Reviewerly/exHarmony huggingface dataset:
Evolving scattering networks for engineering disorder

Abstract Network science provides a powerful tool for unraveling the complexities of social, technological and biological systems. Constructing networks using wave phenomena is also of great interest in devising advanced hardware for machine learning, as shown in optical neural networks. Although most wave-based networks have employed static network models, the impact of evolving models in network science provides strong motivation to apply dynamical network modeling to wave physics. Here the concept of evolving scattering networks for scattering phenomena is developed. The network is defined by links, node degrees and their evolution processes modeling multi-particle interferences, which directly determine scattering from disordered materials. I demonstrate the concept by examining network-based material classification, microstructure screening and preferential attachment in evolutions, which are applied to stealthy hyperuniformity. The results enable independent control of scattering from different length scales, revealing superdense material phases in short-range order. The proposed concept provides a bridge between wave physics and network science to resolve multiscale material complexities and open-system material design.
Quantum networks self-test all entangled states

Certifying quantum properties with minimal assumptions is a fundamental problem in quantum information science. Self-testing is a method to infer the underlying physics of a quantum experiment only from the measured statistics1,2. Although all bipartite pure entangled states can be self-tested3, little is known about how to self-test quantum states of an arbitrary number of systems. Here we introduce a framework of network-assisted self-testing and use it to self-test any pure entangled quantum state of an arbitrary number of systems. The scheme requires the preparation of a number of singlets that scales linearly with the number of systems, and the implementation of standard projective and Bell measurements, all feasible with current technology4. When all the network constraints are exploited, the obtained self-testing certification is stronger than what is achievable in any Bell-type scenario. Our work shows how properly designed networks offer new opportunities for the certification of quantum phenomena. Quantum systems produce correlations that cannot be mimicked by classical resources, which can be used to certify quantum states without trusting the underlying devices. A network can perform this procedure for pure states with any number of systems.
Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm

In this study, a snapshot-based hyperspectral imaging (HSI) algorithm that converts RGB images to HSI images is designed using the Raspberry Pi environment. A Windows-based Python application is also developed to control the Raspberry Pi camera and processor. The mean gray values (MGVs) of two distinct regions of interest (ROIs) are selected from three samples of 100 NTD Taiwanese currency notes and compared with three samples of counterfeit 100 NTD notes. Results suggest that the currency notes can be easily differentiated on the basis of MGV values within shorter wavelengths, between 400 nm and 500 nm. However, the MGV values are similar in longer wavelengths. Moreover, if an ROI has a security feature, then the classification method is considerably more efficient. The key features of the module include portability, lower cost, a lack of moving parts, and no processing of images required.
Omnivision forecasting: Combining satellite and sky images for improved deterministic and probabilistic intra-hour solar energy predictions

Integrating large proportions of intermittent renewable energy sources into electric grids is challenging. A well-established approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply variability to adapt the response of the grid. In solar energy, short-term changes in electricity production caused by occluding clouds can be predicted at different time scales from all-sky cameras (up to 30-min ahead) and satellite observations (up to 6 h ahead). In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting. Both deterministic and probabilistic predictions are evaluated in different weather conditions (clear-sky, cloudy, overcast) and with different input configurations (sky images, satellite observations and/or past irradiance values). Our results show that the hybrid model benefits predictions in clear-sky conditions and improves longer-term forecasting. This study lays the groundwork for future innovative approaches of combining sky images and satellite observations in a single learning framework to advance solar nowcasting.
Integrating intuitionistic preferences into the graph model for conflict resolution with applications to an ecological compensation conflict in Taihu Lake basin

Ecological compensation is an important means to solve the problem of river basin water pollution, which has been widely used at home and abroad. However, the study of ecological compensation problems from the perspective of the management by using the graph model for conflict resolution (GMCR) is still a challenge. Thus, the aim of this article is to construct the graph model with intuitionistic preferences to model and analyze real life ecological compensation conflicts. Specifically, due to the lack of information and the inherent fuzziness of human cognition in ecological compensation conflicts, the intuitionistic preference structure is incorporated into the framework of the GMCR to represent the decision makers' (DMs') uncertain preference. Based on this, two sets of intuitionistic stability definitions are defined to analyze and solve the possible resolution for conflict problems with two DMs and multiple DMs, respectively. Note that a state is called intuitionistic stability for DM in a graph model based on the given intuitionistic stability definitions if and only if there is no reward for a given state to leave the initial state, that is to say, the evaluated value of the focus DM is less than an intuitionistic satisficing threshold of the same DM. Furthermore, the intuitionistic equilibrium is considered as the possible solution to the conflict problems and is intuitionistic stable for all DMs in the graph model. Finally, the ecological compensation conflicts of the Taihu Lake basin are used to demonstrate the usefulness and correctness of the graph model with intuitionistic preferences.
What are the trend and core knowledge of information security? A citation and co-citation analysis

