Reviewerly/exHarmony huggingface dataset:
Is It Coming Soon to Power Systems: Quantum Computing and Its Early Exploration

With the global trend of pursuing clean energy and decarbonization, power systems have been evolving in a fast pace that we have never seen in the history of electrifi-cation. Power system researchers have been leveraging classical high-performance computing (HPC) techniques to increase the computational speed for better decision support. However, there are intractable problems that cannot be handled by classical HPC. Quantum computing (QC) as an emerging technology has been well recognized and holds promises to significantly revolutionize computing for complex power system applications. This paper introduces the fundamentals of quantum computing, briefly summarizes the existing quantum computing applications in literature, and explains the need of exploring quantum computing potentials for power systems. A quantum neural network application in power system contingency analysis is presented as an example to show the potential of quantum computing. More importantly, this paper emphasizes the need for co-existence of classical HPC and quantum computing and recommends three quantum computing research directions in power systems: quantum-inspired algorithm for large-scale optimization, variational quantum algorithms (VQA) translation, and power system data qubitization.
Tomo2Mesh: Fast Porosity Mapping and Visualization for Synchrotron Tomography

Applications of X-ray computed tomography (CT) in porosity characterization of engineering materials often involve an extensive data analysis workflow. This workflow includes CT reconstruction of raw projection data, binarization, labeling, and mesh extraction. Mapping porosity in larger samples presents a significant computational challenge, as it requires processing gigabytes of raw data to extract porosity information, which becomes a critical bottleneck in the analysis. In this study, we present algorithms and an implementation of an end-to-end porosity mapping framework. Our framework processes raw projection data obtained from a synchrotron CT instrument, generating a porosity map and a visualization in the form of a triangular face mesh. To achieve this objective, we introduce a novel subset reconstruction scheme for X-ray CT, combining filtered back-projection and a convolutional neural network. This scheme allows us to reconstruct subsets of a tomography object with arbitrary shapes. Building upon this scheme, we have developed a fast and efficient framework for porosity mapping. Initially, our framework detects potential voids by performing a coarse reconstruction on down-sampled projections. Subsequently, we enhance the shape of these voids by reconstructing selected subsets from the original raw data, providing higher detail. To evaluate the performance of our framework, we measured the processing time from raw data to a triangular face mesh across multiple visualization scenarios. Our experiments were conducted on a single high-performance workstation equipped with a GPU. The results demonstrate that our framework enables the visualization of local porosity within an 8-gigavoxel CT volume (12 gigabytes raw data) in just 1 to 2 minutes. Moreover, for a larger 64-gigavoxel CT volume (100 gigabytes of raw data), the visualization can be generated within 3 to 7 minutes, showcasing the efficiency of our approach.
A Competent Memetic Algorithm for Error Mitigation in Quantum Measurement

Recently, a significant interest is arising in developing techniques capable of correcting errors in noisy quantum computations without using additional quantum resources. These techniques, known as mitigation methods, are aimed at post-processing the quantum outcome without working at quantum hardware level. Among the most error-prone operations on the current quantum devices, there is surely the quantum measurement. Currently, the most popular mitigation method for quantum measurement error consists of computing a so-called mitigation matrix to be applied on the results outputted by a quantum processor to transform them and make them closer to the ideal ones. In this paper, a new measurement error mitigation method based on a competent memetic algorithm is proposed to generate an appropriate mitigation matrix. As shown in the experimental session, the proposed measurement error mitigation method shows better results when compared with the conventional method and the state of the art among the evolutionary approaches.
Storm-RTS: Stream Processing with Stable Performance for Multi-Cloud and Cloud-edge

Stream Processing Engines (SPEs) traditionally de-ploy applications on a set of shared workers (e.g., threads, processes, or containers) requiring complex performance man-agement by SPEs and application developers. We explore a new approach that replaces workers with Rate-based Abstract Ma-chines (RBAMs). This allows SPEs to translate stream operations into FaaS invocations, and exploit guaranteed invocation rates to manage performance. This approach enables SPE applications to achieve transparent and predictable performance. We realize the approach in the Storm-RTS system. Exploring 36 stream processing scenarios over 5 different hardware config-urations, we demonstrate several key advantages. First, Storm-RTS provides stable application performance and can enable flexible reconfiguration across cloud resource configurations. Sec-ond, SPEs built on RBAM can be resource-efficient and scalable. Finally, Storm-RTS allows the stream-processing paradigm to be extended from the cloud to the edge, using its performance stability to hide edge heterogeneity and resource competition. An experiment with 4 cloud and edge sites over 300 cores shows how Storm-RTS can support flexible reconfiguration and simple high-level declarative policies that optimize resource cost or other criteria.
Optimization of Multi-Strategy Machine Translation System Based on AI Technology

