Reviewerly/exHarmony huggingface dataset:
Unsupervised Deep Learning for Structural Health Monitoring

In the last few decades, structural health monitoring has gained relevance in the context of civil engineering, and much effort has been made to automate the process of data acquisition and analysis through the use of data-driven methods. Currently, the main issues arising in automated monitoring processing regard the establishment of a robust approach that covers all intermediate steps from data acquisition to output production and interpretation. To overcome this limitation, we introduce a dedicated artificial-intelligence-based monitoring approach for the assessment of the health conditions of structures in near-real time. The proposed approach is based on the construction of an unsupervised deep learning algorithm, with the aim of establishing a reliable method of anomaly detection for data acquired from sensors positioned on buildings. After preprocessing, the data are fed into various types of artificial neural network autoencoders, which are trained to produce outputs as close as possible to the inputs. We tested the proposed approach on data generated from an OpenSees numerical model of a railway bridge and data acquired from physical sensors positioned on the Historical Tower of Ravenna (Italy). The results show that the approach actually flags the data produced when damage scenarios are activated in the OpenSees model as coming from a damaged structure. The proposed method is also able to reliably detect anomalous structural behaviors of the tower, preventing critical scenarios. Compared to other state-of-the-art methods for anomaly detection, the proposed approach shows very promising results.
Multi-Class Document Classification Using Lexical Ontology-Based Deep Learning

With the recent growth of the Internet, the volume of data has also increased. In particular, the increase in the amount of unstructured data makes it difficult to manage data. Classification is also needed in order to be able to use the data for various purposes. Since it is difficult to manually classify the ever-increasing volume data for the purpose of various types of analysis and evaluation, automatic classification methods are needed. In addition, the performance of imbalanced and multi-class classification is a challenging task. As the number of classes increases, so does the number of decision boundaries a learning algorithm has to solve. Therefore, in this paper, an improvement model is proposed using WordNet lexical ontology and BERT to perform deeper learning on the features of text, thereby improving the classification effect of the model. It was observed that classification success increased when using WordNet 11 general lexicographer files based on synthesis sets, syntactic categories, and logical groupings. WordNet was used for feature dimension reduction. In experimental studies, word embedding methods were used without dimension reduction. Afterwards, Random Forest (RF), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) algorithms were employed to perform classification. These studies were then repeated with dimension reduction performed by WordNet. In addition to the machine learning model, experiments were also conducted with the pretrained BERT model with and without WordNet. The experimental results showed that, on an unstructured, seven-class, imbalanced dataset, the highest accuracy value of 93.77% was obtained when using our proposed model.
Bitcoin Is Full of Surprises

Bitcoin has been embraced by many individuals with strong right-leaning views on freedom, property rights, and self-sovereignty. Among left-leaning progressives, Bitcoin is often quickly dismissed as irrelevant or a major source of carbon emissions. Bitcoin seems, however, to be full of surprises. A rapidly advancing body of anecdotal evidence suggests that its adoption may affect causes important to progressives, mitigating greenhouse gas emissions, accelerating electrification of economies, alleviating poverty, and supporting human rights for people living under political repression, even though scientific confirmation is lagging. In this paper, I highlight how a Pragmatist perspective can be applied to Bitcoin, a technological and financial innovation that may well reshape how humans perceive and use money, preserve wealth, and structure governance. I first cover Bitcoin’s technological and financial fundamentals and some core concepts of Pragmatism, before outlining how Bitcoin might surprise progressives. Pragmatism offers a philosophical and political grounding for left-leaning “progressive Bitcoiners” who prioritize environmental and social well-being and view inclusive deliberative democracy as the preferred form of governance.
Understanding Policy and Technical Aspects of AI-enabled Smart Video Surveillance to Address Public Safety

Abstract Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48% to 73.72% in anomaly detection and 96% to 83.07% in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds.
An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization

With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper, which provides a new idea for solving high-dimensional optimization problems. First, in order to balance the algorithm's search abilities in terms of breadth and depth, the value of parameter G is given by an adaptive dynamic adjustment method. Second, in this paper, a foraging-behavior-improvement strategy is utilized to improve the algorithm's solution accuracy and depth-optimization ability. Third, the artificial fish swarm algorithm (AFSA) is introduced to construct a dual-population collaborative optimization strategy based on chicken swarms and artificial fish swarms, so as to improve the algorithm's ability to jump out of local extrema. The simulation experiments on the 17 benchmark functions preliminarily show that the ADPCCSO algorithm is superior to some swarm-intelligence algorithms such as the artificial fish swarm algorithm (AFSA), the artificial bee colony (ABC) algorithm, and the particle swarm optimization (PSO) algorithm in terms of solution accuracy and convergence performance. In addition, the APDCCSO algorithm is also utilized in the parameter estimation problem of the Richards model to further verify its performance.
Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism

Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to weak computational scalability and the problem of dimensional catastrophe, ignoring potential correlations between sensing data and some important information of certain characteristics. In order to capture the correlation of multidimensional sensing data and improve the accuracy of anomaly detection effectively, GTAF, an anomaly detection model for multivariate sequences based on an improved graph neural network with a transformer, a graph attention mechanism and a multi-channel fusion mechanism, is proposed in this paper. First, we added a multi-channel transformer structure for intrinsic pattern extraction of different data. Then, we combined the multi-channel transformer structure with GDN’s original graph attention network (GAT) to attain better capture of features of time series, better learning of dependencies between time series and hence prediction of future values of adjacent time series. Finally, we added a multi-channel data fusion module, which utilizes channel attention to integrate global information and upgrade anomaly detection accuracy. The results of experiments show that the average accuracies of GTAF, the anomaly detection model proposed in this paper, are 92.83% and 96.59% on two datasets from unmanned systems, respectively, which has higher accuracy and computational efficiency compared with other methods.
A Survey on Sparsity Exploration in Transformer-Based Accelerators

Transformer models have emerged as the state-of-the-art in many natural language processing and computer vision applications due to their capability of attending to longer sequences of tokens and supporting parallel processing more efficiently. Nevertheless, the training and inference of transformer models are computationally expensive and memory intensive. Meanwhile, utilizing the sparsity in deep learning models has proven to be an effective approach to alleviate the computation challenge as well as help to fit large models in edge devices. As high-performance CPUs and GPUs are generally not flexible enough to explore low-level sparsity, a number of specialized hardware accelerators have been proposed for transformer models. This paper provides a comprehensive review of hardware transformer accelerators that have been proposed to explore sparsity for computation and memory optimizations. We classify existing works based on the strategies of utilizing sparsity and identify their pros and cons in those strategies. Based on our analysis, we point out promising directions and recommendations for future works on improving the effective sparse execution of transformer hardware accelerators.
My Lovely Granny’s Farm: An immersive virtual reality training system for children with autism spectrum disorder

One of the biggest difficulties faced by children with Autism Spectrum Disorder during their learning process and general life, is communication and social interaction. In recent years, researchers and practitioners have invested in different approaches to improving aspects of their communication and learning. However, there is still no consolidated approach and the community is still looking for new approaches that can meet this need. Addressing this challenge, in this article we propose a novelty approach (i.e., an Adaptive Immersive Virtual Reality Training System), aiming to enrich social interaction and communication skills for children with Autism Spectrum Disorder. In this adaptive system (called My Lovely Granny's Farm), the behavior of the virtual trainer changes depending on the mood and actions of the users (i.e., patients/learners). Additionally, we conducted an initial observational study by monitoring the behavior of children with autism in a virtual environment. In the initial study, the system was offered to users with a high degree of interactivity so that they might practice various social situations in a safe and controlled environment. The results demonstrate that the use of the system can allow patients who needed treatment to receive therapy without leaving home. Our approach is the first experience of treating children with autism in Kazakhstan and can contribute to improving the communication and social interaction of children with Autism Spectrum Disorder. We contribute to the community of educational technologies and mental health by providing a system that can improve communication among children with autism and providing insights on how to design this kind of system.
Outside Inn: Exploring the Heritage of a Historic Hotel through 360-Panoramas

This research project investigates the potential of 360-panorama tours to improve the situated and contextual interpretation, virtual visitation, and spatial understanding of recorded or simulated built heritage sites. Our chosen case study was the Subiaco Hotel, a significant heritage building designed by Summerhayes Architecture, which we documented using 360-degree photographs and linked with other media to create an interactive 360-panorama tour. Today, 360-degree panorama tours such as Google Street View enable the virtual exploration of heritage sites and historic buildings. They demonstrate limited interaction and immersion across a range of platforms and devices, without the requirement of expensive virtual reality headsets, but typically do not integrate other media to leverage spatially richer ways to communicate the historical developments of architectural interiors and exteriors. The primary goals of this study were to establish a comprehensive step-by-step workflow for creating an interactive tour of a significant heritage site, demonstrate how other media such as text, videos, and 3D models can be linked, gather feedback from cultural heritage professionals, and offer future research directions and development guidelines. Apart from detailing an optimized workflow for developing interactive 360-degree virtual tours for heritage buildings, we also offer guidelines for optimal panoramic tour creation and implementation.
Design of an SoC Based on 32-Bit RISC-V Processor with Low-Latency Lightweight Cryptographic Cores in FPGA

The security of Internet of Things (IoTs) devices in recent years has created interest in developing implementations of lightweight cryptographic algorithms for such systems. Additionally, open-source hardware and field-programable gate arrays (FPGAs) are gaining traction via newly developed tools, frameworks, and HDLs. This enables new methods of creating hardware and systems faster, more simply, and more efficiently. In this paper, the implementation of a system-on-chip (SoC) based on a 32-bit RISC-V processor with lightweight cryptographic accelerator cores in FPGA and an open-source integrating framework is presented. The system consists of a 32-bit VexRiscv processor, written in SpinalHDL, and lightweight cryptographic accelerator cores for the PRINCE block cipher, the PRESENT-80 block cipher, the ChaCha stream cipher, and the SHA3-512 hash function, written in Verilog HDL and optimized for low latency with fewer clock cycles. The primary aim of this work was to develop a customized SoC platform with a register-controlled bus suitable for integrating lightweight cryptographic cores to become compact embedded systems that require encryption functionalities. Additionally, custom firmware was developed to verify the functionality of the SoC with all integrated accelerator cores, and to evaluate the speed of cryptographic processing. The proposed system was successfully implemented in a Xilinx Nexys4 DDR FPGA development board. The resources of the system in the FPGA were low with 11,830 LUTs and 9552 FFs. The proposed system can be applicable to enhancing the security of Internet of Things systems.
A framework using nested partitions algorithm for convergence analysis of population distribution-based methods

Stochastic optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), estimation of distribution algorithms (EDAs), and nested partitions algorithm (NPA) are used in many problems including nonlinear model predictive control and task assignment. Some of these algorithms, however, lack global convergence guarantee such as PSO, or require strict convergence assumptions such as NPA. To enhance these methods in terms of convergence, a common underlying framework towards representing the seemingly unrelated methods is established as the updating of the distribution of the population through iterative sampling, and the methods that fit into this framework are called population distribution-based methods. Global convergence conditions for this framework are innovatively developed by building a shadow NPA structure for the population evolution process. The result is generic and is capable of analyzing convergence of many methods including GA, PSO, EDA, and NPA. It can be further exploited to improve convergence by modifying these methods. The existing and modified variants of these methods are then applied to case studies to show the improvement.
SPT: Single Pedestrian Tracking Framework with Re-Identification-Based Learning Using the Siamese Model

Pedestrian tracking is a challenging task in the area of visual object tracking research and it is a vital component of various vision-based applications such as surveillance systems, human-following robots, and autonomous vehicles. In this paper, we proposed a single pedestrian tracking (SPT) framework for identifying each instance of a person across all video frames through a tracking-by-detection paradigm that combines deep learning and metric learning-based approaches. The SPT framework comprises three main modules: detection, re-identification, and tracking. Our contribution is a significant improvement in the results by designing two compact metric learning-based models using Siamese architecture in the pedestrian re-identification module and combining one of the most robust re-identification models for data associated with the pedestrian detector in the tracking module. We carried out several analyses to evaluate the performance of our SPT framework for single pedestrian tracking in the videos. The results of the re-identification module validate that our two proposed re-identification models surpass existing state-of-the-art models with increased accuracies of 79.2% and 83.9% on the large dataset and 92% and 96% on the small dataset. Moreover, the proposed SPT tracker, along with six state-of-the-art (SOTA) tracking models, has been tested on various indoor and outdoor video sequences. A qualitative analysis considering six major environmental factors verifies the effectiveness of our SPT tracker under illumination changes, appearance variations due to pose changes, changes in target position, and partial occlusions. In addition, quantitative analysis based on experimental results also demonstrates that our proposed SPT tracker outperforms the GOTURN, CSRT, KCF, and SiamFC trackers with a success rate of 79.7% while beating the DiamSiamRPN, SiamFC, CSRT, GOTURN, and SiamMask trackers with an average of 18 tracking frames per second.
Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting machine (LightGBM) prediction model (NARX-LSTM-LightGBM) was constructed based on the combined modal decomposition. Specifically, this paper uses a dataset that includes ambient temperature, irradiance, inverter temperature, module temperature, etc. Firstly, the feature variables with high correlation effects on PV power were selected by Pearson correlation analysis. Furthermore, the PV power is decomposed into a new feature matrix by (EMD), (EEMD) and (CEEMDAN), i.e., the combination decomposition (CD), which deeply explores the intrinsic connection of PV power historical series information and reduces the non-smoothness of PV power. Finally, preliminary PV power prediction values and error correction vector are obtained by NARX prediction. Both are embedded into the NARX-LSTM-LightGBM model pair for PV power prediction, and then the error inverse method is used for weighted optimization to improve the accuracy of the PV power prediction. The experiments were conducted with the measured data from Andre Agassi College, USA, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model under different weather conditions were lower than 1.665 kw, 0.892 kw and 0.211, respectively, which are better than the prediction results of other models and proved the effectiveness of the model.
Understanding the role of external pull requests in the NPM ecosystem

The risk to using third-party libraries in a software application is that much needed maintenance is solely carried out by library maintainers. These libraries may rely on a core team of maintainers (who might be a single maintainer that is unpaid and overworked) to serve a massive client user-base. On the other hand, being open source has the benefit of receiving contributions (in the form of External PRs) to help fix bugs and add new features. In this paper, we investigate the role by which External PRs (contributions from outside the core team of maintainers) contribute to a library. Through a preliminary analysis, we find that External PRs are prevalent, and just as likely to be accepted as maintainer PRs. We find that 26.75% of External PRs submitted fix existing issues. Moreover, fixes also belong to labels such as breaking changes, urgent, and on-hold. Differently from Internal PRs, External PRs cover documentation changes (44 out of 384 PRs), while not having as much refactoring (34 out of 384 PRs). On the other hand, External PRs also cover new features (380 out of 384 PRs) and bugs (120 out of 384 PRs). Our results lay the groundwork for understanding how maintainers decide which external contributions they select to evolve their libraries and what role they play in reducing the workload.