Information technology brings business success opportunities, but also causes potential safety hazards to organizations. In response to the increasing academia and industry concerns regarding information security (ISec), this study systematically explored extant ISec research and identified eight core knowledge groups, including (1) intrusion detection, (2) privacy protection, (3) secure machine learning, (4) cryptosystem, (5) data service security, (6) malware analysis, (7) security decision-making, and (8) security management. The detection of research hotspots shows that data service security and risk management garner the most current research attention. Furthermore, we establish a comprehensive ISec framework to help systematically understand and achieve ISec.
PSUMNet: Unified Modality Part Streams Are All You Need for Efficient Pose-Based Action Recognition

Pose-based action recognition is predominantly tackled by approaches which treat the input skeleton in a monolithic fashion, i.e. joints in the pose tree are processed as a whole. However, such approaches ignore the fact that action categories are often characterized by localized action dynamics involving only small subsets of part joint groups involving hands (e.g. 'Thumbs up') or legs (e.g. 'Kicking'). Although part-grouping based approaches exist, each part group is not considered within the global pose frame, causing such methods to fall short. Further, conventional approaches employ independent modality streams (e.g. joint, bone, joint velocity, bone velocity) and train their network multiple times on these streams, which massively increases the number of training parameters. To address these issues, we introduce PSUMNet, a novel approach for scalable and efficient pose-based action recognition. At the representation level, we propose a global frame based part stream approach as opposed to conventional modality based streams. Within each part stream, the associated data from multiple modalities is unified and consumed by the processing pipeline. Experimentally, PSUMNet achieves state of the art performance on the widely used NTURGB+D 60/120 dataset and dense joint skeleton dataset NTU 60-X/120-X. PSUMNet is highly efficient and outperforms competing methods which use 100%–400% more parameters. PSUMNet also generalizes to the SHREC hand gesture dataset with competitive performance. Overall, PSUMNet's scalability, performance and efficiency makes it an attractive choice for action recognition and for deployment on compute-restricted embedded and edge devices. Code and pretrained models can be accessed at https://github.com/skelemoa/psumnet .
Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI &amp; AIM 2022 Challenge: Report

As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI challenge, the target was to develop an efficient end-to-end AI-based bokeh effect rendering approach that can run on modern smartphone GPUs using TensorFlow Lite. The participants were provided with a large-scale EBB! bokeh dataset consisting of 5K shallow/wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The runtime of the resulting models was evaluated on the Kirin 9000's Mali GPU that provides excellent acceleration results for the majority of common deep learning ops. A detailed description of all models developed in this challenge is provided in this paper.
A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks

Climate change and global warming drive many governments and scientists to investigate new renewable and green energy sources. Special attention is on solar panel technology, since solar energy is considered one of the primary renewable sources and solar panels can be installed in domestic neighborhoods. Photovoltaic (PV) power prediction is essential to match supply and demand and ensure grid stability. However, the PV system has assertive stochastic behavior, requiring advanced forecasting methods, such as machine learning and deep learning, to predict day-ahead PV power accurately. Machine learning models need a rich historical dataset that includes years of PV power outputs to capture hidden patterns between essential variables to predict day-ahead PV power production accurately. Therefore, this study presents a framework based on the transfer learning method to use reliable trained deep learning models of old PV plants in newly installed PV plants in the same neighborhoods. The numerical results show the effectiveness of transfer learning in day-ahead PV prediction in newly established PV plants where a sizable historical dataset of them is unavailable. Among all nine models presented in this study, the LSTM models have better performance in PV power prediction. The new LSTM model using the inadequate dataset has 0.55 mean square error (MSE) and 47.07% weighted mean absolute percentage error (wMAPE), while the transferred LSTM model improves prediction accuracy to 0.168 MSE and 32.04% wMAPE.
Graph-Augmented Co-Attention Model for Socio-Sequential Recommendation

A sequential recommendation has become a hot research topic, which seeks to predict the next interesting item for each user based on his action sequence. While previous methods have made many efforts to capture the dynamics of sequential patterns, we contend that they still suffer from two inherent limitations: 1) they fail to model item transition patterns in an efficient and time-sensitive manner and 2) they are unaware of the importance of dynamically capturing social influence, resulting in suboptimal performance. We introduce a new concept dubbed socio-sequential recommendation, where the challenge mainly lies in dynamically modeling social influences and capturing item-to-item transition patterns in a time-sensitive manner. In light of this, we contribute a novel solution named GCARec (short for graph-augmented co-attention model), which takes into account the joint effect of dynamic sequential patterns and dynamic social influences. GCARec decomposes socio-sequential recommendation workflow into two steps. First, we adopt a light graph embedding module to model long-term user preference. Then, we propose a time-sensitive attention mechanism and a social-aware attention mechanism to capture dynamic patterns at sequential-level and social-level, respectively. Extensive experiments have been conducted on eight real-world datasets from different scenarios, demonstrating the superiority of GCARec against several state-of-the-art methods. The codes and datasets have been released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wubinzzu/GCARec</uri> .
Using Virtual Replicas to Improve Mixed Reality Remote Collaboration

