Reviewerly/exHarmony huggingface dataset:
Diurnal Variation in Clouds and Radiative Budgets Over the Tibetan Plateau During Summer Using CERES Data

Abstract Diurnal variations in clouds and radiation budgets over four subareas of the Tibetan Plateau (TP) during summer (June–August) are analyzed using the Clouds and the Earth's Radiant Energy System (CERES) synoptic 1° (SYN1deg) data from 2000 to 2020. The results show that the total cloud amount decreases from southeast to northwest and is larger during daytime (71.1%) than nighttime (67.2%) over the entire TP. High‐clouds develop in the afternoon, persist during nighttime, and dissipate after sunrise. Low clouds develop after sunrise and dissipate in the afternoon over the entire TP, but show opposite temporal variation over the Kunlun Mountains. The net radiation budget at the top‐of‐atmosphere reaches its maximum at noon. The surface net radiation budget is positive in the daytime and negative at nighttime. These features are mainly adjusted by the cloud distribution. The diurnal variations in heating rate over the four subareas are similar in the upper atmosphere but different in the lower layer. The low‐atmosphere heating rate shows a maximum value over the center‐south (CS) subarea, while it is lowest over the west (W) subarea. Internal cloud forcing has distinct regional differences over the four subareas: it shows a heating effect in the low atmosphere and a cooling effect in the middle atmosphere over the CS subarea, whereas over the W subarea it shows a radiative cooling effect in the low atmosphere and no significant radiative effect in the middle layer. The findings of this study help toward improving our understanding of the TP's energy cycle.
On the way to a smart school: a look from the classroom

The article presents a ten-year experience of the development and implementation of a personalized mastery-based learning model to renew the educational process in a public school. The model assumes that 1) externally set learning goals should be processed by the students and become their personal goals, 2) the success of learning is ensured by individualizing the pace, organization and methods of teaching, educational materials and special eﬀorts of teachers to develop personal qualities of the student. Six interrelated tasks that the educational organization systematically solved during the experiment are described. The eﬀectiveness of the developed model of educational work is evidenced by the high marks of students in the state exams in informatics and the results of the annual state graduation tests.
Blockchain in education: the influence of trust on adoption and implementation

Purpose This paper aims to investigate the influence of trust on adopting and implementing blockchain technology in higher education institutions (HEIs) in Brazil. Design/methodology/approach This study uses an exploratory qualitative approach to understand the construct of trust in the context of the educational sector. Data were collected through semistructured questionnaires and online interviews. Findings The research identified that, for most potential blockchain users, trust positively influences the HEIs, because benefits such as secure data sharing and transaction transparency could optimize the daily routine and avoid fraud in academic documents, providing a cooperative and reliable working environment. In addition, the results suggest that trust is needed to overcome challenges related to issues such as costs and privacy. Research limitations/implications This study contributes to the advances in the emerging literature on blockchain in the educational sector as a system with the potential to generate trust, as well as the literature on the technology acceptance models. Practical implications For HEI managers and practitioners, this study highlights the need for a greater understanding of the influence of trust in the relationships between HEIs and other stakeholders. Social implications This work shows that adopting blockchain technologies would allow users to build social relationships of trust in a cooperative work environment and develop trusted behavior by sharing data securely and transparently. Originality/value To the best of the authors’ knowledge, this is one of the first studies on the adoption and implementation of blockchain in the education sector in Brazil.
Why functional program synthesis matters (in the realm of genetic programming)

In Genetic Programming (GP) systems, particularly those that target general program synthesis problems, it is common to use imperative programming languages to represent evolving code. In this work, we consider the benefits of using a purely functional, rather than an imperative, approach. We then demonstrate some of these benefits via an experimental comparison of the pure functional language Haskell and the imperative language Python when solving program synthesis benchmarks within a grammar-guided GP system. Notably, we discover that the Haskell programs yield a higher success rate on unseen data, and that the evolved programs often have a higher degree of interpretability. We also discuss the broader issues of adapting a grammar-based GP system to functional languages, and highlight some of the challenges involved with carrying out comparisons using existing benchmark suites.
A layered learning estimation of distribution algorithm

