Reviewerly/exHarmony huggingface dataset:
Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A System for Galician and Mobile Opportunistic Scenarios

Systems with voice control are an attractive option for increasing technological integration, not only for people with little knowledge on technology or constrained Internet access, but also for people with certain disabilities. In addition, devices based on Alexa or Google Home provide an interesting alternative for interacting with Internet of Things (IoT) devices, but they usually rely on an Internet connection to a cloud server for their full operation. Furthermore, many voice-recognition systems are only available in a limited number of languages, which tend to be those with the highest number of speakers, thus excluding minority-language speakers. To address the previously mentioned issues, this article presents a solution based on Edge Computing and voice commands that carries out offline voice processing and that is able to interact with IoT-based systems. The proposed system performs local speech inference, providing a communication interface with IoT devices in a Bluetooth mesh, all in a fast way and without the need for an Internet connection. In addition, the proposed solution can be adapted easily for voice recognition of languages with few resources. Such a feature is demonstrated with the Galician language, which is spoken by less than 3 million people worldwide. In particular, different Automatic Speech Recognition (ASR) models based on three of the most popular ASR development frameworks (wav2vec2, DistilHubert, Whisper) were developed to transcribe short speech and to translate it into IoT commands that perform specific home-automation actions. Such models were fine-tuned for Galician with a corpus of approximately 20 hours and were evaluated in static and mobile opportunistic scenarios in terms of accuracy, energy consumption and latency on an embedded platform (that acts as an edge device) and on a cloud server. The obtained results show that inference is performed in less than 2 seconds on a Raspberry Pi 4 for the two smallest models and in less than 500 ms on a high-end Android smartphone when processing all data locally with CPU-only inference (i.e., without hardware acceleration or external processing). The results of the transcriptions are accurate enough to be able to use simple text distance algorithms to detect keywords in the speech and perform commands on IoT devices. In particular, a maximum success rate of 92% was achieved for detecting the indicated commands when using models optimized for being executed on embedded devices. For selected home scenarios, command actions were sent via Bluetooth with average response times of up to 113 ms.
Zero-Knowledge Proof Intelligent Recommendation System to Protect Students’ Data Privacy in the Digital Age

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Digital soil mapping of lithium in Australia

Abstract. With a higher demand for lithium (Li), a better understanding of its concentration and spatial distribution is important to delineate potential anomalous areas. This study uses a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates that affect soil formation to predict and map aqua-regia-extractable Li content across the 7.6×106 km2 area of Australia. Catchment outlet sediment samples (i.e. soils formed on alluvial parent material) were collected by the National Geochemical Survey of Australia at 1315 sites, with both top (0–10 cm depth) and bottom (on average ∼60–80 cm depth) catchment outlet sediments sampled. We developed 50 bootstrap models using a cubist regression tree algorithm for each depth. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction with a root mean square error of 3.32 mg kg−1 (which is 44.2 % of the interquartile range) for the top depth. The model for the bottom depth has yet to be validated. The variables of importance for the models indicated that the first three Landsat 30+ Barest Earth bands (red, green, blue) and gamma radiometric dose have a strong impact on the development of regression-based Li prediction. The bootstrapped models were then used to generate digital soil Li prediction maps for both depths, which could identify and delineate areas with anomalously high Li concentrations in the regolith. The predicted maps show high Li concentration around existing mines and other potentially anomalous Li areas that have yet to be verified. The same mapping principles can potentially be applied to other elements. The Li geochemical data for calibration and validation are available from de Caritat and Cooper (2011b; https://doi.org/10.11636/Record.2011.020) and Main et al. (2019; https://doi.org/10.11636/Record.2019.002), respectively. The covariate data used for this study were sourced from the Terrestrial Ecosystem Research Network (TERN) infrastructure, which is enabled by the Australian Government's National Collaborative Research Infrastructure Strategy (NCRIS; https://esoil.io/TERNLandscapes/Public/Products/TERN/Covariates/Mosaics/90m/, last access: 6 December 2022; TERN, 2019). The final predictive map is available at https://doi.org/10.5281/zenodo.7895482 (Ng et al., 2023).
Comparing Stacking Ensemble and Deep Learning for Software Project Effort Estimation