In this paper, we explore how virtual replicas can enhance Mixed Reality (MR) remote collaboration with a 3D reconstruction of the task space. People in different locations may need to work together remotely on complicated tasks. For example, a local user could follow a remote expert's instructions to complete a physical task. However, it could be challenging for the local user to fully understand the remote expert's intentions without effective spatial referencing and action demonstration. In this research, we investigate how virtual replicas can work as a spatial communication cue to improve MR remote collaboration. This approach segments the foreground manipulable objects in the local environment and creates corresponding virtual replicas of physical task objects. The remote user can then manipulate these virtual replicas to explain the task and guide their partner. This enables the local user to rapidly and accurately understand the remote expert's intentions and instructions. Our user study with an object assembly task found that using virtual replica manipulation was more efficient than using 3D annotation drawing in an MR remote collaboration scenario. We report and discuss the findings and limitations of our system and study, and present directions for future research.
Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization

Leukemia is a type of cancer that affects blood cells and causes fatal infection and premature death. Modern technology enabled by the machine and advanced deep learning algorithms can assist doctors in diagnosing the disease in the early stage. Automatic classification of disease based on advanced deep learning always require a huge number of labeled dataset. This paper presents an image dataset of 750 microscopic blood smears. The dataset contains images of Chronic Lymphocytic Leukemia, Acute Lymphoblastic Leukemia, Chronic Myeloid Leukemia, and Acute Myeloid Leukemia. The dataset is enlarged by merging images of a dataset present in the literature, containing 500 images of Acute Lymphoblastic Leukemia, Acute Myeloid Leukemia, and normal cases. This dataset is used for automated detection and classification of leukemia using deep transfer learning, which is the major task of the proposed work. The impact of subject-independent test dataset on the robustness of trained models is also analyzed on different data splits for training and testing. For the classification task, the proposed work obtained an accuracy of 84% on a subject-independent test dataset when support vector machine is trained on features extracted by a pruned VGG16 whose last three convolutional layers are fine tuned. The features responsible for classifying an image to a particular class are visualized with the help of Gradient-weighted Class Activation Mapping technique. The dataset is being created by taking the opinion of various experts that will assist the scientific community in conducting medical research supported by machine learning models.
Towards Wearable Augmented Reality in Healthcare: A Comparative Survey and Analysis of Head-Mounted Displays

Head-mounted displays (HMDs) have the potential to greatly impact the surgical field by maintaining sterile conditions in healthcare environments. Google Glass (GG) and Microsoft HoloLens (MH) are examples of optical HMDs. In this comparative survey related to wearable augmented reality (AR) technology in the medical field, we examine the current developments in wearable AR technology, as well as the medical aspects, with a specific emphasis on smart glasses and HoloLens. The authors searched recent articles (between 2017 and 2022) in the PubMed, Web of Science, Scopus, and ScienceDirect databases and a total of 37 relevant studies were considered for this analysis. The selected studies were divided into two main groups; 15 of the studies (around 41%) focused on smart glasses (e.g., Google Glass) and 22 (59%) focused on Microsoft HoloLens. Google Glass was used in various surgical specialities and preoperative settings, namely dermatology visits and nursing skill training. Moreover, Microsoft HoloLens was used in telepresence applications and holographic navigation of shoulder and gait impairment rehabilitation, among others. However, some limitations were associated with their use, such as low battery life, limited memory size, and possible ocular pain. Promising results were obtained by different studies regarding the feasibility, usability, and acceptability of using both Google Glass and Microsoft HoloLens in patient-centric settings as well as medical education and training. Further work and development of rigorous research designs are required to evaluate the efficacy and cost-effectiveness of wearable AR devices in the future.
An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly machine learning models, are successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due to the lack of transparency behind their behavior, users need more understanding of how these models make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex machine learning systems utilize less transparent algorithms, thereby exacerbating the problem. This survey analyzes the significance and evolution of explainable AI (XAI) research across various domains and applications. Throughout this study, a rich repository of explainability classifications and summaries has been developed, along with their applications and practical use cases. We believe this study will make it easier for researchers to understand all explainability methods and access their applications simultaneously.
DeepSpeed- Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale

The landscape of transformer model inference is increasingly diverse in model size, model characteristics, latency and throughput requirements, hardware requirements, etc. With such diversity, designing a versatile inference system is challenging. DeepSpeed-Inference addresses these challenges by (1) a multi-GPU inference solution to minimize latency while maximizing throughput for both dense and sparse transformers when the model fits in aggregate GPU memory, and (2) a heterogeneous inference solution that leverages CPU/NVMe/GPU memory to enable high-throughput inference for models larger than aggregate GPU memory. DeepSpeed-Inference reduces latency by 6.4× and increases throughput by 1.5 ×over the state-of-the-art. It enables trillion parameter scale inference under real-time latency constraints by leveraging hundreds of GPUs, an unprecedented scale for inference. It can inference 25 ×larger models than with GPU-only solutions, while delivering a high throughput of 84 TFLOPS (over 50% of A6000 peak).
An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA.