Reviewerly/exHarmony huggingface dataset:
Deep Gaussian processes for biogeophysical parameter retrieval and model inversion

Parameter retrieval and model inversion are key problems in remote sensing and Earth observation. Currently, different approximations exist: a direct, yet costly, inversion of radiative transfer models (RTMs); the statistical inversion with in situ data that often results in problems with extrapolation outside the study area; and the most widely adopted hybrid modeling by which statistical models, mostly nonlinear and non-parametric machine learning algorithms, are applied to invert RTM simulations. We will focus on the latter. Among the different existing algorithms, in the last decade kernel based methods, and Gaussian Processes (GPs) in particular, have provided useful and informative solutions to such RTM inversion problems. This is in large part due to the confidence intervals they provide, and their predictive accuracy. However, RTMs are very complex, highly nonlinear, and typically hierarchical models, so that very often a single (shallow) GP model cannot capture complex feature relations for inversion. This motivates the use of deeper hierarchical architectures, while still preserving the desirable properties of GPs. This paper introduces the use of deep Gaussian Processes (DGPs) for bio-geo-physical model inversion. Unlike shallow GP models, DGPs account for complicated (modular, hierarchical) processes, provide an efficient solution that scales well to big datasets, and improve prediction accuracy over their single layer counterpart. In the experimental section, we provide empirical evidence of performance for the estimation of surface temperature and dew point temperature from infrared sounding data, as well as for the prediction of chlorophyll content, inorganic suspended matter, and coloured dissolved matter from multispectral data acquired by the Sentinel-3 OLCI sensor. The presented methodology allows for more expressive forms of GPs in big remote sensing model inversion problems.
A Dual Input-aware Factorization Machine for CTR Prediction

Factorization Machines (FMs) refer to a class of general predictors working with real valued feature vectors, which are well-known for their ability to estimate model parameters under significant sparsity and have found successful applications in many areas such as the click-through rate (CTR) prediction. However, standard FMs only produce a single fixed representation for each feature across different input instances, which may limit the CTR model’s expressive and predictive power. Inspired by the success of Input-aware Factorization Machines (IFMs), which aim to learn more flexible and informative representations of a given feature according to different input instances, we propose a novel model named Dual Input-aware Factorization Machines (DIFMs) that can adaptively reweight the original feature representations at the bit-wise and vector-wise levels simultaneously. Furthermore, DIFMs strategically integrate various components including Multi-Head Self-Attention, Residual Networks and DNNs into a unified end-to-end model. Comprehensive experiments on two real-world CTR prediction datasets show that the DIFM model can outperform several state-of-the-art models consistently.
Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding

Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner.It has in general two sub-tasks: (i) extracting aspects from each review, and (ii) classifying aspectbased reviews by sentiment polarity.In this paper, we propose a weakly-supervised approach for aspect-based sentiment analysis, which uses only a few keywords describing each aspect/sentiment without using any labeled examples.Existing methods are either designed only for one of the sub-tasks, neglecting the benefit of coupling both, or are based on topic models that may contain overlapping concepts.We propose to first learn sentiment, aspect joint topic embeddings in the word embedding space by imposing regularizations to encourage topic distinctiveness, and then use neural models to generalize the word-level discriminative information by pre-training the classifiers with embedding-based predictions and self-training them on unlabeled data.Our comprehensive performance analysis shows that our method generates quality joint topics and outperforms the baselines significantly (7.4% and 5.1% F1-score gain on average for aspect and sentiment classification respectively) on benchmark datasets 1 .
Few-Shot Compositional Font Generation with Dual Memory

Generating a new font library is a very labor-intensive and time-consuming job for glyph-rich scripts. Despite the remarkable success of existing font generation methods, they have significant drawbacks; they require a large number of reference images to generate a new font set, or they fail to capture detailed styles with only a few samples. In this paper, we focus on compositional scripts, a widely used letter system in the world, where each glyph can be decomposed by several components. By utilizing the compositionality of compositional scripts, we propose a novel font generation framework, named Dual Memory-Augmented Font Generation Network (DM-Font), which enables us to generate a high-quality font library with only a few samples. We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization compared to the state-of-the-art generation methods quantitatively and qualitatively. Source code is available at https://github.com/clovaai/dmfont .
A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids

Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems.In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators.This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids' components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM).Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure.We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching.Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids.The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid.The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction).Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr(4.40% reduction), which are notable values.The results advocate the quality and functionality of the proposed framework.
mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines

Machine learning (ML) for classification and prediction based on a set of features is used to make decisions in healthcare, economics, criminal justice and more. However, implementing an ML pipeline including preprocessing, model selection, and evaluation can be time-consuming, confusing, and difficult. Here, we present mikropml (prononced "meek-ROPE em el"), an easy-to-use R package that implements ML pipelines using regression, support vector machines, decision trees, random forest, or gradient-boosted trees. The package is available on GitHub, CRAN, and conda.
Enhancement of underwater vision by fully exploiting the polarization information from the Stokes vector

Underwater imaging method based on polarization information is extremely popular due to its ability to effectively remove the backscattered light. The Stokes vector contains the information of both the degree and angle of polarization of the light wave. However, this aspect has been rarely utilized in image reconstruction. In this study, an underwater polarimetric imaging model is established by fully exploiting this feature of Stokes vectors. The transmission of light wave is described in terms of the polarization information derived from the Stokes vector. Then, an optimization function is designed based on the independent characteristics of target light and backscattered light to estimate the target and backscattered field information. The real-world experiments and mean squared error analysis verify that the proposed method can remove the backscattered light and recover the target information accurately.
Cluster learning-assisted directed evolution

Directed evolution, a strategy for protein engineering, optimizes protein properties (that is, fitness) by expensive and time-consuming screening or selection of a large mutational sequence space. Machine learning-assisted directed evolution (MLDE), which screens sequence properties in silico, can accelerate the optimization and reduce the experimental burden. This work introduces an MLDE framework, cluster learning-assisted directed evolution (CLADE), which combines hierarchical unsupervised clustering sampling and supervised learning to guide protein engineering. The clustering sampling selectively picks and screens variants in targeted subspaces, which guides the subsequent generation of diverse training sets. In the last stage, accurate predictions via supervised learning models improve the final outcomes. By sequentially screening 480 sequences out of 160,000 in a four-site combinatorial library with five equal experimental batches, CLADE achieves global maximal fitness hit rates of up to 91.0% and 34.0% for the GB1 and PhoQ datasets, respectively, improved from the values of 18.6% and 7.2% obtained by random sampling-based MLDE. A machine learning-assisted directed evolution method is developed, combining hierarchical unsupervised clustering and supervised learning, to guide protein engineering by iteratively exploring the large mutational sequence space.
Impact of mining on land use land cover change and water quality in the Asutifi North District of Ghana, West Africa

Surface and groundwater are important resources for human life and health. Land use changes and mining activities, however, have the potential to compromise the safety of these resources. This study aims to investigate the land cover conditions before and during mining activities and assess the impact on forests, surface, and groundwater resources in the Asutifi North District, Ghana. Landsat data of 30 m resolution was used to assess the land cover change from 2000 to 2020. A cross-sectional analysis of data between April to June 2004 to 2019 was used to assess water quality. The overall accuracy of the land cover classification ranged between (91.33–99.41)% and (78.01–85.95)% respectively for ground-truth validation and validation using the MODIS annual 500 m land cover dataset. Forest cover decreased by 11.47% while agriculture doubled from 12.13% to 24.06% between 2000 and 2020. The introduction of mining is responsible for major land cover changes including the conversion of forest to agricultural land through shifting cultivation. For a general assessment of water quality, the percentage of pH, TB, TDS, NH3, Mg and SO4 measurements outside the EPA admissible limits for drinking water is 16.67%, 83.33%, 0.69%, 0%, 10.42%, and 16.67% respectively. Turbidity is the most impacted water quality parameter affected by land cover changes while NH3 is the least impacted parameter. The incidences of water quality outside EPA/WHO limits for drinking water at all monitoring points call for education and collective action to safeguard water sources from further deterioration. It is necessary mining companies adhere to environmental protection guidelines.
Leaders, practitioners and scientists' awareness of artificial intelligence in libraries: a pilot study