This study focuses on improving the accuracy of effort estimation by employing ensemble, deep learning, and transfer learning techniques.An ensemble approach is utilized, incorporating XGBoost, Random Forest, and Histogram Gradient Boost as generators to enhance predictive capabilities.The performance of the ensemble method is compared against both the deep learning approach and the PFA-IFPUG technique.Statistical criteria including MAE, SA, MMRE, PRED(0.25),MBRE, MIBRE, and relevant information related to MMRE and PRED(0.25)are employed for evaluation.The results demonstrate that combining regression models with Random Forest as the final regressor and XGBoost and Histogram Gradient Boost as prior generators yields more accurate effort estimation than other combinations.Furthermore, the findings highlight the potential of transfer learning in the deep learning method, which exhibits superior performance over the ensemble approach.This approach leverages pre-trained models and continuously improves performance by training on new datasets, providing valuable insights for cross-company and cross-time effort estimation problems.The ISBSG dataset is used to build the pretrained model, and the inductive transfer learning approach is verified based on the Desharnais, Albrecht, Kitchenham, and China datasets.The study underscores the significance of transfer learning and the integration of domain-specific knowledge from existing models to enhance the performance of new models, thereby improving accuracy, reducing errors, and enhancing predictive capabilities in effort estimation.
Blockchain

This chapter provides an overview of blockchain technology and its potential use in library and information centers. It explains the main components of blockchain technology, including nodes, transactions, blocks, and chains, and the different types of blockchain networks. The three pillars of blockchain technology, namely decentralization, transparency, and immutability, are also discussed. The chapter highlights the advantages of using blockchain in libraries, including digital preservation, tracking, and data storage. It also notes the challenges in implementing blockchain technology in libraries, such as complex architecture and start-up costs. Despite the obstacles, the potential benefits of blockchain in libraries are significant. The chapter concludes by highlighting the importance of further research and development in this field, and how emerging technologies like blockchain could shape the future of libraries and information centers.
Underwater-image super-resolution via range-dependency learning of multiscale features

Optical imaging instruments have been widely deployed in underwater-engineering systems, playing an important role in underwater-object localization, detection, and recognition. However, underwater images suffer from the adverse effects of local distortion and global haze. Consequently, raw underwater images have limitations when used for display and vision tasks. Super resolution (SR) has been increasingly exploited for perceptual image quality improvement. However, underwater-image SR is a relatively underexplored area, most existing methods being unable to reduce the adverse effects of underwater images. Moreover, in contrast to land-based high-performance platforms, the power supply and computational resources of underwater platforms are limited, making them difficult to use in large-scale models. To solve these problems, this study developed a novel range-dependency learning network to present the short- and long-range dependency of multiscale features. Such a mechanism could provide more detailed and accurate texture information for underwater-image SR, improving underwater-image SR performance. Moreover, a channel-splitting module was designed to generate the channel bands which could extract texture details and global structural information at different scales while reducing the number of parameters, thus accelerating the training speed of the model and maintaining good performance. Our novel network could reach an optimal tradeoff between the underwater-image SR performance and efficiency, which was demonstrated by experimental comparisons and an ablation study.
Characterizing the Development of Photovoltaic Power Stations and Their Impacts on Vegetation Conditions from Landsat Time Series during 1990–2022

To achieve carbon peaking and carbon neutrality in China, photovoltaic (PV) power generation has become increasingly important for promoting a low-carbon transition. The central and western desert areas of China have been identified as major areas for the construction of large PV bases. Remote sensing technology has been used to map the spatial distribution and development status of PV power stations quickly and accurately in ecologically fragile areas, as well as assess the ecological and environmental impact of their construction. However, current remote sensing monitoring of PV power stations focuses mainly on mapping and time series analysis to measure their development process and assess the environmental conditions on a large scale over a long period of time. Therefore, we constructed a random forest model based on image spectral and texture features and mapped 2022 PV power stations in the junction area of Hobq Desert, Ulan Buh Desert, Tengger Desert, and Mu Us Sands in China. Following that, we identified the construction time of the PV power stations by identifying the turning points of the normalized construction land index (NDBI) time series from 1990–2022 using the LandTrendr method. To assess the ecological impact of PV power stations, we used the NDVI to measure the change in vegetation condition before and after the construction of PV power stations and constructed NDVI changes for PV power stations constructed in different years. The results showed that this mapping method achieved an overall classification accuracy of 96.65% and a Kappa coefficient of 0.92. The root mean square error (RMSE) for construction year identification was less than 0.5, and the number of new PV power stations increased significantly after 2010, reaching a total area of 14.52 km2 by 2016, which is consistent with the trend driven by national and regional development plans. Furthermore, the study found that the vegetation cover level could be restored to the average level before construction within 5–6 years and continued to increase after that. These findings may help government policymakers and practitioners make decisions on PV power station planning and ecological environment protection, thus contributing promptly to the achievement of China’s dual carbon goals.
CNN Combined with FC Classifier to Combat Artificial Penta-Digit Text-Based Captcha