As one of many methods of Artificial intelligence (AI), deep learning is inspired by the structure and function of human brain. By simulating the interconnection between neurons, an artificial neural network is constructed for repeated training until the expected value is output. Traditional English machine translation (MT) relies too much on expert summary, but there is a big error in English translation under weakening rules. In this paper, the semantic is Gaussian marginalized to obtain its rectangular window function and window feature vector, and the semantic correlation factors are added to the information entropy and information gain of the text. Compared with the traditional MT model, the evaluation accuracy of the proposed algorithm is improved by 25.68%. When English translation is based on AI technology, it is first necessary to translate the semantics of words. Mechanical intelligent recognition is used to select the coherent and key terms in semantics, and a certain language model is constructed to construct the English context and semantic connection. Combined with the fuzzy semantics of translation, the best translation solution is finally selected through discrimination and comparison.
ProtoDINet: End-to-End Interpretable Prototypical Model for Insulator Damage Detection

Damaged grid components such as insulators in transmission line can cause widespread impact in downstream connections such as outages. Preventing such failures requires frequent laborious inspection of thousands of power grid components, hence currently is done quite infrequently. In an era of societal electrification, there is an increasing need for an automated approach to continuous monitoring and inspection of grid components. In this paper, we propose an explainable deep learning based approach for damage detection and automated inspection of transmission lines from aerial images. We propose ProtoDINet - an interpretable end-to-end trained prototypical damaged insulator detection network. Unlike the black box machine learning models, ProtoDINet learns explainable prototypes during training-making it inherently interpretable. The most relevant prototypes are then automatically identified using a combinatorial multi-armed bandit (MAB) method targeted to improve class distinction. The final learned prototypes facilitate end users’ understanding of the model’s decision making process. We report promising results in F1 and AUC scores, visualize meaningful learnt prototypes, and demonstrate the effectiveness of the MAB-based prototype selection both quantitatively and qualitatively. This demonstrates the potential for more trustworthy interpretable solutions compared to existing black-box methods and encourages further research in the direction of prototypical networks for power grid.
GDTS: GAN-Based Distributed Tabular Synthesizer

Generative Adversarial Networks (GANs) are typically trained to synthesize data, from images and more recently tabular data, under the assumption of directly accessible training data. While learning image GANs on Federated Learning (FL) and Multi-Discriminator (MD) systems has just been demonstrated, it is unknown if tabular GANs can be learned from decentralized data sources. Different from image GANs, state-of-the-art tabular GANs require prior knowledge on the data distribution of each (discrete and continuous) column to agree on a common encoding - risking privacy guarantees. In this paper, we propose GDTS, a distributed framework for GAN-based tabular synthesizer. GDTS provides different system architectures to match the two training paradigms termed GDTS_FL and GDTS_MD. Key to enable learning on distributed data is the proposed novel privacy-preserving multi-source feature encoding to capture the global data properties. In addition GDTS encompasses a weighting strategy based on table similarity to counter the detrimental effects of non-IID data and a validation pipeline to easily assess and compare the performance of different paradigms and hyper parameters. We evaluate the effectiveness of GDTS in terms of synthetic data quality, and overall training scalability. Experiments show that GDTS_FL achieves better statistical similarity and machine learning utility between generated and original data compared to GDTS_MD.
Robust Density-Based Data Clustering Using a Quantum-Inspired Genetic Algorithm

Density-based clustering methods such as DBSCAN are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which are not trivial. In this paper, the user-defined parameters of DBSCAN are evolved using a quantum-inspired genetic algorithm (QGA). The quantum-bit or Q-bit representation of a partition is an improvement over the more popular binary label-based representations and real-coded representation of partition cluster centers. A resulting algorithm called DBSCAN-QGA in the relational data space is proposed, and three different fitness functions are devised to evaluate partitions both in terms of cluster compactness and separation, and the relative number of entities classified as noise. The performance of the proposed algorithm is compared to synthetic and benchmark datasets from the UCI machine learning repository with encouraging results.
Balancing Privacy and Utility in Surveillance Systems: An Overview

This paper delves into the critical issue of privacy protection in video surveillance systems, emphasizing the need for comprehensive and interdisciplinary approaches. The study presents recent advancements in de-and re-identification technologies aimed at ensuring personal information protection. The mechanisms for creating high-quality anonymized images that do not compromise the original data distribution and methodologies for effective facial image restoration are explored. Special attention is given to the operational dynamics of systems under specific conditions and their management of distinct facial features. The necessity for an integrated approach in constructing AI systems in edge computing is highlighted, focusing on anonymization techniques, re-identification prevention, and data distribution maintenance. Through these research efforts, we anticipate balancing privacy protection and data identifiability, offering a solid foundation for future exploration in this field.
GridViz: a Toolkit for Interactive and Multi-Modal Power Grid Data Visualization

Understanding the states and evolution of power systems serves as one of the foundational modules of power systems. The major challenge in this context is the data sources are dispersed from system operators to utilities and independent organizations, while there is lack of an interactive visualization platform to integrate and present power system information along with geospatial features in an effective way. We propose GridViz, an open-source data analytics and visualization toolkit for efficient integration and analysis of multi-modal transmission grid data. By collecting and transforming different layers of data coming from ISOs, substations and OpenStreet map, and utilizing Geographic Information Systems (GIS), GridViz provides users an easy-to-use interface for analyzing time-series data as well as geographical insights regarding power grid operations. We demonstrate GridViz can help enhance grid situational awareness for a wide of targeted users ranging from engineers to market operators and electricity energy end users. Our visualization tools can produce grid visualization which are easily viewable, editable, repeatable and can be produced as animations, and such methods can be served as backbones for a range of tasks such as grid marginal carbon emissions and contingencies analysis.
Wawel: Architecture for Scalable Attestation of Heterogeneous Virtual Execution Environments

Existing attestation mechanisms lack scalability and support for heterogeneous virtual execution environments (VEEs), such as virtual machines and containers executed inside or outside hardware isolation on different vendors' hardware in clouds managed by various organizations. To overcome these limitations, hardware vendors and cloud providers implement proprietary mechanisms (Intel DCAP, Amazon NitroTPM, Google Titan) to support their offerings. However, due to their plurality, the attestation becomes cumbersome because it increases maintenance and integration costs and reduces portability required in hybrid- and multi-cloud deployments. We introduce WAWEL, a framework that enables scalable attestation of heterogeneous VEEs. WAWEL can be plugged into existing hardware-specific attestation mechanisms, offering a unified interface. WAWEL supports the widely adopted trusted platform module (TPM) attestation standard. We implemented a prototype and integrated it with three different VEEs. It supports runtime integrity attestation with Linux integrity measurement architecture (IMA) and legacy applications requiring zero-code changes. The evaluation demonstrated that the WAWEL prototype achieves very good performance and scalability despite the indirections between the VEE and hardware root of trust.
Tracker Learning Surgical Images By Self-Supervised Learning: An Enhanced Unsupervised Deep Tracking Approach

Tracking the surgical site in surgical images is a method that can help doctors. Therefore, we proposed a tracker model that learns by self-supervised learning for tissue organization in surgical images. We constructed a model based on unsupervised deep tracking (UDT) as a base line Our proposed model incorporates forward and backward prediction in conjunction with a Siamese network, a feature that sets it apart as a robust tracking solution. Through a series of extensive experiments, we established the effectiveness of our proposed model in comparison with existing models in the field. Our model achieved an Expected Average Overlap (EAO) score of 0.405, demonstrating significant improvements over existing models: TransT (0.274), KIT (0.223), SRV (0.293), and MEDCVR (0.302), with respective score improvements of 0.131, 0.182, 0.112, and 0.103. These results confirm that our proposed model can be helpful in performing effective tracking in surgical videos.
Few-Shot Object Detection Based on Contrastive Learning

Few-shot object detection is a challenging research topic in the field of computer vision. In scenarios with a limited number of training samples, traditional object detection algorithms often suffer from overfitting, leading to subpar classification accuracy and imprecise localization. To address these challenges, we propose a few-shot object detection algorithm based on contrastive learning, encompassing the design of data strategies and model structures. To mitigate the issues arising from limited data and insufficient intra-class diversity, we introduce data augmentation strategies involving saliency-mixed image enhancement and data resampling. Additionally, to tackle problems such as misclassification of new instances and inaccurate object localization, we design a comprehensive model structure for few-shot object detection based on contrastive learning. Experimental evaluations are conducted on general datasets PASCAL VOC. The results demonstrate the high effectiveness and practicality of the proposed approach in few-shot object detection tasks, underscoring its significant research significance and practical application value.
Evolving Intertask Mappings for Transfer in Reinforcement Learning

