Reviewerly/exHarmony huggingface dataset:
Distributed stochastic configuration networks with cooperative learning paradigm

As a new category of randomized neural networks (RNNs), stochastic configuration networks (SCNs) have demonstrated great potential for data analytics. Unlike conventional randomized learning techniques, e.g., random vector functional-link (RVFL) networks, SCNs provide a stochastic configuration mechanism on the assignment of input parameters which guarantees the universal approximation capability of a resulting learner model. In this paper, a distributed version of SCN is developed for decentralized datasets in cooperative learning paradigm. This paper proposes an approach to deal with datasets stored across a network of multiple learning agents without any fusion center. Specifically, we formulate the centralized learning problem as an equivalent form with the decomposition of subproblems coupled in a network and a consensus restriction. Then, a cooperative configuration scheme is proposed for randomly assigning the input weights and bias. Finally, based on the well-known parallel alternating direction method of multipliers (ADMM), the output weights are evaluated iteratively. Simulation studies with comparisons on three benchmark datasets are carried out. The experimental results indicate that our proposed learning scheme performs well and outperforms distributed RVFL networks.
Genetic programming-based learning of texture classification descriptors from Local Edge Signature

Describing texture is a very challenging problem for many image-based expert and intelligent systems (e.g. defective product detection, people re-identification, abnormality investigation in medical imaging and remote sensing applications…) since the process of texture classification relies on the quality of the extracted features. Indeed, detecting and extracting features is a hard and time-consuming task that requires the intervention of an expert, notably when dealing with challenging textures. Thus, machine learning-based descriptors have emerged as another alternative to deal with the difficulty of feature extracting. In this work, we propose a new operator, which we named Local Edge Signature (LES) descriptor, to locally represent texture. The proposed texture descriptor is based on statistical information on edge pixels’ arrangement and orientation in a specific local region, and it is insensitive to rotation and scale changes. A genetic programming-based approach is then fitted to automatically learn a global texture descriptor that we called Genetic Texture Signature (GTS). In fact, a tree representation of individuals is used to generate global texture features by applying elementary operations on LES elements at a set of keypoints, and a fitness function evaluates the descriptors considering intra-class homogeneity and inter-class discrimination properties of their generated features. The obtained results, on six challenging texture datasets (Brodatz, Outex_TC_00000, Outex_TC_00013, KTH-TIPS, KTH-TIPS2b and UIUCTex), show that the proposed classification method, which is fully automated, achieves state-of-the-art performance, especially when the number of available training samples is limited.
Constructing a gridded direct normal irradiance dataset in China during 1981–2014

High-quality direct normal irradiance (DNI) observations are of vital importance for the optimal design, installation and profitability of CST plants. Numerous models have been developed for estimating DNI at regional or global scales. The goal of this study is to generate a gridded DNI dataset for all-sky conditions over mainland China during 1981–2014, based on a broadband DNI estimates for clear-sky conditions (obtained with the REST2_v9.1 model) and cloud transmittance estimates using sunshine observations. The results indicate that the REST2_v9.1 model can be used to estimate DNI with high accuracy and consistency, owing to its robust two-band parameterization of the radiation transfer processes. Comparing daily DNI modeled predictions to measurements at 6 BSRN (Baseline Surface Radiation Network) stations in East Asia results in relatively low errors statistics: RMSE, MAE, RMSER, MAER and R of 1.436 MJm−2, 0.900 MJm−2, 22.26%, 13.95% and 0.972. Somewhat lessened agreement is found at the 17 CMA (China meteorological administrations) stations: 4.064 MJm−2, 2.864 MJm−2, 34.41%, 24.26% and 0.914, respectively. A gridded DNI dataset is constructed using sunshine duration measurements at 2474 CMA meteorological stations and the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) reanalysis products. The spatial and temporal variations of DNI in different climate zones throughout China are also investigated. The gridded DNI datasets generated in this study would assist in numerous solar resource studies and solar energy applications.
A semi-automated forensic investigation model for online social networks

Investigating the online social network profiles of victims, suspects, and witnesses are now part of almost every legal investigation, either it involves a criminal offense, financial fraud, or domestic lawsuit. However, investigating online social networks (OSN) is a technically complicated process that becomes more challenging due to the legal issues of privacy and authentication. Completely manual investigative methods are not feasible for OSN investigations due to the immense size and heterogeneity of social networks. However, the existing models for digital forensic investigation are not supporting automated or semi-automated forensic investigation processes. Furthermore, they are not addressing the fundamental differences and specific requirements of online social networks. The model presented in this work incorporates the robust features of standard investigation models and proposes a digital forensic investigation process model that explicitly addresses the necessities of OSN investigations. This work is addressing the issues of automating the forensic collection and analysis processes, defining crime scene boundaries, and outlining reasonable iterative collection procedures in online social network forensic investigation. This work is evaluated using a case study and is compared with existing practices and standards.
Towards unstructured mortality prediction with free-text clinical notes

