Reviewerly/exHarmony huggingface dataset:
Exploring the Evolution of Sensemaking Strategies in Immersive Space to Think

Existing research on immersive analytics to support the sensemaking process focuses on single-session sensemaking tasks. However, in the wild, sensemaking can take days or months to complete. In order to understand the full benefits of immersive analytic systems, we need to understand how immersive analytic systems provide flexibility for the dynamic nature of the sensemaking process. In our work, we build upon an existing immersive analytic system – Immersive Space to Think, to evaluate how immersive analytic systems can support sensemaking tasks over time. We conducted a user study with eight participants with three separate analysis sessions each. We found significant differences between analysis strategies between sessions one, two, and three, which suggest that immersive space to think can benefit analysts during multiple stages in the sensemaking process.
Analysis of existing methods to reduce the dimensionality of input data

The explosive growth of data arrays, both in the number of records and in attributes, has triggered the development of a number of platforms for handling big data (Amazon Web Services, Google, IBM, Infoworks, Oracle, etc.), as well as parallel algorithms for data analysis (classification, clustering, associative rules). This, in turn, has prompted the use of dimensionality reduction techniques. Feature selection, as a data preprocessing strategy, has proven to be effective and efficient in preparing data (especially high-dimensional data) for various data collection and machine learning tasks. Dimensionality reduction is not only useful for speeding up algorithm execution, but can also help in the final classification/clustering accuracy. Too noisy or even erroneous input data often results in less than desirable algorithm performance. Removing uninformative or low-informative columns of data can actually help the algorithm find more general areas and classification rules and generally achieve better performance. This article discusses commonly used data dimensionality reduction methods and their classification. Data transformation consists of two steps: feature generation and feature selection. A dis tinction is made between scalar feature selection and vector methods (wrapper methods, filtering methods, embedded methods and hybrid methods). Each method has its own advantages and disadvantages, which are outlined in the article. It describes the application of one of the most effective methods of dimensionality reduction - the method of correspondence analysis for CSE-CIC-IDS2018 dataset. The effectiveness of this method in the tasks of dimensionality reduction of the specified dataset in the detection of computer attacks is checked.
A BP Neural Network-Based Early Warning Model for Student Performance in the Context of Big Data

Nowadays, educational data mining technology has received more and more attention from scholars in China, and the application of correlation between student behavior data and student achievement to teaching management has become a hot research topic. Starting from the study of the potential association between book borrowing and student achievement in the big data environment, the paper analyzes the correlation between book borrowing and student achievement based on the Apriori algorithm and concludes that there is a strong correlation rule between book borrowing and student achievement. Based on BP neural network prediction algorithm, the paper constructs an early warning model for student performance by predicting book borrowing through course performance. The absolute value of the error between the predicted value of book borrowing and the real value of borrowing is used as a basis to make early warning for students’ performance, so as to realize the monitoring of students’ learning situation, thereby providing theoretical suggestions for teachers’ teaching and promoting the school’s management of students.
Taking the pain out of data sharing

Despite agreeing to make raw data available, some authors fail to comply. The right strategies and platforms can ease the task. Despite agreeing to make raw data available, some authors fail to comply. The right strategies and platforms can ease the task.
DAM

Short video streaming has been increasingly popular in recent years. Due to its unique user behavior of watching and sliding, a critical technique issue is to design a preload algorithm deciding which video chunk to download next, bitrate selection and the pause time, in order to improve user experience while reducing bandwidth wastage. However, designing such a preload algorithm is non-trivial, especially taking into account conflicting goals of improving QoE and reducing bandwidth wastage. In this paper, we propose a deep reinforcement learning-based approach to simultaneously decide the aforementioned three decision variables via learning an optimal policy under a complex environment of varying network conditions and unpredictable user behavior. In particular, we incorporate domain knowledge into the decision procedure via action masking to make decisions more transparent, and accelerate the model training. Experimental results validate the proposed approach significantly outperforms baseline algorithms in terms of QoE metrics and bandwidth wastage.
Attention-Driven Graph Neural Network for Deep Face Super-Resolution