Purpose This study investigates the level of artificial intelligence (AI) awareness among library leaders, practitioners and scientists of Indonesian academic libraries to elucidate the benefits of AI implementation and its necessary infrastructure and challenges. Design/methodology/approach The study adopted a purposive sampling technique to select the 38 participants and thematic analysis to analyze the data, identifying eight themes: understanding of AI, AI adoption, benefits of AI, competencies needed to support AI, facilities to support AI, factors supporting AI adoption, AI-inhibiting factors and expectations of AI. Findings Different viewpoints provided full awareness among library stakeholders and sufficient information to begin AI initiatives in Indonesian libraries as leaders, practitioners and scientists had a favorable, open and encouraging outlook on AI. Research limitations/implications The study does not investigate variations in perspectives between the participants, but it examines their understanding of AI and elaborates the results into the concept of an intelligent library. Moreover, this study only uses samples from academic libraries. Practical implications Libraries can take these results into consideration before implementing AI, especially in technology and facilities, librarian competency with regard to AI and leadership roles in AI projects. Social implications Library boards and library associations can use this research as a source to create guidelines about AI implementation in academic libraries. Originality/value The study addresses the gap in the research on university libraries' readiness and awareness to implement AI, especially in developing countries.
Medical domain knowledge in domain-agnostic generative AI

The text-guided diffusion model GLIDE (Guided Language to Image Diffusion for Generation and Editing) is the state of the art in text-to-image generative artificial intelligence (AI). GLIDE has rich representations, but medical applications of this model have not been systematically explored. If GLIDE had useful medical knowledge, it could be used for medical image analysis tasks, a domain in which AI systems are still highly engineered towards a single use-case. Here we show that the publicly available GLIDE model has reasonably strong representations of key topics in cancer research and oncology, in particular the general style of histopathology images and multiple facets of diseases, pathological processes and laboratory assays. However, GLIDE seems to lack useful representations of the style and content of radiology data. Our findings demonstrate that domain-agnostic generative AI models can learn relevant medical concepts without explicit training. Thus, GLIDE and similar models might be useful for medical image processing tasks in the future - particularly with additional domain-specific fine-tuning.
Comparative Analysis of Skeleton-Based Human Pose Estimation

Human pose estimation (HPE) has become a prevalent research topic in computer vision. The technology can be applied in many areas, such as video surveillance, medical assistance, and sport motion analysis. Due to higher demand for HPE, many HPE libraries have been developed in the last 20 years. In the last 5 years, more and more skeleton-based HPE algorithms have been developed and packaged into libraries to provide ease of use for researchers. Hence, the performance of these libraries is important when researchers intend to integrate them into real-world applications for video surveillance, medical assistance, and sport motion analysis. However, a comprehensive performance comparison of these libraries has yet to be conducted. Therefore, this paper aims to investigate the strengths and weaknesses of four popular state-of-the-art skeleton-based HPE libraries for human pose detection, including OpenPose, PoseNet, MoveNet, and MediaPipe Pose. A comparative analysis of these libraries based on images and videos is presented in this paper. The percentage of detected joints (PDJ) was used as the evaluation metric in all comparative experiments to reveal the performance of the HPE libraries. MoveNet showed the best performance for detecting different human poses in static images and videos.
Determinants of Using AI-Based Chatbots for Knowledge Sharing: Evidence From PLS-SEM and Fuzzy Sets (fsQCA)

While adopting chatbots powered by artificial intelligence could enhance knowledge sharing, it also causes challenges due to the “dark side” of these agents. However, research on the factors influencing chatbots for knowledge sharing is lacking. To bridge this gap, we developed the integrated chatbot acceptance-avoidance model, which looks at the positive and negative determinants of using chatbots for knowledge sharing. Through a comprehensive questionnaire survey of 447 students, the research model is evaluated using the partial least squares-structural equation modeling (PLS-SEM), a symmetric approach, and fuzzy set qualitative comparative analysis (fsQCA) as an asymmetric approach. The PLS-SEM results supported the positive role of performance expectancy, effort expectancy, and habit and the negative role of perceived threats in affecting chatbot use for knowledge sharing. Although PLS-SEM results revealed that social influence, facilitating conditions, and hedonic motivation have no impact on chatbot use, the fsQCA analysis revealed that all factors might play a role in shaping the use of chatbots. In addition to the theoretical contributions, the findings provide several managerial implications for universities, instructors, and chatbot developers to help them make insightful decisions and promote the use of chatbots.
HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers

Reducing energy consumption in cloud data centers is one of the prime issue in the cloud community. It reduces energy related costs and increases lifespan of high performance computing resources deployed in cloud data centers and it also helps in reducing carbon emissions. Along with energy efficiency, problem of task scheduling is also one of the important problem considered in cloud data centers and it belongs to NP-class problems. With the energy consumption consideration, problem of task scheduling becomes more complex to solve. Metaheuristic algorithms are proven to generate near optimal solutions for task scheduling problem but their scheduling overhead increases vastly as the number of tasks or number of resources increases. In addition to this, metaheuristic-based scheduling algorithms lives most of time in local-optimal region of solution space and it is possible that it can finalizes the solution in the local-optimal region only. Primary motivation behind this work is from genetic algorithm itself, we have experimented with genetic algorithm in many ways to solve the problem of energy efficient task scheduling and come up with new hybrid scheme to solve it. In this work, we tackles the problem of energy efficient task scheduling on modern cloud data center architecture and proposes a novel hybrid metaheuristic scheme harmony-inspired genetic algorithm (HIGA). It also addresses issues associated with metaheuristic algorithm. HIGA combines the exploration capability of genetic algorithm and exploitation capability of harmony search by which it intelligently senses local as well as global optimal region without wasting time (iterations) in local or global optimal region and provides quick convergence. Our primary objectives in this work are to reduce makespan and computing energy and secondary objectives are to reduce the energy consumed by the resources other than computing resources and reduce execution overhead associated with scheduler. Collectively these objectives guides HIGA for better energy efficiency and performance while reducing the number of required resources (i.e. active racks). It indirectly also reduces cooling energy as we can switch off the rack components (air blower, cooling inlets) once racks become idle. Simulation analysis has been performed over independent task applications as well as real-world scientific applications like CyberShake, Epigenomics and Montage. The result clearly manifests that proposed HIGA provides by up to 33% of energy savings and 47% of improvement in application performance (makespan) that too with 39% less execution overhead.
Evaluating the Effects of Human Activity over the Last Decades on the Soil Organic Carbon Pool Using Satellite Imagery and GIS Techniques in the Nile Delta Area, Egypt

The study aims to clarify the relationship between soil organic carbon (SOC) and human activity under arid conditions, in the east area of the Nile Delta, Egypt. SOC is one of the critical factors in food production and plays an important role in the climate change because it affects the physio-chemical soil characteristics, plant growth, and contributes to sustainable development on global levels. For the purpose of our investigations, 120 soil samples (0–30 cm) were collected throughout different land uses and soil types of the study area. Multiple linear regressions (MLR) were used to investigate the spatiotemporal relationship of SOC, soil characteristics, and environmental factors. Remote sensing data acquired from Landsat 5 TM in July 1995 and operational land imager (OLI) in July 2018 were used to model SOC pool. The results revealed significant variations of soil organic carbon pool (SOCP) among different soil textures and land-uses. Soil with high clay content revealed an increase in the percentage of soil organic carbon, and had mean SOCP of 6.08 ± 1.91 Mg C ha−1, followed by clay loams and loamy soils. The higher values of SOCP were observed in the northern regions of the study area. The phenomenon is associated with the expansion of the human activity of initiating fish ponds that reflected higher values of SOC that were related to the organic additions used as nutrients for fish. Nevertheless, the SOC values decreased in southeast of the study area with the decrease of soil moisture contents and the increase in the heavy texture profiles. As a whole, our findings pointed out that the human factor has had a significant impact on the variation of soil organic carbon values in the Eastern Nile Delta from 1995 to 2018. As land use changes from agricultural activity to fish ponds, the SOCP significantly increased. The agriculture land-use revealed higher SOCP with 60.77 Mg C ha−1 in clay soils followed by fish ponds with 53.43 Mg C ha−1. The results also showed a decrease in SOCP values due to an increasing in land surface temperature (LST) thus highlighting that influence of temperature and ambient soil conditions linked to land-use changes have a marked impact on surface SOCP and C sequestration.