Captcha technology has grown in popularity along with the growth of the Internet. The completely automated public Turing test to tell computers and humans apart uses captcha technology to discriminate between humans and robots. The most common type of CAPTCHA is text-based. The majority of CAPTCHAs that use text have been broken. However, prior research has primarily relied on complex and ineffective preparation methods to attack text CAPTCHAs. This study builds a deep CNN network model that can recognize a 5-character text captcha by researching captcha recognition technology.
Fully-integrated photonic tensor core for image convolutions

Convolutions are one of the most critical signal and image processing operations. From spectral analysis to computer vision, convolutional filtering is often related to spatial information processing involving neighbourhood operations. As convolution operations are based around the product of two functions, vectors or matrices, dot products play a key role in the performance of such operations; for example, advanced image processing techniques require fast, dense matrix multiplications that typically take more than 90% of the computational capacity dedicated to solving convolutional neural networks. Silicon photonics has been demonstrated to be an ideal candidate to accelerate information processing involving parallel matrix multiplications. In this work, we experimentally demonstrate a multiwavelength approach with fully integrated modulators, tunable filters as microring resonator weight banks, and a balanced detector to perform matrix multiplications for image convolution operations. We develop a scattering matrix model that matches the experiment to simulate large-scale versions of these photonic systems with which we predict performance and physical constraints, including inter-channel cross-talk and bit resolution.
Extraordinary MHNet: Military high-level camouflage object detection network and dataset

We present the first systematic work on Military High-level Camouflage object Detection (MHCD), aiming to identify objects visibly embedded in chaotic backgrounds. The high intrinsic similarities (e.g., texture, intensity, color, etc.) between the attention object and its background give the task far more challenging than general object detection. In this paper, we construct a benchmark MHCD2022 dataset, which consists of 3000 images with dense annotations covering 5 categories from multiple real-world scenes. Remarkably, based on the observation that biological vision usually first obtains perception from global search and strives to recover the complete object, we propose a novel Military High-level detection Network, called MHNet, which is characterized by four ingenious modules: Subject Perception Gathering (SPG), Part-object Relationships Mining (PRM), Concept Recovery/Feature Clue Supplement (CR/FCS) and Springboard Selection (SS). Firstly, a SPG is designed for global foreground rough perception by the exploitation of depth information. Second, a PRM is particularly used to mine part-object potential relations in diverse environments. After that, we propose CR/FCS and SS to enhance the destroyed instance-level representation and suppress the domain imbalance problem, respectively. Extensive experimental results show that previous methods suffered from poor performance, MHNet significantly outperforms camouflage baselines and competing methods on the MHCD2022 for the high-level camouflaged object. Finally, we also present and highlight the practical application value and several future directions of the research.
Assessing the impact of soiling on photovoltaic efficiency using supervised learning techniques

The accumulation of dust and other particles on solar panels, known as soiling, is a significant factor that affects their performance, leading to reduced efficiency if not addressed properly. In this study, we propose a new methodology to estimate soiling on solar photovoltaic panels. To address this issue, we utilised data from the University of Jaén and satellite information from NASA. We applied five different machine learning models, including Linear Regression, Random Forest, Decision Tree, Multilayer Perceptron and Long Short-Term memory neural networks to estimate the extent of soiling on the panels. The input data consisted of weather data, as well as operational data of the solar panels. Our results showed that the MLP model had the lowest average error of 0.0003, indicating its effectiveness in estimating the extent of soiling on the panels. This is significantly lower compared to previous proposals in the literature, which had an average error of 0.026. This study demonstrates the effectiveness of using machine learning methods to forecast soiling on photovoltaic panels accurately. The implications of our findings are essential for optimising energy production and improving the efficiency of solar power systems.
An adaptive pixel value ordering based reversible data hiding scheme for images