With the help of convolutional neural networks (CNNs), deep learning-based methods have achieved remarkable performance in face super-resolution (FSR) task. Despite their success, most of the existing methods neglect non-local correlations of face images, leaving much room for improvement. In this paper, we introduce a novel end-to-end trainable attention-driven graph neural network (AD-GNN) for more discriminative feature extraction and feature relation modeling. This is achieved by two major components. The first component is a cross-scale dynamic graph (CDG) block. The CDG block considers cross-scale relationships of patches in distant areas and employs two dynamic graphs to construct enhanced features. The second component is a series of channel attention and spatial dynamic graph (CASDG) blocks. A CASDG block has a channel-wise attention unit and a spatial-aware dynamic graph (SDG) unit. The SDG unit extracts informative features by exploring spatial non-local self-similarity information of the patches using dynamic graph convolution. Using these two components, facial details can be effectively reconstructed with the help of information supplemented by similar but spatially remote patches and structural information of faces. Extensive experiments on two public benchmarks demonstrate the superiority of AD-GNN over the state-of-the-art FSR methods.
Model predictive control for robust quantum state preparation

A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment closely match. Consequently, realizing high-fidelity quantum processes with model-based control requires careful device characterization. In quantum processors based on cold atoms, the Hamiltonian can be well-characterized. For superconducting qubits operating at milli-Kelvin temperatures, the Hamiltonian is not as well-characterized. Unaccounted for physics (i.e., mode discrepancy), coherent disturbances, and increased noise compromise traditional model-based control. This work introduces model predictive control (MPC) for quantum control applications. MPC is a closed-loop optimization framework that (i) inherits a natural degree of disturbance rejection by incorporating measurement feedback, (ii) utilizes finite-horizon model-based optimizations to control complex multi-input, multi-output dynamical systems under state and input constraints, and (iii) is flexible enough to develop synergistically alongside other modern control strategies. We show how MPC can be used to generate practical optimized control sequences in representative examples of quantum state preparation. Specifically, we demonstrate for a qubit, a weakly-anharmonic qubit, and a system undergoing crosstalk, that MPC can realize successful model-based control even when the model is inadequate. These examples showcase why MPC is an important addition to the quantum engineering control suite.
A Lightweight Hybrid Scheme for Hiding Text Messages in Colour Images Using LSB, Lah Transform and Chaotic Techniques

Data security can involve embedding hidden images, text, audio, or video files within other media to prevent hackers from stealing encrypted data. Existing mechanisms suffer from a high risk of security breaches or large computational costs, however. The method proposed in this work incorporates low-complexity encryption and steganography mechanisms to enhance security during transmission while lowering computational complexity. In message encryption, it is recommended that text file data slicing in binary representation, to achieve different lengths of string, be conducted before text file data masking based on the lightweight Lucas series and mod function to ensure the retrieval of text messages is impossible. The steganography algorithm starts by generating a random key stream using a hybrid of two low-complexity chaotic maps, the Tent map and the Ikeda map. By finding a position vector parallel to the input image vector, these keys are used based on the previously generated position vector to randomly select input image data and create four vectors that can be later used as input for the Lah transform. In this paper, we present an approach for hiding encrypted text files using LSB colour image steganography by applying a low-complexity XOR operation to the most significant bits in 24-bit colour cover images. It is necessary to perform inverse Lah transformation to recover the image pixels and ensure that invisible data cannot be retrieved in a particular sequence. Evaluation of the quality of the resulting stego-images and comparison with other ways of performing encryption and message concealment shows that the stego-image has a higher PSNR, a lower MSE, and an SSIM value close to one, illustrating the suitability of the proposed method. It is also considered lightweight in terms of having lower computational overhead.
A Novel Color Image Encryption Scheme Based on Hyperchaos and Hopfield Chaotic Neural Network