Though estimation of distribution algorithms (EDAs) have witnessed success in problem optimization, most of them suffer from sharp shrink of covariance, which may lead to premature convergence. To alleviate this issue, this paper proposes a layered learning estimation of distribution algorithm (LLEDA) by maintaining multiple probability distribution models. Specifically, LLEDA first separates the population into several layers based on fitness. Then, the mean position of each layer is computed. Subsequently, we let the estimated mean position in each lower layer randomly learn from the one of a randomly selected higher layer, so that the mean positions of lower layers could be promoted to be close to promising areas found in the current population. At last, the covariance of each layer is estimated based on the generated new mean position and the individuals in this layer. By this means, multiple probability models with high quality are maintained and then are used to sample promising and diversified offspring separately. Comparative experiments conducted on a widely used benchmark problem set demonstrate that the proposed LLEDA achieves competitive or even much better performance than several state-of-the-art and representative EDAs.
Combining GIN and PMD for code improvements

Software developers are increasingly dependent on question and answer portals and blogs for coding solutions. While such interfaces provide useful information, there are concerns that code hosted here is often incorrect, insecure or incomplete. Previous work indeed detected a range of faults in code provided on Stack Overflow through the use of static analysis. Static analysis may go a far way towards quickly establishing the health of software code available online. In addition, mechanisms that enable rapid automated program improvement may then enhance such code. Accordingly, we present this proof of concept. We use the PMD static analysis tool to detect performance faults for a sample of Stack Overflow Java code snippets, before performing mutations on these snippets using GIN. We then re-analyse the performance faults in these snippets after the GIN mutations. GIN's RandomSampler was used to perform 17,986 unique line and statement patches on 3,034 snippets where PMD violations were removed from 770 patched versions. Our outcomes indicate that static analysis techniques may be combined with automated program improvement methods to enhance publicly available code with very little resource requirements. We discuss our planned research agenda in this regard.
Using structural bias to analyse the behaviour of modular CMA-ES

The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a commonly used iterative optimisation heuristic for optimising black-box functions. CMA-ES comes in many flavours with different configuration settings. In this work, we investigate whether CMA-ES suffers from structural bias and which modules and parameters affect the strength and type of structural bias. Structural bias occurs when an algorithm or a component of the algorithm biases the search towards a specific direction in the search space irrespective of the objective function. In addition to this investigation, we propose a method to assess the relationship between structural bias and the performance of configurations with different types of bias on the BBOB suite of benchmark functions. Surprisingly for such a popular algorithm, 90.3% of the 1 620 CMA-ES configurations were found to have Structural Bias. Some interesting patterns between module settings and bias types are presented and further insights are discussed.
Survivor selection in a crossoverless evolutionary algorithm

The plant propagation algorithm is a crossoverless population based evolutionary algorithm, defaultly deploying the plus-selection method for survivor selection - combining parents and offspring population and then selecting the best popSize individuals. In this study, we explore eight different survivor selection methods (plus and comma selection, tournament selection with and without replacement, elitist tournament selection, linear ranking selection and (elitist) roulette wheel selection) on 59 continuous benchmark test function instances and compare the results.
Optimizing LLVM pass sequences with shackleton

In this paper we explore the novel application of a linear genetic programming framework, Shackleton, to optimizing sequences of LLVM optimization passes. The algorithm underpinning Shackleton is discussed, with an emphasis on the effects of different features unique to the framework when applied to LLVM pass sequences. Combined with analysis of different hyperparameter settings, we report the results on automatically optimizing pass sequences with Shackleton for two software applications at differing complexity levels. Finally, we reflect on the advantages and limitations of our current implementation and lay out a path for further improvements. These improvements aim to surpass hand-crafted solutions with an automatic discovery method for an optimal pass sequence.
Towards the evolutionary assessment of neural transformers trained on source code

In recent years, deep learning have become popular for solving tasks in a wide range of domains. With this growing diffusion, combined with architectures becoming increasingly complex and sophisticated, understanding how deep models make their predictions is now a crucial and challenging research issue.
Incorporating sub-programs as knowledge in program synthesis by PushGP and adaptive replacement mutation

Program synthesis aims to build an intelligent agent that composes computer programs to solve problems. Genetic programming (GP) provides an evolutionary solution for the program synthesis task. A typical GP includes a random initialization, an unguided variation, and a fitness-guided selection to search for a solution program. However, several recent studies have shown the importance of using prior knowledge in different components of the GP. This study investigates the effectiveness of incorporating sub-programs as "prior knowledge" into the variation process of GP by Replacement Mutation. We further design an adaptive strategy that allows the automatic selection of the helpful sub-programs to the search process from an archive (including helpful and unhelpful ones). With handcrafted sub-program archives, we verify the effectiveness of the Adaptive Replacement Mutation method in success rate. We demonstrate the effectiveness of our approach with transferred archives on two composite problems.
Neural architecture search using genetic algorithm for facial expression recognition