Recently, there has been a focus on using transfer learning to reduce the sample complexity in reinforcement learning. One component that enables transfer is an intertask mapping that relates a pair of tasks. Automatic methods attempt to learn task relationships either by evaluating all possible mappings in a brute force manner, or by using techniques such as neural networks to represent the mapping. However, brute force methods do not scale well in problems since there is an exponential number of possible mappings, and automatic methods that use complex representations generate mappings that are not always interpretable. In this paper, we describe a population-based algorithm that generates intertask mappings in a tractable amount of time. The idea is to use an explicit representation of an intertask mapping, and to combine an evolutionary algorithm with an offline evaluation scheme to search for the optimal mapping. Experiments on two transfer learning problems show that our approach is capable of finding highly-fit mappings and searching a space that is infeasible for a brute force approach. Furthermore, agents that learn using the mappings found by our approach are able to reach a performance target faster than agents that learn without transfer.
Towards Confidential Computing: A Secure Cloud Architecture for Big Data Analytics and AI

Cloud computing provisions computer resources at a cost-effective way based on demand. Therefore it has become a viable solution for big data analytics and artificial intelligence which have been widely adopted in various domain science. Data security in certain fields such as biomedical research remains a major concern when moving their workflows to cloud, because cloud environments are generally outsourced which are more exposed to risks. We present a secure cloud architecture and describes how it enables workflow packaging and scheduling while keeping its data, logic and computation secure in transit, in use and at rest.
Mapping the Repository Landscape: Harnessing Similarity with RepoSim and RepoSnipy

The rapid growth of scientific software development has led to the emergence of large and complex codebases, making it challenging to search, find, and compare software repositories within the scientific research community. In this paper, we propose a solution by leveraging deep learning techniques to learn embeddings that capture semantic similarities among repositories. Our approach focuses on identifying repositories with similar semantics, even when their code fragments and documentation exhibit different syntax. To address this challenge, we introduce two complementary open-source tools: RepoSim and RepoSnipy. RepoSim is a command-line toolbox designed to represent repositories at both the source code and documentation levels. It utilizes the UniXcoder pre-trained language model, which has demonstrated remarkable performance in code-related understanding tasks. RepoSnipy is a web-based neural semantic search engine that utilizes the powerful capabilities of RepoSim and offers a user-friendly search interface, allowing researchers and practitioners to query public repositories hosted on GitHub and discover semantically similar repositories. RepoSim and RepoSnipy empower researchers, developers, and practitioners by facilitating the comparison and analysis of software repositories. They not only enable efficient collaboration and code reuse but also accelerate the development of scientific software.
Research on Safety Testing and Evaluation Technology of Artificial Intelligence System for Automatic Driving

Automatic driving is the main application field of artificial intelligence technology. With the continuous expansion of technology and application and the frequent occurrence of safety accidents, the safety of automatic driving artificial intelligence system has gradually become a new research hotspot. This paper introduces the development status of security evaluation technology of artificial intelligence system for automatic driving from the aspects of adversarial examples attack algorithm, data set quality evaluation, testing and evaluation platform and so on.
A Network Synthesis and Analytics Pipeline with Applications to Sustainable Energy in Smart Grid

Transitioning to clean and low-carbon energy is becoming a crucial goal for many entities in the energy systems sector such as governments, power utilities, and policymakers. This shift to clean energy is supported by a diverse portfolio of data products such as satellite data, smart meter data, power networks, green energy datasets (e.g., solar installations & electric vehicles), microgrid networks, and building stock data. Among these, network datasets are becoming increasingly common in addressing a wide array of issues in residential energy, especially in applications that focus on social good. Thus, streamlining the process of generating different types of networks will be helpful. In this work, we propose a versatile network synthesis and analytics pipeline developed using software design principles that make it modular, scalable, and extensible. Three case studies are presented to illustrate the significance of network data in sustainable energy applications.
Negatively Correlated Search for Constrained Optimization

Evolutionary algorithms (EAs) combined with constraint handling techniques (CHTs) are very effective in solving constrained optimization problems (COPs). Recently, negatively correlated search (NCS) has been shown to be powerful for real-world multimodal optimization problems (MMOP). However, there has been few works on utilizing NCS to solve COPs. In this paper, we present a novel constrained optimization evolutionary algorithm (COEA) named NCS-E which combined NCS with a new CHT ensemble method to deal with COPs. It integrates three complementary CHTs as voters, and individuals are first voted by each voter, then the individual with more weighted votes is considered better. In addition, the negative correlation information of the voted individuals is utilized to adaptively adjust the voting weights. To demonstrate the performance of NCS-E, NCS-E is compared with four NCS-based COEAs and two state-of-the-art methods, ∊MAgES and MAgES-VMCH, on 57 real-world COPs. The statistical results show that NCS-E exhibits the best performance among the compared methods. Moreover, NCS-E was applied to solve the Deep Neural Network pruning problem and compared with the state-of-the-art pruning method OLMP on three reference models. Empirical results show that the proposed method improves the performance of OLMP on all three DNN pruning tasks.