Problems such as insufficient key space, lack of a one-time pad, and a simple encryption structure may emerge in existing encryption schemes. To solve these problems, and keep sensitive information safe, this paper proposes a plaintext-related color image encryption scheme. Firstly, a new five-dimensional hyperchaotic system is constructed in this paper, and its performance is analyzed. Secondly, this paper applies the Hopfield chaotic neural network together with the novel hyperchaotic system to propose a new encryption algorithm. The plaintext-related keys are generated by image chunking. The pseudo-random sequences iterated by the aforementioned systems are used as key streams. Therefore, the proposed pixel-level scrambling can be completed. Then the chaotic sequences are utilized to dynamically select the rules of DNA operations to complete the diffusion encryption. This paper also presents a series of security analyses of the proposed encryption scheme and compares it with other schemes to evaluate its performance. The results show that the key streams generated by the constructed hyperchaotic system and the Hopfield chaotic neural network improve the key space. The proposed encryption scheme provides a satisfying visual hiding result. Furthermore, it is resistant to a series of attacks and the problem of structural degradation caused by the simplicity of the encryption system's structure.
Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.
Being Precise About Precision Mental Health

This Viewpoint discusses the limitations of precision medicine and compares it with evidence-based medicine.
An Improved Sea Lion Optimization for Workload Elasticity Prediction with Neural Networks

Abstract The work in this paper presents a study into nature-inspired optimization applied to workload elasticity prediction using neural networks. Currently, the trend is for proactive decision support in increasing or decreasing the available resource in cloud computing. The aim is to avoid overprovision leading to resource waste and to avoid resource under-provisioning. The combination of optimization and neural networks has potential for the performance, accuracy, and stability of the prediction solution. In this context, we initially proposed an improved variant of sea lion optimization (ISLO) to boost the efficiency of the original in solving optimization problems. The designed optimization results are validated against eight well-known metaheuristic algorithms on 20 benchmark functions of CEC’2014 and CEC’2015. After that, improved sea lion optimization (ISLO) is used to train a hybrid neural network. Finally, the trained neural model is used for resource auto-scaling based on workload prediction with 4 real and public datasets. The experiments show that our neural network model provides improved results in comparison with other models, especially in comparison with neural networks trained using the original sea lion optimization. The proposed ISLO proved efficiency and improvement in solving problems ranging from global optimization with swarm intelligence to the prediction of workload elasticity.
Neural architecture search using property guided synthesis

In the past few years, neural architecture search (NAS) has become an increasingly important tool within the deep learning community. Despite the many recent successes of NAS, however, most existing approaches operate within highly structured design spaces, and hence explore only a small fraction of the full search space of neural architectures while also requiring significant manual effort from domain experts. In this work, we develop techniques that enable efficient NAS in a significantly larger design space. To accomplish this, we propose to perform NAS in an abstract search space of program properties. Our key insights are as follows: (1) the abstract search space is significantly smaller than the original search space, and (2) architectures with similar program properties also have similar performance; thus, we can search more efficiently in the abstract search space. To enable this approach, we also propose a novel efficient synthesis procedure, which accepts a set of promising program properties, and returns a satisfying neural architecture. We implement our approach, $\alpha$NAS, within an evolutionary framework, where the mutations are guided by the program properties. Starting with a ResNet-34 model, $\alpha$NAS produces a model with slightly improved accuracy on CIFAR-10 but 96% fewer parameters. On ImageNet, $\alpha$NAS is able to improve over Vision Transformer (30% fewer FLOPS and parameters), ResNet-50 (23% fewer FLOPS, 14% fewer parameters), and EfficientNet (7% fewer FLOPS and parameters) without any degradation in accuracy.
Edge Machine Learning for the Automated Decision and Visual Computing of the Robots, IoT Embedded Devices or UAV-Drones

This paper presents edge machine learning (ML) technology and the challenges of its implementation into various proof-of-concept solutions developed by the authors. Paper presents the concept of Edge ML from a variety of perspectives, describing different implementations such as: a tech-glove smart device (IoT embedded device) for controlling teleoperated robots or an UAVs (unmanned aerial vehicles/drones) that is processing data locally (at the device level) using machine learning techniques and artificial intelligence neural networks (deep learning algorithms), to make decisions without interrogating the cloud platforms. Implementation challenges used in Edge ML are described and analyzed in comparisons with other solutions. An IoT embedded device integrated into a tech glove, which controls a teleoperated robot, is used to run the AI neural network inference. The neural network was trained in an ML cloud for better control. Implementation developments, behind the UAV device capable of visual computation using machine learning, are presented.
Detecting Stealthy Cyberattacks on Automated Vehicles via Generative Adversarial Networks