Facial expression is one of the most powerful, natural, and universal signals for human beings to express emotional states and intentions. Thus, it is evident the importance of correct and innovative facial expression recognition (FER) approaches in Artificial Intelligence. The current common practice for FER is to correctly design convolutional neural networks' architectures (CNNs) using human expertise. However, finding a well-performing architecture is often a very tedious and error-prone process for deep learning researchers. Neural architecture search (NAS) is an area of growing interest as demonstrated by the large number of scientific works published in recent years thanks to the impressive results achieved in recent years. We propose a genetic algorithm approach that uses an ingenious encoding-decoding mechanism that allows to automatically evolve CNNs on FER tasks attaining high accuracy classification rates. The experimental results demonstrate that the proposed algorithm achieves the best-known results on the CK+ and FERG datasets as well as competitive results on the JAFFE dataset.
Denoising autoencoder genetic programming for real-world symbolic regression

Denoising Autoencoder Genetic Programming (DAE-GP) is a novel neural-network based estimation of distribution genetic programming algorithm that uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard recombination and mutation operators of genetic programming (GP). Recent work demonstrated that the DAE-GP outperforms standard GP. However, results are limited to the generalization of the royal tree problem. In this work, we apply the DAE-GP to real-world symbolic regression. On the Airfoil dataset and given a fixed number of fitness evaluations, we find that the DAE-GP generates significantly better and smaller (number of nodes) best candidate solutions than standard GP. The results highlight that the DAE-GP may be a good alternative for generating good and interpretable solutions for real-world symbolic regression.
Rich feature distillation with feature affinity module for efficient image dehazing

Single-image haze removal is a long-standing hurdle for computer vision applications. Several works have been focused on transferring advances from image classification, detection, and segmentation to the niche of image dehazing, primarily focusing on contrastive learning and knowledge distillation. However, these approaches prove computationally expensive, raising concern regarding their applicability to on-the-edge use-cases. This work introduces a simple, lightweight, and efficient framework for single-image haze removal, exploiting rich “dark-knowledge” information from a lightweight pre-trained super-resolution model via the notion of heterogeneous knowledge distillation. We designed a feature affinity module to maximize the flow of rich feature semantics from the super-resolution teacher to the student dehazing network. In order to evaluate the efficacy of our proposed framework, its performance as a plug-and-play setup to a baseline model is examined. Our experiments are carried out on the RESIDE-Standard dataset to demonstrate the robustness of our framework to the synthetic and real-world domains. The extensive qualitative and quantitative results provided establish the effectiveness of the framework, achieving gains of up to 15% (PSNR) while reducing the model size by ∼20 times.
Genesis of altered slate type ores in the Huangjindong gold deposit, Jiangnan Orogenic Belt, South China

Altered rock ores, closely associated with fluid-rock interactions, represent a typical mineralization style in hydrothermal gold deposits. In the Huangjindong gold deposit of the Jiangnan Orogenic Belt, South China, altered slate ores are generally characterized with bleaching, but the genesis remains not understood. Based on field work, petrographic observations and Tescan Integrated Mineral Analyzer (TIMA) analysis, the alteration of the slate can be defined as carbonatization-sericitization, characterized with the abundant siderite spots and sericite, as well as locally occurred cryptocrystalline quartz. Abundant gold-bearing sulfides are locally present in altered rocks, which constitute the altered slate ores. In altered slates, pyrite and arsenopyrite aggregate near carbonate spots, sharing similar geochemical compositions with those in quartz veins. These sulfides locally crosscut siderite grains, demonstrating that the carbonatization-sericitization alteration took place before gold mineralization. μ-XRF analysis and mass balance calculation show that Fe is not prominently gained or lost but aggregated to form siderite spots in the alteration zone. In addition, K contents increased during alteration, consistent with the presence of sericitization. Thermodynamic modeling of the dissolution of siderite manifests that the chemical reactions between siderite and S-Au-bearing fluids leads to the co-precipitation of gold, pyrite, arsenopyrite and quartz. Moreover, taking account of the siderite contents in altered slates and Au contents in sulfides, the water-rock interaction could generate altered slate ores with economic significance. These results show that large amounts of siderite spots are generated during the pre-mineralization fluid-rock reaction, and thus provide favorable chemical traps for gold mineralization. Abundant siderites contribute to gold mineralization by triggering sulfidation, which is the major genesis for the altered slate ores in the Huangjindong gold deposit.
Intelligent ensembling of auto-ML system outputs for solving classification problems