Pixel value ordering (PVO) is a prominent method for reversible data hiding, which has gained increasing research interest in recent years due to its effective performance producing high-fidelity marked images. PVO-based methods performance lies on block dimensions and thresholds for classification. However, most methods determine proper parameters by performing exhaustive searches in restricted search spaces. In this paper, we present a novel Pixel Value Ordering technique based on Genetic Algorithms (PVO-GA) to address these issues. PVO-GA uses flexible sized blocks to take advantage of different block features, to better exploit image redundancy. A genetic algorithm is introduced to optimize the embedding process since the solutions space is greatly expanded. Results showed that PVO-GA achieves satisfactory levels in terms of the introduced distortion while significantly reduces the computational cost needed to find the optimal set of insertion parameters when compared to previous methods.
Daily prediction method of dust accumulation on photovoltaic (PV) panels using echo state network with delay output

Dust accumulation over time can be one of the main causes of uncertainty in the output of photovoltaic (PV) systems. In order to better understand these losses, this paper established a daily dust accumulation prediction model for PV panels based on the delay output echo state network (DESN). A pigeon-inspired optimization (PIO) algorithm with adaptive Cauchy (AC) mutation strategy was proposed, which can optimize the reservoir parameters (such as leakage rate, spectral radius, and input scaling) of DESN, shorten the solution time, and improve search speed. Based on typical meteorological and air quality data, as well as daily accumulated dust weight recorded from the experimental platform, model training and testing were carried out. According to the Pearson correlation coefficient, the relationship between the environment parameters (humidity, wind speed, wind direction, PM2.5, PM10, and rainfall) and the dust accumulation was obtained. The results show that the prediction accuracy of AC-PIO-DESN is better than other methods for meteorological and air quality data. The mean absolute percentage error (MAPE) for humidity, irradiance, PM2.5 and PM10 were 4.0743%, 4.4958%, 10.6231% and 12.8402%, respectively. In addition, the proposed daily dust prediction model has good robustness for 10-day samples, with an average relative error ranging from 0.65% to 54%. This method can provide data support for grid scheduling and PV panel cleaning strategy of PV power plants.
Dual-scale Contrastive Learning for multi-behavior recommendation

Multi-behavior recommendation (MBR) aims to improve the prediction of the target behavior (i.e., purchase) by exploiting multi-typed auxiliary behaviors, such as page view, cart and favorite. Recently, leveraging Graph Neural Networks (GNNs) to capture collaborative signals has been the mainstream paradigm for MBR. However, GNN-based MBR suffers from data sparsity in real-world scenarios and thus performs mediocrely. Excitingly, contrastive learning which can mine additional self-supervised signals from raw data, holds great potential to alleviate this problem. Naturally, we seek to exploit contrastive learning to enhance MBR, while two key challenges have yet to be addressed: (i) Difficult to learn reliable representations under different behaviors; (ii) Sparse supervised signals under target behavior. To tackle the above challenges, in this paper, we propose a novel Dual-Scale Contrastive Learning (DSCL) framework. Unlike traditional contrastive learning methods that artificially construct two views through data augmentation, we comprehensively consider two different views for MBR, including the collaborative view and the semantic view. Specifically, we regard the user–item graph as a collaborative view and the user–user graph as a semantic view. In particular, we develop two novel contrastive learning objectives at two scales. For the first challenge, we devise local-to-context contrastive learning within behaviors on collaborative view, which enhances the representation learning by incorporating potential neighbors into the contrastive learning from the graph topological space and the semantic space, respectively. As for the second challenge, we design local-to-local contrastive learning across behaviors on a semantic view, which has the benefit of capturing commonalities between different behaviors and integrating them into the target behavior to alleviate the sparse supervised signal problem of the target behavior. In addition, we also propose an adaptive weight network to efficiently customize the integration of all losses. Extensive experiments on three real-world benchmark datasets show that our proposed DSCL is significantly superior to various state-of-the-art recommendation methods.