• Hierarchically model free-text notes to predict mortality. • Performance compared to severity scores and RNN’s utilizing physiological data. • Notes consistently better than both baselines and multi-modal model achieves highest metrics. • More unstructured data needs to be used in clinical prediction. Healthcare data continues to flourish yet a relatively small portion, mostly structured, is being utilized effectively for predicting clinical outcomes. The rich subjective information available in unstructured clinical notes can possibly facilitate higher discrimination but tends to be under-utilized in mortality prediction. This work attempts to assess the gain in performance when multiple notes that have been minimally preprocessed are used as an input for prediction. A hierarchical architecture consisting of both convolutional and recurrent layers is used to concurrently model the different notes compiled in an individual hospital stay. This approach is evaluated on predicting in-hospital mortality on the MIMIC-III dataset. On comparison to approaches utilizing structured data, it achieved higher metrics despite requiring less cleaning and preprocessing. This demonstrates the potential of unstructured data in enhancing mortality prediction and signifies the need to incorporate more raw unstructured data into current clinical prediction methods.
Characterization and identification of HPC applications at leadership computing facility

High Performance Computing (HPC) is an important method for scientific discovery via large-scale simulation, data analysis, or artificial intelligence. Leadership-class supercomputers are expensive, but essential to run large HPC applications. The Petascale era of supercomputers began in 2008, with the first machines achieving performance in excess of one petaflops, and with the advent of new supercomputers in 2021 (e.g., Aurora, Frontier), the Exascale era will soon begin. However, the high theoretical computing capability (i.e., peak FLOPS) of a machine is not the only meaningful target when designing a supercomputer, as the resources demand of applications varies. A deep understanding of the characterization of applications that run on a leadership supercomputer is one of the most important ways for planning its design, development and operation.
Enhancing the learning of multi-level undergraduate Chinese language with a 3D immersive experience - An exploratory study

The article reports on a study that incorporated an immersive 3 D virtual environment, created in Second Life (SL) into the curriculum of a Chinese program, including its development, implementation and evaluation. This innovation supplemented classroom teaching of Chinese language throughout one semester at a regional Australian university. Using the existing resource called Chinese Island (CI) created by a major urban Australian university, the primary aim of this study was to probe student’s perceptions about the resource. Due to curriculum differences between two universities, customised language and culture learning tasks based on the learnt textbook content were designed on CI, with instructions and associated quizzes hosted in a private Moodle site—a widely-used learning management system (LMS) environment. Twenty-eight mixed-level undergraduate Chinese as a foreign language (CFL) students participated in a survey, and seven of these students were interviewed. The questionnaire and interview data were collected and analysed during the study. Following analysis, results indicated that students welcomed the experiential learning opportunity to explore Chinese culture and language outside the classroom afforded by CI. Anecdotal evidence suggested that students became more aware of the complicated pragmatic issues in real life communication. The survey results, including the participants’ responses to open-ended questions, are discussed in this paper. Pedagogical implications and directions for further research are also considered.
Fast Learning Through Deep Multi-Net CNN Model For Violence Recognition In Video Surveillance

Abstract The violence detection is mostly achieved through handcrafted feature descriptors, while some researchers have also employed deep learning-based representation models for violent activity recognition. Deep learning-based models have achieved encouraging results for fight activity recognition on benchmark data sets such as hockey and movies. However, these models have limitations in learning discriminating features for violence activity classification with abrupt camera motion. This research work investigated deep representation models using transfer learning for handling the issue of abrupt camera motion. Consequently, a novel deep multi-net (DMN) architecture based on AlexNet and GoogleNet is proposed for violence detection in videos. AlexNet and GoogleNet are top-ranked pre-trained models for image classification with distinct pre-learnt potential features. The fusion of these models can yield superior performance. The proposed DMN unleashed the integrated potential by concurrently coalescing both networks. The results confirmed that DMN outperformed state-of-the-art methods by learning finest discriminating features and achieved 99.82% and 100% accuracy on hockey and movies data sets, respectively. Moreover, DMN has faster learning capability i.e. 1.33 and 2.28 times faster than AlexNet and GoogleNet, which makes it an effective learning architecture on images and videos.
Preparation of in-service measurement data for ship operation and performance analysis

An increasing number of ships are being equipped with sensors and devices for monitoring of operational behavior, and the amount and access to operational data is gradually increasing. Due to various reasons described in this paper, the operational data may contain erroneous data points that are critical to assess prior to performing data analysis or building mathematical and statistical models. In this paper, a stepwise method for preparation of data for ship operation and performance analysis is presented. The method deals with removing jumps in the time series data, including loss of time synchronization between different measurement subsystems, outlier detection, including repeated samples, dropouts and spikes and data selection and extraction, including stationarity detection. The final result is a data set free from disturbances, distortions and undesired physical effects, that can be used to improve the quality of a ship operation and performance analysis.
The Nudge Deck

The idea of nudging - that subtle changes in the 'choice architecture' can alter people's behaviors in predictable ways - was eagerly adopted by HCI researchers and practitioners over the past decade. Yet, the design of effective nudging interventions is far from trivial, with theoretical knowledge being unstructured, with over a hundred cognitive biases found online, and inaccessible to practitioners during design meetings. We present the design and evaluation of the Nudge Deck, a card-based, design support tool that provides actionable knowledge for the design of technology-mediated nudges. The tool was evaluated through two case studies where 58 participants were asked to design nudging interventions, in the contexts of physical activity promotion and misinformation mitigation, with and without the cards. We report on how the cards enhanced designers' self-efficacy, and led to more theoretically grounded, creative and appropriate for the context, ideas.
The anatomy of a distributed predictive modeling framework: online learning, blockchain network, and consensus algorithm

Cross-institutional distributed healthcare/genomic predictive modeling is an emerging technology that fulfills both the need of building a more generalizable model and of protecting patient data by only exchanging the models but not the patient data. In this article, the implementation details are presented for one specific blockchain-based approach, ExplorerChain, from a software development perspective. The healthcare/genomic use cases of myocardial infarction, cancer biomarker, and length of hospitalization after surgery are also described.ExplorerChain's 3 main technical components, including online machine learning, metadata of transaction, and the Proof-of-Information-Timed (PoINT) algorithm, are introduced in this study. Specifically, the 3 algorithms (ie, core, new network, and new site/data) are described in detail.ExplorerChain was implemented and the design details of it were illustrated, especially the development configurations in a practical setting. Also, the system architecture and programming languages are introduced. The code was also released in an open source repository available at https://github.com/tsungtingkuo/explorerchain.The designing considerations of semi-trust assumption, data format normalization, and non-determinism was discussed. The limitations of the implementation include fixed-number participating sites, limited join-or-leave capability during initialization, advanced privacy technology yet to be included, and further investigation in ethical, legal, and social implications.This study can serve as a reference for the researchers who would like to implement and even deploy blockchain technology. Furthermore, the off-the-shelf software can also serve as a cornerstone to accelerate the development and investigation of future healthcare/genomic blockchain studies.
Deep support vector neural networks

Kernel based Support Vector Machines, SVM, one of the most popular machine learning models, usually achieve top performances in two-class classification and regression problems. However, their training cost is at least quadratic on sample size, making them thus unsuitable for large sample problems. However, Deep Neural Networks (DNNs), with a cost linear on sample size, are able to solve big data problems relatively easily. In this work we propose to combine the advanced representations that DNNs can achieve in their last hidden layers with the hinge and ϵ insensitive losses that are used in two-class SVM classification and regression. We can thus have much better scalability while achieving performances comparable to those of SVMs. Moreover, we will also show that the resulting Deep SVM models are competitive with standard DNNs in two-class classification problems but have an edge in regression ones.
Learning from Data Journeys

Abstract The introduction discusses the idea of data journeys and its characteristics as an investigative tool and theoretical framework for this volume and broader scholarship on data. Building on a relational and historicized understanding of data as lineages , it reflects on the methodological and conceptual challenges involved in mapping, analyzing and comparing the production, movement and use of data within and across research fields and approaches, and the strategies developed to cope with such difficulties. The introduction then provides an overview of significant variation among data practices in different research areas that emerge from the analyses of data journeys garnered in this volume. In closing, it discusses the significance of this approach towards addressing the challenges raised by data-centric science and the emergence of big and open data.
Human action recognition in videos based on spatiotemporal features and bag-of-poses

Currently, there is a large number of methods that use 2D poses to represent and recognize human action in videos. Most of these methods use information computed from raw 2D poses based on the straight line segments that form the body parts in a 2D pose model in order to extract features (e.g., angles and trajectories). In our work, we propose a new method of representing 2D poses. Instead of directly using the straight line segments, firstly, the 2D pose is converted to the parameter space in which each segment is mapped to a point. Then, from the parameter space, spatiotemporal features are extracted and encoded using a Bag-of-Poses approach, then used for human action recognition in the video. Experiments on two well-known public datasets, Weizmann and KTH, showed that the proposed method using 2D poses encoded in parameter space can improve the recognition rates, obtaining competitive accuracy rates compared to state-of-the-art methods.
Replay Enactments

As we design increasingly complex systems, we run up against fundamental limitations of human imagination. To support practice, it becomes essential to use authentic data and algorithms as design materials to augment designers' intuitions. Recent work has explored some dimensions of using data as a design material, suggesting the contours of a new space of design and prototyping methods. In this paper, we present Replay Enactments (REs, an extension of the User Enactments methods that uses data replay as a boundary object, making complex system behavior tangible to designers and stakeholders. We reflect on a set of case studies that have instantiated REs in diverse ways and discuss trade-offs between different ways of using data replays in design. We conclude by highlighting opportunities and challenges for future work.
Anomaly Detection Based on RBM-LSTM Neural Network for CPS in Advanced Driver Assistance System

Advanced Driver Assistance System (ADAS) is a typical Cyber Physical System (CPS) application for human–computer interaction. In the process of vehicle driving, we use the information from CPS on ADAS to not only help us understand the driving condition of the car but also help us change the driving strategies to drive in a better and safer way. After getting the information, the driver can evaluate the feedback information of the vehicle, so as to enhance the ability to assist in driving of the ADAS system. This completes a complete human–computer interaction process. However, the data obtained during the interaction usually form a large dimension, and irrelevant features sometimes hide the occurrence of anomalies, which poses a significant challenge to us to better understand the driving states of the car. To solve this problem, we propose an anomaly detection framework based on RBM-LSTM. In this hybrid framework, RBM is trained to extract general underlying features from data collected by CPS, and LSTM is trained from the features learned by RBM. This framework can effectively improve the prediction speed and present a good prediction accuracy to show vehicle driving condition. Besides, drivers are allowed to evaluate the prediction results, so as to improve the accuracy of prediction. Through the experimental results, we can find that the proposed framework not only simplifies the training of the entire neural network and increases the training speed but also greatly improves the accuracy of the interaction-driven data analysis. It is a valid method to analyze the data generated during the human interaction.
On the Design of a Blockchain-based Student Quality Assessment System

Blockchain technology has been applied in different fields with the advantages of decentralization, anonymity, immutability and reliability. There are quite a lot of problems, such as data decentralization, low utilization rate, high cost and unguaranteed security when managing students' study records. Therefore, we design a blockchain-based system for students' comprehensive quality assessment. This system adopts a solution of combining “onchain and offchain” data, where we use the smart contract, the interplanetary file system (IPFS) and Web Service to ensure that data flow is efficient, safe and reliable. In this paper, a network architecture based on education consortium blockchain is proposed, where we design a blockchain data structure and storage model to conveniently access to students' quality assessment data. And a data flow mechanism is designed for student data's trusted sharing and authentication protection, in which we combine the advantages of RBFT-based Hyperchain consortium blockchain and traditional data persistence. Then we use the microservices architecture to build the whole system and propose a performance optimization scheme to ensure high availability of the system. Sharing student data for research purposes will boost research innovation in education.
Quantum Speedup for Aeroscience and Engineering

Algorithms and hardware for quantum computing (QC) are reaching a critical stage in their development and have the potential to generate a paradigm shift in computing capability across a range of fields.Opportunities are growing for genuine impact of these systems over a timescale of 10-15 years, and there has been significant investment both from government agencies and private industry in the development of quantum computing.However, utilization of quantum phenomena is extraordinarily challenging due to its delicate nature and difficulties in measurement and control.A clear path exists towards demonstrating the advantages of QC over existing high-performance computing for some physics and materials science problems, but addressing practical computational challenges in other fields -though promising -is at an early stage of development.Reaching the next level of development will require strategic coordination between physicists, computer & information scientists, mathematicians, and engineers, in order to transition this technology from the laboratory to robust and scalable computations for practical problems, especially those of interest to the aeroscience and engineering community.This community has been relying on high performance computing heavily, and will surely want to be informed of the developments in QC.This survey introduces the background and current state-of-the-art in QC, as well as its perceived opportunities and challenges.