The emergence of vehicles with driver-assist features, including adaptive cruise control (ACC) or other automated driving capabilities, introduces the possibility of cyberattacks where a select number of automated vehicles (AVs) are compromised to drive with adversarial controls. While obvious attacks that force vehicles to crash may be easily detectable, more subtle attacks are harder to detect and could change vehicle driving behavior, resulting in a network-wide increase in congestion and fuel consumption. To address this pressing problem, we first characterize two scenarios of potential cyberattacks, namely malicious attack on individual vehicles and data injection attack on sensor measurements. Then, a generative adversarial network (GAN)-based anomaly detection model is proposed for real-time detection of such attacks. Finally, to demonstrate the effectiveness of the proposed method we conduct numerical experiments using both synthetic vehicle trajectory data, and real-world ACC trajectory data with synthetic sensor attacks injected. The results show that the proposed model is capable of detecting attacks using a short period of trajectory data.
Tunable narrowband excitonic Optical Tamm states enabled by a metal-free all-organic structure

Abstract Optical Tamm states (OTS) are confined optical modes that can occur at the interface between two highly reflective structures. However, due to the strong reflectance required, their implementation with highly processable and metal-free flexible materials has proven challenging. Herein, we develop the first structure supporting OTS based only on organic polymeric materials, demonstrating a photonic platform based on non-critical, widely available and easily processable materials. The structures fabricated present large areas and consist of a narrowband multi-layered polymeric distributed Bragg reflector (DBR) followed by a thin film of J-aggregate molecular excitonic material that can act as a highly reflective surface within a narrowband range. We take advantage of the narrowband spectral response of the DBR and of the reflective molecular layer to tune the OTS band by varying the periodicity of the multilayer, opening the door for the fabrication of OTS structures based on lightweight integrable excitonic devices with cost-effective procedures.
Coherent momentum control of forbidden excitons

A double-edged sword in two-dimensional material science and technology is an optically forbidden dark exciton. On the one hand, it is fascinating for condensed matter physics, quantum information processing, and optoelectronics due to its long lifetime. On the other hand, it is notorious for being optically inaccessible from both excitation and detection standpoints. Here, we provide an efficient and low-loss solution to the dilemma by reintroducing photonics bound states in the continuum (BICs) to manipulate dark excitons in the momentum space. In a monolayer tungsten diselenide under normal incidence, we observed a giant enhancement with an enhancement factor of ~3,100 for dark excitons enabled by transverse magnetic BICs with intrinsic out-of-plane electric fields. By further employing widely tunable Friedrich-Wintgen BICs, we demonstrated highly directional emission from the dark excitons with a divergence angle of merely 7 degrees. We found that the directional emission is coherent at room temperature, unambiguously shown in polarization analyses and interference measurements. Therefore, the BICs reintroduced as a momentum-space photonic environment could be an intriguing platform to reshape and redefine light-matter interactions in nearby quantum materials, such as low-dimensional materials, otherwise challenging or even impossible to achieve.
Improving Medical X-ray Report Generation by Using Knowledge Graph

In clinical diagnosis, radiological reports are essential to guide the patient’s treatment. However, writing radiology reports is a critical and time-consuming task for radiologists. Existing deep learning methods often ignore the interplay between medical findings, which may be a bottleneck limiting the quality of generated radiology reports. Our paper focuses on the automatic generation of medical reports from input chest X-ray images. In this work, we mine the associations between medical discoveries in the given texts and construct a knowledge graph based on the associations between medical discoveries. The patient’s chest X-ray image and clinical history file were used as input to extract the image–text hybrid features. Then, this feature is used as the input of the adjacency matrix of the knowledge graph, and the graph neural network is used to aggregate and transfer the information between each node to generate the situational representation of the disease with prior knowledge. These disease situational representations with prior knowledge are fed into the generator for self-supervised learning to generate radiology reports. We evaluate the performance of the proposed method using metrics from natural language generation and clinical efficacy on two public datasets. Our experiments show that our method outperforms state-of-the-art methods with the help of a knowledge graph constituted by prior knowledge of the patient.
Choosing Representation, Mutation, and Crossover in Genetic Algorithms

This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.