regions and renders visually pleasing results. Extensive experiments demonstrate that our model outperforms state-of-the-art ones.
A Machine-Learning Approach for the Reconstruction of Ground-Shaking Fields in Real Time

ABSTRACT Real-time seismic monitoring is of primary importance for rapid and targeted emergency operations after potentially destructive earthquakes. A key aspect in determining the impact of an earthquake is the reconstruction of the ground-shaking field, usually expressed as the ground-motion parameter. Traditional algorithms compute the ground-shaking field from the punctual data at the stations relying on ground-motion prediction equations computed on estimates of the earthquake location and magnitude when the instrumental data are missing. The results of such algorithms are then subordinate to the evaluation of location and magnitude, which can take several minutes. To fill the temporal gap between the arrival of the data and the estimate of these parameters, a new data-driven algorithm that exploits the information from the station data only is introduced. This algorithm, consisting of an ensemble of convolutional neural networks (CNNs) trained on a database of ground-shaking maps produced with traditional algorithms, can provide estimates of the ground-shaking maps and their associated uncertainties in real time. Because CNNs cannot handle sparse data, a Voronoi tessellation of a selected peak ground parameter recorded at the stations is computed and used as the input to the CNNs; site effects and network geometry are accounted for using a (normalized) VS30 map and a station location map, respectively. The developed method is robust to noise, can handle network geometry changes over time without the need for retraining, and can resolve multiple simultaneous events. Although having a lower resolution, the results obtained are statistically compatible with the ones from traditional methods. A fully operational version of the algorithm is running on the servers at the Department of Mathematics and Geosciences of the University of Trieste, showing real-time capabilities in handling stations from multiple Italian strong-motion networks and outputting results with a resolution of 0.05° × 0.05°.
Crypto-environment network connectivity and Bitcoin returns distribution tail behaviour

This study explores whether and to what extent cryptocurrency ecosystem network connectivity predicts Bitcoin returns across quantiles of the return distribution. The facets of cryptocurrency ecosystem network connectivity we consider include connectivity between the on- and off-chain segments of the Bitcoin market, the intensity and synchronization of social and traditional crypto-focused media activity, the intensity of network correlations between cryptocurrencies. We identify tail behaviour predictors employing a quantile regression approach. The results demonstrate the effectiveness of several connectivity measures in predicting both price spikes and downfalls, but in a different way before and during the COVID-19 outbreak.
Drone Surveillance in the Modern Agriculture

Drone technology is no longer available for military organizations only. It is so advanced and inexpensive, that every technology enthusiast could purchase and start using it in just a couple of minutes, without the need of any specific training. This level of accessibility of the drone technology allows its application to be expanded and reach various niche industries, including agriculture. Amongst the sheer activities that the modern drones are capable of, there is one that could help the nowadays farmer not only reduce their need on additional manpower, but also their monthly cost required to raise and protect their livestock, regularly monitoring it from the sky.
Assemble Foundation Models for Automatic Code Summarization

Automatic code summarization is beneficial to software development and maintenance since it reduces the burden of manual tasks. Currently, artificial intelligence is undergoing a paradigm shift. The foundation models pretrained on massive data and finetuned to downstream tasks surpass specially customized models. This trend inspired us to consider reusing foundation models instead of learning from scratch. Based on this, we propose a flexible and robust approach for automatic code summarization based on neural networks. We assemble available foundation models, such as CodeBERT and GPT-2, into a single model named AdaMo. Moreover, we utilize Gaussian noise as the simulation of contextual information to optimize the latent representation. Furthermore, we introduce two adaptive schemes from the perspective of knowledge transfer, namely continuous pretraining and intermediate finetuning, and design intermediate stage tasks for general sequence-to-sequence learning. Finally, we evaluate AdaMo against a benchmark dataset for code summarization, by comparing it with state-of-the-art models.
Faster NTRU on ARM Cortex-M4 With TMVP-Based Multiplication

This paper focuses on speeding up NTRU - one of the lattice-based finalists of the NIST PQC competition - by improving the ring multiplication. The Number Theoretic Transform (NTT), Toom-Cook, and Karatsuba are the most commonly used algorithms for implementing NTRU. In this paper, we propose Toeplitz matrix-vector product (TMVP) based algorithms for multiplication for all parameter sets of NTRU. We implement the proposed algorithms on ARM Cortex-M4. The results show that the TMVP-based multiplication algorithms we propose are more efficient than the others in the literature in most cases. Our algorithm for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ntruhps2048509</i> outperform the Toom-Cook and NTT methods in the literature by 25.4% and 21.5%. We also observe the impact of these improvements on the overall performance of NTRU. We speed up the key generation, encryption, decryption, encapsulation, and decapsulation algorithms of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ntruhps2048509</i> by 12.5%, 14.3%, 17.7%, 3.9%, and 14.7%, respectively, compared to state-of-the-art implementation. Moreover, our algorithms require less stack space than the others.
HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment

Network alignment, or identifying the same entities (anchors) across multiple networks, has significant applications across diverse fields. Unsupervised approaches for network alignment, though popular, strictly assume that the anchor nodes' structure and attributes remain consistent across different networks. However, in practice, strictly adhering to these constraints makes it difficult to deal with networks with high variance in the structural characteristics and inherent structural noises like missing nodes and edges, resulting in poor generalization. In order to handle these shortcomings, we propose HCNA: Hyperbolic Contrastive Learning Framework for Self-Supervised Network Alignment, a novel self-supervised contrastive learning model which learns from the multiple augmented views of each network, thereby making HCNA robust to the inherent multi-network characteristics. Furthermore, we propose multi-order hyperbolic graph convolution networks to generate node embedding for each network which can handle the hierarchical structure of networks. The main objective of HCNA is to obtain structure-preserving embeddings that are also robust to noises and variations for better alignment results. The major novelty lies in generating augmented multiple graph views for contrastive learning that are driven by real world network dynamics. Rigorous investigations on 4 real datasets show that HCNA consistently outperforms the baselines by at least 1–84% in terms of accuracy score. Furthermore, HCNA is also more resilient to structural and attributes noises, as evidenced by its adaptivity analysis on adversarial conditions.
Fast unsupervised embedding learning with anchor-based graph

As graph technology is widely used in unsupervised dimensionality reduction, many methods automatically construct a full connection graph to learn the structure of data, and then preserve critical information on data in subspace. The construction of a full connection graph with heavy computational complexity, however, is separated from the optimization of transformation matrix. In order to solve significant computational burden, we design anchor-based graph and unify the construction of graph and optimization of transformation matrix into a framework called fast unsupervised embedding learning with anchor-based graph (FUAG) which not only can avoid the impact of noises and redundant features in original space, but also can capture local structure of data in subspace precisely. Our method additionally incorporates the discriminant information of data captured by using trace difference form. Meanwhile, it optimizes the anchor-based graph partitioning problem with Constrained Laplacian Rank in order to ensure that the number of connected components is exactly equal to the number of classes. We also impose ℓ0 norm constraint on each point to avoid trivial solutions and propose an efficient iterative algorithm. Experimental results on both synthetic and real-world datasets demonstrate the promising performance of the proposed algorithm.
Dlsa: Semi-supervised partial label learning via dependence-maximized label set assignment

Semi-supervised partial label learning is an emerging weakly supervised learning paradigm dealing with partially labeled data and unlabeled data simultaneously. The supervision information is weak and even missing for training instances. Most existing researches simply propagate labels to unlabeled data and focus on inducing a weighted classifier model, ignoring the importance of acquiring reliable label confidences for training instances. In this paper, by label set assignment, the valid label supervision information can be preserved and propagated to unlabeled instances to a greater extent. Then, the training instances are projected into a lower-dimensional feature space. Reliable label confidence can be obtained by maximizing the dependence between the feature description and the associated label information. After that, the predictive model can be induced by preserving the intrinsic manifold structure in both feature space and label space. Accordingly, a novel semi-supervised partial label learning approach via dependence-maximized label set assignment (Dlsa) is proposed, where an accurate and robust model can be induced based on reliable label confidences obtained by label set assignment and dependence-maximized dimensionality reduction. Extensive experiments on five real-world data sets validate the effectiveness and superiority of the proposed method.
Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition

Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic properties, such as their ability to work in low illumination, harsh weather conditions, and being low-cost and compact, making them highly preferable for a gesture recognition solution. However, most literature work focuses on solutions with a limited range that is lower than a meter. We propose a novel architecture for a long-range (1m – 2m) gesture recognition solution that leverages a point cloud-based cross-learning approach from camera point cloud to 60-GHz FMCW radar point cloud, which allows learning better representations while suppressing noise. We use a variant of Dynamic Graph CNN (DGCNN) for the cross-learning, enabling us to model relationships between the points at a local and global level and to model the temporal dynamics a Bidirectional Long short-term memory (LSTM) network is employed. In the experimental results section, we demonstrate our model's overall accuracy of 98.4% for five gestures and its generalization capability.
Data Drift in DL: Lessons Learned from Encrypted Traffic Classification

Deep learning models have shown to achieve high performance in encrypted traffic classification. However, when it comes to production use, multiple factors challenge the performance of these models. The emergence of new network traffic protocols, especially at the application-layer, as well as updates to previous protocols affect the patterns in input data, making the model's previously learned patterns obsolete. Furthermore, proposed model architectures are usually tested on datasets collected in controlled settings, which makes the reported performances unreliable for production use. In this paper, we study how the performances of two high-performing traffic classifiers change on multiple real-world datasets collected over the course of two years. We investigate the changes in traffic data patterns showing the extent to which these changes reduce the performance of the two models. Furthermore, we propose architectural adaptations to a flow time-series based traffic classifier, showing that they improve accuracy by 4.8%.
Veritaa-IoT: A Distributed Public Key Infrastructure for the Internet of Things

With the growth of the Internet of Things (IoT) and improved interoperability between various IoT devices of different manufacturers and owners, verifying the authenticity and integrity of data becomes a challenge. Cryptographic signatures together with Distributed Public Key Infrastructures (DPKI) and Distributed Ledger Technology (DLT) are promising solutions to this challenge. In this work, we extended the Veritaa framework, a DLT-based DPKI with an immutable signature store, to support IoT applications and evaluate its applicability. Therefore, we propose the systems architecture and implement a low-power IoT client. We built a real-world testbed with different sensors to evaluate the proposed architecture and extensions. In the evaluation, we analyze the time required to secure sensor data on the DLT, the overhead introduced to assert authenticity, integrity, and immutability of the data, and the security of our proposed solution.
Efficient Pairing-Free Identity-Based Signcryption Scheme for Cloud-Assisted IoT

The Internet of Things (IoT) has become a part of our everyday life. Due to limited storage and computational capabilities, data collected by IoT devices outsource to cloud servers. Although cloud servers provide many benefits, confidentiality and authenticity are the major issues. Signcryption is a cryptographic technique to address the above issues. Several identity-based signcryption schemes are proposed; however, these schemes create heavy computation and communication overhead because of bilinear pairings. This paper proposes an Efficient Pairing-Free Identity-based Signcryption (EPFIBSC) scheme based on Elliptic Curve Cryptography (ECC), which reduces computation and communication overhead. The EPFIBSC scheme's security is proven under Elliptic Curve Discrete Logarithm Problem (ECDLP). The scheme also meets the security requirements such as confidentiality, authenticity, and unforgeability. In performance analysis, the authors compare the scheme with some of the existing schemes; the comparison shows that this scheme is more efficient in computation and communication costs.
Designing a Data Visualisation for Interdisciplinary Scientists. How to Transparently Convey Data Frictions?

This study investigates how the frictions that emerge while synthesising disparate datasets can be transparently conveyed in a single data visualisation. We encountered this need while being embedded in an academic consortium of four epistemologically-distant scientific teams, who wanted to develop new interdisciplinary hypotheses from their merged datasets. By inviting these scientists to collaboratively develop visualisation prototypes of their data within their own and then towards the other disciplines, we uncovered four data frictions that relate to discipline-specific interpretations of data, methodological approaches, ways of handling data uncertainties, as well as the large differences in dataset scale and granularity. We then recognised how the resulting visualisation prototypes contained several promising techniques that addressed these frictions transparently, such as retaining their overall visualisation context and using visual translators to mediate between differing scales. Driven by critical data discourse that calls for frictions to be foregrounded rather than be occluded, we generalised these techniques into a series of actionable design considerations. While originating from a single case of an interdisciplinary collaboration, we believe that our findings form a crucial step towards enabling a more transparent and accountable interdisciplinary data visualisation practice.
Smart grid stability prediction using genetic algorithm-based extreme learning machine

The cutting-edge electrical utility industry is quick advancing into a keen blend of best-in-class digital advancements with the actual foundation, famously alluded to as cyber-physical system (CPS). These CPSs, which depend entirely on an extensive intelligent computing algorithm-based communication network, are vulnerable to digital assaults and are likewise touchy to various technical and socioeconomic variables trading off its stability. Evaluation and forecast of the stability of these CPSs are fundamental in this unique situation. Figuring such countless variables while designing a framework of stability assessment is humanly inconceivable and hence deployment of cutting-edge computational methods, for example, the machine learning-based model, is found generally reasonable for such purposes. In this work, a novel improved genetic algorithm (GA)-based extreme learning machine (ELM) model for smart-grid-CPS stability forecast has been proposed. The exploratory outcome in regards to the proposed model is then contrasted with other contemporary AI and profound learning models.
Unbiased and consistent rendering using biased estimators

We introduce a general framework for transforming biased estimators into unbiased and consistent estimators for the same quantity. We show how several existing unbiased and consistent estimation strategies in rendering are special cases of this framework, and are part of a broader debiasing principle. We provide a recipe for constructing estimators using our generalized framework and demonstrate its applicability by developing novel unbiased forms of transmittance estimation, photon mapping, and finite differences.