Automatic Machine Learning (Auto-ML) tools enable the automatic solution of real-world problems through machine learning techniques. These tools tend to be more time consuming than standard machine learning libraries, therefore, exploiting all the available resources to the full is a valuable feature. This paper presents a two-phase optimization system for solving classification problems. The system is designed to produce more robust classifiers by exploiting the different architectures that are generated while solving classification problems with Auto-ML tools, particularly AutoGOAL. In the first phase, the system follows a probabilistic strategy to find the best combination of algorithms and hyperparameters to generate a collection of base models according to certain diversity criteria; and in the second, it follows similar Auto-ML strategies to ensemble those models. The HAHA 2019 challenge corpus and the Adult dataset were used to evaluate the system. The experimental results show that: i) a better solution can be built by ensembling a subset of the already tested models; ii) the performance of ensemble methods depends on the collection of base models used; and, iii) ensuring diversity using the double-fault measure produces better results than the disagreement measure. The source code is available online for the research community.
Admissibility of Π2-Inference Rules: interpolation, model completion, and contact algebras

We devise three strategies for recognizing admissibility of non-standard inference rules via interpolation, uniform interpolation, and model completions. We apply our machinery to the case of symmetric implication calculus S2IC, where we also supply a finite axiomatization of the model completion of its algebraic counterpart, via the equivalent theory of contact algebras. Using this result we obtain a finite basis for admissible Π2-rules.
Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems

Recent work done on traffic sign and traffic light detection focus on improving detection accuracy in complex scenarios, yet many fail to deliver real-time performance, specifically with limited computational resources. In this work, we propose a simple deep learning based end-to-end detection framework, which effectively tackles challenges inherent to traffic sign and traffic light detection such as small size, large number of classes and complex road scenarios. We optimize the detection models using TensorRT and integrate with Robot Operating System to deploy on an Nvidia Jetson AGX Xavier as our embedded device. The overall system achieves a high inference speed of 63 frames per second, demonstrating the capability of our system to perform in real-time. Furthermore, we introduce CeyRo, which is the first ever large-scale traffic sign and traffic light detection dataset for the Sri Lankan context. Our dataset consists of 7984 total images with 10176 traffic sign and traffic light instances covering 70 traffic sign and 5 traffic light classes. The images have a high resolution of 1920 x 1080 and capture a wide range of challenging road scenarios with different weather and lighting conditions. Our work is publicly available at https://github.com/oshadajay/CeyRo.
Multitask Network for Joint Object Detection, Semantic Segmentation and Human Pose Estimation in Vehicle Occupancy Monitoring

In order to ensure safe autonomous driving, precise information about the conditions in and around the vehicle must be available. Accordingly, the monitoring of occupants and objects inside the vehicle is crucial. In the state-of-the-art, single or multiple deep neural networks are used for either object recognition, semantic segmentation, or human pose estimation. In contrast, we propose our Multitask Detection, Segmentation and Pose Estimation Network (MDSP) – the first multitask network solving all these three tasks jointly in the area of occupancy monitoring. Due to the shared architecture, memory and computing costs can be saved while achieving higher accuracy. Furthermore, our architecture allows a flexible combination of the three mentioned tasks during a simple end-to-end training. We perform comprehensive evaluations on the public datasets SVIRO and TiCaM in order to demonstrate the superior performance.
The impact of flooding on rice production in the Krueng Kluet Watershed, Aceh Province, Indonesia

Abstract Kluet Utara is a sub-district in South Aceh Regency, Aceh Province. This sub-district is the largest rice field area in South Aceh Regency with an area of ± 1,627.34 Ha. Currently, lowland rice farmers in this district often suffer losses due to flooding from the Krueng Kluet watershed which has an impact on crop failure and the drifting of rice production that is carried away by flood currents, this greatly affects the decline in lowland rice production. This study aims to examine correlation the impact of flooding on lowland rice production and the relationship between flood-prone areas and the of lowland rice production in Kluet Utara Regency. In conclusion, before the flood the total production was 9,827 tons, productivity was 6.03 tons/ha. Meanwhile, when there was a flood, the production of lowland rice was 2,827 tons, the productivity was 1.73 tons/ha. The relationship of lowland rice production with moderate and high flood areas has a very strong correlation value. That is, there is a very large relationship on the decline in production during a flood, while in very high flood areas there is a correlation between the impact of flooding on a decrease in rice production.